From 028f2720d12a3a19a30e689347e98c5eb07631eb Mon Sep 17 00:00:00 2001
From: Douglas Thor <dougthor42@users.noreply.github.com>
Date: Tue, 2 Jul 2024 14:27:41 -0700
Subject: [PATCH 01/17] Add .bazelversion (#790)

This repo is already using `bazelisk` but does not currently pin a Bazel
version.

Add `.bazelversion` to pin Bazel based on the version used in a recent
CI run
(https://github.com/quantumlib/Stim/actions/runs/9657364117/job/26636504001).

Related to
https://github.com/qh-lab/pyle/pull/46833#issuecomment-2204296035

/cc @Strilanc
---
 .bazelversion | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 .bazelversion

diff --git a/.bazelversion b/.bazelversion
new file mode 100644
index 00000000..0ee843cc
--- /dev/null
+++ b/.bazelversion
@@ -0,0 +1 @@
+7.2.0

From cfcf16352901ba53901ba6586695800bff285f78 Mon Sep 17 00:00:00 2001
From: Douglas Thor <dougthor42@users.noreply.github.com>
Date: Tue, 2 Jul 2024 14:57:12 -0700
Subject: [PATCH 02/17] bazelbuild/setup-bazelisk is deprecated; use
 bazel-contib/setup-bazel instead (#791)

[bazelbuild/setup-bazelisk](https://github.com/bazelbuild/setup-bazelisk)
is deprecated; use
[bazel-contib/setup-bazel](https://github.com/bazel-contrib/setup-bazel)
instead.
---
 .github/workflows/ci.yml | 42 ++++++++++++++++++++++++++++++++++------
 1 file changed, 36 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index a7dbd443..3c6709c6 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -215,7 +215,12 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v1
-    - uses: bazelbuild/setup-bazelisk@v1
+    - uses: bazel-contrib/setup-bazel@0.8.5
+      with:
+        bazelisk-cache: true
+        disk-cache: ${{ github.workflow }}
+        repository-cache: true
+        bazelisk-version: 1.x
     - run: bazel build :all
     - run: bazel test :stim_test
   build_clang:
@@ -310,7 +315,12 @@ jobs:
     steps:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v3
-      - uses: bazelbuild/setup-bazelisk@v1
+      - uses: bazel-contrib/setup-bazel@0.8.5
+        with:
+          bazelisk-cache: true
+          disk-cache: ${{ github.workflow }}
+          repository-cache: true
+          bazelisk-version: 1.x
       - uses: actions/setup-node@v1
         with:
           node-version: 16.x
@@ -342,7 +352,12 @@ jobs:
     steps:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v3
-      - uses: bazelbuild/setup-bazelisk@v1
+      - uses: bazel-contrib/setup-bazel@0.8.5
+        with:
+          bazelisk-cache: true
+          disk-cache: ${{ github.workflow }}
+          repository-cache: true
+          bazelisk-version: 1.x
       - run: bazel build :stim_dev_wheel
       - run: pip install bazel-bin/stim-0.0.dev0-py3-none-any.whl
       - run: pip install pytest
@@ -353,7 +368,12 @@ jobs:
     steps:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v3
-      - uses: bazelbuild/setup-bazelisk@v1
+      - uses: bazel-contrib/setup-bazel@0.8.5
+        with:
+          bazelisk-cache: true
+          disk-cache: ${{ github.workflow }}
+          repository-cache: true
+          bazelisk-version: 1.x
       - run: bazel build :stim_dev_wheel
       - run: pip install bazel-bin/stim-0.0.dev0-py3-none-any.whl
       - run: pip install -e glue/cirq
@@ -365,7 +385,12 @@ jobs:
     steps:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v3
-      - uses: bazelbuild/setup-bazelisk@v1
+      - uses: bazel-contrib/setup-bazel@0.8.5
+        with:
+          bazelisk-cache: true
+          disk-cache: ${{ github.workflow }}
+          repository-cache: true
+          bazelisk-version: 1.x
       - run: bazel build :stim_dev_wheel
       - run: pip install bazel-bin/stim-0.0.dev0-py3-none-any.whl
       - run: pip install -e glue/sample
@@ -378,7 +403,12 @@ jobs:
     steps:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v3
-      - uses: bazelbuild/setup-bazelisk@v1
+      - uses: bazel-contrib/setup-bazel@0.8.5
+        with:
+          bazelisk-cache: true
+          disk-cache: ${{ github.workflow }}
+          repository-cache: true
+          bazelisk-version: 1.x
       - run: bazel build :stim_dev_wheel
       - run: pip install bazel-bin/stim-0.0.dev0-py3-none-any.whl
       - run: pip install -e glue/zx

From e0d99ebe9f0c29b9ad5559efff9c9ab4952c89da Mon Sep 17 00:00:00 2001
From: Craig Gidney <craiggidney@google.com>
Date: Tue, 2 Jul 2024 20:49:18 -0700
Subject: [PATCH 03/17] Fix PauliString.before reset not working (#792)

- Also switch `circuit.insert` to doing auto-fusion of operations
---
 doc/python_api_reference_vDev.md              |  10 +-
 doc/stim.pyi                                  |  10 +-
 glue/python/src/stim/__init__.pyi             |  10 +-
 src/stim/circuit/circuit.cc                   |  32 +++-
 src/stim/circuit/circuit.h                    |   1 +
 src/stim/circuit/circuit.pybind.cc            |  10 +-
 src/stim/circuit/circuit.test.cc              | 139 ++++++++++++++++++
 .../stabilizers/pauli_string_pybind_test.py   |  27 ++++
 src/stim/stabilizers/pauli_string_ref.h       |   1 +
 src/stim/stabilizers/pauli_string_ref.inl     |  39 ++++-
 10 files changed, 247 insertions(+), 32 deletions(-)

diff --git a/doc/python_api_reference_vDev.md b/doc/python_api_reference_vDev.md
index d05fb9dc..8846c0ac 100644
--- a/doc/python_api_reference_vDev.md
+++ b/doc/python_api_reference_vDev.md
@@ -2236,9 +2236,8 @@ def insert(
 ) -> None:
     """Inserts an operation at the given index, pushing existing operations forward.
 
-    Note that, unlike when appending operations or parsing stim circuit files,
-    inserted operations aren't automatically fused into the preceding operation.
-    This is to avoid creating complicated situations where it's difficult to reason
+    Beware that inserted operations are automatically fused with the preceding
+    and following operations, if possible. This can make it complex to reason
     about how the indices of operations change in response to insertions.
 
     Args:
@@ -2249,7 +2248,7 @@ def insert(
             indices relative to the end of the circuit instead of the start.
 
             Instructions before the index are not shifted. Instructions that
-            were at or after the index are shifted forwards.
+            were at or after the index are shifted forwards as needed.
         operation: The object to insert. This can be a single
             stim.CircuitInstruction or an entire stim.Circuit.
 
@@ -2273,8 +2272,7 @@ def insert(
         stim.Circuit('''
             H 0
             Y 3 4 5
-            S 1
-            S 999
+            S 1 999
             CX 0 1
             CZ 2 3
             X 2
diff --git a/doc/stim.pyi b/doc/stim.pyi
index 8d603dd7..9e15ce30 100644
--- a/doc/stim.pyi
+++ b/doc/stim.pyi
@@ -1607,9 +1607,8 @@ class Circuit:
     ) -> None:
         """Inserts an operation at the given index, pushing existing operations forward.
 
-        Note that, unlike when appending operations or parsing stim circuit files,
-        inserted operations aren't automatically fused into the preceding operation.
-        This is to avoid creating complicated situations where it's difficult to reason
+        Beware that inserted operations are automatically fused with the preceding
+        and following operations, if possible. This can make it complex to reason
         about how the indices of operations change in response to insertions.
 
         Args:
@@ -1620,7 +1619,7 @@ class Circuit:
                 indices relative to the end of the circuit instead of the start.
 
                 Instructions before the index are not shifted. Instructions that
-                were at or after the index are shifted forwards.
+                were at or after the index are shifted forwards as needed.
             operation: The object to insert. This can be a single
                 stim.CircuitInstruction or an entire stim.Circuit.
 
@@ -1644,8 +1643,7 @@ class Circuit:
             stim.Circuit('''
                 H 0
                 Y 3 4 5
-                S 1
-                S 999
+                S 1 999
                 CX 0 1
                 CZ 2 3
                 X 2
diff --git a/glue/python/src/stim/__init__.pyi b/glue/python/src/stim/__init__.pyi
index 8d603dd7..9e15ce30 100644
--- a/glue/python/src/stim/__init__.pyi
+++ b/glue/python/src/stim/__init__.pyi
@@ -1607,9 +1607,8 @@ class Circuit:
     ) -> None:
         """Inserts an operation at the given index, pushing existing operations forward.
 
-        Note that, unlike when appending operations or parsing stim circuit files,
-        inserted operations aren't automatically fused into the preceding operation.
-        This is to avoid creating complicated situations where it's difficult to reason
+        Beware that inserted operations are automatically fused with the preceding
+        and following operations, if possible. This can make it complex to reason
         about how the indices of operations change in response to insertions.
 
         Args:
@@ -1620,7 +1619,7 @@ class Circuit:
                 indices relative to the end of the circuit instead of the start.
 
                 Instructions before the index are not shifted. Instructions that
-                were at or after the index are shifted forwards.
+                were at or after the index are shifted forwards as needed.
             operation: The object to insert. This can be a single
                 stim.CircuitInstruction or an entire stim.Circuit.
 
@@ -1644,8 +1643,7 @@ class Circuit:
             stim.Circuit('''
                 H 0
                 Y 3 4 5
-                S 1
-                S 999
+                S 1 999
                 CX 0 1
                 CZ 2 3
                 X 2
diff --git a/src/stim/circuit/circuit.cc b/src/stim/circuit/circuit.cc
index 615a20e3..86b762f7 100644
--- a/src/stim/circuit/circuit.cc
+++ b/src/stim/circuit/circuit.cc
@@ -225,13 +225,21 @@ void circuit_read_single_operation(Circuit &circuit, char lead_char, SOURCE read
 }
 
 void Circuit::try_fuse_last_two_ops() {
-    auto &ops = operations;
-    size_t n = ops.size();
-    if (n > 1 && ops[n - 2].can_fuse(ops[n - 1])) {
-        fuse_data(ops[n - 2].targets, ops[n - 1].targets, target_buf);
-        ops.pop_back();
+    if (operations.size() >= 2) {
+        try_fuse_after(operations.size() - 2);
     }
 }
+
+void Circuit::try_fuse_after(size_t index) {
+    if (index + 1 >= operations.size()) {
+        return;
+    }
+    if (operations[index].can_fuse(operations[index + 1])) {
+        fuse_data(operations[index].targets, operations[index + 1].targets, target_buf);
+        operations.erase(operations.begin() + index + 1);
+    }
+}
+
 template <typename SOURCE>
 void circuit_read_operations(Circuit &circuit, SOURCE read_char, READ_CONDITION read_condition) {
     auto &ops = circuit.operations;
@@ -358,6 +366,12 @@ void Circuit::safe_insert(size_t index, const CircuitInstruction &instruction) {
     copy.args = arg_buf.take_copy(copy.args);
     copy.targets = target_buf.take_copy(copy.targets);
     operations.insert(operations.begin() + index, copy);
+
+    // Fuse at boundaries.
+    try_fuse_after(index);
+    if (index > 0) {
+        try_fuse_after(index - 1);
+    }
 }
 
 void Circuit::safe_insert(size_t index, const Circuit &circuit) {
@@ -381,6 +395,14 @@ void Circuit::safe_insert(size_t index, const Circuit &circuit) {
             operations[k].args = arg_buf.take_copy(operations[k].args);
         }
     }
+
+    // Fuse at boundaries.
+    if (!circuit.operations.empty()) {
+        try_fuse_after(index + circuit.operations.size() - 1);
+        if (index > 0) {
+            try_fuse_after(index - 1);
+        }
+    }
 }
 
 void Circuit::safe_insert_repeat_block(size_t index, uint64_t repeat_count, const Circuit &block) {
diff --git a/src/stim/circuit/circuit.h b/src/stim/circuit/circuit.h
index e8670e83..f8268dea 100644
--- a/src/stim/circuit/circuit.h
+++ b/src/stim/circuit/circuit.h
@@ -280,6 +280,7 @@ struct Circuit {
     std::string describe_instruction_location(size_t instruction_offset) const;
 
     void try_fuse_last_two_ops();
+    void try_fuse_after(size_t index);
 };
 
 void vec_pad_add_mul(std::vector<double> &target, SpanRef<const double> offset, uint64_t mul = 1);
diff --git a/src/stim/circuit/circuit.pybind.cc b/src/stim/circuit/circuit.pybind.cc
index 608b60bf..f364cc4e 100644
--- a/src/stim/circuit/circuit.pybind.cc
+++ b/src/stim/circuit/circuit.pybind.cc
@@ -1134,9 +1134,8 @@ void stim_pybind::pybind_circuit_methods(pybind11::module &, pybind11::class_<Ci
             Inserts an operation at the given index, pushing existing operations forward.
             @signature def insert(self, index: int, operation: Union[stim.CircuitInstruction, stim.Circuit]) -> None:
 
-            Note that, unlike when appending operations or parsing stim circuit files,
-            inserted operations aren't automatically fused into the preceding operation.
-            This is to avoid creating complicated situations where it's difficult to reason
+            Beware that inserted operations are automatically fused with the preceding
+            and following operations, if possible. This can make it complex to reason
             about how the indices of operations change in response to insertions.
 
             Args:
@@ -1147,7 +1146,7 @@ void stim_pybind::pybind_circuit_methods(pybind11::module &, pybind11::class_<Ci
                     indices relative to the end of the circuit instead of the start.
 
                     Instructions before the index are not shifted. Instructions that
-                    were at or after the index are shifted forwards.
+                    were at or after the index are shifted forwards as needed.
                 operation: The object to insert. This can be a single
                     stim.CircuitInstruction or an entire stim.Circuit.
 
@@ -1171,8 +1170,7 @@ void stim_pybind::pybind_circuit_methods(pybind11::module &, pybind11::class_<Ci
                 stim.Circuit('''
                     H 0
                     Y 3 4 5
-                    S 1
-                    S 999
+                    S 1 999
                     CX 0 1
                     CZ 2 3
                     X 2
diff --git a/src/stim/circuit/circuit.test.cc b/src/stim/circuit/circuit.test.cc
index cdde5e37..a3592888 100644
--- a/src/stim/circuit/circuit.test.cc
+++ b/src/stim/circuit/circuit.test.cc
@@ -1769,3 +1769,142 @@ TEST(circuit, generate_test_circuit_with_all_operations) {
     }
     ASSERT_EQ(seen.size(), NUM_DEFINED_GATES);
 }
+
+TEST(circuit, insert_circuit) {
+    Circuit c(R"CIRCUIT(
+        CX 0 1
+        H 0
+        S 0
+        CX 0 1
+    )CIRCUIT");
+    c.safe_insert(2, Circuit(R"CIRCUIT(
+        H 1
+        X 3
+        S 2
+    )CIRCUIT"));
+    ASSERT_EQ(c.operations.size(), 5);
+    ASSERT_EQ(c, Circuit(R"CIRCUIT(
+        CX 0 1
+        H 0 1
+        X 3
+        S 2 0
+        CX 0 1
+    )CIRCUIT"));
+
+    c = Circuit(R"CIRCUIT(
+        CX 0 1
+        H 0
+        S 0
+        CX 0 1
+    )CIRCUIT");
+    c.safe_insert(0, Circuit(R"CIRCUIT(
+        H 1
+        X 3
+        S 2
+    )CIRCUIT"));
+    ASSERT_EQ(c, Circuit(R"CIRCUIT(
+        H 1
+        X 3
+        S 2
+        CX 0 1
+        H 0
+        S 0
+        CX 0 1
+    )CIRCUIT"));
+
+    c = Circuit(R"CIRCUIT(
+        CX 0 1
+        H 0
+        S 0
+        CX 0 1
+    )CIRCUIT");
+    c.safe_insert(4, Circuit(R"CIRCUIT(
+        H 1
+        X 3
+        S 2
+    )CIRCUIT"));
+    ASSERT_EQ(c, Circuit(R"CIRCUIT(
+        CX 0 1
+        H 0
+        S 0
+        CX 0 1
+        H 1
+        X 3
+        S 2
+    )CIRCUIT"));
+}
+
+TEST(circuit, insert_instruction) {
+    Circuit c = Circuit(R"CIRCUIT(
+        CX 0 1
+        H 0
+        S 0
+        CX 0 1
+    )CIRCUIT");
+    c.safe_insert(2, Circuit("H 1").operations[0]);
+    ASSERT_EQ(c, Circuit(R"CIRCUIT(
+        CX 0 1
+        H 0 1
+        S 0
+        CX 0 1
+    )CIRCUIT"));
+
+    c = Circuit(R"CIRCUIT(
+        CX 0 1
+        H 0
+        S 0
+        CX 0 1
+    )CIRCUIT");
+    c.safe_insert(2, Circuit("S 1").operations[0]);
+    ASSERT_EQ(c, Circuit(R"CIRCUIT(
+        CX 0 1
+        H 0
+        S 1 0
+        CX 0 1
+    )CIRCUIT"));
+
+    c = Circuit(R"CIRCUIT(
+        CX 0 1
+        H 0
+        S 0
+        CX 0 1
+    )CIRCUIT");
+    c.safe_insert(2, Circuit("X 1").operations[0]);
+    ASSERT_EQ(c, Circuit(R"CIRCUIT(
+        CX 0 1
+        H 0
+        X 1
+        S 0
+        CX 0 1
+    )CIRCUIT"));
+
+    c = Circuit(R"CIRCUIT(
+        CX 0 1
+        H 0
+        S 0
+        CX 0 1
+    )CIRCUIT");
+    c.safe_insert(0, Circuit("X 1").operations[0]);
+    ASSERT_EQ(c, Circuit(R"CIRCUIT(
+        X 1
+        CX 0 1
+        H 0
+        S 0
+        CX 0 1
+    )CIRCUIT"));
+
+    c = Circuit(R"CIRCUIT(
+        CX 0 1
+        H 0
+        S 0
+        CX 0 1
+    )CIRCUIT");
+    c.safe_insert(4, Circuit("X 1").operations[0]);
+    ASSERT_EQ(c, Circuit(R"CIRCUIT(
+        CX 0 1
+        H 0
+        S 0
+        CX 0 1
+        X 1
+    )CIRCUIT"));
+}
diff --git a/src/stim/stabilizers/pauli_string_pybind_test.py b/src/stim/stabilizers/pauli_string_pybind_test.py
index 5058e6a5..4d7b5af1 100644
--- a/src/stim/stabilizers/pauli_string_pybind_test.py
+++ b/src/stim/stabilizers/pauli_string_pybind_test.py
@@ -928,3 +928,30 @@ def test_pauli_indices():
     assert stim.PauliString("_XYZ").pauli_indices("_") == [0]
     with pytest.raises(ValueError, match="Invalid character"):
         assert stim.PauliString("_XYZ").pauli_indices("k")
+
+
+def test_before_reset():
+    assert stim.PauliString("Z").before(stim.Circuit("R 0")) == stim.PauliString("_")
+    assert stim.PauliString("Z").before(stim.Circuit("MR 0")) == stim.PauliString("_")
+    assert stim.PauliString("Z").before(stim.Circuit("M 0")) == stim.PauliString("Z")
+
+    assert stim.PauliString("X").before(stim.Circuit("RX 0")) == stim.PauliString("_")
+    assert stim.PauliString("X").before(stim.Circuit("MRX 0")) == stim.PauliString("_")
+    assert stim.PauliString("X").before(stim.Circuit("MX 0")) == stim.PauliString("X")
+
+    assert stim.PauliString("Y").before(stim.Circuit("RY 0")) == stim.PauliString("_")
+    assert stim.PauliString("Y").before(stim.Circuit("MRY 0")) == stim.PauliString("_")
+    assert stim.PauliString("Y").before(stim.Circuit("MY 0")) == stim.PauliString("Y")
+
+    with pytest.raises(ValueError):
+        stim.PauliString("Z").before(stim.Circuit("RX 0"))
+    with pytest.raises(ValueError):
+        stim.PauliString("Z").before(stim.Circuit("RY 0"))
+    with pytest.raises(ValueError):
+        stim.PauliString("Z").before(stim.Circuit("MRX 0"))
+    with pytest.raises(ValueError):
+        stim.PauliString("Z").before(stim.Circuit("MRY 0"))
+    with pytest.raises(ValueError):
+        stim.PauliString("Z").before(stim.Circuit("MX 0"))
+    with pytest.raises(ValueError):
+        stim.PauliString("Z").before(stim.Circuit("MY 0"))
diff --git a/src/stim/stabilizers/pauli_string_ref.h b/src/stim/stabilizers/pauli_string_ref.h
index d93ca517..b87599b3 100644
--- a/src/stim/stabilizers/pauli_string_ref.h
+++ b/src/stim/stabilizers/pauli_string_ref.h
@@ -159,6 +159,7 @@ struct PauliStringRef {
     void check_avoids_MPP(const CircuitInstruction &inst);
     void check_avoids_reset(const CircuitInstruction &inst);
     void check_avoids_measurement(const CircuitInstruction &inst);
+    void undo_reset_xyz(const CircuitInstruction &inst);
 
     void do_single_cx(const CircuitInstruction &inst, uint32_t c, uint32_t t);
     void do_single_cy(const CircuitInstruction &inst, uint32_t c, uint32_t t);
diff --git a/src/stim/stabilizers/pauli_string_ref.inl b/src/stim/stabilizers/pauli_string_ref.inl
index 5b49756a..73c5e546 100644
--- a/src/stim/stabilizers/pauli_string_ref.inl
+++ b/src/stim/stabilizers/pauli_string_ref.inl
@@ -172,6 +172,39 @@ void PauliStringRef<W>::do_circuit(const Circuit &circuit) {
     });
 }
 
+template <size_t W>
+void PauliStringRef<W>::undo_reset_xyz(const CircuitInstruction &inst) {
+    bool x_dep, z_dep;
+    if (inst.gate_type == GateType::R || inst.gate_type == GateType::MR) {
+        x_dep = true;
+        z_dep = false;
+    } else if (inst.gate_type == GateType::RX || inst.gate_type == GateType::MRX) {
+        x_dep = false;
+        z_dep = true;
+    } else if (inst.gate_type == GateType::RY || inst.gate_type == GateType::MRY) {
+        x_dep = true;
+        z_dep = true;
+    } else {
+        throw std::invalid_argument("Unrecognized measurement type: " + inst.str());
+    }
+    for (const auto &t : inst.targets) {
+        assert(t.is_qubit_target());
+        auto q = t.qubit_value();
+        if (q < num_qubits && ((xs[q] & x_dep) ^ (zs[q] & z_dep))) {
+            std::stringstream ss;
+            ss << "The pauli observable '" << *this;
+            ss << "' doesn't have a well specified value before '" << inst;
+            ss << "' because it anticommutes with the reset.";
+            throw std::invalid_argument(ss.str());
+        }
+    }
+    for (const auto &t : inst.targets) {
+        auto q = t.qubit_value();
+        xs[q] = false;
+        zs[q] = false;
+    }
+}
+
 template <size_t W>
 void PauliStringRef<W>::check_avoids_measurement(const CircuitInstruction &inst) {
     bool x_dep, z_dep;
@@ -193,7 +226,7 @@ void PauliStringRef<W>::check_avoids_measurement(const CircuitInstruction &inst)
         if (q < num_qubits && ((xs[q] & x_dep) ^ (zs[q] & z_dep))) {
             std::stringstream ss;
             ss << "The pauli observable '" << *this;
-            ss << "' doesn't have a well specified value after '" << inst;
+            ss << "' doesn't have a well specified value across '" << inst;
             ss << "' because it anticommutes with the measurement.";
             throw std::invalid_argument(ss.str());
         }
@@ -238,7 +271,7 @@ void PauliStringRef<W>::check_avoids_MPP(const CircuitInstruction &inst) {
         if (anticommutes) {
             std::stringstream ss;
             ss << "The pauli observable '" << *this;
-            ss << "' doesn't have a well specified value after '" << inst;
+            ss << "' doesn't have a well specified value across '" << inst;
             ss << "' because it anticommutes with the measurement.";
             throw std::invalid_argument(ss.str());
         }
@@ -552,7 +585,7 @@ void PauliStringRef<W>::undo_instruction(const CircuitInstruction &inst) {
         case GateType::MR:
         case GateType::MRX:
         case GateType::MRY:
-            check_avoids_reset(inst);
+            undo_reset_xyz(inst);
             break;
 
         case GateType::M:

From 0685b5a2e784c24b8898ceb1a01e01db34ab47fd Mon Sep 17 00:00:00 2001
From: Craig Gidney <craiggidney@google.com>
Date: Sat, 13 Jul 2024 02:12:29 -0700
Subject: [PATCH 04/17] Add `stim.Circuit.flow_generators` (#794)

- Fix `MPAD` targets counting towards `circuit.num_qubits`
- Fix `stim::PauliString` move constructor not clearing `num_qubits`
- Fix `stim.has_flow` not ignoring noise
- Add `stim.CircuitInstruction.num_measurements`
- Add `stim::PauliString::operator<`
- Fix `SPP` and `SPP_DAG` not being supported by
`stim.PauliString.after/before`
---
 doc/python_api_reference_vDev.md              | 100 ++++
 doc/stim.pyi                                  |  84 +++
 file_lists/test_files                         |   1 +
 glue/python/src/stim/__init__.pyi             |  84 +++
 src/stim.h                                    |   1 +
 src/stim/circuit/circuit.pybind.cc            |  76 ++-
 src/stim/circuit/circuit_instruction.cc       |   6 +-
 .../circuit/circuit_instruction.pybind.cc     |  25 +
 .../circuit_instruction_pybind_test.py        |   7 +
 src/stim/mem/sparse_xor_vec.h                 |  35 +-
 .../py/compiled_detector_sampler.pybind.cc    |  14 +-
 src/stim/py/numpy.pybind.cc                   |  21 +-
 src/stim/simulators/dem_sampler.pybind.cc     |   6 +-
 src/stim/simulators/frame_simulator.pybind.cc |   3 +-
 ...measurements_to_detection_events.pybind.cc |   3 +-
 src/stim/stabilizers/flow.h                   |   1 +
 src/stim/stabilizers/flow.inl                 |  20 +
 src/stim/stabilizers/flow.test.cc             |  32 +-
 src/stim/stabilizers/pauli_string.h           |  16 +-
 src/stim/stabilizers/pauli_string.inl         |  61 ++-
 src/stim/stabilizers/pauli_string_ref.h       |   1 +
 src/stim/stabilizers/pauli_string_ref.inl     |  42 ++
 src/stim/util_top/circuit_flow_generators.h   |  51 ++
 src/stim/util_top/circuit_flow_generators.inl | 515 ++++++++++++++++++
 .../util_top/circuit_flow_generators.test.cc  | 266 +++++++++
 src/stim/util_top/circuit_inverse_qec.test.cc |   2 +-
 src/stim/util_top/export_qasm.test.cc         |   2 +-
 src/stim/util_top/has_flow.inl                |   5 +-
 src/stim/util_top/reference_sample_tree.cc    |  34 +-
 src/stim/util_top/reference_sample_tree.h     |  12 +-
 src/stim/util_top/reference_sample_tree.inl   |  13 +-
 .../util_top/reference_sample_tree.perf.cc    |   6 +-
 .../util_top/reference_sample_tree.test.cc    | 198 ++++---
 src/stim/util_top/stabilizers_to_tableau.inl  |   2 +-
 34 files changed, 1551 insertions(+), 194 deletions(-)
 create mode 100644 src/stim/util_top/circuit_flow_generators.h
 create mode 100644 src/stim/util_top/circuit_flow_generators.inl
 create mode 100644 src/stim/util_top/circuit_flow_generators.test.cc

diff --git a/doc/python_api_reference_vDev.md b/doc/python_api_reference_vDev.md
index 8846c0ac..49dd374d 100644
--- a/doc/python_api_reference_vDev.md
+++ b/doc/python_api_reference_vDev.md
@@ -33,6 +33,7 @@ API references for stable versions are kept on the [stim github wiki](https://gi
     - [`stim.Circuit.diagram`](#stim.Circuit.diagram)
     - [`stim.Circuit.explain_detector_error_model_errors`](#stim.Circuit.explain_detector_error_model_errors)
     - [`stim.Circuit.flattened`](#stim.Circuit.flattened)
+    - [`stim.Circuit.flow_generators`](#stim.Circuit.flow_generators)
     - [`stim.Circuit.from_file`](#stim.Circuit.from_file)
     - [`stim.Circuit.generated`](#stim.Circuit.generated)
     - [`stim.Circuit.get_detector_coordinates`](#stim.Circuit.get_detector_coordinates)
@@ -80,6 +81,7 @@ API references for stable versions are kept on the [stim github wiki](https://gi
     - [`stim.CircuitInstruction.__str__`](#stim.CircuitInstruction.__str__)
     - [`stim.CircuitInstruction.gate_args_copy`](#stim.CircuitInstruction.gate_args_copy)
     - [`stim.CircuitInstruction.name`](#stim.CircuitInstruction.name)
+    - [`stim.CircuitInstruction.num_measurements`](#stim.CircuitInstruction.num_measurements)
     - [`stim.CircuitInstruction.targets_copy`](#stim.CircuitInstruction.targets_copy)
 - [`stim.CircuitRepeatBlock`](#stim.CircuitRepeatBlock)
     - [`stim.CircuitRepeatBlock.__eq__`](#stim.CircuitRepeatBlock.__eq__)
@@ -1825,6 +1827,64 @@ def flattened(
     """
 ```
 
+<a name="stim.Circuit.flow_generators"></a>
+```python
+# stim.Circuit.flow_generators
+
+# (in class stim.Circuit)
+def flow_generators(
+    self,
+) -> List[stim.Flow]:
+    """Returns a list of flows that generate all of the circuit's flows.
+
+    Every stabilizer flow that the circuit implements is a product of some
+    subset of the returned generators. Every returned flow will be a flow
+    of the circuit.
+
+    Returns:
+        A list of flow generators for the circuit.
+
+    Examples:
+        >>> import stim
+
+        >>> stim.Circuit("H 0").flow_generators()
+        [stim.Flow("X -> Z"), stim.Flow("Z -> X")]
+
+        >>> stim.Circuit("M 0").flow_generators()
+        [stim.Flow("1 -> Z xor rec[0]"), stim.Flow("Z -> rec[0]")]
+
+        >>> stim.Circuit("RX 0").flow_generators()
+        [stim.Flow("1 -> X")]
+
+        >>> for flow in stim.Circuit("MXX 0 1").flow_generators():
+        ...     print(flow)
+        1 -> XX xor rec[0]
+        _X -> _X
+        X_ -> _X xor rec[0]
+        ZZ -> ZZ
+
+        >>> for flow in stim.Circuit.generated(
+        ...     "repetition_code:memory",
+        ...     rounds=2,
+        ...     distance=3,
+        ...     after_clifford_depolarization=1e-3,
+        ... ).flow_generators():
+        ...     print(flow)
+        1 -> rec[0]
+        1 -> rec[1]
+        1 -> rec[2]
+        1 -> rec[3]
+        1 -> rec[4]
+        1 -> rec[5]
+        1 -> rec[6]
+        1 -> ____Z
+        1 -> ___Z_
+        1 -> __Z__
+        1 -> _Z___
+        1 -> Z____
+    """
+```
+
 <a name="stim.Circuit.from_file"></a>
 ```python
 # stim.Circuit.from_file
@@ -2064,6 +2124,8 @@ def has_all_flows(
     because, behind the scenes, the circuit can be iterated once instead of once
     per flow.
 
+    This method ignores any noise in the circuit.
+
     Args:
         flows: An iterable of `stim.Flow` instances representing the flows to check.
         unsigned: Defaults to False. When False, the flows must be correct including
@@ -2131,6 +2193,8 @@ def has_flow(
     A flow like P -> 1 means the circuit measures P.
     A flow like 1 -> 1 means the circuit contains a check (could be a DETECTOR).
 
+    This method ignores any noise in the circuit.
+
     Args:
         flow: The flow to check for.
         unsigned: Defaults to False. When False, the flows must be correct including
@@ -2171,6 +2235,14 @@ def has_flow(
         ... ))
         True
 
+        >>> stim.Circuit('''
+        ...     RY 0
+        ...     X_ERROR(0.1) 0
+        ... ''').has_flow(stim.Flow(
+        ...     output=stim.PauliString("Y"),
+        ... ))
+        True
+
         >>> stim.Circuit('''
         ...     RY 0
         ... ''').has_flow(stim.Flow(
@@ -3727,6 +3799,34 @@ def name(
     """
 ```
 
+<a name="stim.CircuitInstruction.num_measurements"></a>
+```python
+# stim.CircuitInstruction.num_measurements
+
+# (in class stim.CircuitInstruction)
+@property
+def num_measurements(
+    self,
+) -> int:
+    """Returns the number of bits produced when running this instruction.
+
+    Examples:
+        >>> import stim
+        >>> stim.CircuitInstruction('H', [0]).num_measurements
+        0
+        >>> stim.CircuitInstruction('M', [0]).num_measurements
+        1
+        >>> stim.CircuitInstruction('M', [2, 3, 5, 7, 11]).num_measurements
+        5
+        >>> stim.CircuitInstruction('MXX', [0, 1, 4, 5, 11, 13]).num_measurements
+        3
+        >>> stim.Circuit('MPP X0*X1 X0*Z1*Y2')[0].num_measurements
+        2
+        >>> stim.CircuitInstruction('HERALDED_ERASE', [0]).num_measurements
+        1
+    """
+```
+
 <a name="stim.CircuitInstruction.targets_copy"></a>
 ```python
 # stim.CircuitInstruction.targets_copy
diff --git a/doc/stim.pyi b/doc/stim.pyi
index 9e15ce30..cd0d0258 100644
--- a/doc/stim.pyi
+++ b/doc/stim.pyi
@@ -1243,6 +1243,57 @@ class Circuit:
             ... ''').flattened_operations()
             [('H', [6], 0), ('H', [6], 0)]
         """
+    def flow_generators(
+        self,
+    ) -> List[stim.Flow]:
+        """Returns a list of flows that generate all of the circuit's flows.
+
+        Every stabilizer flow that the circuit implements is a product of some
+        subset of the returned generators. Every returned flow will be a flow
+        of the circuit.
+
+        Returns:
+            A list of flow generators for the circuit.
+
+        Examples:
+            >>> import stim
+
+            >>> stim.Circuit("H 0").flow_generators()
+            [stim.Flow("X -> Z"), stim.Flow("Z -> X")]
+
+            >>> stim.Circuit("M 0").flow_generators()
+            [stim.Flow("1 -> Z xor rec[0]"), stim.Flow("Z -> rec[0]")]
+
+            >>> stim.Circuit("RX 0").flow_generators()
+            [stim.Flow("1 -> X")]
+
+            >>> for flow in stim.Circuit("MXX 0 1").flow_generators():
+            ...     print(flow)
+            1 -> XX xor rec[0]
+            _X -> _X
+            X_ -> _X xor rec[0]
+            ZZ -> ZZ
+
+            >>> for flow in stim.Circuit.generated(
+            ...     "repetition_code:memory",
+            ...     rounds=2,
+            ...     distance=3,
+            ...     after_clifford_depolarization=1e-3,
+            ... ).flow_generators():
+            ...     print(flow)
+            1 -> rec[0]
+            1 -> rec[1]
+            1 -> rec[2]
+            1 -> rec[3]
+            1 -> rec[4]
+            1 -> rec[5]
+            1 -> rec[6]
+            1 -> ____Z
+            1 -> ___Z_
+            1 -> __Z__
+            1 -> _Z___
+            1 -> Z____
+        """
     @staticmethod
     def from_file(
         file: Union[io.TextIOBase, str, pathlib.Path],
@@ -1449,6 +1500,8 @@ class Circuit:
         because, behind the scenes, the circuit can be iterated once instead of once
         per flow.
 
+        This method ignores any noise in the circuit.
+
         Args:
             flows: An iterable of `stim.Flow` instances representing the flows to check.
             unsigned: Defaults to False. When False, the flows must be correct including
@@ -1509,6 +1562,8 @@ class Circuit:
         A flow like P -> 1 means the circuit measures P.
         A flow like 1 -> 1 means the circuit contains a check (could be a DETECTOR).
 
+        This method ignores any noise in the circuit.
+
         Args:
             flow: The flow to check for.
             unsigned: Defaults to False. When False, the flows must be correct including
@@ -1549,6 +1604,14 @@ class Circuit:
             ... ))
             True
 
+            >>> stim.Circuit('''
+            ...     RY 0
+            ...     X_ERROR(0.1) 0
+            ... ''').has_flow(stim.Flow(
+            ...     output=stim.PauliString("Y"),
+            ... ))
+            True
+
             >>> stim.Circuit('''
             ...     RY 0
             ... ''').has_flow(stim.Flow(
@@ -2816,6 +2879,27 @@ class CircuitInstruction:
     ) -> str:
         """The name of the instruction (e.g. `H` or `X_ERROR` or `DETECTOR`).
         """
+    @property
+    def num_measurements(
+        self,
+    ) -> int:
+        """Returns the number of bits produced when running this instruction.
+
+        Examples:
+            >>> import stim
+            >>> stim.CircuitInstruction('H', [0]).num_measurements
+            0
+            >>> stim.CircuitInstruction('M', [0]).num_measurements
+            1
+            >>> stim.CircuitInstruction('M', [2, 3, 5, 7, 11]).num_measurements
+            5
+            >>> stim.CircuitInstruction('MXX', [0, 1, 4, 5, 11, 13]).num_measurements
+            3
+            >>> stim.Circuit('MPP X0*X1 X0*Z1*Y2')[0].num_measurements
+            2
+            >>> stim.CircuitInstruction('HERALDED_ERASE', [0]).num_measurements
+            1
+        """
     def targets_copy(
         self,
     ) -> List[stim.GateTarget]:
diff --git a/file_lists/test_files b/file_lists/test_files
index efab5d9b..9de5d724 100644
--- a/file_lists/test_files
+++ b/file_lists/test_files
@@ -79,6 +79,7 @@ src/stim/util_bot/probability_util.test.cc
 src/stim/util_bot/str_util.test.cc
 src/stim/util_bot/test_util.test.cc
 src/stim/util_bot/twiddle.test.cc
+src/stim/util_top/circuit_flow_generators.test.cc
 src/stim/util_top/circuit_inverse_qec.test.cc
 src/stim/util_top/circuit_inverse_unitary.test.cc
 src/stim/util_top/circuit_to_detecting_regions.test.cc
diff --git a/glue/python/src/stim/__init__.pyi b/glue/python/src/stim/__init__.pyi
index 9e15ce30..cd0d0258 100644
--- a/glue/python/src/stim/__init__.pyi
+++ b/glue/python/src/stim/__init__.pyi
@@ -1243,6 +1243,57 @@ class Circuit:
             ... ''').flattened_operations()
             [('H', [6], 0), ('H', [6], 0)]
         """
+    def flow_generators(
+        self,
+    ) -> List[stim.Flow]:
+        """Returns a list of flows that generate all of the circuit's flows.
+
+        Every stabilizer flow that the circuit implements is a product of some
+        subset of the returned generators. Every returned flow will be a flow
+        of the circuit.
+
+        Returns:
+            A list of flow generators for the circuit.
+
+        Examples:
+            >>> import stim
+
+            >>> stim.Circuit("H 0").flow_generators()
+            [stim.Flow("X -> Z"), stim.Flow("Z -> X")]
+
+            >>> stim.Circuit("M 0").flow_generators()
+            [stim.Flow("1 -> Z xor rec[0]"), stim.Flow("Z -> rec[0]")]
+
+            >>> stim.Circuit("RX 0").flow_generators()
+            [stim.Flow("1 -> X")]
+
+            >>> for flow in stim.Circuit("MXX 0 1").flow_generators():
+            ...     print(flow)
+            1 -> XX xor rec[0]
+            _X -> _X
+            X_ -> _X xor rec[0]
+            ZZ -> ZZ
+
+            >>> for flow in stim.Circuit.generated(
+            ...     "repetition_code:memory",
+            ...     rounds=2,
+            ...     distance=3,
+            ...     after_clifford_depolarization=1e-3,
+            ... ).flow_generators():
+            ...     print(flow)
+            1 -> rec[0]
+            1 -> rec[1]
+            1 -> rec[2]
+            1 -> rec[3]
+            1 -> rec[4]
+            1 -> rec[5]
+            1 -> rec[6]
+            1 -> ____Z
+            1 -> ___Z_
+            1 -> __Z__
+            1 -> _Z___
+            1 -> Z____
+        """
     @staticmethod
     def from_file(
         file: Union[io.TextIOBase, str, pathlib.Path],
@@ -1449,6 +1500,8 @@ class Circuit:
         because, behind the scenes, the circuit can be iterated once instead of once
         per flow.
 
+        This method ignores any noise in the circuit.
+
         Args:
             flows: An iterable of `stim.Flow` instances representing the flows to check.
             unsigned: Defaults to False. When False, the flows must be correct including
@@ -1509,6 +1562,8 @@ class Circuit:
         A flow like P -> 1 means the circuit measures P.
         A flow like 1 -> 1 means the circuit contains a check (could be a DETECTOR).
 
+        This method ignores any noise in the circuit.
+
         Args:
             flow: The flow to check for.
             unsigned: Defaults to False. When False, the flows must be correct including
@@ -1549,6 +1604,14 @@ class Circuit:
             ... ))
             True
 
+            >>> stim.Circuit('''
+            ...     RY 0
+            ...     X_ERROR(0.1) 0
+            ... ''').has_flow(stim.Flow(
+            ...     output=stim.PauliString("Y"),
+            ... ))
+            True
+
             >>> stim.Circuit('''
             ...     RY 0
             ... ''').has_flow(stim.Flow(
@@ -2816,6 +2879,27 @@ class CircuitInstruction:
     ) -> str:
         """The name of the instruction (e.g. `H` or `X_ERROR` or `DETECTOR`).
         """
+    @property
+    def num_measurements(
+        self,
+    ) -> int:
+        """Returns the number of bits produced when running this instruction.
+
+        Examples:
+            >>> import stim
+            >>> stim.CircuitInstruction('H', [0]).num_measurements
+            0
+            >>> stim.CircuitInstruction('M', [0]).num_measurements
+            1
+            >>> stim.CircuitInstruction('M', [2, 3, 5, 7, 11]).num_measurements
+            5
+            >>> stim.CircuitInstruction('MXX', [0, 1, 4, 5, 11, 13]).num_measurements
+            3
+            >>> stim.Circuit('MPP X0*X1 X0*Z1*Y2')[0].num_measurements
+            2
+            >>> stim.CircuitInstruction('HERALDED_ERASE', [0]).num_measurements
+            1
+        """
     def targets_copy(
         self,
     ) -> List[stim.GateTarget]:
diff --git a/src/stim.h b/src/stim.h
index d1f55e3a..adc845a5 100644
--- a/src/stim.h
+++ b/src/stim.h
@@ -105,6 +105,7 @@
 #include "stim/util_bot/probability_util.h"
 #include "stim/util_bot/str_util.h"
 #include "stim/util_bot/twiddle.h"
+#include "stim/util_top/circuit_flow_generators.h"
 #include "stim/util_top/circuit_inverse_qec.h"
 #include "stim/util_top/circuit_inverse_unitary.h"
 #include "stim/util_top/circuit_to_detecting_regions.h"
diff --git a/src/stim/circuit/circuit.pybind.cc b/src/stim/circuit/circuit.pybind.cc
index f364cc4e..454b6f91 100644
--- a/src/stim/circuit/circuit.pybind.cc
+++ b/src/stim/circuit/circuit.pybind.cc
@@ -37,6 +37,7 @@
 #include "stim/simulators/measurements_to_detection_events.pybind.h"
 #include "stim/simulators/tableau_simulator.h"
 #include "stim/stabilizers/flow.h"
+#include "stim/util_top/circuit_flow_generators.h"
 #include "stim/util_top/circuit_inverse_qec.h"
 #include "stim/util_top/circuit_to_detecting_regions.h"
 #include "stim/util_top/circuit_vs_tableau.h"
@@ -178,11 +179,7 @@ std::string py_likeliest_error_sat_problem(const Circuit &self, int quantization
     return stim::likeliest_error_sat_problem(dem, quantization, format);
 }
 
-void circuit_insert(
-    Circuit &self,
-    pybind11::ssize_t &index,
-    pybind11::object &operation) {
-
+void circuit_insert(Circuit &self, pybind11::ssize_t &index, pybind11::object &operation) {
     if (index < 0) {
         index += self.operations.size();
     }
@@ -1176,7 +1173,7 @@ void stim_pybind::pybind_circuit_methods(pybind11::module &, pybind11::class_<Ci
                     X 2
                 ''')
         )DOC")
-             .data());
+            .data());
 
     c.def(
         "append_from_stim_program_text",
@@ -2820,6 +2817,8 @@ void stim_pybind::pybind_circuit_methods(pybind11::module &, pybind11::class_<Ci
             A flow like P -> 1 means the circuit measures P.
             A flow like 1 -> 1 means the circuit contains a check (could be a DETECTOR).
 
+            This method ignores any noise in the circuit.
+
             Args:
                 flow: The flow to check for.
                 unsigned: Defaults to False. When False, the flows must be correct including
@@ -2860,6 +2859,14 @@ void stim_pybind::pybind_circuit_methods(pybind11::module &, pybind11::class_<Ci
                 ... ))
                 True
 
+                >>> stim.Circuit('''
+                ...     RY 0
+                ...     X_ERROR(0.1) 0
+                ... ''').has_flow(stim.Flow(
+                ...     output=stim.PauliString("Y"),
+                ... ))
+                True
+
                 >>> stim.Circuit('''
                 ...     RY 0
                 ... ''').has_flow(stim.Flow(
@@ -2941,6 +2948,8 @@ void stim_pybind::pybind_circuit_methods(pybind11::module &, pybind11::class_<Ci
             because, behind the scenes, the circuit can be iterated once instead of once
             per flow.
 
+            This method ignores any noise in the circuit.
+
             Args:
                 flows: An iterable of `stim.Flow` instances representing the flows to check.
                 unsigned: Defaults to False. When False, the flows must be correct including
@@ -2984,6 +2993,61 @@ void stim_pybind::pybind_circuit_methods(pybind11::module &, pybind11::class_<Ci
         )DOC")
             .data());
 
+    c.def(
+        "flow_generators",
+        &circuit_flow_generators<MAX_BITWORD_WIDTH>,
+        clean_doc_string(R"DOC(
+            @signature def flow_generators(self) -> List[stim.Flow]:
+            Returns a list of flows that generate all of the circuit's flows.
+
+            Every stabilizer flow that the circuit implements is a product of some
+            subset of the returned generators. Every returned flow will be a flow
+            of the circuit.
+
+            Returns:
+                A list of flow generators for the circuit.
+
+            Examples:
+                >>> import stim
+
+                >>> stim.Circuit("H 0").flow_generators()
+                [stim.Flow("X -> Z"), stim.Flow("Z -> X")]
+
+                >>> stim.Circuit("M 0").flow_generators()
+                [stim.Flow("1 -> Z xor rec[0]"), stim.Flow("Z -> rec[0]")]
+
+                >>> stim.Circuit("RX 0").flow_generators()
+                [stim.Flow("1 -> X")]
+
+                >>> for flow in stim.Circuit("MXX 0 1").flow_generators():
+                ...     print(flow)
+                1 -> XX xor rec[0]
+                _X -> _X
+                X_ -> _X xor rec[0]
+                ZZ -> ZZ
+
+                >>> for flow in stim.Circuit.generated(
+                ...     "repetition_code:memory",
+                ...     rounds=2,
+                ...     distance=3,
+                ...     after_clifford_depolarization=1e-3,
+                ... ).flow_generators():
+                ...     print(flow)
+                1 -> rec[0]
+                1 -> rec[1]
+                1 -> rec[2]
+                1 -> rec[3]
+                1 -> rec[4]
+                1 -> rec[5]
+                1 -> rec[6]
+                1 -> ____Z
+                1 -> ___Z_
+                1 -> __Z__
+                1 -> _Z___
+                1 -> Z____
+        )DOC")
+            .data());
+
     c.def(
         "time_reversed_for_flows",
         [](const Circuit &self,
diff --git a/src/stim/circuit/circuit_instruction.cc b/src/stim/circuit/circuit_instruction.cc
index c2f397c8..840c5f93 100644
--- a/src/stim/circuit/circuit_instruction.cc
+++ b/src/stim/circuit/circuit_instruction.cc
@@ -62,8 +62,10 @@ void CircuitInstruction::add_stats_to(CircuitStats &out, const Circuit *host) co
     for (auto t : targets) {
         auto v = t.data & TARGET_VALUE_MASK;
         // Qubit counting.
-        if (!(t.data & (TARGET_RECORD_BIT | TARGET_SWEEP_BIT))) {
-            out.num_qubits = std::max(out.num_qubits, v + 1);
+        if (gate_type != GateType::MPAD) {
+            if (!(t.data & (TARGET_RECORD_BIT | TARGET_SWEEP_BIT))) {
+                out.num_qubits = std::max(out.num_qubits, v + 1);
+            }
         }
         // Lookback counting.
         if (t.data & TARGET_RECORD_BIT) {
diff --git a/src/stim/circuit/circuit_instruction.pybind.cc b/src/stim/circuit/circuit_instruction.pybind.cc
index 3cae0347..adef47c2 100644
--- a/src/stim/circuit/circuit_instruction.pybind.cc
+++ b/src/stim/circuit/circuit_instruction.pybind.cc
@@ -162,6 +162,31 @@ void stim_pybind::pybind_circuit_instruction_methods(pybind11::module &m, pybind
         )DOC")
             .data());
 
+    c.def_property_readonly(
+        "num_measurements",
+        [](const PyCircuitInstruction &self) -> uint64_t {
+            return self.as_operation_ref().count_measurement_results();
+        },
+        clean_doc_string(R"DOC(
+            Returns the number of bits produced when running this instruction.
+
+            Examples:
+                >>> import stim
+                >>> stim.CircuitInstruction('H', [0]).num_measurements
+                0
+                >>> stim.CircuitInstruction('M', [0]).num_measurements
+                1
+                >>> stim.CircuitInstruction('M', [2, 3, 5, 7, 11]).num_measurements
+                5
+                >>> stim.CircuitInstruction('MXX', [0, 1, 4, 5, 11, 13]).num_measurements
+                3
+                >>> stim.Circuit('MPP X0*X1 X0*Z1*Y2')[0].num_measurements
+                2
+                >>> stim.CircuitInstruction('HERALDED_ERASE', [0]).num_measurements
+                1
+        )DOC")
+            .data());
+
     c.def(pybind11::self == pybind11::self, "Determines if two `stim.CircuitInstruction`s are identical.");
     c.def(pybind11::self != pybind11::self, "Determines if two `stim.CircuitInstruction`s are different.");
     c.def(
diff --git a/src/stim/circuit/circuit_instruction_pybind_test.py b/src/stim/circuit/circuit_instruction_pybind_test.py
index 9703e622..d0592713 100644
--- a/src/stim/circuit/circuit_instruction_pybind_test.py
+++ b/src/stim/circuit/circuit_instruction_pybind_test.py
@@ -50,3 +50,10 @@ def test_hashable():
     c = stim.CircuitInstruction("X_ERROR", [stim.GateTarget(5)], [0.5])
     assert hash(a) == hash(c)
     assert len({a, b, c}) == 2
+
+
+def test_num_measurements():
+    assert stim.CircuitInstruction("X", [1, 2, 3]).num_measurements == 0
+    assert stim.CircuitInstruction("MXX", [1, 2]).num_measurements == 1
+    assert stim.CircuitInstruction("M", [1, 2]).num_measurements == 2
+    assert stim.CircuitInstruction("MPAD", [0, 1, 0]).num_measurements == 3
diff --git a/src/stim/mem/sparse_xor_vec.h b/src/stim/mem/sparse_xor_vec.h
index 777f1368..787bef07 100644
--- a/src/stim/mem/sparse_xor_vec.h
+++ b/src/stim/mem/sparse_xor_vec.h
@@ -120,6 +120,25 @@ struct SparseXorVec;
 template <typename T>
 std::ostream &operator<<(std::ostream &out, const SparseXorVec<T> &v);
 
+template <typename T>
+inline void xor_item_into_sorted_vec(const T &item, std::vector<T> &sorted_items) {
+    // Just do a linear scan to find the insertion point, instead of a binary search.
+    // This is faster at small sizes, and complexity is linear anyways due to the shifting of later items.
+    for (size_t k = 0; k < sorted_items.size(); k++) {
+        const auto &v = sorted_items[k];
+        if (v < item) {
+            continue;
+        } else if (v == item) {
+            sorted_items.erase(sorted_items.begin() + k);
+            return;
+        } else {
+            sorted_items.insert(sorted_items.begin() + k, item);
+            return;
+        }
+    }
+    sorted_items.push_back(item);
+}
+
 /// A sparse set of integers that supports efficient xoring (computing the symmetric difference).
 template <typename T>
 struct SparseXorVec {
@@ -161,21 +180,7 @@ struct SparseXorVec {
     }
 
     void xor_item(const T &item) {
-        // Just do a linear scan to find the insertion point, instead of a binary search.
-        // This is faster at small sizes, and complexity is linear anyways due to the shifting of later items.
-        for (size_t k = 0; k < sorted_items.size(); k++) {
-            const auto &v = sorted_items[k];
-            if (v < item) {
-                continue;
-            } else if (v == item) {
-                sorted_items.erase(sorted_items.begin() + k);
-                return;
-            } else {
-                sorted_items.insert(sorted_items.begin() + k, item);
-                return;
-            }
-        }
-        sorted_items.push_back(item);
+        xor_item_into_sorted_vec(item, sorted_items);
     }
 
     SparseXorVec &operator^=(const SparseXorVec<T> &other) {
diff --git a/src/stim/py/compiled_detector_sampler.pybind.cc b/src/stim/py/compiled_detector_sampler.pybind.cc
index 1733f5e4..27cbd52e 100644
--- a/src/stim/py/compiled_detector_sampler.pybind.cc
+++ b/src/stim/py/compiled_detector_sampler.pybind.cc
@@ -32,7 +32,13 @@ CompiledDetectorSampler::CompiledDetectorSampler(Circuit init_circuit, std::mt19
 }
 
 pybind11::object CompiledDetectorSampler::sample_to_numpy(
-    size_t num_shots, bool prepend_observables, bool append_observables, bool separate_observables, bool bit_packed, pybind11::object dets_out, pybind11::object obs_out) {
+    size_t num_shots,
+    bool prepend_observables,
+    bool append_observables,
+    bool separate_observables,
+    bool bit_packed,
+    pybind11::object dets_out,
+    pybind11::object obs_out) {
     if (separate_observables && (append_observables || prepend_observables)) {
         throw std::invalid_argument(
             "Can't specify separate_observables=True with append_observables=True or prepend_observables=True");
@@ -50,7 +56,8 @@ pybind11::object CompiledDetectorSampler::sample_to_numpy(
 
     pybind11::object py_obs_data = pybind11::none();
     if (separate_observables || !obs_out.is_none()) {
-        py_obs_data = simd_bit_table_to_numpy(obs_data, circuit_stats.num_observables, num_shots, bit_packed, true, obs_out);
+        py_obs_data =
+            simd_bit_table_to_numpy(obs_data, circuit_stats.num_observables, num_shots, bit_packed, true, obs_out);
     }
 
     pybind11::object py_det_data = pybind11::none();
@@ -67,7 +74,8 @@ pybind11::object CompiledDetectorSampler::sample_to_numpy(
         }
         py_det_data = simd_bit_table_to_numpy(concat_data, num_concat, num_shots, bit_packed, true, dets_out);
     } else {
-        py_det_data = simd_bit_table_to_numpy(det_data, circuit_stats.num_detectors, num_shots, bit_packed, true, dets_out);
+        py_det_data =
+            simd_bit_table_to_numpy(det_data, circuit_stats.num_detectors, num_shots, bit_packed, true, dets_out);
     }
 
     if (separate_observables) {
diff --git a/src/stim/py/numpy.pybind.cc b/src/stim/py/numpy.pybind.cc
index c03448ae..1aaecbe4 100644
--- a/src/stim/py/numpy.pybind.cc
+++ b/src/stim/py/numpy.pybind.cc
@@ -18,8 +18,10 @@ using namespace stim;
 using namespace stim_pybind;
 
 static pybind11::object transposed_simd_bit_table_to_numpy_uint8(
-    const simd_bit_table<MAX_BITWORD_WIDTH> &table, size_t num_major_in, size_t num_minor_in, pybind11::object out_buffer) {
-
+    const simd_bit_table<MAX_BITWORD_WIDTH> &table,
+    size_t num_major_in,
+    size_t num_minor_in,
+    pybind11::object out_buffer) {
     size_t num_major_bytes_in = (num_major_in + 7) / 8;
 
     if (out_buffer.is_none()) {
@@ -61,8 +63,10 @@ static pybind11::object transposed_simd_bit_table_to_numpy_uint8(
 }
 
 static pybind11::object transposed_simd_bit_table_to_numpy_bool8(
-    const simd_bit_table<MAX_BITWORD_WIDTH> &table, size_t num_major_in, size_t num_minor_in, pybind11::object out_buffer) {
-
+    const simd_bit_table<MAX_BITWORD_WIDTH> &table,
+    size_t num_major_in,
+    size_t num_minor_in,
+    pybind11::object out_buffer) {
     if (out_buffer.is_none()) {
         auto numpy = pybind11::module::import("numpy");
         out_buffer = numpy.attr("empty")(pybind11::make_tuple(num_minor_in, num_major_in), numpy.attr("bool_"));
@@ -99,7 +103,6 @@ static pybind11::object transposed_simd_bit_table_to_numpy_bool8(
 
 static pybind11::object simd_bit_table_to_numpy_uint8(
     const simd_bit_table<MAX_BITWORD_WIDTH> &table, size_t num_major, size_t num_minor, pybind11::object out_buffer) {
-
     size_t num_minor_bytes = (num_minor + 7) / 8;
     if (out_buffer.is_none()) {
         auto numpy = pybind11::module::import("numpy");
@@ -144,7 +147,6 @@ static pybind11::object simd_bit_table_to_numpy_uint8(
 
 static pybind11::object simd_bit_table_to_numpy_bool8(
     const simd_bit_table<MAX_BITWORD_WIDTH> &table, size_t num_major, size_t num_minor, pybind11::object out_buffer) {
-
     if (out_buffer.is_none()) {
         auto numpy = pybind11::module::import("numpy");
         out_buffer = numpy.attr("empty")(pybind11::make_tuple(num_major, num_minor), numpy.attr("bool_"));
@@ -179,7 +181,12 @@ static pybind11::object simd_bit_table_to_numpy_bool8(
 }
 
 pybind11::object stim_pybind::simd_bit_table_to_numpy(
-    const simd_bit_table<MAX_BITWORD_WIDTH> &table, size_t num_major, size_t num_minor, bool bit_pack_result, bool transposed, pybind11::object out_buffer) {
+    const simd_bit_table<MAX_BITWORD_WIDTH> &table,
+    size_t num_major,
+    size_t num_minor,
+    bool bit_pack_result,
+    bool transposed,
+    pybind11::object out_buffer) {
     if (transposed) {
         if (bit_pack_result) {
             return transposed_simd_bit_table_to_numpy_uint8(table, num_major, num_minor, out_buffer);
diff --git a/src/stim/simulators/dem_sampler.pybind.cc b/src/stim/simulators/dem_sampler.pybind.cc
index 699a1245..3b89a66d 100644
--- a/src/stim/simulators/dem_sampler.pybind.cc
+++ b/src/stim/simulators/dem_sampler.pybind.cc
@@ -72,8 +72,10 @@ pybind11::object dem_sampler_py_sample(
     if (return_errors) {
         err_out = simd_bit_table_to_numpy(self.err_buffer, self.num_errors, shots, bit_packed, true, pybind11::none());
     }
-    pybind11::object det_out = simd_bit_table_to_numpy(self.det_buffer, self.num_detectors, shots, bit_packed, true, pybind11::none());
-    pybind11::object obs_out = simd_bit_table_to_numpy(self.obs_buffer, self.num_observables, shots, bit_packed, true, pybind11::none());
+    pybind11::object det_out =
+        simd_bit_table_to_numpy(self.det_buffer, self.num_detectors, shots, bit_packed, true, pybind11::none());
+    pybind11::object obs_out =
+        simd_bit_table_to_numpy(self.obs_buffer, self.num_observables, shots, bit_packed, true, pybind11::none());
     return pybind11::make_tuple(det_out, obs_out, err_out);
 }
 
diff --git a/src/stim/simulators/frame_simulator.pybind.cc b/src/stim/simulators/frame_simulator.pybind.cc
index 51282577..05386a4c 100644
--- a/src/stim/simulators/frame_simulator.pybind.cc
+++ b/src/stim/simulators/frame_simulator.pybind.cc
@@ -138,7 +138,8 @@ pybind11::object sliced_table_to_numpy(
             auto data = table.read_across_majors_at_minor_index(0, num_major_exact, *minor_index);
             return simd_bits_to_numpy(data, num_major_exact, bit_packed);
         } else {
-            return simd_bit_table_to_numpy(table, num_major_exact, num_minor_exact, bit_packed, false, pybind11::none());
+            return simd_bit_table_to_numpy(
+                table, num_major_exact, num_minor_exact, bit_packed, false, pybind11::none());
         }
     }
 }
diff --git a/src/stim/simulators/measurements_to_detection_events.pybind.cc b/src/stim/simulators/measurements_to_detection_events.pybind.cc
index fa45886b..cdfd6495 100644
--- a/src/stim/simulators/measurements_to_detection_events.pybind.cc
+++ b/src/stim/simulators/measurements_to_detection_events.pybind.cc
@@ -129,7 +129,8 @@ pybind11::object CompiledMeasurementsToDetectionEventsConverter::convert(
                 obs_slice.clear();
             }
         }
-        obs_data = simd_bit_table_to_numpy(obs_table, circuit_stats.num_observables, num_shots, bit_pack_result, true, pybind11::none());
+        obs_data = simd_bit_table_to_numpy(
+            obs_table, circuit_stats.num_observables, num_shots, bit_pack_result, true, pybind11::none());
     }
 
     // Caution: only do this after extracting the observable data, lest it leak into the packed bytes.
diff --git a/src/stim/stabilizers/flow.h b/src/stim/stabilizers/flow.h
index 15052c98..8146d5b8 100644
--- a/src/stim/stabilizers/flow.h
+++ b/src/stim/stabilizers/flow.h
@@ -32,6 +32,7 @@ struct Flow {
     std::vector<int32_t> measurements;
 
     static Flow<W> from_str(std::string_view text);
+    bool operator<(const Flow<W> &other) const;
     bool operator==(const Flow<W> &other) const;
     bool operator!=(const Flow<W> &other) const;
     std::string str() const;
diff --git a/src/stim/stabilizers/flow.inl b/src/stim/stabilizers/flow.inl
index ce612563..019747ee 100644
--- a/src/stim/stabilizers/flow.inl
+++ b/src/stim/stabilizers/flow.inl
@@ -75,6 +75,11 @@ Flow<W> Flow<W>::from_str(std::string_view text) {
         }
         PauliString<W> out(0);
         std::vector<int32_t> measurements;
+        bool flip_out = false;
+        if (parts[k].starts_with('-')) {
+            flip_out = true;
+            parts[k] = parts[k].substr(1);
+        }
         if (!parts[k].empty() && parts[k][0] != 'r') {
             out = parse_non_empty_pauli_string_allowing_i<W>(parts[k], &imag_out);
         } else {
@@ -84,6 +89,7 @@ Flow<W> Flow<W>::from_str(std::string_view text) {
             }
             measurements.push_back(rec);
         }
+        out.sign ^= flip_out;
         k++;
         while (k < parts.size()) {
             if (parts[k] != "xor" || k + 1 == parts.size()) {
@@ -113,6 +119,20 @@ bool Flow<W>::operator==(const Flow<W> &other) const {
     return input == other.input && output == other.output && measurements == other.measurements;
 }
 
+template <size_t W>
+bool Flow<W>::operator<(const Flow<W> &other) const {
+    if (input != other.input) {
+        return input < other.input;
+    }
+    if (output != other.output) {
+        return output < other.output;
+    }
+    if (measurements != other.measurements) {
+        return SpanRef<const int32_t>(measurements) < SpanRef<const int32_t>(other.measurements);
+    }
+    return false;
+}
+
 template <size_t W>
 bool Flow<W>::operator!=(const Flow<W> &other) const {
     return !(*this == other);
diff --git a/src/stim/stabilizers/flow.test.cc b/src/stim/stabilizers/flow.test.cc
index f43e5cf2..b0f20c43 100644
--- a/src/stim/stabilizers/flow.test.cc
+++ b/src/stim/stabilizers/flow.test.cc
@@ -1,24 +1,9 @@
-// Copyright 2021 Google LLC
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
 #include "stim/stabilizers/flow.h"
 
 #include "gtest/gtest.h"
 
 #include "stim/circuit/circuit.h"
 #include "stim/mem/simd_word.test.h"
-#include "stim/util_bot/test_util.test.h"
 
 using namespace stim;
 
@@ -44,6 +29,13 @@ TEST_EACH_WORD_SIZE_W(stabilizer_flow, from_str, {
             .output = PauliString<W>::from_str(""),
             .measurements = {},
         }));
+    ASSERT_EQ(
+        Flow<W>::from_str("1 -> -rec[0]"),
+        (Flow<W>{
+            .input = PauliString<W>::from_str(""),
+            .output = PauliString<W>::from_str("-"),
+            .measurements = {0},
+        }));
     ASSERT_EQ(
         Flow<W>::from_str("i -> -i"),
         (Flow<W>{
@@ -151,3 +143,13 @@ TEST_EACH_WORD_SIZE_W(stabilizer_flow, str_and_from_str, {
         Flow<W>::from_str("-1 -> -X xor rec[-1] xor rec[-3]"),
         (Flow<W>{PauliString<W>::from_str("-"), PauliString<W>::from_str("-X"), {-1, -3}}));
 })
+
+TEST_EACH_WORD_SIZE_W(stabilizer_flow, ordering, {
+    ASSERT_FALSE(Flow<W>::from_str("1 -> 1") < Flow<W>::from_str("1 -> 1"));
+    ASSERT_FALSE(Flow<W>::from_str("X -> 1") < Flow<W>::from_str("1 -> 1"));
+    ASSERT_FALSE(Flow<W>::from_str("1 -> X") < Flow<W>::from_str("1 -> 1"));
+    ASSERT_FALSE(Flow<W>::from_str("1 -> rec[-1]") < Flow<W>::from_str("1 -> 1"));
+    ASSERT_TRUE(Flow<W>::from_str("1 -> 1") < Flow<W>::from_str("X -> 1"));
+    ASSERT_TRUE(Flow<W>::from_str("1 -> 1") < Flow<W>::from_str("1 -> X"));
+    ASSERT_TRUE(Flow<W>::from_str("1 -> 1") < Flow<W>::from_str("1 -> rec[-1]"));
+})
diff --git a/src/stim/stabilizers/pauli_string.h b/src/stim/stabilizers/pauli_string.h
index 697f622b..53edd696 100644
--- a/src/stim/stabilizers/pauli_string.h
+++ b/src/stim/stabilizers/pauli_string.h
@@ -78,8 +78,15 @@ struct PauliString {
     /// Paulis are xz-encoded (P=xz: I=00, X=10, Y=11, Z=01) pairwise across the two bit vectors.
     simd_bits<W> xs, zs;
 
-    /// Identity constructor.
+    /// Standard constructors.
     explicit PauliString(size_t num_qubits);
+    PauliString(const PauliStringRef<W> &other);  // NOLINT(google-explicit-constructor)
+    PauliString(const PauliString<W> &other);
+    PauliString(PauliString<W> &&other) noexcept;
+    PauliString &operator=(const PauliStringRef<W> &other);
+    PauliString &operator=(const PauliString<W> &other);
+    PauliString &operator=(PauliString<W> &&other);
+
     /// Parse constructor.
     explicit PauliString(std::string_view text);
     /// Factory method for creating a PauliString whose Pauli entries are returned by a function.
@@ -89,17 +96,14 @@ struct PauliString {
     /// Factory method for creating a PauliString with uniformly random sign and Pauli entries.
     static PauliString<W> random(size_t num_qubits, std::mt19937_64 &rng);
 
-    /// Copy constructor.
-    PauliString(const PauliStringRef<W> &other);  // NOLINT(google-explicit-constructor)
-    /// Overwrite assignment.
-    PauliString &operator=(const PauliStringRef<W> &other) noexcept;
-
     /// Equality.
     bool operator==(const PauliStringRef<W> &other) const;
     bool operator==(const PauliString<W> &other) const;
     /// Inequality.
     bool operator!=(const PauliStringRef<W> &other) const;
     bool operator!=(const PauliString<W> &other) const;
+    bool operator<(const PauliStringRef<W> &other) const;
+    bool operator<(const PauliString<W> &other) const;
 
     /// Implicit conversion to a reference.
     operator PauliStringRef<W>();
diff --git a/src/stim/stabilizers/pauli_string.inl b/src/stim/stabilizers/pauli_string.inl
index 9e7ea851..67ff6af4 100644
--- a/src/stim/stabilizers/pauli_string.inl
+++ b/src/stim/stabilizers/pauli_string.inl
@@ -45,6 +45,50 @@ PauliString<W>::PauliString(const PauliStringRef<W> &other)
     : num_qubits(other.num_qubits), sign((bool)other.sign), xs(other.xs), zs(other.zs) {
 }
 
+template <size_t W>
+PauliString<W>::PauliString(const PauliString<W> &other)
+    : num_qubits(other.num_qubits), sign((bool)other.sign), xs(other.xs), zs(other.zs) {
+}
+
+template <size_t W>
+PauliString<W>::PauliString(PauliString<W> &&other) noexcept
+    : num_qubits(other.num_qubits), sign((bool)other.sign), xs(std::move(other.xs)), zs(std::move(other.zs)) {
+    other.num_qubits = 0;
+    other.sign = false;
+}
+
+template <size_t W>
+PauliString<W> &PauliString<W>::operator=(const PauliStringRef<W> &other) {
+    xs = other.xs;
+    zs = other.zs;
+    num_qubits = other.num_qubits;
+    sign = other.sign;
+    return *this;
+}
+
+template <size_t W>
+PauliString<W> &PauliString<W>::operator=(const PauliString<W> &other) {
+    xs = other.xs;
+    zs = other.zs;
+    num_qubits = other.num_qubits;
+    sign = other.sign;
+    return *this;
+}
+
+template <size_t W>
+PauliString<W> &PauliString<W>::operator=(PauliString<W> &&other) {
+    if (&other == this) {
+        return *this;
+    }
+    xs = std::move(other.xs);
+    zs = std::move(other.zs);
+    num_qubits = other.num_qubits;
+    sign = other.sign;
+    other.num_qubits = 0;
+    other.sign = false;
+    return *this;
+}
+
 template <size_t W>
 const PauliStringRef<W> PauliString<W>::ref() const {
     size_t nw = (num_qubits + W - 1) / W;
@@ -67,13 +111,6 @@ std::string PauliString<W>::str() const {
     return ref().str();
 }
 
-template <size_t W>
-PauliString<W> &PauliString<W>::operator=(const PauliStringRef<W> &other) noexcept {
-    (*this).~PauliString();
-    new (this) PauliString(other);
-    return *this;
-}
-
 template <size_t W>
 PauliString<W> PauliString<W>::from_func(bool sign, size_t num_qubits, const std::function<char(size_t)> &func) {
     PauliString<W> result(num_qubits);
@@ -144,6 +181,16 @@ bool PauliString<W>::operator!=(const PauliString<W> &other) const {
     return ref() != other.ref();
 }
 
+template <size_t W>
+bool PauliString<W>::operator<(const PauliString<W> &other) const {
+    return ref() < other.ref();
+}
+
+template <size_t W>
+bool PauliString<W>::operator<(const PauliStringRef<W> &other) const {
+    return ref() < other;
+}
+
 template <size_t W>
 void PauliString<W>::ensure_num_qubits(size_t min_num_qubits, double resize_pad_factor) {
     assert(resize_pad_factor >= 1);
diff --git a/src/stim/stabilizers/pauli_string_ref.h b/src/stim/stabilizers/pauli_string_ref.h
index b87599b3..1b35421c 100644
--- a/src/stim/stabilizers/pauli_string_ref.h
+++ b/src/stim/stabilizers/pauli_string_ref.h
@@ -60,6 +60,7 @@ struct PauliStringRef {
     bool operator==(const PauliStringRef<W> &other) const;
     /// Inequality.
     bool operator!=(const PauliStringRef<W> &other) const;
+    bool operator<(const PauliStringRef<W> &other) const;
 
     /// Overwrite assignment.
     PauliStringRef<W> &operator=(const PauliStringRef<W> &other);
diff --git a/src/stim/stabilizers/pauli_string_ref.inl b/src/stim/stabilizers/pauli_string_ref.inl
index 73c5e546..1be812e7 100644
--- a/src/stim/stabilizers/pauli_string_ref.inl
+++ b/src/stim/stabilizers/pauli_string_ref.inl
@@ -16,6 +16,7 @@
 #include <sstream>
 
 #include "stim/circuit/circuit.h"
+#include "stim/circuit/gate_decomposition.h"
 #include "stim/mem/simd_util.h"
 #include "stim/stabilizers/pauli_string.h"
 #include "stim/stabilizers/tableau.h"
@@ -84,6 +85,25 @@ bool PauliStringRef<W>::operator!=(const PauliStringRef<W> &other) const {
     return !(*this == other);
 }
 
+template <size_t W>
+bool PauliStringRef<W>::operator<(const PauliStringRef<W> &other) const {
+    size_t n = std::min(num_qubits, other.num_qubits);
+    for (size_t q = 0; q < n; q++) {
+        uint8_t p1 = (xs[q] ^ zs[q]) + zs[q] * 2;
+        uint8_t p2 = (other.xs[q] ^ other.zs[q]) + other.zs[q] * 2;
+        if (p1 != p2) {
+            return p1 < p2;
+        }
+    }
+    if (num_qubits != other.num_qubits) {
+        return num_qubits < other.num_qubits;
+    }
+    if (sign != other.sign) {
+        return sign < other.sign;
+    }
+    return false;
+}
+
 template <size_t W>
 PauliStringRef<W> &PauliStringRef<W>::operator*=(const PauliStringRef<W> &rhs) {
     uint8_t log_i = inplace_right_mul_returning_log_i_scalar(rhs);
@@ -429,6 +449,13 @@ void PauliStringRef<W>::do_instruction(const CircuitInstruction &inst) {
             check_avoids_MPP(inst);
             break;
 
+        case GateType::SPP:
+        case GateType::SPP_DAG:
+            decompose_spp_or_spp_dag_operation(inst, num_qubits, false, [&](CircuitInstruction sub_inst) {
+                do_instruction(sub_inst);
+            });
+            break;
+
         case GateType::X_ERROR:
         case GateType::Y_ERROR:
         case GateType::Z_ERROR:
@@ -569,6 +596,21 @@ void PauliStringRef<W>::undo_instruction(const CircuitInstruction &inst) {
             do_YCZ<true>(inst);
             break;
 
+        case GateType::SPP:
+        case GateType::SPP_DAG: {
+            std::vector<GateTarget> buf_targets;
+            buf_targets.insert(buf_targets.end(), inst.targets.begin(), inst.targets.end());
+            std::reverse(buf_targets.begin(), buf_targets.end());
+            decompose_spp_or_spp_dag_operation(
+                CircuitInstruction{inst.gate_type, {}, buf_targets},
+                num_qubits,
+                false,
+                [&](CircuitInstruction sub_inst) {
+                    undo_instruction(sub_inst);
+                });
+            break;
+        }
+
         case GateType::DETECTOR:
         case GateType::OBSERVABLE_INCLUDE:
         case GateType::TICK:
diff --git a/src/stim/util_top/circuit_flow_generators.h b/src/stim/util_top/circuit_flow_generators.h
new file mode 100644
index 00000000..329c8961
--- /dev/null
+++ b/src/stim/util_top/circuit_flow_generators.h
@@ -0,0 +1,51 @@
+#ifndef _STIM_UTIL_TOP_CIRCUIT_FLOW_GENERATORS_H
+#define _STIM_UTIL_TOP_CIRCUIT_FLOW_GENERATORS_H
+
+#include "stim/circuit/circuit.h"
+
+namespace stim {
+
+/// Finds a basis of flows for the circuit.
+template <size_t W>
+std::vector<Flow<W>> circuit_flow_generators(const Circuit &circuit);
+
+template <size_t W>
+struct CircuitFlowGeneratorSolver {
+    std::vector<Flow<W>> table;
+    size_t num_qubits;
+    size_t num_measurements;
+    size_t num_measurements_in_past;
+    std::vector<Flow<W>> measurements_only_table;
+    std::vector<size_t> buf_for_rows_with;
+    std::vector<int32_t> buf_for_xor_merge;
+    std::vector<GateTarget> buf_targets;
+
+    CircuitFlowGeneratorSolver(CircuitStats stats);
+    Flow<W> &add_row();
+    void add_1q_measure_terms(CircuitInstruction inst, bool x, bool z);
+    void add_2q_measure_terms(CircuitInstruction inst, bool x, bool z);
+    void remove_single_qubit_reset_terms(CircuitInstruction inst);
+    void handle_anticommutations(std::span<const size_t> anticommutation_set);
+    void check_for_2q_anticommutations(CircuitInstruction inst, bool x, bool z);
+    void check_for_1q_anticommutations(CircuitInstruction inst, bool x, bool z);
+    void mult_row_into(size_t src_row, size_t dst_row);
+    void undo_mrb(CircuitInstruction inst, bool x, bool z);
+    void undo_mb(CircuitInstruction inst, bool x, bool z);
+    void undo_rb(CircuitInstruction inst, bool x, bool z);
+    void undo_2q_m(CircuitInstruction inst, bool x, bool z);
+    void undo_instruction(CircuitInstruction inst);
+    void elimination_step(std::span<const size_t> elimination_set, size_t &num_eliminated);
+    void canonicalize_over_qubits();
+    void final_canonicalize_into_table();
+    void undo_feedback_capable_instruction(CircuitInstruction inst, bool x, bool z);
+    std::span<const size_t> rows_anticommuting_with(uint32_t q, bool x, bool z);
+
+    template <typename PREDICATE>
+    std::span<const size_t> rows_with(PREDICATE predicate);
+};
+
+}  // namespace stim
+
+#include "stim/util_top/circuit_flow_generators.inl"
+
+#endif
diff --git a/src/stim/util_top/circuit_flow_generators.inl b/src/stim/util_top/circuit_flow_generators.inl
new file mode 100644
index 00000000..de8b45df
--- /dev/null
+++ b/src/stim/util_top/circuit_flow_generators.inl
@@ -0,0 +1,515 @@
+#include "stim/util_top/circuit_inverse_qec.h"
+
+namespace stim {
+
+template <size_t W>
+CircuitFlowGeneratorSolver<W>::CircuitFlowGeneratorSolver(CircuitStats stats)
+    : table(),
+      num_qubits(stats.num_qubits),
+      num_measurements(stats.num_measurements),
+      num_measurements_in_past(stats.num_measurements),
+      measurements_only_table(),
+      buf_for_rows_with(),
+      buf_for_xor_merge() {
+    if (num_measurements_in_past > INT32_MAX) {
+        throw std::invalid_argument("Circuit is too large. Max flow measurement index is " + std::to_string(INT32_MAX));
+    }
+}
+
+template <size_t W>
+Flow<W> &CircuitFlowGeneratorSolver<W>::add_row() {
+    table.push_back(Flow<W>{
+        .input = PauliString<W>(num_qubits),
+        .output = PauliString<W>(num_qubits),
+        .measurements = {},
+    });
+    return table.back();
+}
+
+template <size_t W>
+void CircuitFlowGeneratorSolver<W>::add_1q_measure_terms(CircuitInstruction inst, bool x, bool z) {
+    for (size_t k = inst.targets.size(); k--;) {
+        num_measurements_in_past--;
+
+        auto t = inst.targets[k];
+        if (!t.is_qubit_target()) {
+            throw std::invalid_argument("Bad target in " + inst.str());
+        }
+        uint32_t q = t.qubit_value();
+        auto &row = add_row();
+        row.measurements.push_back(num_measurements_in_past);
+        row.input.xs[q] = x;
+        row.input.zs[q] = z;
+        row.input.sign ^= t.is_inverted_result_target();
+    }
+}
+
+template <size_t W>
+void CircuitFlowGeneratorSolver<W>::add_2q_measure_terms(CircuitInstruction inst, bool x, bool z) {
+    size_t k = inst.targets.size();
+    while (k > 0) {
+        k -= 2;
+        num_measurements_in_past--;
+
+        auto t1 = inst.targets[k];
+        auto t2 = inst.targets[k + 1];
+        if (!t1.is_qubit_target() || !t2.is_qubit_target()) {
+            throw std::invalid_argument("Bad target in " + inst.str());
+        }
+        uint32_t q1 = t1.qubit_value();
+        uint32_t q2 = t2.qubit_value();
+        auto &row = add_row();
+        row.measurements.push_back(num_measurements_in_past);
+        row.input.xs[q1] = x;
+        row.input.zs[q1] = z;
+        row.input.xs[q2] = x;
+        row.input.zs[q2] = z;
+        row.input.sign ^= t1.is_inverted_result_target();
+        row.input.sign ^= t2.is_inverted_result_target();
+    }
+}
+
+template <size_t W>
+void CircuitFlowGeneratorSolver<W>::remove_single_qubit_reset_terms(CircuitInstruction inst) {
+    for (auto t : inst.targets) {
+        if (!t.is_qubit_target()) {
+            throw std::invalid_argument("Bad target in " + inst.str());
+        }
+        uint32_t q = t.qubit_value();
+        for (auto &row : table) {
+            row.input.xs[q] = 0;
+            row.input.zs[q] = 0;
+        }
+    }
+}
+
+template <size_t W>
+void CircuitFlowGeneratorSolver<W>::handle_anticommutations(std::span<const size_t> anticommutation_set) {
+    if (anticommutation_set.empty()) {
+        return;
+    }
+
+    // Sacrifice the first anticommutation to save the others.
+    for (size_t k = 1; k < buf_for_rows_with.size(); k++) {
+        mult_row_into(anticommutation_set[0], anticommutation_set[k]);
+    }
+    table.erase(table.begin() + anticommutation_set[0]);
+}
+
+template <size_t W>
+void CircuitFlowGeneratorSolver<W>::check_for_2q_anticommutations(CircuitInstruction inst, bool x, bool z) {
+    size_t k = inst.targets.size();
+    while (k > 0) {
+        k -= 2;
+
+        auto t1 = inst.targets[k];
+        auto t2 = inst.targets[k + 1];
+        if (!t1.is_qubit_target() || !t2.is_qubit_target()) {
+            throw std::invalid_argument("Bad target in " + inst.str());
+        }
+        uint32_t q1 = t1.qubit_value();
+        uint32_t q2 = t2.qubit_value();
+
+        auto anticommutations = rows_with([&](const Flow<W> &flow) {
+            bool anticommutes = false;
+            anticommutes ^= flow.input.xs[q1] & z;
+            anticommutes ^= flow.input.zs[q1] & x;
+            anticommutes ^= flow.input.xs[q2] & z;
+            anticommutes ^= flow.input.zs[q2] & x;
+            return anticommutes;
+        });
+
+        handle_anticommutations(anticommutations);
+    }
+}
+
+template <size_t W>
+void CircuitFlowGeneratorSolver<W>::check_for_1q_anticommutations(CircuitInstruction inst, bool x, bool z) {
+    for (auto t : inst.targets) {
+        if (!t.is_qubit_target()) {
+            throw std::invalid_argument("Bad target in " + inst.str());
+        }
+        uint32_t q = t.qubit_value();
+
+        handle_anticommutations(rows_anticommuting_with(q, x, z));
+    }
+}
+
+template <size_t W>
+void CircuitFlowGeneratorSolver<W>::mult_row_into(size_t src_row, size_t dst_row) {
+    auto &src = table[src_row];
+    auto &dst = table[dst_row];
+
+    // Combine the pauli strings.
+    uint8_t log_i = 0;
+    log_i += dst.input.ref().inplace_right_mul_returning_log_i_scalar(src.input);
+    log_i -= dst.output.ref().inplace_right_mul_returning_log_i_scalar(src.output);
+    if (log_i & 1) {
+        throw std::invalid_argument("Unexpected anticommutation while solving for flow generators.");
+    }
+    if (log_i & 2) {
+        dst.input.sign ^= 1;
+    }
+
+    // Xor-merge-sort the measurement indices.
+    buf_for_xor_merge.resize(std::max(buf_for_xor_merge.size(), dst.measurements.size() + src.measurements.size() + 1));
+    const int32_t *end = xor_merge_sort(
+        SpanRef<const int32_t>(dst.measurements), SpanRef<const int32_t>(src.measurements), buf_for_xor_merge.data());
+    size_t n = end - buf_for_xor_merge.data();
+    dst.measurements.resize(n);
+    if (n > 0) {
+        memcpy(dst.measurements.data(), buf_for_xor_merge.data(), n * sizeof(int32_t));
+    }
+}
+
+template <size_t W>
+void CircuitFlowGeneratorSolver<W>::undo_mrb(CircuitInstruction inst, bool x, bool z) {
+    check_for_1q_anticommutations(inst, x, z);
+    remove_single_qubit_reset_terms(inst);
+    add_1q_measure_terms(inst, x, z);
+}
+template <size_t W>
+void CircuitFlowGeneratorSolver<W>::undo_mb(CircuitInstruction inst, bool x, bool z) {
+    check_for_1q_anticommutations(inst, x, z);
+    add_1q_measure_terms(inst, x, z);
+}
+template <size_t W>
+void CircuitFlowGeneratorSolver<W>::undo_rb(CircuitInstruction inst, bool x, bool z) {
+    check_for_1q_anticommutations(inst, x, z);
+    remove_single_qubit_reset_terms(inst);
+}
+template <size_t W>
+void CircuitFlowGeneratorSolver<W>::undo_2q_m(CircuitInstruction inst, bool x, bool z) {
+    check_for_2q_anticommutations(inst, x, z);
+    add_2q_measure_terms(inst, x, z);
+}
+
+template <size_t W>
+void CircuitFlowGeneratorSolver<W>::undo_feedback_capable_instruction(CircuitInstruction inst, bool x, bool z) {
+    size_t k = inst.targets.size();
+    while (k > 0) {
+        k -= 2;
+
+        auto t1 = inst.targets[k];
+        auto t2 = inst.targets[k + 1];
+        bool m1 = t1.is_measurement_record_target();
+        bool m2 = t2.is_measurement_record_target();
+        bool f1 = t1.is_qubit_target();
+        bool f2 = t2.is_qubit_target();
+        if ((m1 && f2) || (m2 && f1)) {
+            uint32_t q = f1 ? t1.qubit_value() : t2.qubit_value();
+            int32_t t = (m1 ? t1.value() : t2.value()) + num_measurements_in_past;
+            if (t < 0) {
+                throw std::invalid_argument("Referred to measurement before start of time in " + inst.str());
+            }
+            for (auto r : rows_anticommuting_with(q, x, z)) {
+                xor_item_into_sorted_vec(t, table[r].measurements);
+            }
+        } else if (f1 && f2) {
+            CircuitInstruction sub_inst = CircuitInstruction{inst.gate_type, {}, inst.targets.sub(k, k + 2)};
+            for (auto &row : table) {
+                row.input.ref().undo_instruction(sub_inst);
+            }
+        }
+    }
+}
+
+template <size_t W>
+void CircuitFlowGeneratorSolver<W>::undo_instruction(CircuitInstruction inst) {
+    if (table.size() > num_qubits * 3) {
+        canonicalize_over_qubits();
+    }
+
+    switch (inst.gate_type) {
+        case GateType::MRX:
+        case GateType::MRY:
+        case GateType::MR:
+            undo_mrb(inst, inst.gate_type != GateType::MR, inst.gate_type != GateType::MRX);
+            break;
+
+        case GateType::MX:
+        case GateType::MY:
+        case GateType::M:
+            undo_mb(inst, inst.gate_type != GateType::M, inst.gate_type != GateType::MX);
+            break;
+
+        case GateType::RX:
+        case GateType::RY:
+        case GateType::R:
+            undo_rb(inst, inst.gate_type != GateType::R, inst.gate_type != GateType::RX);
+            break;
+
+        case GateType::MXX:
+        case GateType::MYY:
+        case GateType::MZZ:
+            undo_2q_m(inst, inst.gate_type != GateType::MZZ, inst.gate_type != GateType::MXX);
+            break;
+
+        case GateType::DETECTOR:
+        case GateType::OBSERVABLE_INCLUDE:
+        case GateType::TICK:
+        case GateType::QUBIT_COORDS:
+        case GateType::SHIFT_COORDS:
+        case GateType::DEPOLARIZE1:
+        case GateType::DEPOLARIZE2:
+        case GateType::X_ERROR:
+        case GateType::Y_ERROR:
+        case GateType::Z_ERROR:
+        case GateType::PAULI_CHANNEL_1:
+        case GateType::PAULI_CHANNEL_2:
+        case GateType::E:
+        case GateType::ELSE_CORRELATED_ERROR:
+            // Ignored.
+            break;
+
+        case GateType::HERALDED_ERASE:
+        case GateType::HERALDED_PAULI_CHANNEL_1:
+            // Heralds.
+            for (auto t : inst.targets) {
+                num_measurements_in_past--;
+                if (!t.is_qubit_target()) {
+                    throw std::invalid_argument("Bad target in " + inst.str());
+                }
+                auto &row = add_row();
+                row.measurements.push_back(num_measurements_in_past);
+            }
+            break;
+
+        case GateType::MPAD:
+            // Pads.
+            for (auto t : inst.targets) {
+                num_measurements_in_past--;
+                if (!t.is_qubit_target()) {
+                    throw std::invalid_argument("Bad target in " + inst.str());
+                }
+                auto &row = add_row();
+                row.measurements.push_back(num_measurements_in_past);
+                if (t.qubit_value()) {
+                    row.output.sign = 1;
+                }
+            }
+            break;
+
+        case GateType::CX:
+        case GateType::XCZ:
+            undo_feedback_capable_instruction(inst, true, false);
+            break;
+
+        case GateType::YCZ:
+        case GateType::CY:
+            undo_feedback_capable_instruction(inst, true, true);
+            break;
+
+        case GateType::CZ:
+            undo_feedback_capable_instruction(inst, false, true);
+            break;
+
+        case GateType::XCX:
+        case GateType::XCY:
+        case GateType::YCX:
+        case GateType::YCY:
+        case GateType::H:
+        case GateType::H_XY:
+        case GateType::H_YZ:
+        case GateType::I:
+        case GateType::X:
+        case GateType::Y:
+        case GateType::Z:
+        case GateType::C_XYZ:
+        case GateType::C_ZYX:
+        case GateType::SQRT_X:
+        case GateType::SQRT_X_DAG:
+        case GateType::SQRT_Y:
+        case GateType::SQRT_Y_DAG:
+        case GateType::S:
+        case GateType::S_DAG:
+        case GateType::SQRT_XX:
+        case GateType::SQRT_XX_DAG:
+        case GateType::SQRT_YY:
+        case GateType::SQRT_YY_DAG:
+        case GateType::SQRT_ZZ:
+        case GateType::SQRT_ZZ_DAG:
+        case GateType::SPP:
+        case GateType::SPP_DAG:
+        case GateType::SWAP:
+        case GateType::ISWAP:
+        case GateType::CXSWAP:
+        case GateType::SWAPCX:
+        case GateType::CZSWAP:
+        case GateType::ISWAP_DAG:
+            for (auto &row : table) {
+                row.input.ref().undo_instruction(inst);
+            }
+            break;
+
+        case GateType::MPP:
+            buf_targets.clear();
+            buf_targets.insert(buf_targets.end(), inst.targets.begin(), inst.targets.end());
+            std::reverse(buf_targets.begin(), buf_targets.end());
+            decompose_mpp_operation(
+                CircuitInstruction{inst.gate_type, {}, buf_targets}, num_qubits, [&](CircuitInstruction sub_inst) {
+                    undo_instruction(sub_inst);
+                });
+            break;
+
+        default:
+            throw std::invalid_argument("Not handled by circuit flow generators method: " + inst.str());
+    }
+}
+
+template <size_t W>
+std::span<const size_t> CircuitFlowGeneratorSolver<W>::rows_anticommuting_with(uint32_t q, bool x, bool z) {
+    return rows_with([&](const Flow<W> &flow) {
+        bool anticommutes = false;
+        anticommutes ^= flow.input.xs[q] & z;
+        anticommutes ^= flow.input.zs[q] & x;
+        return anticommutes;
+    });
+}
+
+template <size_t W>
+template <typename PREDICATE>
+std::span<const size_t> CircuitFlowGeneratorSolver<W>::rows_with(PREDICATE predicate) {
+    buf_for_rows_with.clear();
+    for (size_t r = 0; r < table.size(); r++) {
+        if (predicate(table[r])) {
+            buf_for_rows_with.push_back(r);
+        }
+    }
+    return buf_for_rows_with;
+}
+
+template <size_t W>
+void CircuitFlowGeneratorSolver<W>::elimination_step(std::span<const size_t> elimination_set, size_t &num_eliminated) {
+    size_t pivot = SIZE_MAX;
+    for (auto p : elimination_set) {
+        if (p >= num_eliminated) {
+            pivot = p;
+            break;
+        }
+    }
+    if (pivot == SIZE_MAX) {
+        return;
+    }
+
+    for (auto p : elimination_set) {
+        if (p != pivot) {
+            mult_row_into(pivot, p);
+        }
+    }
+    std::swap(table[pivot], table[num_eliminated]);
+    num_eliminated++;
+}
+
+template <size_t W>
+void CircuitFlowGeneratorSolver<W>::canonicalize_over_qubits() {
+    size_t num_eliminated = 0;
+    for (size_t q = 0; q < num_qubits; q++) {
+        elimination_step(
+            rows_with([&](const Flow<W> &flow) {
+                return flow.input.xs[q];
+            }),
+            num_eliminated);
+        elimination_step(
+            rows_with([&](const Flow<W> &flow) {
+                return flow.input.zs[q];
+            }),
+            num_eliminated);
+        elimination_step(
+            rows_with([&](const Flow<W> &flow) {
+                return flow.output.xs[q];
+            }),
+            num_eliminated);
+        elimination_step(
+            rows_with([&](const Flow<W> &flow) {
+                return flow.output.zs[q];
+            }),
+            num_eliminated);
+    }
+
+    for (size_t r = 0; r < table.size(); r++) {
+        if (table[r].input.ref().has_no_pauli_terms() && table[r].output.ref().has_no_pauli_terms()) {
+            measurements_only_table.push_back(std::move(table[r]));
+            std::swap(table[r], table.back());
+            table.pop_back();
+            r--;
+        }
+    }
+}
+
+template <size_t W>
+void CircuitFlowGeneratorSolver<W>::final_canonicalize_into_table() {
+    for (auto &row : measurements_only_table) {
+        table.push_back(std::move(row));
+    }
+
+    size_t num_eliminated = 0;
+    for (size_t q = 0; q < num_qubits; q++) {
+        elimination_step(
+            rows_with([&](const Flow<W> &flow) {
+                return flow.input.xs[q];
+            }),
+            num_eliminated);
+        elimination_step(
+            rows_with([&](const Flow<W> &flow) {
+                return flow.input.zs[q];
+            }),
+            num_eliminated);
+    }
+    for (size_t q = 0; q < num_qubits; q++) {
+        elimination_step(
+            rows_with([&](const Flow<W> &flow) {
+                return flow.output.xs[q];
+            }),
+            num_eliminated);
+        elimination_step(
+            rows_with([&](const Flow<W> &flow) {
+                return flow.output.zs[q];
+            }),
+            num_eliminated);
+    }
+    for (size_t m = 0; m < num_measurements; m++) {
+        elimination_step(
+            rows_with([&](const Flow<W> &flow) {
+                return std::find(flow.measurements.begin(), flow.measurements.end(), (int32_t)m) !=
+                       flow.measurements.end();
+            }),
+            num_eliminated);
+    }
+    for (auto &row : table) {
+        row.output.sign ^= row.input.sign;
+        row.input.sign = 0;
+        if (row.input.ref().has_no_pauli_terms()) {
+            row.input.xs.destructive_resize(0);
+            row.input.zs.destructive_resize(0);
+            row.input.num_qubits = 0;
+        }
+        if (row.output.ref().has_no_pauli_terms()) {
+            row.output.xs.destructive_resize(0);
+            row.output.zs.destructive_resize(0);
+            row.output.num_qubits = 0;
+        }
+    }
+
+    std::sort(table.begin(), table.end());
+}
+
+template <size_t W>
+std::vector<Flow<W>> circuit_flow_generators(const Circuit &circuit) {
+    CircuitFlowGeneratorSolver<W> solver(circuit.compute_stats());
+    for (size_t q = 0; q < solver.num_qubits; q++) {
+        auto &x = solver.add_row();
+        x.output.xs[q] = 1;
+        x.input.xs[q] = 1;
+        auto &z = solver.add_row();
+        z.output.zs[q] = 1;
+        z.input.zs[q] = 1;
+    }
+    circuit.for_each_operation_reverse([&](CircuitInstruction inst) {
+        solver.undo_instruction(inst);
+    });
+    solver.final_canonicalize_into_table();
+    return solver.table;
+}
+
+}  // namespace stim
diff --git a/src/stim/util_top/circuit_flow_generators.test.cc b/src/stim/util_top/circuit_flow_generators.test.cc
new file mode 100644
index 00000000..8075a74b
--- /dev/null
+++ b/src/stim/util_top/circuit_flow_generators.test.cc
@@ -0,0 +1,266 @@
+#include "stim/util_top/circuit_flow_generators.h"
+
+#include "gtest/gtest.h"
+
+#include "stim/circuit/circuit.test.h"
+#include "stim/gen/gen_surface_code.h"
+#include "stim/mem/simd_word.test.h"
+#include "stim/util_top/circuit_inverse_qec.h"
+#include "stim/util_top/has_flow.h"
+
+using namespace stim;
+
+TEST_EACH_WORD_SIZE_W(circuit_flow_generators, various, {
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{}));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            X 0
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("X -> X"),
+            Flow<W>::from_str("Z -> -Z"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            H 0
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("X -> Z"),
+            Flow<W>::from_str("Z -> X"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            M 0
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("1 -> Z xor rec[0]"),
+            Flow<W>::from_str("Z -> rec[0]"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            M 0 0
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("1 -> rec[0] xor rec[1]"),
+            Flow<W>::from_str("1 -> Z xor rec[1]"),
+            Flow<W>::from_str("Z -> rec[1]"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            MXX 2 0
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("1 -> X_X xor rec[0]"),
+            Flow<W>::from_str("__X -> __X"),
+            Flow<W>::from_str("_X_ -> _X_"),
+            Flow<W>::from_str("_Z_ -> _Z_"),
+            Flow<W>::from_str("X__ -> __X xor rec[0]"),
+            Flow<W>::from_str("Z_Z -> Z_Z"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            MYY 3 1 2 3
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("1 -> __YY xor rec[1]"),
+            Flow<W>::from_str("1 -> _Y_Y xor rec[0]"),
+            Flow<W>::from_str("___Y -> ___Y"),
+            Flow<W>::from_str("__Y_ -> ___Y xor rec[1]"),
+            Flow<W>::from_str("_XZZ -> _ZZX xor rec[0]"),
+            Flow<W>::from_str("_ZZZ -> _ZZZ"),
+            Flow<W>::from_str("X___ -> X___"),
+            Flow<W>::from_str("Z___ -> Z___"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            MZZ 3 1 2 3
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("1 -> __ZZ xor rec[1]"),
+            Flow<W>::from_str("1 -> _Z_Z xor rec[0]"),
+            Flow<W>::from_str("___Z -> ___Z"),
+            Flow<W>::from_str("__Z_ -> ___Z xor rec[1]"),
+            Flow<W>::from_str("_XXX -> _XXX"),
+            Flow<W>::from_str("_Z__ -> ___Z xor rec[0]"),
+            Flow<W>::from_str("X___ -> X___"),
+            Flow<W>::from_str("Z___ -> Z___"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            ISWAP 3 1 2 3
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("___X -> _YZ_"),
+            Flow<W>::from_str("___Z -> _Z__"),
+            Flow<W>::from_str("__X_ -> __ZY"),
+            Flow<W>::from_str("__Z_ -> ___Z"),
+            Flow<W>::from_str("_X__ -> -_ZXZ"),
+            Flow<W>::from_str("_Z__ -> __Z_"),
+            Flow<W>::from_str("X___ -> X___"),
+            Flow<W>::from_str("Z___ -> Z___"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            S 0
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("X -> Y"),
+            Flow<W>::from_str("Z -> Z"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            S_DAG 0
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("X -> -Y"),
+            Flow<W>::from_str("Z -> Z"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            SPP Z0
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("X -> Y"),
+            Flow<W>::from_str("Z -> Z"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            SQRT_X 0
+            S 0
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("X -> Y"),
+            Flow<W>::from_str("Z -> X"),
+        }));
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            SPP X0 Z0
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("X -> Y"),
+            Flow<W>::from_str("Z -> X"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            SPP X0*X1
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("_X -> _X"),
+            Flow<W>::from_str("_Z -> -XY"),
+            Flow<W>::from_str("X_ -> X_"),
+            Flow<W>::from_str("Z_ -> -YX"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            SPP_DAG Z0
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("X -> -Y"),
+            Flow<W>::from_str("Z -> Z"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            M 0
+            CX rec[-1] 0
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("1 -> Z"),
+            Flow<W>::from_str("Z -> rec[0]"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            R 0
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("1 -> Z"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            MR 0
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("1 -> Z"),
+            Flow<W>::from_str("Z -> rec[0]"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            M 0
+            XCZ 0 rec[-1]
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("1 -> Z"),
+            Flow<W>::from_str("Z -> rec[0]"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            MPAD 0 1 1 0
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("1 -> rec[0]"),
+            Flow<W>::from_str("1 -> rec[3]"),
+            Flow<W>::from_str("1 -> -rec[1]"),
+            Flow<W>::from_str("1 -> -rec[2]"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            M 0
+            CY rec[-1] 1
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("1 -> Z_ xor rec[0]"),
+            Flow<W>::from_str("_X -> _X xor rec[0]"),
+            Flow<W>::from_str("_Z -> _Z xor rec[0]"),
+            Flow<W>::from_str("Z_ -> rec[0]"),
+        }));
+
+    EXPECT_EQ(
+        circuit_flow_generators<W>(Circuit(R"CIRCUIT(
+            HERALDED_ERASE(0.04) 1
+            HERALDED_PAULI_CHANNEL_1(0.01, 0.02, 0.03, 0.04) 1
+            TICK
+            MPP X0*Y1*Z2 Z0*Z1
+        )CIRCUIT")),
+        (std::vector<Flow<W>>{
+            Flow<W>::from_str("1 -> rec[0]"),
+            Flow<W>::from_str("1 -> rec[1]"),
+            Flow<W>::from_str("1 -> XYZ xor rec[2]"),
+            Flow<W>::from_str("1 -> ZZ_ xor rec[3]"),
+            Flow<W>::from_str("__Z -> __Z"),
+            Flow<W>::from_str("_ZX -> _ZX"),
+            Flow<W>::from_str("XXX -> _ZY xor rec[2]"),
+            Flow<W>::from_str("Z_X -> _ZX xor rec[3]"),
+        }));
+})
+
+TEST_EACH_WORD_SIZE_W(circuit_flow_generators, all_operations, {
+    auto circuit = generate_test_circuit_with_all_operations();
+    auto generators = circuit_flow_generators<W>(circuit);
+    auto rng = externally_seeded_rng();
+    auto passes = sample_if_circuit_has_stabilizer_flows<W>(256, rng, circuit, generators);
+    for (size_t k = 0; k < passes.size(); k++) {
+        EXPECT_TRUE(passes[k]) << k << ": " << generators[k];
+    }
+})
diff --git a/src/stim/util_top/circuit_inverse_qec.test.cc b/src/stim/util_top/circuit_inverse_qec.test.cc
index 135a1166..9438dd41 100644
--- a/src/stim/util_top/circuit_inverse_qec.test.cc
+++ b/src/stim/util_top/circuit_inverse_qec.test.cc
@@ -2,9 +2,9 @@
 
 #include "gtest/gtest.h"
 
-#include "circuit_to_detecting_regions.h"
 #include "stim/gen/gen_surface_code.h"
 #include "stim/mem/simd_word.test.h"
+#include "stim/util_top/circuit_to_detecting_regions.h"
 
 using namespace stim;
 
diff --git a/src/stim/util_top/export_qasm.test.cc b/src/stim/util_top/export_qasm.test.cc
index 75377cd2..34a7d89b 100644
--- a/src/stim/util_top/export_qasm.test.cc
+++ b/src/stim/util_top/export_qasm.test.cc
@@ -196,7 +196,7 @@ TEST(export_qasm, export_open_qasm_mpad) {
     ASSERT_EQ(out.str(), R"QASM(OPENQASM 3.0;
 include "stdgates.inc";
 
-qreg q[2];
+qreg q[1];
 creg rec[4];
 
 h q[0];
diff --git a/src/stim/util_top/has_flow.inl b/src/stim/util_top/has_flow.inl
index 7451e034..b106bdd5 100644
--- a/src/stim/util_top/has_flow.inl
+++ b/src/stim/util_top/has_flow.inl
@@ -38,7 +38,7 @@ static GateTarget measurement_index_to_target(int32_t m, uint64_t num_measuremen
 }
 
 template <size_t W>
-bool _sample_if_circuit_has_stabilizer_flow(
+bool _sample_if_noiseless_circuit_has_stabilizer_flow(
     size_t num_samples, std::mt19937_64 &rng, const Circuit &circuit, const Flow<W> &flow) {
     uint32_t num_qubits = (uint32_t)circuit.count_qubits();
     uint64_t num_measurements = circuit.count_measurements();
@@ -76,9 +76,10 @@ bool _sample_if_circuit_has_stabilizer_flow(
 template <size_t W>
 std::vector<bool> sample_if_circuit_has_stabilizer_flows(
     size_t num_samples, std::mt19937_64 &rng, const Circuit &circuit, std::span<const Flow<W>> flows) {
+    const auto &noiseless = circuit.aliased_noiseless_circuit();
     std::vector<bool> result;
     for (const auto &flow : flows) {
-        result.push_back(_sample_if_circuit_has_stabilizer_flow(num_samples, rng, circuit, flow));
+        result.push_back(_sample_if_noiseless_circuit_has_stabilizer_flow(num_samples, rng, noiseless, flow));
     }
     return result;
 }
diff --git a/src/stim/util_top/reference_sample_tree.cc b/src/stim/util_top/reference_sample_tree.cc
index bb8764aa..457a417f 100644
--- a/src/stim/util_top/reference_sample_tree.cc
+++ b/src/stim/util_top/reference_sample_tree.cc
@@ -9,7 +9,7 @@ bool ReferenceSampleTree::empty() const {
     if (!prefix_bits.empty()) {
         return false;
     }
-    for (const auto &child: suffix_children) {
+    for (const auto &child : suffix_children) {
         if (!child.empty()) {
             return false;
         }
@@ -48,7 +48,7 @@ void ReferenceSampleTree::flatten_and_simplify_into(std::vector<ReferenceSampleT
         if (dst.prefix_bits == src.prefix_bits && dst.suffix_children == src.suffix_children) {
             dst.repetitions += src.repetitions;
 
-        // Fuse children with unrepeated contents if they can be fused.
+            // Fuse children with unrepeated contents if they can be fused.
         } else if (src.repetitions == 1 && dst.repetitions == 1 && dst.suffix_children.empty()) {
             dst.suffix_children = std::move(src.suffix_children);
             dst.prefix_bits.insert(dst.prefix_bits.end(), src.prefix_bits.begin(), src.prefix_bits.end());
@@ -78,9 +78,9 @@ void ReferenceSampleTree::flatten_and_simplify_into(std::vector<ReferenceSampleT
         out.push_back(std::move(result));
     } else {
         out.push_back(ReferenceSampleTree{
-            .prefix_bits={},
-            .suffix_children=std::move(fused),
-            .repetitions=repetitions,
+            .prefix_bits = {},
+            .suffix_children = std::move(fused),
+            .repetitions = repetitions,
         });
     }
 }
@@ -90,7 +90,8 @@ uint64_t stim::max_feedback_lookback_in_loop(const Circuit &loop) {
     uint64_t furthest_lookback = 0;
     for (const auto &inst : loop.operations) {
         if (inst.gate_type == GateType::REPEAT) {
-            furthest_lookback = std::max(furthest_lookback, max_feedback_lookback_in_loop(inst.repeat_block_body(loop)));
+            furthest_lookback =
+                std::max(furthest_lookback, max_feedback_lookback_in_loop(inst.repeat_block_body(loop)));
         } else {
             auto f = GATE_DATA[inst.gate_type].flags;
             if ((f & GateFlags::GATE_CAN_TARGET_BITS) && (f & GateFlags::GATE_TARGETS_PAIRS)) {
@@ -136,10 +137,10 @@ ReferenceSampleTree ReferenceSampleTree::simplified() const {
     ReferenceSampleTree result;
     result.repetitions = 1;
 
-     // Take payload from first child.
+    // Take payload from first child.
     if (flat[0].repetitions == 1 && flat[0].suffix_children.empty()) {
-         result = std::move(flat[0]);
-         flat.erase(flat.begin());
+        result = std::move(flat[0]);
+        flat.erase(flat.begin());
     }
 
     result.suffix_children = std::move(flat);
@@ -148,7 +149,7 @@ ReferenceSampleTree ReferenceSampleTree::simplified() const {
 
 size_t ReferenceSampleTree::size() const {
     size_t result = prefix_bits.size();
-    for (const auto &child: suffix_children) {
+    for (const auto &child : suffix_children) {
         result += child.size();
     }
     return result * repetitions;
@@ -157,7 +158,7 @@ size_t ReferenceSampleTree::size() const {
 void ReferenceSampleTree::decompress_into(std::vector<bool> &output) const {
     for (uint64_t k = 0; k < repetitions; k++) {
         output.insert(output.end(), prefix_bits.begin(), prefix_bits.end());
-        for (const auto &child: suffix_children) {
+        for (const auto &child : suffix_children) {
             child.decompress_into(output);
         }
     }
@@ -166,12 +167,8 @@ void ReferenceSampleTree::decompress_into(std::vector<bool> &output) const {
 ReferenceSampleTree ReferenceSampleTree::from_circuit_reference_sample(const Circuit &circuit) {
     auto stats = circuit.compute_stats();
     std::mt19937_64 irrelevant_rng{0};
-    CompressedReferenceSampleHelper<MAX_BITWORD_WIDTH> helper(
-        TableauSimulator<MAX_BITWORD_WIDTH>(
-            std::move(irrelevant_rng),
-            stats.num_qubits,
-            +1,
-            MeasureRecord(stats.max_lookback)));
+    CompressedReferenceSampleHelper<MAX_BITWORD_WIDTH> helper(TableauSimulator<MAX_BITWORD_WIDTH>(
+        std::move(irrelevant_rng), stats.num_qubits, +1, MeasureRecord(stats.max_lookback)));
     return helper.do_loop_with_tortoise_hare_folding(circuit, 1).simplified();
 }
 
@@ -182,8 +179,7 @@ std::string ReferenceSampleTree::str() const {
 }
 
 bool ReferenceSampleTree::operator==(const ReferenceSampleTree &other) const {
-    return repetitions == other.repetitions &&
-           prefix_bits == other.prefix_bits &&
+    return repetitions == other.repetitions && prefix_bits == other.prefix_bits &&
            suffix_children == other.suffix_children;
 }
 bool ReferenceSampleTree::operator!=(const ReferenceSampleTree &other) const {
diff --git a/src/stim/util_top/reference_sample_tree.h b/src/stim/util_top/reference_sample_tree.h
index 2b439b61..4b6f677f 100644
--- a/src/stim/util_top/reference_sample_tree.h
+++ b/src/stim/util_top/reference_sample_tree.h
@@ -60,21 +60,15 @@ struct CompressedReferenceSampleHelper {
     ///
     /// Loops containing within the body of this loop (or circuit body) may
     /// still be compressed. Only the top-level loop is not folded.
-    ReferenceSampleTree do_loop_with_no_folding(
-        const Circuit &loop,
-        uint64_t reps);
+    ReferenceSampleTree do_loop_with_no_folding(const Circuit &loop, uint64_t reps);
 
     /// Runs tortoise-and-hare analysis of the loop while simulating its
     /// reference sample, in order to attempt to return a compressed
     /// representation.
-    ReferenceSampleTree do_loop_with_tortoise_hare_folding(
-        const Circuit &loop,
-        uint64_t reps);
+    ReferenceSampleTree do_loop_with_tortoise_hare_folding(const Circuit &loop, uint64_t reps);
 
     bool in_same_recent_state_as(
-        const CompressedReferenceSampleHelper<W> &other,
-        uint64_t max_record_lookback,
-        bool allow_false_negative) const;
+        const CompressedReferenceSampleHelper<W> &other, uint64_t max_record_lookback, bool allow_false_negative) const;
 };
 
 uint64_t max_feedback_lookback_in_loop(const Circuit &loop);
diff --git a/src/stim/util_top/reference_sample_tree.inl b/src/stim/util_top/reference_sample_tree.inl
index 746920dc..94c52e27 100644
--- a/src/stim/util_top/reference_sample_tree.inl
+++ b/src/stim/util_top/reference_sample_tree.inl
@@ -26,7 +26,7 @@ ReferenceSampleTree CompressedReferenceSampleHelper<W>::do_loop_with_no_folding(
         for (const auto &inst : loop.operations) {
             if (inst.gate_type == GateType::REPEAT) {
                 uint64_t repeats = inst.repeat_block_rep_count();
-                const auto& block = inst.repeat_block_body(loop);
+                const auto &block = inst.repeat_block_body(loop);
                 flush_recorded_into_result();
                 result.suffix_children.push_back(do_loop_with_tortoise_hare_folding(block, repeats));
                 start_size = sim.measurement_record.storage.size();
@@ -41,7 +41,8 @@ ReferenceSampleTree CompressedReferenceSampleHelper<W>::do_loop_with_no_folding(
 }
 
 template <size_t W>
-ReferenceSampleTree CompressedReferenceSampleHelper<W>::do_loop_with_tortoise_hare_folding(const Circuit &loop, uint64_t reps) {
+ReferenceSampleTree CompressedReferenceSampleHelper<W>::do_loop_with_tortoise_hare_folding(
+    const Circuit &loop, uint64_t reps) {
     if (reps < 10) {
         // Probably not worth the overhead of tortoise-and-hare. Just run it raw.
         return do_loop_with_no_folding(loop, reps);
@@ -99,7 +100,9 @@ ReferenceSampleTree CompressedReferenceSampleHelper<W>::do_loop_with_tortoise_ha
     // Add skipped iterations' measurement data into the sim's measurement record.
     loop_contents.repetitions = 1;
     sim.measurement_record.discard_results_past_max_lookback();
-    for (size_t k = 0; k < period_steps_left && sim.measurement_record.storage.size() < sim.measurement_record.max_lookback * 2; k++) {
+    for (size_t k = 0;
+         k < period_steps_left && sim.measurement_record.storage.size() < sim.measurement_record.max_lookback * 2;
+         k++) {
         loop_contents.decompress_into(sim.measurement_record.storage);
     }
     sim.measurement_record.discard_results_past_max_lookback();
@@ -116,9 +119,7 @@ ReferenceSampleTree CompressedReferenceSampleHelper<W>::do_loop_with_tortoise_ha
 
 template <size_t W>
 bool CompressedReferenceSampleHelper<W>::in_same_recent_state_as(
-    const CompressedReferenceSampleHelper<W> &other,
-    uint64_t max_record_lookback,
-    bool allow_false_negative) const {
+    const CompressedReferenceSampleHelper<W> &other, uint64_t max_record_lookback, bool allow_false_negative) const {
     const auto &s1 = sim.measurement_record.storage;
     const auto &s2 = other.sim.measurement_record.storage;
 
diff --git a/src/stim/util_top/reference_sample_tree.perf.cc b/src/stim/util_top/reference_sample_tree.perf.cc
index c245f157..8d655202 100644
--- a/src/stim/util_top/reference_sample_tree.perf.cc
+++ b/src/stim/util_top/reference_sample_tree.perf.cc
@@ -14,8 +14,7 @@ BENCHMARK(reference_sample_tree_surface_code_d31_r1000000000) {
     benchmark_go([&]() {
         auto result = ReferenceSampleTree::from_circuit_reference_sample(circuit);
         total += result.empty();
-    })
-        .goal_millis(25);
+    }).goal_millis(25);
     if (total) {
         std::cerr << "data dependence";
     }
@@ -44,8 +43,7 @@ BENCHMARK(reference_sample_tree_nested_circuit) {
     benchmark_go([&]() {
         auto result = ReferenceSampleTree::from_circuit_reference_sample(circuit);
         total += result.empty();
-    })
-        .goal_micros(230);
+    }).goal_micros(230);
     if (total) {
         std::cerr << "data dependence";
     }
diff --git a/src/stim/util_top/reference_sample_tree.test.cc b/src/stim/util_top/reference_sample_tree.test.cc
index 858a8e66..3d09171b 100644
--- a/src/stim/util_top/reference_sample_tree.test.cc
+++ b/src/stim/util_top/reference_sample_tree.test.cc
@@ -19,72 +19,83 @@ void expect_tree_matches_normal_reference_sample_of(const ReferenceSampleTree &t
 
 TEST(ReferenceSampleTree, equality) {
     ReferenceSampleTree empty1{
-        .prefix_bits={},
-        .suffix_children={},
-        .repetitions=0,
+        .prefix_bits = {},
+        .suffix_children = {},
+        .repetitions = 0,
     };
     ReferenceSampleTree empty2;
     ASSERT_EQ(empty1, empty2);
 
     ASSERT_FALSE(empty1 != empty2);
-    ASSERT_NE(empty1, (ReferenceSampleTree{.prefix_bits={},.suffix_children{},.repetitions=1}));
-    ASSERT_NE(empty1, (ReferenceSampleTree{.prefix_bits={0},.suffix_children{},.repetitions=0}));
-    ASSERT_NE(empty1, (ReferenceSampleTree{.prefix_bits={},.suffix_children{{}},.repetitions=0}));
+    ASSERT_NE(empty1, (ReferenceSampleTree{.prefix_bits = {}, .suffix_children{}, .repetitions = 1}));
+    ASSERT_NE(empty1, (ReferenceSampleTree{.prefix_bits = {0}, .suffix_children{}, .repetitions = 0}));
+    ASSERT_NE(empty1, (ReferenceSampleTree{.prefix_bits = {}, .suffix_children{{}}, .repetitions = 0}));
 }
 
 TEST(ReferenceSampleTree, str) {
-    ASSERT_EQ((ReferenceSampleTree{
-        .prefix_bits={},
-        .suffix_children={},
-        .repetitions=0,
-    }.str()), "0*('')");
+    ASSERT_EQ(
+        (ReferenceSampleTree{
+            .prefix_bits = {},
+            .suffix_children = {},
+            .repetitions = 0,
+        }
+             .str()),
+        "0*('')");
 
-    ASSERT_EQ((ReferenceSampleTree{
-        .prefix_bits={1, 1, 0, 1},
-        .suffix_children={},
-        .repetitions=0,
-    }.str()), "0*('1101')");
+    ASSERT_EQ(
+        (ReferenceSampleTree{
+            .prefix_bits = {1, 1, 0, 1},
+            .suffix_children = {},
+            .repetitions = 0,
+        }
+             .str()),
+        "0*('1101')");
 
-    ASSERT_EQ((ReferenceSampleTree{
-        .prefix_bits={1, 1, 0, 1},
-        .suffix_children={},
-        .repetitions=2,
-    }.str()), "2*('1101')");
+    ASSERT_EQ(
+        (ReferenceSampleTree{
+            .prefix_bits = {1, 1, 0, 1},
+            .suffix_children = {},
+            .repetitions = 2,
+        }
+             .str()),
+        "2*('1101')");
 
-    ASSERT_EQ((ReferenceSampleTree{
-        .prefix_bits={1, 1, 0, 1},
-        .suffix_children={
-            ReferenceSampleTree{
-                .prefix_bits={1},
-                .suffix_children={},
-                .repetitions=5,
-            }
-        },
-        .repetitions=2,
-    }.str()), "2*('1101'+5*('1'))");
+    ASSERT_EQ(
+        (ReferenceSampleTree{
+            .prefix_bits = {1, 1, 0, 1},
+            .suffix_children = {ReferenceSampleTree{
+                .prefix_bits = {1},
+                .suffix_children = {},
+                .repetitions = 5,
+            }},
+            .repetitions = 2,
+        }
+             .str()),
+        "2*('1101'+5*('1'))");
 }
 
 TEST(ReferenceSampleTree, simplified) {
     ReferenceSampleTree raw{
-        .prefix_bits={},
-        .suffix_children={
-            ReferenceSampleTree{
-                .prefix_bits={},
-                .suffix_children={},
-                .repetitions=1,
-            },
-            ReferenceSampleTree{
-                .prefix_bits={1, 0, 1},
-                .suffix_children={{}},
-                .repetitions=0,
+        .prefix_bits = {},
+        .suffix_children =
+            {
+                ReferenceSampleTree{
+                    .prefix_bits = {},
+                    .suffix_children = {},
+                    .repetitions = 1,
+                },
+                ReferenceSampleTree{
+                    .prefix_bits = {1, 0, 1},
+                    .suffix_children = {{}},
+                    .repetitions = 0,
+                },
+                ReferenceSampleTree{
+                    .prefix_bits = {1, 1, 1},
+                    .suffix_children = {},
+                    .repetitions = 2,
+                },
             },
-            ReferenceSampleTree{
-                .prefix_bits={1, 1, 1},
-                .suffix_children={},
-                .repetitions=2,
-            },
-        },
-        .repetitions=3,
+        .repetitions = 3,
     };
     ASSERT_EQ(raw.simplified().str(), "6*('111')");
 }
@@ -92,36 +103,38 @@ TEST(ReferenceSampleTree, simplified) {
 TEST(ReferenceSampleTree, decompress_into) {
     std::vector<bool> result;
     ReferenceSampleTree{
-        .prefix_bits={1, 1, 0, 1},
-        .suffix_children={
-            ReferenceSampleTree{
-                .prefix_bits={1},
-                .suffix_children={},
-                .repetitions=5,
-            }
-        },
-        .repetitions=2,
-    }.decompress_into(result);
+        .prefix_bits = {1, 1, 0, 1},
+        .suffix_children = {ReferenceSampleTree{
+            .prefix_bits = {1},
+            .suffix_children = {},
+            .repetitions = 5,
+        }},
+        .repetitions = 2,
+    }
+        .decompress_into(result);
     ASSERT_EQ(result, (std::vector<bool>{1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1}));
 
     result.clear();
     ReferenceSampleTree{
-        .prefix_bits={1, 1, 0, 1},
-        .suffix_children={
-            ReferenceSampleTree{
-                .prefix_bits={1, 0, 1},
-                .suffix_children={},
-                .repetitions=8,
+        .prefix_bits = {1, 1, 0, 1},
+        .suffix_children =
+            {
+                ReferenceSampleTree{
+                    .prefix_bits = {1, 0, 1},
+                    .suffix_children = {},
+                    .repetitions = 8,
+                },
+                ReferenceSampleTree{
+                    .prefix_bits = {0, 0},
+                    .suffix_children = {},
+                    .repetitions = 1,
+                },
             },
-            ReferenceSampleTree{
-                .prefix_bits={0, 0},
-                .suffix_children={},
-                .repetitions=1,
-            },
-        },
-        .repetitions=1,
-    }.decompress_into(result);
-    ASSERT_EQ(result, (std::vector<bool>{1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0}));
+        .repetitions = 1,
+    }
+        .decompress_into(result);
+    ASSERT_EQ(result, (std::vector<bool>{1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0,
+                                         1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0}));
 }
 
 TEST(ReferenceSampleTree, simple_circuit) {
@@ -186,7 +199,8 @@ TEST(ReferenceSampleTree, feedback) {
 TEST(max_feedback_lookback_in_loop, simple) {
     ASSERT_EQ(max_feedback_lookback_in_loop(Circuit()), 0);
 
-    ASSERT_EQ(max_feedback_lookback_in_loop(Circuit(R"CIRCUIT(
+    ASSERT_EQ(
+        max_feedback_lookback_in_loop(Circuit(R"CIRCUIT(
         REPEAT 100 {
             REPEAT 100 {
                 M 0
@@ -200,27 +214,36 @@ TEST(max_feedback_lookback_in_loop, simple) {
             X 1
             CX 1 0
         }
-    )CIRCUIT")), 0);
+    )CIRCUIT")),
+        0);
 
-    ASSERT_EQ(max_feedback_lookback_in_loop(Circuit(R"CIRCUIT(
+    ASSERT_EQ(
+        max_feedback_lookback_in_loop(Circuit(R"CIRCUIT(
         CX rec[-1] 0
-    )CIRCUIT")), 1);
+    )CIRCUIT")),
+        1);
 
-    ASSERT_EQ(max_feedback_lookback_in_loop(Circuit(R"CIRCUIT(
+    ASSERT_EQ(
+        max_feedback_lookback_in_loop(Circuit(R"CIRCUIT(
         CZ 0 rec[-2]
-    )CIRCUIT")), 2);
+    )CIRCUIT")),
+        2);
 
-    ASSERT_EQ(max_feedback_lookback_in_loop(Circuit(R"CIRCUIT(
+    ASSERT_EQ(
+        max_feedback_lookback_in_loop(Circuit(R"CIRCUIT(
         CZ 0 rec[-2]
         CY 0 rec[-3]
-    )CIRCUIT")), 3);
+    )CIRCUIT")),
+        3);
 
-    ASSERT_EQ(max_feedback_lookback_in_loop(Circuit(R"CIRCUIT(
+    ASSERT_EQ(
+        max_feedback_lookback_in_loop(Circuit(R"CIRCUIT(
         CZ 0 rec[-2]
         REPEAT 100 {
             CX rec[-5] 0
         }
-    )CIRCUIT")), 5);
+    )CIRCUIT")),
+        5);
 }
 
 TEST(ReferenceSampleTree, nested_loops) {
@@ -240,7 +263,10 @@ TEST(ReferenceSampleTree, nested_loops) {
     )CIRCUIT");
     auto ref = ReferenceSampleTree::from_circuit_reference_sample(circuit);
     expect_tree_matches_normal_reference_sample_of(ref, circuit);
-    ASSERT_EQ(ref.str(), "1*(''+50*('0110')+200*('0')+50*('1001')+200*('1')+50*('1001')+200*('1')+50*('0110')+200*('0')+24*(''+50*('0110')+200*('0')+50*('1001')+200*('1')+50*('1001')+200*('1')+50*('0110')+200*('0')))");
+    ASSERT_EQ(
+        ref.str(),
+        "1*(''+50*('0110')+200*('0')+50*('1001')+200*('1')+50*('1001')+200*('1')+50*('0110')+200*('0')+24*(''+50*('"
+        "0110')+200*('0')+50*('1001')+200*('1')+50*('1001')+200*('1')+50*('0110')+200*('0')))");
 }
 
 TEST(ReferenceSampleTree, surface_code) {
diff --git a/src/stim/util_top/stabilizers_to_tableau.inl b/src/stim/util_top/stabilizers_to_tableau.inl
index 881c846d..c1751c2e 100644
--- a/src/stim/util_top/stabilizers_to_tableau.inl
+++ b/src/stim/util_top/stabilizers_to_tableau.inl
@@ -97,7 +97,7 @@ Tableau<W> stabilizers_to_tableau(
             if (!allow_redundant) {
                 std::stringstream ss;
                 ss << "Some of the given stabilizers are redundant.";
-                ss<< "\nTo allow redundant stabilizers, pass the argument allow_redundant=True.";
+                ss << "\nTo allow redundant stabilizers, pass the argument allow_redundant=True.";
                 ss << "\n";
                 ss << "\nFor example:";
                 ss << "\n    stabilizers[" << k << "] = " << stabilizers[k];

From 3f9082ef8ad17b30ade0f5bdbfa35167e72fdd97 Mon Sep 17 00:00:00 2001
From: Craig Gidney <craiggidney@google.com>
Date: Sat, 13 Jul 2024 23:21:40 -0700
Subject: [PATCH 05/17] Add
 `stim.GateData.{is_symmetric_gate,hadamard_conjugated}` (#796)

- Add some missing example sections to docstrings
- Add a warning about missing example sections when running
documentation tests
- Add `stim.GateData.is_symmetric_gate`
- Add `stim.GateData.hadamard_conjugated(unsigned=False)`
- Add `stim.DemTarget.__init__` with support for parsing strings

Fixes https://github.com/quantumlib/Stim/issues/795
---
 dev/doctest_proper.py                         |   9 +
 doc/python_api_reference_vDev.md              | 347 +++++++++++++++++-
 doc/stim.pyi                                  | 323 +++++++++++++++-
 glue/python/src/stim/__init__.pyi             | 323 +++++++++++++++-
 src/stim/circuit/circuit.pybind.cc            |   4 +-
 src/stim/dem/dem_instruction.cc               |  10 +-
 src/stim/dem/dem_instruction.h                |   3 +
 .../dem/detector_error_model_pybind_test.py   |   2 +-
 ...etector_error_model_repeat_block.pybind.cc |  12 +
 .../dem/detector_error_model_target.pybind.cc |  74 +++-
 ...detector_error_model_target_pybind_test.py |  20 +
 src/stim/gates/gates.cc                       | 142 +++++++
 src/stim/gates/gates.h                        |   3 +
 src/stim/gates/gates.pybind.cc                | 121 ++++++
 src/stim/gates/gates.test.cc                  |  75 ++++
 src/stim/gates/gates_test.py                  |  17 +
 src/stim/simulators/matched_error.pybind.cc   | 184 +++++++++-
 17 files changed, 1594 insertions(+), 75 deletions(-)

diff --git a/dev/doctest_proper.py b/dev/doctest_proper.py
index 59311966..881efac7 100755
--- a/dev/doctest_proper.py
+++ b/dev/doctest_proper.py
@@ -89,6 +89,15 @@ def main():
         module = __import__(module_name)
         out = {}
         gen(obj=module, fullname=module_name, out=out)
+        for k, v in out.items():
+            if v.__doc__ is None:
+                continue
+            v = v.__doc__.lower()
+            if '\n' in v.strip() and 'examples:' not in v and 'example:' not in v and '[deprecated]' not in v:
+                if k.split('.')[-1] not in ['__next__', '__iter__', '__init_subclass__', '__module__', '__eq__', '__ne__', '__str__', '__repr__']:
+                    if all(not (e.startswith('_') and not e.startswith('__')) for e in k.split('.')):
+                        print(f"    Warning: Missing 'examples:' section in docstring of {k!r}", file=sys.stderr)
+
         module.__test__ = {k: v for k, v in out.items()}
         if doctest.testmod(module, globs=globs).failed:
             any_failed = True
diff --git a/doc/python_api_reference_vDev.md b/doc/python_api_reference_vDev.md
index 49dd374d..145d04ac 100644
--- a/doc/python_api_reference_vDev.md
+++ b/doc/python_api_reference_vDev.md
@@ -135,6 +135,7 @@ API references for stable versions are kept on the [stim github wiki](https://gi
     - [`stim.DemRepeatBlock.type`](#stim.DemRepeatBlock.type)
 - [`stim.DemTarget`](#stim.DemTarget)
     - [`stim.DemTarget.__eq__`](#stim.DemTarget.__eq__)
+    - [`stim.DemTarget.__init__`](#stim.DemTarget.__init__)
     - [`stim.DemTarget.__ne__`](#stim.DemTarget.__ne__)
     - [`stim.DemTarget.__repr__`](#stim.DemTarget.__repr__)
     - [`stim.DemTarget.__str__`](#stim.DemTarget.__str__)
@@ -217,10 +218,12 @@ API references for stable versions are kept on the [stim github wiki](https://gi
     - [`stim.GateData.aliases`](#stim.GateData.aliases)
     - [`stim.GateData.flows`](#stim.GateData.flows)
     - [`stim.GateData.generalized_inverse`](#stim.GateData.generalized_inverse)
+    - [`stim.GateData.hadamard_conjugated`](#stim.GateData.hadamard_conjugated)
     - [`stim.GateData.inverse`](#stim.GateData.inverse)
     - [`stim.GateData.is_noisy_gate`](#stim.GateData.is_noisy_gate)
     - [`stim.GateData.is_reset`](#stim.GateData.is_reset)
     - [`stim.GateData.is_single_qubit_gate`](#stim.GateData.is_single_qubit_gate)
+    - [`stim.GateData.is_symmetric_gate`](#stim.GateData.is_symmetric_gate)
     - [`stim.GateData.is_two_qubit_gate`](#stim.GateData.is_two_qubit_gate)
     - [`stim.GateData.is_unitary`](#stim.GateData.is_unitary)
     - [`stim.GateData.name`](#stim.GateData.name)
@@ -2702,8 +2705,8 @@ def reference_sample(
     Examples:
         >>> import stim
         >>> stim.Circuit('''
-        ...    X 1
-        ...    M 0 1
+        ...     X 1
+        ...     M 0 1
         ... ''').reference_sample()
         array([False,  True])
     """
@@ -3502,6 +3505,26 @@ def without_noise(
 # (at top-level in the stim module)
 class CircuitErrorLocation:
     """Describes the location of an error mechanism from a stim circuit.
+
+    Examples:
+        >>> import stim
+        >>> circuit = stim.Circuit.generated(
+        ...     "repetition_code:memory",
+        ...     distance=5,
+        ...     rounds=5,
+        ...     before_round_data_depolarization=1e-3,
+        ... )
+        >>> logical_error = circuit.shortest_graphlike_error()
+        >>> error_location = logical_error[0].circuit_error_locations[0]
+        >>> print(error_location)
+        CircuitErrorLocation {
+            flipped_pauli_product: X0
+            Circuit location stack trace:
+                (after 1 TICKs)
+                at instruction #3 (DEPOLARIZE1) in the circuit
+                at target #1 of the instruction
+                resolving to DEPOLARIZE1(0.001) 0
+        }
     """
 ```
 
@@ -3520,6 +3543,48 @@ def __init__(
     stack_frames: List[stim.CircuitErrorLocationStackFrame],
 ) -> None:
     """Creates a stim.CircuitErrorLocation.
+
+    Examples:
+        >>> import stim
+        >>> err = stim.CircuitErrorLocation(
+        ...     tick_offset=1,
+        ...     flipped_pauli_product=(
+        ...         stim.GateTargetWithCoords(
+        ...             gate_target=stim.target_x(0),
+        ...             coords=[],
+        ...         ),
+        ...     ),
+        ...     flipped_measurement=stim.FlippedMeasurement(
+        ...         record_index=None,
+        ...         observable=(),
+        ...     ),
+        ...     instruction_targets=stim.CircuitTargetsInsideInstruction(
+        ...         gate='DEPOLARIZE1',
+        ...         args=[0.001],
+        ...         target_range_start=0,
+        ...         target_range_end=1,
+        ...         targets_in_range=(stim.GateTargetWithCoords(
+        ...             gate_target=0,
+        ...             coords=[],
+        ...         ),)
+        ...     ),
+        ...     stack_frames=(
+        ...         stim.CircuitErrorLocationStackFrame(
+        ...             instruction_offset=2,
+        ...             iteration_index=0,
+        ...             instruction_repetitions_arg=0,
+        ...         ),
+        ...     ),
+        ... )
+        >>> print(err)
+        CircuitErrorLocation {
+            flipped_pauli_product: X0
+            Circuit location stack trace:
+                (after 1 TICKs)
+                at instruction #3 (DEPOLARIZE1) in the circuit
+                at target #1 of the instruction
+                resolving to DEPOLARIZE1(0.001) 0
+        }
     """
 ```
 
@@ -3533,7 +3598,21 @@ def flipped_measurement(
     self,
 ) -> Optional[stim.FlippedMeasurement]:
     """The measurement that was flipped by the error mechanism.
+
     If the error isn't a measurement error, this will be None.
+
+    Examples:
+        >>> import stim
+        >>> err = stim.Circuit('''
+        ...     R 0
+        ...     M(0.125) 0
+        ...     OBSERVABLE_INCLUDE(0) rec[-1]
+        ... ''').shortest_graphlike_error()
+        >>> err[0].circuit_error_locations[0].flipped_measurement
+        stim.FlippedMeasurement(
+            record_index=0,
+            observable=(stim.GateTargetWithCoords(stim.target_z(0), []),),
+        )
     """
 ```
 
@@ -3547,7 +3626,19 @@ def flipped_pauli_product(
     self,
 ) -> List[stim.GateTargetWithCoords]:
     """The Pauli errors that the error mechanism applied to qubits.
+
     When the error is a measurement error, this will be an empty list.
+
+    Examples:
+        >>> import stim
+        >>> err = stim.Circuit('''
+        ...     R 0
+        ...     Y_ERROR(0.125) 0
+        ...     M 0
+        ...     OBSERVABLE_INCLUDE(0) rec[-1]
+        ... ''').shortest_graphlike_error()
+        >>> err[0].circuit_error_locations[0].flipped_pauli_product
+        [stim.GateTargetWithCoords(stim.target_y(0), [])]
     """
 ```
 
@@ -3575,8 +3666,26 @@ def instruction_targets(
 def stack_frames(
     self,
 ) -> List[stim.CircuitErrorLocationStackFrame]:
-    """Where in the circuit's execution does the error mechanism occur,
-    accounting for things like nested loops that iterate multiple times.
+    """Describes where in the circuit's execution the error happened.
+
+    Multiple frames are needed because the error may occur within a loop,
+    or a loop nested inside a loop, or etc.
+
+    Examples:
+        >>> import stim
+        >>> err = stim.Circuit('''
+        ...     R 0
+        ...     TICK
+        ...     Y_ERROR(0.125) 0
+        ...     M 0
+        ...     OBSERVABLE_INCLUDE(0) rec[-1]
+        ... ''').shortest_graphlike_error()
+        >>> err[0].circuit_error_locations[0].stack_frames
+        [stim.CircuitErrorLocationStackFrame(
+            instruction_offset=2,
+            iteration_index=0,
+            instruction_repetitions_arg=0,
+        )]
     """
 ```
 
@@ -3589,8 +3698,23 @@ def stack_frames(
 def tick_offset(
     self,
 ) -> int:
-    """The number of TICKs that executed before the error mechanism being discussed,
-    including TICKs that occurred multiple times during loops.
+    """The number of TICKs that executed before the error happened.
+
+    This counts TICKs occurring multiple times during loops.
+
+    Examples:
+        >>> import stim
+        >>> err = stim.Circuit('''
+        ...     R 0
+        ...     TICK
+        ...     TICK
+        ...     TICK
+        ...     Y_ERROR(0.125) 0
+        ...     M 0
+        ...     OBSERVABLE_INCLUDE(0) rec[-1]
+        ... ''').shortest_graphlike_error()
+        >>> err[0].circuit_error_locations[0].tick_offset
+        3
     """
 ```
 
@@ -5310,6 +5434,18 @@ def body_copy(
     self,
 ) -> stim.DetectorErrorModel:
     """Returns a copy of the block's body, as a stim.DetectorErrorModel.
+
+    Examples:
+        >>> import stim
+        >>> body = stim.DetectorErrorModel('''
+        ...     error(0.125) D0 D1
+        ...     shift_detectors 1
+        ... ''')
+        >>> repeat_block = stim.DemRepeatBlock(100, body)
+        >>> repeat_block.body_copy() == body
+        True
+        >>> repeat_block.body_copy() is repeat_block.body_copy()
+        False
     """
 ```
 
@@ -5379,6 +5515,33 @@ def __eq__(
     """
 ```
 
+<a name="stim.DemTarget.__init__"></a>
+```python
+# stim.DemTarget.__init__
+
+# (in class stim.DemTarget)
+def __init__(
+    self,
+    arg: object,
+    /,
+) -> None:
+    """Creates a stim.DemTarget from the given object.
+
+    Args:
+        arg: A string to parse as a stim.DemTarget, or some other object to
+            convert into a stim.DemTarget.
+
+    Examples:
+        >>> import stim
+        >>> stim.DemTarget("D5") == stim.target_relative_detector_id(5)
+        True
+        >>> stim.DemTarget("L2") == stim.target_logical_observable_id(2)
+        True
+        >>> stim.DemTarget("^") == stim.target_separator()
+        True
+    """
+```
+
 <a name="stim.DemTarget.__ne__"></a>
 ```python
 # stim.DemTarget.__ne__
@@ -5428,6 +5591,15 @@ def is_logical_observable_id(
 
     In a detector error model file, observable targets are prefixed by `L`. For
     example, in `error(0.25) D0 L1` the `L1` is an observable target.
+
+    Examples:
+        >>> import stim
+        >>> stim.DemTarget("L2").is_logical_observable_id()
+        True
+        >>> stim.DemTarget("D3").is_logical_observable_id()
+        False
+        >>> stim.DemTarget("^").is_logical_observable_id()
+        False
     """
 ```
 
@@ -5443,6 +5615,15 @@ def is_relative_detector_id(
 
     In a detector error model file, detectors are prefixed by `D`. For
     example, in `error(0.25) D0 L1` the `D0` is a relative detector target.
+
+    Examples:
+        >>> import stim
+        >>> stim.DemTarget("L2").is_relative_detector_id()
+        False
+        >>> stim.DemTarget("D3").is_relative_detector_id()
+        True
+        >>> stim.DemTarget("^").is_relative_detector_id()
+        False
     """
 ```
 
@@ -5458,6 +5639,15 @@ def is_separator(
 
     Separates separate the components of a suggested decompositions within an error.
     For example, the `^` in `error(0.25) D1 D2 ^ D3 D4` is the separator.
+
+    Examples:
+        >>> import stim
+        >>> stim.DemTarget("L2").is_separator()
+        False
+        >>> stim.DemTarget("D3").is_separator()
+        False
+        >>> stim.DemTarget("^").is_separator()
+        True
     """
 ```
 
@@ -5558,11 +5748,10 @@ def val(
     """Returns the target's integer value.
 
     Example:
-
         >>> import stim
-        >>> stim.target_relative_detector_id(5).val
+        >>> stim.DemTarget("D5").val
         5
-        >>> stim.target_logical_observable_id(6).val
+        >>> stim.DemTarget("L6").val
         6
     """
 ```
@@ -7480,11 +7669,21 @@ class FlippedMeasurement:
 # (in class stim.FlippedMeasurement)
 def __init__(
     self,
-    *,
-    record_index: int,
-    observable: object,
-) -> None:
+    measurement_record_index: Optional[int],
+    measured_observable: Iterable[stim.GateTargetWithCoords],
+):
     """Creates a stim.FlippedMeasurement.
+
+    Examples:
+        >>> import stim
+        >>> print(stim.FlippedMeasurement(
+        ...     record_index=5,
+        ...     observable=[],
+        ... ))
+        stim.FlippedMeasurement(
+            record_index=5,
+            observable=(),
+        )
     """
 ```
 
@@ -7944,6 +8143,67 @@ def generalized_inverse(
     """
 ```
 
+<a name="stim.GateData.hadamard_conjugated"></a>
+```python
+# stim.GateData.hadamard_conjugated
+
+# (in class stim.GateData)
+def hadamard_conjugated(
+    self,
+    *,
+    unsigned: bool = False,
+) -> Optional[stim.GateData]:
+    """Returns a stim gate equivalent to this gate conjugated by Hadamard gates.
+
+    The Hadamard conjugate can be thought of as the XZ dual of the gate; the gate
+    you get by exchanging the X and Z bases. For example, a SQRT_X will become a
+    SQRT_Z and a CX gate will switch directions into an XCZ.
+
+    If stim doesn't define a gate equivalent to conjugating this gate by Hadamards,
+    the value `None` is returned.
+
+    Args:
+        unsigned: Defaults to False. When False, the returned gate must be *exactly*
+            the Hadamard conjugation of this gate. When True, the returned gate must
+            have the same flows but the sign of the flows can be different (i.e.
+            the returned gate must be the Hadamard conjugate up to Pauli gate
+            differences).
+
+    Returns:
+        A stim.GateData instance of the Hadamard conjugate, if it exists in stim.
+
+        None, if stim doesn't define a gate equal to the Hadamard conjugate.
+
+    Examples:
+        >>> import stim
+
+        >>> stim.gate_data('X').hadamard_conjugated()
+        stim.gate_data('Z')
+        >>> stim.gate_data('CX').hadamard_conjugated()
+        stim.gate_data('XCZ')
+        >>> stim.gate_data('RY').hadamard_conjugated() is None
+        True
+        >>> stim.gate_data('RY').hadamard_conjugated(unsigned=True)
+        stim.gate_data('RY')
+        >>> stim.gate_data('ISWAP').hadamard_conjugated(unsigned=True) is None
+        True
+        >>> stim.gate_data('SWAP').hadamard_conjugated()
+        stim.gate_data('SWAP')
+        >>> stim.gate_data('CXSWAP').hadamard_conjugated()
+        stim.gate_data('SWAPCX')
+        >>> stim.gate_data('MXX').hadamard_conjugated()
+        stim.gate_data('MZZ')
+        >>> stim.gate_data('DEPOLARIZE1').hadamard_conjugated()
+        stim.gate_data('DEPOLARIZE1')
+        >>> stim.gate_data('X_ERROR').hadamard_conjugated()
+        stim.gate_data('Z_ERROR')
+        >>> stim.gate_data('H_XY').hadamard_conjugated(unsigned=True)
+        stim.gate_data('H_YZ')
+        >>> stim.gate_data('DETECTOR').hadamard_conjugated(unsigned=True)
+        stim.gate_data('DETECTOR')
+    """
+```
+
 <a name="stim.GateData.inverse"></a>
 ```python
 # stim.GateData.inverse
@@ -8120,6 +8380,62 @@ def is_single_qubit_gate(
     """
 ```
 
+<a name="stim.GateData.is_symmetric_gate"></a>
+```python
+# stim.GateData.is_symmetric_gate
+
+# (in class stim.GateData)
+@property
+def is_symmetric_gate(
+    self,
+) -> bool:
+    """Returns whether or not the gate is the same when its targets are swapped.
+
+    A two qubit gate is symmetric if it doesn't matter if you swap its targets. It
+    is unaffected when conjugated by the SWAP gate.
+
+    Single qubit gates are vacuously symmetric. A multi-qubit gate is symmetric if
+    swapping any two of its targets has no effect.
+
+    Note that this method is for symmetry *without broadcasting*. For example, SWAP
+    is symmetric even though SWAP 1 2 3 4 isn't equal to SWAP 1 3 2 4.
+
+    Returns:
+        True if the gate is symmetric.
+        False if the gate isn't symmetric.
+
+    Examples:
+        >>> import stim
+
+        >>> stim.gate_data('CX').is_symmetric_gate
+        False
+        >>> stim.gate_data('CZ').is_symmetric_gate
+        True
+        >>> stim.gate_data('ISWAP').is_symmetric_gate
+        True
+        >>> stim.gate_data('CXSWAP').is_symmetric_gate
+        False
+        >>> stim.gate_data('MXX').is_symmetric_gate
+        True
+        >>> stim.gate_data('DEPOLARIZE2').is_symmetric_gate
+        True
+        >>> stim.gate_data('PAULI_CHANNEL_2').is_symmetric_gate
+        False
+        >>> stim.gate_data('H').is_symmetric_gate
+        True
+        >>> stim.gate_data('R').is_symmetric_gate
+        True
+        >>> stim.gate_data('X_ERROR').is_symmetric_gate
+        True
+        >>> stim.gate_data('CORRELATED_ERROR').is_symmetric_gate
+        False
+        >>> stim.gate_data('MPP').is_symmetric_gate
+        False
+        >>> stim.gate_data('DETECTOR').is_symmetric_gate
+        False
+    """
+```
+
 <a name="stim.GateData.is_two_qubit_gate"></a>
 ```python
 # stim.GateData.is_two_qubit_gate
@@ -8136,6 +8452,10 @@ def is_two_qubit_gate(
     Variable-qubit gates like CORRELATED_ERROR and MPP are not
     considered two qubit gates.
 
+    Returns:
+        True if the gate is a two qubit gate.
+        False if the gate isn't a two qubit gate.
+
     Examples:
         >>> import stim
 
@@ -8883,7 +9203,6 @@ class GateTargetWithCoords:
 # (in class stim.GateTargetWithCoords)
 def __init__(
     self,
-    *,
     gate_target: object,
     coords: List[float],
 ) -> None:
diff --git a/doc/stim.pyi b/doc/stim.pyi
index cd0d0258..bbd39402 100644
--- a/doc/stim.pyi
+++ b/doc/stim.pyi
@@ -2001,8 +2001,8 @@ class Circuit:
         Examples:
             >>> import stim
             >>> stim.Circuit('''
-            ...    X 1
-            ...    M 0 1
+            ...     X 1
+            ...     M 0 1
             ... ''').reference_sample()
             array([False,  True])
         """
@@ -2717,6 +2717,26 @@ class Circuit:
         """
 class CircuitErrorLocation:
     """Describes the location of an error mechanism from a stim circuit.
+
+    Examples:
+        >>> import stim
+        >>> circuit = stim.Circuit.generated(
+        ...     "repetition_code:memory",
+        ...     distance=5,
+        ...     rounds=5,
+        ...     before_round_data_depolarization=1e-3,
+        ... )
+        >>> logical_error = circuit.shortest_graphlike_error()
+        >>> error_location = logical_error[0].circuit_error_locations[0]
+        >>> print(error_location)
+        CircuitErrorLocation {
+            flipped_pauli_product: X0
+            Circuit location stack trace:
+                (after 1 TICKs)
+                at instruction #3 (DEPOLARIZE1) in the circuit
+                at target #1 of the instruction
+                resolving to DEPOLARIZE1(0.001) 0
+        }
     """
     def __init__(
         self,
@@ -2728,20 +2748,88 @@ class CircuitErrorLocation:
         stack_frames: List[stim.CircuitErrorLocationStackFrame],
     ) -> None:
         """Creates a stim.CircuitErrorLocation.
+
+        Examples:
+            >>> import stim
+            >>> err = stim.CircuitErrorLocation(
+            ...     tick_offset=1,
+            ...     flipped_pauli_product=(
+            ...         stim.GateTargetWithCoords(
+            ...             gate_target=stim.target_x(0),
+            ...             coords=[],
+            ...         ),
+            ...     ),
+            ...     flipped_measurement=stim.FlippedMeasurement(
+            ...         record_index=None,
+            ...         observable=(),
+            ...     ),
+            ...     instruction_targets=stim.CircuitTargetsInsideInstruction(
+            ...         gate='DEPOLARIZE1',
+            ...         args=[0.001],
+            ...         target_range_start=0,
+            ...         target_range_end=1,
+            ...         targets_in_range=(stim.GateTargetWithCoords(
+            ...             gate_target=0,
+            ...             coords=[],
+            ...         ),)
+            ...     ),
+            ...     stack_frames=(
+            ...         stim.CircuitErrorLocationStackFrame(
+            ...             instruction_offset=2,
+            ...             iteration_index=0,
+            ...             instruction_repetitions_arg=0,
+            ...         ),
+            ...     ),
+            ... )
+            >>> print(err)
+            CircuitErrorLocation {
+                flipped_pauli_product: X0
+                Circuit location stack trace:
+                    (after 1 TICKs)
+                    at instruction #3 (DEPOLARIZE1) in the circuit
+                    at target #1 of the instruction
+                    resolving to DEPOLARIZE1(0.001) 0
+            }
         """
     @property
     def flipped_measurement(
         self,
     ) -> Optional[stim.FlippedMeasurement]:
         """The measurement that was flipped by the error mechanism.
+
         If the error isn't a measurement error, this will be None.
+
+        Examples:
+            >>> import stim
+            >>> err = stim.Circuit('''
+            ...     R 0
+            ...     M(0.125) 0
+            ...     OBSERVABLE_INCLUDE(0) rec[-1]
+            ... ''').shortest_graphlike_error()
+            >>> err[0].circuit_error_locations[0].flipped_measurement
+            stim.FlippedMeasurement(
+                record_index=0,
+                observable=(stim.GateTargetWithCoords(stim.target_z(0), []),),
+            )
         """
     @property
     def flipped_pauli_product(
         self,
     ) -> List[stim.GateTargetWithCoords]:
         """The Pauli errors that the error mechanism applied to qubits.
+
         When the error is a measurement error, this will be an empty list.
+
+        Examples:
+            >>> import stim
+            >>> err = stim.Circuit('''
+            ...     R 0
+            ...     Y_ERROR(0.125) 0
+            ...     M 0
+            ...     OBSERVABLE_INCLUDE(0) rec[-1]
+            ... ''').shortest_graphlike_error()
+            >>> err[0].circuit_error_locations[0].flipped_pauli_product
+            [stim.GateTargetWithCoords(stim.target_y(0), [])]
         """
     @property
     def instruction_targets(
@@ -2755,15 +2843,48 @@ class CircuitErrorLocation:
     def stack_frames(
         self,
     ) -> List[stim.CircuitErrorLocationStackFrame]:
-        """Where in the circuit's execution does the error mechanism occur,
-        accounting for things like nested loops that iterate multiple times.
+        """Describes where in the circuit's execution the error happened.
+
+        Multiple frames are needed because the error may occur within a loop,
+        or a loop nested inside a loop, or etc.
+
+        Examples:
+            >>> import stim
+            >>> err = stim.Circuit('''
+            ...     R 0
+            ...     TICK
+            ...     Y_ERROR(0.125) 0
+            ...     M 0
+            ...     OBSERVABLE_INCLUDE(0) rec[-1]
+            ... ''').shortest_graphlike_error()
+            >>> err[0].circuit_error_locations[0].stack_frames
+            [stim.CircuitErrorLocationStackFrame(
+                instruction_offset=2,
+                iteration_index=0,
+                instruction_repetitions_arg=0,
+            )]
         """
     @property
     def tick_offset(
         self,
     ) -> int:
-        """The number of TICKs that executed before the error mechanism being discussed,
-        including TICKs that occurred multiple times during loops.
+        """The number of TICKs that executed before the error happened.
+
+        This counts TICKs occurring multiple times during loops.
+
+        Examples:
+            >>> import stim
+            >>> err = stim.Circuit('''
+            ...     R 0
+            ...     TICK
+            ...     TICK
+            ...     TICK
+            ...     Y_ERROR(0.125) 0
+            ...     M 0
+            ...     OBSERVABLE_INCLUDE(0) rec[-1]
+            ... ''').shortest_graphlike_error()
+            >>> err[0].circuit_error_locations[0].tick_offset
+            3
         """
 class CircuitErrorLocationStackFrame:
     """Describes the location of an instruction being executed within a
@@ -4098,6 +4219,18 @@ class DemRepeatBlock:
         self,
     ) -> stim.DetectorErrorModel:
         """Returns a copy of the block's body, as a stim.DetectorErrorModel.
+
+        Examples:
+            >>> import stim
+            >>> body = stim.DetectorErrorModel('''
+            ...     error(0.125) D0 D1
+            ...     shift_detectors 1
+            ... ''')
+            >>> repeat_block = stim.DemRepeatBlock(100, body)
+            >>> repeat_block.body_copy() == body
+            True
+            >>> repeat_block.body_copy() is repeat_block.body_copy()
+            False
         """
     @property
     def repeat_count(
@@ -4137,6 +4270,26 @@ class DemTarget:
     ) -> bool:
         """Determines if two `stim.DemTarget`s are identical.
         """
+    def __init__(
+        self,
+        arg: object,
+        /,
+    ) -> None:
+        """Creates a stim.DemTarget from the given object.
+
+        Args:
+            arg: A string to parse as a stim.DemTarget, or some other object to
+                convert into a stim.DemTarget.
+
+        Examples:
+            >>> import stim
+            >>> stim.DemTarget("D5") == stim.target_relative_detector_id(5)
+            True
+            >>> stim.DemTarget("L2") == stim.target_logical_observable_id(2)
+            True
+            >>> stim.DemTarget("^") == stim.target_separator()
+            True
+        """
     def __ne__(
         self,
         arg0: stim.DemTarget,
@@ -4160,6 +4313,15 @@ class DemTarget:
 
         In a detector error model file, observable targets are prefixed by `L`. For
         example, in `error(0.25) D0 L1` the `L1` is an observable target.
+
+        Examples:
+            >>> import stim
+            >>> stim.DemTarget("L2").is_logical_observable_id()
+            True
+            >>> stim.DemTarget("D3").is_logical_observable_id()
+            False
+            >>> stim.DemTarget("^").is_logical_observable_id()
+            False
         """
     def is_relative_detector_id(
         self,
@@ -4168,6 +4330,15 @@ class DemTarget:
 
         In a detector error model file, detectors are prefixed by `D`. For
         example, in `error(0.25) D0 L1` the `D0` is a relative detector target.
+
+        Examples:
+            >>> import stim
+            >>> stim.DemTarget("L2").is_relative_detector_id()
+            False
+            >>> stim.DemTarget("D3").is_relative_detector_id()
+            True
+            >>> stim.DemTarget("^").is_relative_detector_id()
+            False
         """
     def is_separator(
         self,
@@ -4176,6 +4347,15 @@ class DemTarget:
 
         Separates separate the components of a suggested decompositions within an error.
         For example, the `^` in `error(0.25) D1 D2 ^ D3 D4` is the separator.
+
+        Examples:
+            >>> import stim
+            >>> stim.DemTarget("L2").is_separator()
+            False
+            >>> stim.DemTarget("D3").is_separator()
+            False
+            >>> stim.DemTarget("^").is_separator()
+            True
         """
     @staticmethod
     def logical_observable_id(
@@ -4248,11 +4428,10 @@ class DemTarget:
         """Returns the target's integer value.
 
         Example:
-
             >>> import stim
-            >>> stim.target_relative_detector_id(5).val
+            >>> stim.DemTarget("D5").val
             5
-            >>> stim.target_logical_observable_id(6).val
+            >>> stim.DemTarget("L6").val
             6
         """
 class DemTargetWithCoords:
@@ -5806,11 +5985,21 @@ class FlippedMeasurement:
     """
     def __init__(
         self,
-        *,
-        record_index: int,
-        observable: object,
-    ) -> None:
+        measurement_record_index: Optional[int],
+        measured_observable: Iterable[stim.GateTargetWithCoords],
+    ):
         """Creates a stim.FlippedMeasurement.
+
+        Examples:
+            >>> import stim
+            >>> print(stim.FlippedMeasurement(
+            ...     record_index=5,
+            ...     observable=[],
+            ... ))
+            stim.FlippedMeasurement(
+                record_index=5,
+                observable=(),
+            )
         """
     @property
     def observable(
@@ -6128,6 +6317,60 @@ class GateData:
             >>> stim.gate_data('TICK').generalized_inverse
             stim.gate_data('TICK')
         """
+    def hadamard_conjugated(
+        self,
+        *,
+        unsigned: bool = False,
+    ) -> Optional[stim.GateData]:
+        """Returns a stim gate equivalent to this gate conjugated by Hadamard gates.
+
+        The Hadamard conjugate can be thought of as the XZ dual of the gate; the gate
+        you get by exchanging the X and Z bases. For example, a SQRT_X will become a
+        SQRT_Z and a CX gate will switch directions into an XCZ.
+
+        If stim doesn't define a gate equivalent to conjugating this gate by Hadamards,
+        the value `None` is returned.
+
+        Args:
+            unsigned: Defaults to False. When False, the returned gate must be *exactly*
+                the Hadamard conjugation of this gate. When True, the returned gate must
+                have the same flows but the sign of the flows can be different (i.e.
+                the returned gate must be the Hadamard conjugate up to Pauli gate
+                differences).
+
+        Returns:
+            A stim.GateData instance of the Hadamard conjugate, if it exists in stim.
+
+            None, if stim doesn't define a gate equal to the Hadamard conjugate.
+
+        Examples:
+            >>> import stim
+
+            >>> stim.gate_data('X').hadamard_conjugated()
+            stim.gate_data('Z')
+            >>> stim.gate_data('CX').hadamard_conjugated()
+            stim.gate_data('XCZ')
+            >>> stim.gate_data('RY').hadamard_conjugated() is None
+            True
+            >>> stim.gate_data('RY').hadamard_conjugated(unsigned=True)
+            stim.gate_data('RY')
+            >>> stim.gate_data('ISWAP').hadamard_conjugated(unsigned=True) is None
+            True
+            >>> stim.gate_data('SWAP').hadamard_conjugated()
+            stim.gate_data('SWAP')
+            >>> stim.gate_data('CXSWAP').hadamard_conjugated()
+            stim.gate_data('SWAPCX')
+            >>> stim.gate_data('MXX').hadamard_conjugated()
+            stim.gate_data('MZZ')
+            >>> stim.gate_data('DEPOLARIZE1').hadamard_conjugated()
+            stim.gate_data('DEPOLARIZE1')
+            >>> stim.gate_data('X_ERROR').hadamard_conjugated()
+            stim.gate_data('Z_ERROR')
+            >>> stim.gate_data('H_XY').hadamard_conjugated(unsigned=True)
+            stim.gate_data('H_YZ')
+            >>> stim.gate_data('DETECTOR').hadamard_conjugated(unsigned=True)
+            stim.gate_data('DETECTOR')
+        """
     @property
     def inverse(
         self,
@@ -6277,6 +6520,55 @@ class GateData:
             False
         """
     @property
+    def is_symmetric_gate(
+        self,
+    ) -> bool:
+        """Returns whether or not the gate is the same when its targets are swapped.
+
+        A two qubit gate is symmetric if it doesn't matter if you swap its targets. It
+        is unaffected when conjugated by the SWAP gate.
+
+        Single qubit gates are vacuously symmetric. A multi-qubit gate is symmetric if
+        swapping any two of its targets has no effect.
+
+        Note that this method is for symmetry *without broadcasting*. For example, SWAP
+        is symmetric even though SWAP 1 2 3 4 isn't equal to SWAP 1 3 2 4.
+
+        Returns:
+            True if the gate is symmetric.
+            False if the gate isn't symmetric.
+
+        Examples:
+            >>> import stim
+
+            >>> stim.gate_data('CX').is_symmetric_gate
+            False
+            >>> stim.gate_data('CZ').is_symmetric_gate
+            True
+            >>> stim.gate_data('ISWAP').is_symmetric_gate
+            True
+            >>> stim.gate_data('CXSWAP').is_symmetric_gate
+            False
+            >>> stim.gate_data('MXX').is_symmetric_gate
+            True
+            >>> stim.gate_data('DEPOLARIZE2').is_symmetric_gate
+            True
+            >>> stim.gate_data('PAULI_CHANNEL_2').is_symmetric_gate
+            False
+            >>> stim.gate_data('H').is_symmetric_gate
+            True
+            >>> stim.gate_data('R').is_symmetric_gate
+            True
+            >>> stim.gate_data('X_ERROR').is_symmetric_gate
+            True
+            >>> stim.gate_data('CORRELATED_ERROR').is_symmetric_gate
+            False
+            >>> stim.gate_data('MPP').is_symmetric_gate
+            False
+            >>> stim.gate_data('DETECTOR').is_symmetric_gate
+            False
+        """
+    @property
     def is_two_qubit_gate(
         self,
     ) -> bool:
@@ -6287,6 +6579,10 @@ class GateData:
         Variable-qubit gates like CORRELATED_ERROR and MPP are not
         considered two qubit gates.
 
+        Returns:
+            True if the gate is a two qubit gate.
+            False if the gate isn't a two qubit gate.
+
         Examples:
             >>> import stim
 
@@ -6852,7 +7148,6 @@ class GateTargetWithCoords:
     """
     def __init__(
         self,
-        *,
         gate_target: object,
         coords: List[float],
     ) -> None:
diff --git a/glue/python/src/stim/__init__.pyi b/glue/python/src/stim/__init__.pyi
index cd0d0258..bbd39402 100644
--- a/glue/python/src/stim/__init__.pyi
+++ b/glue/python/src/stim/__init__.pyi
@@ -2001,8 +2001,8 @@ class Circuit:
         Examples:
             >>> import stim
             >>> stim.Circuit('''
-            ...    X 1
-            ...    M 0 1
+            ...     X 1
+            ...     M 0 1
             ... ''').reference_sample()
             array([False,  True])
         """
@@ -2717,6 +2717,26 @@ class Circuit:
         """
 class CircuitErrorLocation:
     """Describes the location of an error mechanism from a stim circuit.
+
+    Examples:
+        >>> import stim
+        >>> circuit = stim.Circuit.generated(
+        ...     "repetition_code:memory",
+        ...     distance=5,
+        ...     rounds=5,
+        ...     before_round_data_depolarization=1e-3,
+        ... )
+        >>> logical_error = circuit.shortest_graphlike_error()
+        >>> error_location = logical_error[0].circuit_error_locations[0]
+        >>> print(error_location)
+        CircuitErrorLocation {
+            flipped_pauli_product: X0
+            Circuit location stack trace:
+                (after 1 TICKs)
+                at instruction #3 (DEPOLARIZE1) in the circuit
+                at target #1 of the instruction
+                resolving to DEPOLARIZE1(0.001) 0
+        }
     """
     def __init__(
         self,
@@ -2728,20 +2748,88 @@ class CircuitErrorLocation:
         stack_frames: List[stim.CircuitErrorLocationStackFrame],
     ) -> None:
         """Creates a stim.CircuitErrorLocation.
+
+        Examples:
+            >>> import stim
+            >>> err = stim.CircuitErrorLocation(
+            ...     tick_offset=1,
+            ...     flipped_pauli_product=(
+            ...         stim.GateTargetWithCoords(
+            ...             gate_target=stim.target_x(0),
+            ...             coords=[],
+            ...         ),
+            ...     ),
+            ...     flipped_measurement=stim.FlippedMeasurement(
+            ...         record_index=None,
+            ...         observable=(),
+            ...     ),
+            ...     instruction_targets=stim.CircuitTargetsInsideInstruction(
+            ...         gate='DEPOLARIZE1',
+            ...         args=[0.001],
+            ...         target_range_start=0,
+            ...         target_range_end=1,
+            ...         targets_in_range=(stim.GateTargetWithCoords(
+            ...             gate_target=0,
+            ...             coords=[],
+            ...         ),)
+            ...     ),
+            ...     stack_frames=(
+            ...         stim.CircuitErrorLocationStackFrame(
+            ...             instruction_offset=2,
+            ...             iteration_index=0,
+            ...             instruction_repetitions_arg=0,
+            ...         ),
+            ...     ),
+            ... )
+            >>> print(err)
+            CircuitErrorLocation {
+                flipped_pauli_product: X0
+                Circuit location stack trace:
+                    (after 1 TICKs)
+                    at instruction #3 (DEPOLARIZE1) in the circuit
+                    at target #1 of the instruction
+                    resolving to DEPOLARIZE1(0.001) 0
+            }
         """
     @property
     def flipped_measurement(
         self,
     ) -> Optional[stim.FlippedMeasurement]:
         """The measurement that was flipped by the error mechanism.
+
         If the error isn't a measurement error, this will be None.
+
+        Examples:
+            >>> import stim
+            >>> err = stim.Circuit('''
+            ...     R 0
+            ...     M(0.125) 0
+            ...     OBSERVABLE_INCLUDE(0) rec[-1]
+            ... ''').shortest_graphlike_error()
+            >>> err[0].circuit_error_locations[0].flipped_measurement
+            stim.FlippedMeasurement(
+                record_index=0,
+                observable=(stim.GateTargetWithCoords(stim.target_z(0), []),),
+            )
         """
     @property
     def flipped_pauli_product(
         self,
     ) -> List[stim.GateTargetWithCoords]:
         """The Pauli errors that the error mechanism applied to qubits.
+
         When the error is a measurement error, this will be an empty list.
+
+        Examples:
+            >>> import stim
+            >>> err = stim.Circuit('''
+            ...     R 0
+            ...     Y_ERROR(0.125) 0
+            ...     M 0
+            ...     OBSERVABLE_INCLUDE(0) rec[-1]
+            ... ''').shortest_graphlike_error()
+            >>> err[0].circuit_error_locations[0].flipped_pauli_product
+            [stim.GateTargetWithCoords(stim.target_y(0), [])]
         """
     @property
     def instruction_targets(
@@ -2755,15 +2843,48 @@ class CircuitErrorLocation:
     def stack_frames(
         self,
     ) -> List[stim.CircuitErrorLocationStackFrame]:
-        """Where in the circuit's execution does the error mechanism occur,
-        accounting for things like nested loops that iterate multiple times.
+        """Describes where in the circuit's execution the error happened.
+
+        Multiple frames are needed because the error may occur within a loop,
+        or a loop nested inside a loop, or etc.
+
+        Examples:
+            >>> import stim
+            >>> err = stim.Circuit('''
+            ...     R 0
+            ...     TICK
+            ...     Y_ERROR(0.125) 0
+            ...     M 0
+            ...     OBSERVABLE_INCLUDE(0) rec[-1]
+            ... ''').shortest_graphlike_error()
+            >>> err[0].circuit_error_locations[0].stack_frames
+            [stim.CircuitErrorLocationStackFrame(
+                instruction_offset=2,
+                iteration_index=0,
+                instruction_repetitions_arg=0,
+            )]
         """
     @property
     def tick_offset(
         self,
     ) -> int:
-        """The number of TICKs that executed before the error mechanism being discussed,
-        including TICKs that occurred multiple times during loops.
+        """The number of TICKs that executed before the error happened.
+
+        This counts TICKs occurring multiple times during loops.
+
+        Examples:
+            >>> import stim
+            >>> err = stim.Circuit('''
+            ...     R 0
+            ...     TICK
+            ...     TICK
+            ...     TICK
+            ...     Y_ERROR(0.125) 0
+            ...     M 0
+            ...     OBSERVABLE_INCLUDE(0) rec[-1]
+            ... ''').shortest_graphlike_error()
+            >>> err[0].circuit_error_locations[0].tick_offset
+            3
         """
 class CircuitErrorLocationStackFrame:
     """Describes the location of an instruction being executed within a
@@ -4098,6 +4219,18 @@ class DemRepeatBlock:
         self,
     ) -> stim.DetectorErrorModel:
         """Returns a copy of the block's body, as a stim.DetectorErrorModel.
+
+        Examples:
+            >>> import stim
+            >>> body = stim.DetectorErrorModel('''
+            ...     error(0.125) D0 D1
+            ...     shift_detectors 1
+            ... ''')
+            >>> repeat_block = stim.DemRepeatBlock(100, body)
+            >>> repeat_block.body_copy() == body
+            True
+            >>> repeat_block.body_copy() is repeat_block.body_copy()
+            False
         """
     @property
     def repeat_count(
@@ -4137,6 +4270,26 @@ class DemTarget:
     ) -> bool:
         """Determines if two `stim.DemTarget`s are identical.
         """
+    def __init__(
+        self,
+        arg: object,
+        /,
+    ) -> None:
+        """Creates a stim.DemTarget from the given object.
+
+        Args:
+            arg: A string to parse as a stim.DemTarget, or some other object to
+                convert into a stim.DemTarget.
+
+        Examples:
+            >>> import stim
+            >>> stim.DemTarget("D5") == stim.target_relative_detector_id(5)
+            True
+            >>> stim.DemTarget("L2") == stim.target_logical_observable_id(2)
+            True
+            >>> stim.DemTarget("^") == stim.target_separator()
+            True
+        """
     def __ne__(
         self,
         arg0: stim.DemTarget,
@@ -4160,6 +4313,15 @@ class DemTarget:
 
         In a detector error model file, observable targets are prefixed by `L`. For
         example, in `error(0.25) D0 L1` the `L1` is an observable target.
+
+        Examples:
+            >>> import stim
+            >>> stim.DemTarget("L2").is_logical_observable_id()
+            True
+            >>> stim.DemTarget("D3").is_logical_observable_id()
+            False
+            >>> stim.DemTarget("^").is_logical_observable_id()
+            False
         """
     def is_relative_detector_id(
         self,
@@ -4168,6 +4330,15 @@ class DemTarget:
 
         In a detector error model file, detectors are prefixed by `D`. For
         example, in `error(0.25) D0 L1` the `D0` is a relative detector target.
+
+        Examples:
+            >>> import stim
+            >>> stim.DemTarget("L2").is_relative_detector_id()
+            False
+            >>> stim.DemTarget("D3").is_relative_detector_id()
+            True
+            >>> stim.DemTarget("^").is_relative_detector_id()
+            False
         """
     def is_separator(
         self,
@@ -4176,6 +4347,15 @@ class DemTarget:
 
         Separates separate the components of a suggested decompositions within an error.
         For example, the `^` in `error(0.25) D1 D2 ^ D3 D4` is the separator.
+
+        Examples:
+            >>> import stim
+            >>> stim.DemTarget("L2").is_separator()
+            False
+            >>> stim.DemTarget("D3").is_separator()
+            False
+            >>> stim.DemTarget("^").is_separator()
+            True
         """
     @staticmethod
     def logical_observable_id(
@@ -4248,11 +4428,10 @@ class DemTarget:
         """Returns the target's integer value.
 
         Example:
-
             >>> import stim
-            >>> stim.target_relative_detector_id(5).val
+            >>> stim.DemTarget("D5").val
             5
-            >>> stim.target_logical_observable_id(6).val
+            >>> stim.DemTarget("L6").val
             6
         """
 class DemTargetWithCoords:
@@ -5806,11 +5985,21 @@ class FlippedMeasurement:
     """
     def __init__(
         self,
-        *,
-        record_index: int,
-        observable: object,
-    ) -> None:
+        measurement_record_index: Optional[int],
+        measured_observable: Iterable[stim.GateTargetWithCoords],
+    ):
         """Creates a stim.FlippedMeasurement.
+
+        Examples:
+            >>> import stim
+            >>> print(stim.FlippedMeasurement(
+            ...     record_index=5,
+            ...     observable=[],
+            ... ))
+            stim.FlippedMeasurement(
+                record_index=5,
+                observable=(),
+            )
         """
     @property
     def observable(
@@ -6128,6 +6317,60 @@ class GateData:
             >>> stim.gate_data('TICK').generalized_inverse
             stim.gate_data('TICK')
         """
+    def hadamard_conjugated(
+        self,
+        *,
+        unsigned: bool = False,
+    ) -> Optional[stim.GateData]:
+        """Returns a stim gate equivalent to this gate conjugated by Hadamard gates.
+
+        The Hadamard conjugate can be thought of as the XZ dual of the gate; the gate
+        you get by exchanging the X and Z bases. For example, a SQRT_X will become a
+        SQRT_Z and a CX gate will switch directions into an XCZ.
+
+        If stim doesn't define a gate equivalent to conjugating this gate by Hadamards,
+        the value `None` is returned.
+
+        Args:
+            unsigned: Defaults to False. When False, the returned gate must be *exactly*
+                the Hadamard conjugation of this gate. When True, the returned gate must
+                have the same flows but the sign of the flows can be different (i.e.
+                the returned gate must be the Hadamard conjugate up to Pauli gate
+                differences).
+
+        Returns:
+            A stim.GateData instance of the Hadamard conjugate, if it exists in stim.
+
+            None, if stim doesn't define a gate equal to the Hadamard conjugate.
+
+        Examples:
+            >>> import stim
+
+            >>> stim.gate_data('X').hadamard_conjugated()
+            stim.gate_data('Z')
+            >>> stim.gate_data('CX').hadamard_conjugated()
+            stim.gate_data('XCZ')
+            >>> stim.gate_data('RY').hadamard_conjugated() is None
+            True
+            >>> stim.gate_data('RY').hadamard_conjugated(unsigned=True)
+            stim.gate_data('RY')
+            >>> stim.gate_data('ISWAP').hadamard_conjugated(unsigned=True) is None
+            True
+            >>> stim.gate_data('SWAP').hadamard_conjugated()
+            stim.gate_data('SWAP')
+            >>> stim.gate_data('CXSWAP').hadamard_conjugated()
+            stim.gate_data('SWAPCX')
+            >>> stim.gate_data('MXX').hadamard_conjugated()
+            stim.gate_data('MZZ')
+            >>> stim.gate_data('DEPOLARIZE1').hadamard_conjugated()
+            stim.gate_data('DEPOLARIZE1')
+            >>> stim.gate_data('X_ERROR').hadamard_conjugated()
+            stim.gate_data('Z_ERROR')
+            >>> stim.gate_data('H_XY').hadamard_conjugated(unsigned=True)
+            stim.gate_data('H_YZ')
+            >>> stim.gate_data('DETECTOR').hadamard_conjugated(unsigned=True)
+            stim.gate_data('DETECTOR')
+        """
     @property
     def inverse(
         self,
@@ -6277,6 +6520,55 @@ class GateData:
             False
         """
     @property
+    def is_symmetric_gate(
+        self,
+    ) -> bool:
+        """Returns whether or not the gate is the same when its targets are swapped.
+
+        A two qubit gate is symmetric if it doesn't matter if you swap its targets. It
+        is unaffected when conjugated by the SWAP gate.
+
+        Single qubit gates are vacuously symmetric. A multi-qubit gate is symmetric if
+        swapping any two of its targets has no effect.
+
+        Note that this method is for symmetry *without broadcasting*. For example, SWAP
+        is symmetric even though SWAP 1 2 3 4 isn't equal to SWAP 1 3 2 4.
+
+        Returns:
+            True if the gate is symmetric.
+            False if the gate isn't symmetric.
+
+        Examples:
+            >>> import stim
+
+            >>> stim.gate_data('CX').is_symmetric_gate
+            False
+            >>> stim.gate_data('CZ').is_symmetric_gate
+            True
+            >>> stim.gate_data('ISWAP').is_symmetric_gate
+            True
+            >>> stim.gate_data('CXSWAP').is_symmetric_gate
+            False
+            >>> stim.gate_data('MXX').is_symmetric_gate
+            True
+            >>> stim.gate_data('DEPOLARIZE2').is_symmetric_gate
+            True
+            >>> stim.gate_data('PAULI_CHANNEL_2').is_symmetric_gate
+            False
+            >>> stim.gate_data('H').is_symmetric_gate
+            True
+            >>> stim.gate_data('R').is_symmetric_gate
+            True
+            >>> stim.gate_data('X_ERROR').is_symmetric_gate
+            True
+            >>> stim.gate_data('CORRELATED_ERROR').is_symmetric_gate
+            False
+            >>> stim.gate_data('MPP').is_symmetric_gate
+            False
+            >>> stim.gate_data('DETECTOR').is_symmetric_gate
+            False
+        """
+    @property
     def is_two_qubit_gate(
         self,
     ) -> bool:
@@ -6287,6 +6579,10 @@ class GateData:
         Variable-qubit gates like CORRELATED_ERROR and MPP are not
         considered two qubit gates.
 
+        Returns:
+            True if the gate is a two qubit gate.
+            False if the gate isn't a two qubit gate.
+
         Examples:
             >>> import stim
 
@@ -6852,7 +7148,6 @@ class GateTargetWithCoords:
     """
     def __init__(
         self,
-        *,
         gate_target: object,
         coords: List[float],
     ) -> None:
diff --git a/src/stim/circuit/circuit.pybind.cc b/src/stim/circuit/circuit.pybind.cc
index 454b6f91..4c870e61 100644
--- a/src/stim/circuit/circuit.pybind.cc
+++ b/src/stim/circuit/circuit.pybind.cc
@@ -721,8 +721,8 @@ void stim_pybind::pybind_circuit_methods(pybind11::module &, pybind11::class_<Ci
             Examples:
                 >>> import stim
                 >>> stim.Circuit('''
-                ...    X 1
-                ...    M 0 1
+                ...     X 1
+                ...     M 0 1
                 ... ''').reference_sample()
                 array([False,  True])
         )DOC")
diff --git a/src/stim/dem/dem_instruction.cc b/src/stim/dem/dem_instruction.cc
index b15c47b8..fd3452c5 100644
--- a/src/stim/dem/dem_instruction.cc
+++ b/src/stim/dem/dem_instruction.cc
@@ -11,9 +11,6 @@ using namespace stim;
 constexpr uint64_t OBSERVABLE_BIT = uint64_t{1} << 63;
 constexpr uint64_t SEPARATOR_SYGIL = UINT64_MAX;
 
-constexpr uint64_t MAX_OBS = 0xFFFFFFFF;
-constexpr uint64_t MAX_DET = (uint64_t{1} << 62) - 1;
-
 DemTarget DemTarget::observable_id(uint64_t id) {
     if (id > MAX_OBS) {
         throw std::invalid_argument("id > 0xFFFFFFFF");
@@ -79,6 +76,9 @@ void DemTarget::shift_if_detector_id(int64_t offset) {
     }
 }
 DemTarget DemTarget::from_text(std::string_view text) {
+    if (text == "^") {
+        return DemTarget::separator();
+    }
     if (!text.empty()) {
         bool is_det = text[0] == 'D';
         bool is_obs = text[0] == 'L';
@@ -86,9 +86,9 @@ DemTarget DemTarget::from_text(std::string_view text) {
             int64_t parsed = 0;
             if (parse_int64(text.substr(1), &parsed)) {
                 if (parsed >= 0) {
-                    if (is_det && parsed <= (int64_t)MAX_DET) {
+                    if (is_det && (uint64_t)parsed <= MAX_DET) {
                         return DemTarget::relative_detector_id(parsed);
-                    } else if (is_obs && parsed <= (int64_t)MAX_OBS) {
+                    } else if (is_obs && (uint64_t)parsed <= MAX_OBS) {
                         return DemTarget::observable_id(parsed);
                     }
                 }
diff --git a/src/stim/dem/dem_instruction.h b/src/stim/dem/dem_instruction.h
index d75c4f34..42361112 100644
--- a/src/stim/dem/dem_instruction.h
+++ b/src/stim/dem/dem_instruction.h
@@ -10,6 +10,9 @@
 
 namespace stim {
 
+constexpr uint64_t MAX_OBS = 0xFFFFFFFF;
+constexpr uint64_t MAX_DET = (uint64_t{1} << 62) - 1;
+
 enum class DemInstructionType : uint8_t {
     DEM_ERROR,
     DEM_SHIFT_DETECTORS,
diff --git a/src/stim/dem/detector_error_model_pybind_test.py b/src/stim/dem/detector_error_model_pybind_test.py
index cd479aa9..955937b8 100644
--- a/src/stim/dem/detector_error_model_pybind_test.py
+++ b/src/stim/dem/detector_error_model_pybind_test.py
@@ -155,7 +155,7 @@ def test_append_bad():
     m.append("shift_detectors", [], [5])
     m += m * 3
 
-    with pytest.raises(ValueError, match=r"Bad target 'stim.target_relative_detector_id\(0\)' for instruction 'shift_detectors'"):
+    with pytest.raises(ValueError, match=r"Bad target 'stim.DemTarget\('D0'\)' for instruction 'shift_detectors'"):
         m.append("shift_detectors", [0.125, 0.25], [stim.target_relative_detector_id(0)])
     with pytest.raises(ValueError, match="takes 1 argument"):
         m.append("error", [0.125, 0.25], [stim.target_relative_detector_id(0)])
diff --git a/src/stim/dem/detector_error_model_repeat_block.pybind.cc b/src/stim/dem/detector_error_model_repeat_block.pybind.cc
index cc6f37ca..91b2ed98 100644
--- a/src/stim/dem/detector_error_model_repeat_block.pybind.cc
+++ b/src/stim/dem/detector_error_model_repeat_block.pybind.cc
@@ -78,6 +78,18 @@ void stim_pybind::pybind_detector_error_model_repeat_block_methods(
         &ExposedDemRepeatBlock::body_copy,
         clean_doc_string(R"DOC(
             Returns a copy of the block's body, as a stim.DetectorErrorModel.
+
+            Examples:
+                >>> import stim
+                >>> body = stim.DetectorErrorModel('''
+                ...     error(0.125) D0 D1
+                ...     shift_detectors 1
+                ... ''')
+                >>> repeat_block = stim.DemRepeatBlock(100, body)
+                >>> repeat_block.body_copy() == body
+                True
+                >>> repeat_block.body_copy() is repeat_block.body_copy()
+                False
         )DOC")
             .data());
     c.def_property_readonly(
diff --git a/src/stim/dem/detector_error_model_target.pybind.cc b/src/stim/dem/detector_error_model_target.pybind.cc
index cedc4d41..43b442e6 100644
--- a/src/stim/dem/detector_error_model_target.pybind.cc
+++ b/src/stim/dem/detector_error_model_target.pybind.cc
@@ -16,6 +16,7 @@
 
 #include "stim/dem/detector_error_model.pybind.h"
 #include "stim/py/base.pybind.h"
+#include "stim/util_bot/arg_parse.h"
 
 using namespace stim;
 using namespace stim_pybind;
@@ -27,6 +28,42 @@ pybind11::class_<ExposedDemTarget> stim_pybind::pybind_detector_error_model_targ
 
 void stim_pybind::pybind_detector_error_model_target_methods(
     pybind11::module &m, pybind11::class_<ExposedDemTarget> &c) {
+
+    c.def(
+        pybind11::init([](const pybind11::object &arg) -> ExposedDemTarget {
+            if (pybind11::isinstance<ExposedDemTarget>(arg)) {
+                return pybind11::cast<ExposedDemTarget>(arg);
+            }
+            if (pybind11::isinstance<pybind11::str>(arg)) {
+                std::string_view contents = pybind11::cast<std::string_view>(arg);
+                return DemTarget::from_text(contents);
+            }
+
+            std::stringstream ss;
+            ss << "Don't know how to convert this into a stim.DemTarget: ";
+            ss << pybind11::repr(arg);
+            throw pybind11::type_error(ss.str());
+        }),
+        pybind11::arg("arg"),
+        pybind11::pos_only(),
+        clean_doc_string(R"DOC(
+            Creates a stim.DemTarget from the given object.
+
+            Args:
+                arg: A string to parse as a stim.DemTarget, or some other object to
+                    convert into a stim.DemTarget.
+
+            Examples:
+                >>> import stim
+                >>> stim.DemTarget("D5") == stim.target_relative_detector_id(5)
+                True
+                >>> stim.DemTarget("L2") == stim.target_logical_observable_id(2)
+                True
+                >>> stim.DemTarget("^") == stim.target_separator()
+                True
+        )DOC")
+            .data());
+
     m.def(
         "target_relative_detector_id",
         &ExposedDemTarget::relative_detector_id,
@@ -52,6 +89,7 @@ void stim_pybind::pybind_detector_error_model_target_methods(
                 ''')
         )DOC")
             .data());
+
     m.def(
         "target_logical_observable_id",
         &ExposedDemTarget::observable_id,
@@ -188,6 +226,15 @@ void stim_pybind::pybind_detector_error_model_target_methods(
 
             In a detector error model file, detectors are prefixed by `D`. For
             example, in `error(0.25) D0 L1` the `D0` is a relative detector target.
+
+            Examples:
+                >>> import stim
+                >>> stim.DemTarget("L2").is_relative_detector_id()
+                False
+                >>> stim.DemTarget("D3").is_relative_detector_id()
+                True
+                >>> stim.DemTarget("^").is_relative_detector_id()
+                False
         )DOC")
             .data());
 
@@ -199,6 +246,15 @@ void stim_pybind::pybind_detector_error_model_target_methods(
 
             In a detector error model file, observable targets are prefixed by `L`. For
             example, in `error(0.25) D0 L1` the `L1` is an observable target.
+
+            Examples:
+                >>> import stim
+                >>> stim.DemTarget("L2").is_logical_observable_id()
+                True
+                >>> stim.DemTarget("D3").is_logical_observable_id()
+                False
+                >>> stim.DemTarget("^").is_logical_observable_id()
+                False
         )DOC")
             .data());
 
@@ -209,11 +265,10 @@ void stim_pybind::pybind_detector_error_model_target_methods(
             Returns the target's integer value.
 
             Example:
-
                 >>> import stim
-                >>> stim.target_relative_detector_id(5).val
+                >>> stim.DemTarget("D5").val
                 5
-                >>> stim.target_logical_observable_id(6).val
+                >>> stim.DemTarget("L6").val
                 6
         )DOC")
             .data());
@@ -226,6 +281,15 @@ void stim_pybind::pybind_detector_error_model_target_methods(
 
             Separates separate the components of a suggested decompositions within an error.
             For example, the `^` in `error(0.25) D1 D2 ^ D3 D4` is the separator.
+
+            Examples:
+                >>> import stim
+                >>> stim.DemTarget("L2").is_separator()
+                False
+                >>> stim.DemTarget("D3").is_separator()
+                False
+                >>> stim.DemTarget("^").is_separator()
+                True
         )DOC")
             .data());
 
@@ -237,11 +301,11 @@ void stim_pybind::pybind_detector_error_model_target_methods(
 std::string ExposedDemTarget::repr() const {
     std::stringstream out;
     if (is_relative_detector_id()) {
-        out << "stim.target_relative_detector_id(" << raw_id() << ")";
+        out << "stim.DemTarget('D" << raw_id() << "')";
     } else if (is_separator()) {
         out << "stim.target_separator()";
     } else {
-        out << "stim.target_logical_observable_id(" << raw_id() << ")";
+        out << "stim.DemTarget('L" << raw_id() << "')";
     }
     return out.str();
 }
diff --git a/src/stim/dem/detector_error_model_target_pybind_test.py b/src/stim/dem/detector_error_model_target_pybind_test.py
index 1119c74d..743a07f3 100644
--- a/src/stim/dem/detector_error_model_target_pybind_test.py
+++ b/src/stim/dem/detector_error_model_target_pybind_test.py
@@ -75,3 +75,23 @@ def test_hashable():
     c = stim.DemTarget.relative_detector_id(3)
     assert hash(a) == hash(c)
     assert len({a, b, c}) == 2
+
+
+def test_init():
+    assert stim.DemTarget("D0") == stim.target_relative_detector_id(0)
+    assert stim.DemTarget("D5") == stim.target_relative_detector_id(5)
+    assert stim.DemTarget("L0") == stim.target_logical_observable_id(0)
+    assert stim.DemTarget("L5") == stim.target_logical_observable_id(5)
+    assert stim.DemTarget("^") == stim.target_separator()
+    assert stim.DemTarget(f"D{2**62 - 1}") == stim.target_relative_detector_id(2**62 - 1)
+    assert stim.DemTarget(f"L{0xFFFFFFFF}") == stim.target_logical_observable_id(0xFFFFFFFF)
+    with pytest.raises(ValueError, match="Failed to parse"):
+        _ = stim.DemTarget(f"D{2**62}")
+    with pytest.raises(ValueError, match="Failed to parse"):
+        _ = stim.DemTarget(f"L{0x100000000}")
+    with pytest.raises(ValueError, match="Failed to parse"):
+        _ = stim.DemTarget(f"L-1")
+    with pytest.raises(ValueError, match="Failed to parse"):
+        _ = stim.DemTarget(f"X5")
+    with pytest.raises(ValueError, match="Failed to parse"):
+        _ = stim.DemTarget(f"5")
diff --git a/src/stim/gates/gates.cc b/src/stim/gates/gates.cc
index e374d05f..09f46adf 100644
--- a/src/stim/gates/gates.cc
+++ b/src/stim/gates/gates.cc
@@ -45,6 +45,148 @@ GateDataMap::GateDataMap() {
     }
 }
 
+GateType Gate::hadamard_conjugated(bool ignoring_sign) const {
+    switch (id) {
+    case GateType::DETECTOR:
+    case GateType::OBSERVABLE_INCLUDE:
+    case GateType::TICK:
+    case GateType::QUBIT_COORDS:
+    case GateType::SHIFT_COORDS:
+    case GateType::MPAD:
+    case GateType::H:
+    case GateType::DEPOLARIZE1:
+    case GateType::DEPOLARIZE2:
+    case GateType::Y_ERROR:
+    case GateType::I:
+    case GateType::Y:
+    case GateType::SQRT_YY:
+    case GateType::SQRT_YY_DAG:
+    case GateType::MYY:
+    case GateType::SWAP:
+        return id;
+
+    case GateType::MY:
+    case GateType::MRY:
+    case GateType::RY:
+    case GateType::YCY:
+        return ignoring_sign ? id : GateType::NOT_A_GATE;
+
+    case GateType::ISWAP:
+    case GateType::CZSWAP:
+    case GateType::ISWAP_DAG:
+        return GateType::NOT_A_GATE;
+
+    case GateType::XCY:
+        return ignoring_sign ? GateType::CY : GateType::NOT_A_GATE;
+    case GateType::CY:
+        return ignoring_sign ? GateType::XCY : GateType::NOT_A_GATE;
+    case GateType::YCX:
+        return ignoring_sign ? GateType::YCZ : GateType::NOT_A_GATE;
+    case GateType::YCZ:
+        return ignoring_sign ? GateType::YCX : GateType::NOT_A_GATE;
+    case GateType::C_XYZ:
+        return ignoring_sign ? GateType::C_ZYX : GateType::NOT_A_GATE;
+    case GateType::C_ZYX:
+        return ignoring_sign ? GateType::C_XYZ : GateType::NOT_A_GATE;
+    case GateType::H_XY:
+        return ignoring_sign ? GateType::H_YZ : GateType::NOT_A_GATE;
+    case GateType::H_YZ:
+        return ignoring_sign ? GateType::H_XY : GateType::NOT_A_GATE;
+
+    case GateType::X:
+        return GateType::Z;
+    case GateType::Z:
+        return GateType::X;
+    case GateType::SQRT_Y:
+        return GateType::SQRT_Y_DAG;
+    case GateType::SQRT_Y_DAG:
+        return GateType::SQRT_Y;
+    case GateType::MX:
+        return GateType::M;
+    case GateType::M:
+        return GateType::MX;
+    case GateType::MRX:
+        return GateType::MR;
+    case GateType::MR:
+        return GateType::MRX;
+    case GateType::RX:
+        return GateType::R;
+    case GateType::R:
+        return GateType::RX;
+    case GateType::XCX:
+        return GateType::CZ;
+    case GateType::XCZ:
+        return GateType::CX;
+    case GateType::CX:
+        return GateType::XCZ;
+    case GateType::CZ:
+        return GateType::XCX;
+    case GateType::X_ERROR:
+        return GateType::Z_ERROR;
+    case GateType::Z_ERROR:
+        return GateType::X_ERROR;
+    case GateType::SQRT_X:
+        return GateType::S;
+    case GateType::SQRT_X_DAG:
+        return GateType::S_DAG;
+    case GateType::S:
+        return GateType::SQRT_X;
+    case GateType::S_DAG:
+        return GateType::SQRT_X_DAG;
+    case GateType::SQRT_XX:
+        return GateType::SQRT_ZZ;
+    case GateType::SQRT_XX_DAG:
+        return GateType::SQRT_ZZ_DAG;
+    case GateType::SQRT_ZZ:
+        return GateType::SQRT_XX;
+    case GateType::SQRT_ZZ_DAG:
+        return GateType::SQRT_XX_DAG;
+    case GateType::CXSWAP:
+        return GateType::SWAPCX;
+    case GateType::SWAPCX:
+        return GateType::CXSWAP;
+    case GateType::MXX:
+        return GateType::MZZ;
+    case GateType::MZZ:
+        return GateType::MXX;
+    default:
+        return GateType::NOT_A_GATE;
+    }
+}
+
+bool Gate::is_symmetric() const {
+    if (flags & GATE_IS_SINGLE_QUBIT_GATE) {
+        return true;
+    }
+
+    if (flags & GATE_TARGETS_PAIRS) {
+        switch (id) {
+            case GateType::XCX:
+            case GateType::YCY:
+            case GateType::CZ:
+            case GateType::DEPOLARIZE2:
+            case GateType::SWAP:
+            case GateType::ISWAP:
+            case GateType::CZSWAP:
+            case GateType::ISWAP_DAG:
+            case GateType::MXX:
+            case GateType::MYY:
+            case GateType::MZZ:
+            case GateType::SQRT_XX:
+            case GateType::SQRT_YY:
+            case GateType::SQRT_ZZ:
+            case GateType::SQRT_XX_DAG:
+            case GateType::SQRT_YY_DAG:
+            case GateType::SQRT_ZZ_DAG:
+                return true;
+            default:
+                return false;
+        }
+    }
+
+    return false;
+}
+
 std::array<float, 3> Gate::to_euler_angles() const {
     if (unitary_data.size() != 2) {
         throw std::out_of_range(std::string(name) + " doesn't have 1q unitary data.");
diff --git a/src/stim/gates/gates.h b/src/stim/gates/gates.h
index 291dbd99..32fba97b 100644
--- a/src/stim/gates/gates.h
+++ b/src/stim/gates/gates.h
@@ -282,6 +282,9 @@ struct Gate {
 
     std::vector<std::vector<std::complex<float>>> unitary() const;
 
+    bool is_symmetric() const;
+    GateType hadamard_conjugated(bool ignoring_sign) const;
+
     /// Converts a single qubit unitary gate into an euler-angles rotation.
     ///
     /// Returns:
diff --git a/src/stim/gates/gates.pybind.cc b/src/stim/gates/gates.pybind.cc
index ceca99ed..df54e498 100644
--- a/src/stim/gates/gates.pybind.cc
+++ b/src/stim/gates/gates.pybind.cc
@@ -495,6 +495,123 @@ void stim_pybind::pybind_gate_data_methods(pybind11::module &m, pybind11::class_
         )DOC")
             .data());
 
+    c.def_property_readonly(
+        "is_symmetric_gate",
+        [](const Gate &self) -> bool {
+            return self.is_symmetric();
+        },
+        clean_doc_string(R"DOC(
+            Returns whether or not the gate is the same when its targets are swapped.
+
+            A two qubit gate is symmetric if it doesn't matter if you swap its targets. It
+            is unaffected when conjugated by the SWAP gate.
+
+            Single qubit gates are vacuously symmetric. A multi-qubit gate is symmetric if
+            swapping any two of its targets has no effect.
+
+            Note that this method is for symmetry *without broadcasting*. For example, SWAP
+            is symmetric even though SWAP 1 2 3 4 isn't equal to SWAP 1 3 2 4.
+
+            Returns:
+                True if the gate is symmetric.
+                False if the gate isn't symmetric.
+
+            Examples:
+                >>> import stim
+
+                >>> stim.gate_data('CX').is_symmetric_gate
+                False
+                >>> stim.gate_data('CZ').is_symmetric_gate
+                True
+                >>> stim.gate_data('ISWAP').is_symmetric_gate
+                True
+                >>> stim.gate_data('CXSWAP').is_symmetric_gate
+                False
+                >>> stim.gate_data('MXX').is_symmetric_gate
+                True
+                >>> stim.gate_data('DEPOLARIZE2').is_symmetric_gate
+                True
+                >>> stim.gate_data('PAULI_CHANNEL_2').is_symmetric_gate
+                False
+                >>> stim.gate_data('H').is_symmetric_gate
+                True
+                >>> stim.gate_data('R').is_symmetric_gate
+                True
+                >>> stim.gate_data('X_ERROR').is_symmetric_gate
+                True
+                >>> stim.gate_data('CORRELATED_ERROR').is_symmetric_gate
+                False
+                >>> stim.gate_data('MPP').is_symmetric_gate
+                False
+                >>> stim.gate_data('DETECTOR').is_symmetric_gate
+                False
+        )DOC")
+            .data());
+
+    c.def(
+        "hadamard_conjugated",
+        [](const Gate &self, bool ignoring_sign) -> pybind11::object {
+            GateType g = self.hadamard_conjugated(ignoring_sign);
+            if (g == GateType::NOT_A_GATE) {
+                return pybind11::none();
+            }
+            return pybind11::cast(GATE_DATA[g]);
+        },
+        pybind11::kw_only(),
+        pybind11::arg("unsigned") = false,
+        clean_doc_string(R"DOC(
+            @signature def hadamard_conjugated(self, *, unsigned: bool = False) -> Optional[stim.GateData]:
+            Returns a stim gate equivalent to this gate conjugated by Hadamard gates.
+
+            The Hadamard conjugate can be thought of as the XZ dual of the gate; the gate
+            you get by exchanging the X and Z bases. For example, a SQRT_X will become a
+            SQRT_Z and a CX gate will switch directions into an XCZ.
+
+            If stim doesn't define a gate equivalent to conjugating this gate by Hadamards,
+            the value `None` is returned.
+
+            Args:
+                unsigned: Defaults to False. When False, the returned gate must be *exactly*
+                    the Hadamard conjugation of this gate. When True, the returned gate must
+                    have the same flows but the sign of the flows can be different (i.e.
+                    the returned gate must be the Hadamard conjugate up to Pauli gate
+                    differences).
+
+            Returns:
+                A stim.GateData instance of the Hadamard conjugate, if it exists in stim.
+
+                None, if stim doesn't define a gate equal to the Hadamard conjugate.
+
+            Examples:
+                >>> import stim
+
+                >>> stim.gate_data('X').hadamard_conjugated()
+                stim.gate_data('Z')
+                >>> stim.gate_data('CX').hadamard_conjugated()
+                stim.gate_data('XCZ')
+                >>> stim.gate_data('RY').hadamard_conjugated() is None
+                True
+                >>> stim.gate_data('RY').hadamard_conjugated(unsigned=True)
+                stim.gate_data('RY')
+                >>> stim.gate_data('ISWAP').hadamard_conjugated(unsigned=True) is None
+                True
+                >>> stim.gate_data('SWAP').hadamard_conjugated()
+                stim.gate_data('SWAP')
+                >>> stim.gate_data('CXSWAP').hadamard_conjugated()
+                stim.gate_data('SWAPCX')
+                >>> stim.gate_data('MXX').hadamard_conjugated()
+                stim.gate_data('MZZ')
+                >>> stim.gate_data('DEPOLARIZE1').hadamard_conjugated()
+                stim.gate_data('DEPOLARIZE1')
+                >>> stim.gate_data('X_ERROR').hadamard_conjugated()
+                stim.gate_data('Z_ERROR')
+                >>> stim.gate_data('H_XY').hadamard_conjugated(unsigned=True)
+                stim.gate_data('H_YZ')
+                >>> stim.gate_data('DETECTOR').hadamard_conjugated(unsigned=True)
+                stim.gate_data('DETECTOR')
+        )DOC")
+            .data());
+
     c.def_property_readonly(
         "is_two_qubit_gate",
         [](const Gate &self) -> bool {
@@ -508,6 +625,10 @@ void stim_pybind::pybind_gate_data_methods(pybind11::module &m, pybind11::class_
             Variable-qubit gates like CORRELATED_ERROR and MPP are not
             considered two qubit gates.
 
+            Returns:
+                True if the gate is a two qubit gate.
+                False if the gate isn't a two qubit gate.
+
             Examples:
                 >>> import stim
 
diff --git a/src/stim/gates/gates.test.cc b/src/stim/gates/gates.test.cc
index 8dad3d48..edafb88f 100644
--- a/src/stim/gates/gates.test.cc
+++ b/src/stim/gates/gates.test.cc
@@ -23,6 +23,7 @@
 #include "stim/util_bot/str_util.h"
 #include "stim/util_bot/test_util.test.h"
 #include "stim/util_top/has_flow.h"
+#include "stim/util_top/circuit_flow_generators.h"
 
 using namespace stim;
 
@@ -305,3 +306,77 @@ TEST(gate_data, to_euler_angles_axis_reference) {
         }
     }
 }
+
+TEST(gate_data, is_symmetric_vs_flow_generators_of_two_qubit_gates) {
+    for (const auto &g : GATE_DATA.items) {
+        if ((g.flags & stim::GATE_IS_NOISY) && !(g.flags & stim::GATE_PRODUCES_RESULTS)) {
+            continue;
+        }
+        if (g.flags & GATE_TARGETS_PAIRS) {
+            Circuit c1;
+            Circuit c2;
+            c1.safe_append_u(g.name, {0, 1}, {});
+            c2.safe_append_u(g.name, {1, 0}, {});
+            auto f1 = circuit_flow_generators<64>(c1);
+            auto f2 = circuit_flow_generators<64>(c2);
+            EXPECT_EQ(g.is_symmetric(), f1 == f2) << g.name;
+        }
+    }
+}
+
+TEST(gate_data, hadamard_conjugated_vs_flow_generators_of_two_qubit_gates) {
+    auto flow_key = [](const Circuit &circuit, bool ignore_sign) {
+        auto f = circuit_flow_generators<64>(circuit);
+        if (ignore_sign) {
+            for (auto &e : f) {
+                e.input.sign = false;
+                e.output.sign = false;
+            }
+        }
+        std::stringstream ss;
+        ss << comma_sep(f);
+        return ss.str();
+    };
+    std::map<std::string, GateType> known_flows_s;
+    std::map<std::string, std::vector<GateType>> known_flows_u;
+
+    for (const auto &g : GATE_DATA.items) {
+        if (g.arg_count != 0 && g.arg_count != ARG_COUNT_SYGIL_ZERO_OR_ONE && g.arg_count != ARG_COUNT_SYGIL_ANY) {
+            continue;
+        }
+        if ((g.flags & GATE_TARGETS_PAIRS) || (g.flags & GATE_IS_SINGLE_QUBIT_GATE)) {
+            Circuit c;
+            c.safe_append_u(g.name, {0, 1}, {});
+            auto key_s = flow_key(c, false);
+            auto key_u = flow_key(c, true);
+            ASSERT_EQ(known_flows_s.find(key_s), known_flows_s.end());
+            known_flows_s[key_s] = g.id;
+            known_flows_u[key_u].push_back(g.id);
+        }
+    }
+    for (const auto &g : GATE_DATA.items) {
+        if (g.arg_count != 0 && g.arg_count != ARG_COUNT_SYGIL_ZERO_OR_ONE && g.arg_count != ARG_COUNT_SYGIL_ANY) {
+            continue;
+        }
+        if ((g.flags & GATE_TARGETS_PAIRS) || (g.flags & GATE_IS_SINGLE_QUBIT_GATE)) {
+            Circuit c;
+            c.safe_append_u("H", {0, 1}, {});
+            c.safe_append_u(g.name, {0, 1}, {});
+            c.safe_append_u("H", {0, 1}, {});
+            auto key_s = flow_key(c, false);
+            auto key_u = flow_key(c, true);
+            auto other_s = known_flows_s.find(key_s);
+            auto &other_us = known_flows_u[key_u];
+            if (other_us.empty()) {
+                other_us.push_back(GateType::NOT_A_GATE);
+            }
+
+            GateType expected_s = other_s == known_flows_s.end() ? GateType::NOT_A_GATE : other_s->second;
+            GateType actual_s = g.hadamard_conjugated(false);
+            GateType actual_u = g.hadamard_conjugated(true);
+            bool found = std::find(other_us.begin(), other_us.end(), actual_u) != other_us.end();
+            EXPECT_EQ(actual_s, expected_s) << "signed " << g.name << " -> " << GATE_DATA[actual_s].name << " != " << GATE_DATA[expected_s].name;
+            EXPECT_TRUE(found) << "unsigned " << g.name << " -> " << GATE_DATA[actual_u].name << " not in " << GATE_DATA[other_us[0]].name;
+        }
+    }
+}
diff --git a/src/stim/gates/gates_test.py b/src/stim/gates/gates_test.py
index b1cae79b..aae2b00c 100644
--- a/src/stim/gates/gates_test.py
+++ b/src/stim/gates/gates_test.py
@@ -81,3 +81,20 @@ def test_gate_data_flows():
         stim.Flow("X -> Z"),
         stim.Flow("Z -> X"),
     ]
+
+
+def test_gate_is_symmetric():
+    assert stim.GateData('SWAP').is_symmetric_gate
+    assert stim.GateData('H').is_symmetric_gate
+    assert stim.GateData('MYY').is_symmetric_gate
+    assert stim.GateData('DEPOLARIZE2').is_symmetric_gate
+    assert not stim.GateData('PAULI_CHANNEL_2').is_symmetric_gate
+    assert not stim.GateData('DETECTOR').is_symmetric_gate
+    assert not stim.GateData('TICK').is_symmetric_gate
+
+
+def test_gate_hadamard_conjugated():
+    assert stim.GateData('CZSWAP').hadamard_conjugated(unsigned=True) is None
+    assert stim.GateData('TICK').hadamard_conjugated() == stim.GateData('TICK')
+    assert stim.GateData('MYY').hadamard_conjugated() == stim.GateData('MYY')
+    assert stim.GateData('XCZ').hadamard_conjugated() == stim.GateData('CX')
diff --git a/src/stim/simulators/matched_error.pybind.cc b/src/stim/simulators/matched_error.pybind.cc
index c177283f..984c74c4 100644
--- a/src/stim/simulators/matched_error.pybind.cc
+++ b/src/stim/simulators/matched_error.pybind.cc
@@ -27,11 +27,11 @@ using namespace stim_pybind;
 
 std::string CircuitErrorLocationStackFrame_repr(const CircuitErrorLocationStackFrame &self) {
     std::stringstream out;
-    out << "stim.CircuitErrorLocationStackFrame";
-    out << "(instruction_offset=" << self.instruction_offset;
-    out << ", iteration_index=" << self.iteration_index;
-    out << ", instruction_repetitions_arg=" << self.instruction_repetitions_arg;
-    out << ")";
+    out << "stim.CircuitErrorLocationStackFrame(";
+    out << "\n    instruction_offset=" << self.instruction_offset << ",";
+    out << "\n    iteration_index=" << self.iteration_index << ",";
+    out << "\n    instruction_repetitions_arg=" << self.instruction_repetitions_arg << ",";
+    out << "\n)";
     return out.str();
 }
 
@@ -57,21 +57,26 @@ pybind11::ssize_t CircuitTargetsInsideInstruction_hash(const CircuitTargetsInsid
 std::string GateTargetWithCoords_repr(const GateTargetWithCoords &self) {
     std::stringstream out;
     out << "stim.GateTargetWithCoords";
-    out << "(gate_target=" << self.gate_target;
-    out << ", coords=[" << comma_sep(self.coords) << "]";
+    out << "(" << self.gate_target;
+    out << ", [" << comma_sep(self.coords) << "]";
     out << ")";
     return out.str();
 }
 
 std::string FlippedMeasurement_repr(const FlippedMeasurement &self) {
     std::stringstream out;
-    out << "stim.FlippedMeasurement";
-    out << "(record_index=" << self.measurement_record_index;
-    out << ", observable=(";
+    out << "stim.FlippedMeasurement(";
+    out << "\n    record_index=";
+    if (self.measurement_record_index == UINT64_MAX) {
+        out << "None";
+    } else {
+        out << self.measurement_record_index;
+    }
+    out << ",\n    observable=(";
     for (const auto &e : self.measured_observable) {
         out << GateTargetWithCoords_repr(e) << ",";
     }
-    out << "))";
+    out << "),\n)";
     return out.str();
 }
 
@@ -260,7 +265,6 @@ void stim_pybind::pybind_gate_target_with_coords_methods(
             [](const pybind11::object &gate_target, const std::vector<double> &coords) -> GateTargetWithCoords {
                 return GateTargetWithCoords{obj_to_gate_target(gate_target), coords};
             }),
-        pybind11::kw_only(),
         pybind11::arg("gate_target"),
         pybind11::arg("coords"),
         clean_doc_string(R"DOC(
@@ -379,8 +383,14 @@ void stim_pybind::pybind_flipped_measurement_methods(
     });
     c.def(
         pybind11::init(
-            [](uint64_t measurement_record_index, const pybind11::object &measured_observable) -> FlippedMeasurement {
-                FlippedMeasurement result{measurement_record_index, {}};
+            [](const pybind11::object &measurement_record_index, const pybind11::object &measured_observable) -> FlippedMeasurement {
+                uint64_t u;
+                if (measurement_record_index.is_none()) {
+                    u = UINT64_MAX;
+                } else {
+                    u = pybind11::cast<uint64_t>(measurement_record_index);
+                }
+                FlippedMeasurement result{u, {}};
                 for (const auto &e : measured_observable) {
                     result.measured_observable.push_back(pybind11::cast<GateTargetWithCoords>(e));
                 }
@@ -390,7 +400,19 @@ void stim_pybind::pybind_flipped_measurement_methods(
         pybind11::arg("record_index"),
         pybind11::arg("observable"),
         clean_doc_string(R"DOC(
+            @signature def __init__(self, measurement_record_index: Optional[int], measured_observable: Iterable[stim.GateTargetWithCoords]):
             Creates a stim.FlippedMeasurement.
+
+            Examples:
+                >>> import stim
+                >>> print(stim.FlippedMeasurement(
+                ...     record_index=5,
+                ...     observable=[],
+                ... ))
+                stim.FlippedMeasurement(
+                    record_index=5,
+                    observable=(),
+                )
         )DOC")
             .data());
     c.def("__repr__", &FlippedMeasurement_repr);
@@ -494,7 +516,27 @@ pybind11::class_<CircuitErrorLocation> stim_pybind::pybind_circuit_error_locatio
         "CircuitErrorLocation",
         clean_doc_string(R"DOC(
             Describes the location of an error mechanism from a stim circuit.
-        )DOC")
+
+            Examples:
+                >>> import stim
+                >>> circuit = stim.Circuit.generated(
+                ...     "repetition_code:memory",
+                ...     distance=5,
+                ...     rounds=5,
+                ...     before_round_data_depolarization=1e-3,
+                ... )
+                >>> logical_error = circuit.shortest_graphlike_error()
+                >>> error_location = logical_error[0].circuit_error_locations[0]
+                >>> print(error_location)
+                CircuitErrorLocation {
+                    flipped_pauli_product: X0
+                    Circuit location stack trace:
+                        (after 1 TICKs)
+                        at instruction #3 (DEPOLARIZE1) in the circuit
+                        at target #1 of the instruction
+                        resolving to DEPOLARIZE1(0.001) 0
+                }
+            )DOC")
             .data());
 }
 void stim_pybind::pybind_circuit_error_location_methods(
@@ -503,8 +545,23 @@ void stim_pybind::pybind_circuit_error_location_methods(
         "tick_offset",
         &CircuitErrorLocation::tick_offset,
         clean_doc_string(R"DOC(
-            The number of TICKs that executed before the error mechanism being discussed,
-            including TICKs that occurred multiple times during loops.
+            The number of TICKs that executed before the error happened.
+
+            This counts TICKs occurring multiple times during loops.
+
+            Examples:
+                >>> import stim
+                >>> err = stim.Circuit('''
+                ...     R 0
+                ...     TICK
+                ...     TICK
+                ...     TICK
+                ...     Y_ERROR(0.125) 0
+                ...     M 0
+                ...     OBSERVABLE_INCLUDE(0) rec[-1]
+                ... ''').shortest_graphlike_error()
+                >>> err[0].circuit_error_locations[0].tick_offset
+                3
         )DOC")
             .data());
 
@@ -513,7 +570,19 @@ void stim_pybind::pybind_circuit_error_location_methods(
         &CircuitErrorLocation::flipped_pauli_product,
         clean_doc_string(R"DOC(
             The Pauli errors that the error mechanism applied to qubits.
+
             When the error is a measurement error, this will be an empty list.
+
+            Examples:
+                >>> import stim
+                >>> err = stim.Circuit('''
+                ...     R 0
+                ...     Y_ERROR(0.125) 0
+                ...     M 0
+                ...     OBSERVABLE_INCLUDE(0) rec[-1]
+                ... ''').shortest_graphlike_error()
+                >>> err[0].circuit_error_locations[0].flipped_pauli_product
+                [stim.GateTargetWithCoords(stim.target_y(0), [])]
         )DOC")
             .data());
 
@@ -526,9 +595,24 @@ void stim_pybind::pybind_circuit_error_location_methods(
             return pybind11::cast(self.flipped_measurement);
         },
         clean_doc_string(R"DOC(
+            @signature def flipped_measurement(self) -> Optional[stim.FlippedMeasurement]:
             The measurement that was flipped by the error mechanism.
+
             If the error isn't a measurement error, this will be None.
-            @signature def flipped_measurement(self) -> Optional[stim.FlippedMeasurement]:
+
+            Examples:
+                >>> import stim
+                >>> err = stim.Circuit('''
+                ...     R 0
+                ...     M(0.125) 0
+                ...     OBSERVABLE_INCLUDE(0) rec[-1]
+                ... ''').shortest_graphlike_error()
+                >>> err[0].circuit_error_locations[0].flipped_measurement
+                stim.FlippedMeasurement(
+                    record_index=0,
+                    observable=(stim.GateTargetWithCoords(stim.target_z(0), []),),
+                )
+
         )DOC")
             .data());
 
@@ -546,8 +630,26 @@ void stim_pybind::pybind_circuit_error_location_methods(
         "stack_frames",
         &CircuitErrorLocation::stack_frames,
         clean_doc_string(R"DOC(
-            Where in the circuit's execution does the error mechanism occur,
-            accounting for things like nested loops that iterate multiple times.
+            Describes where in the circuit's execution the error happened.
+
+            Multiple frames are needed because the error may occur within a loop,
+            or a loop nested inside a loop, or etc.
+
+            Examples:
+                >>> import stim
+                >>> err = stim.Circuit('''
+                ...     R 0
+                ...     TICK
+                ...     Y_ERROR(0.125) 0
+                ...     M 0
+                ...     OBSERVABLE_INCLUDE(0) rec[-1]
+                ... ''').shortest_graphlike_error()
+                >>> err[0].circuit_error_locations[0].stack_frames
+                [stim.CircuitErrorLocationStackFrame(
+                    instruction_offset=2,
+                    iteration_index=0,
+                    instruction_repetitions_arg=0,
+                )]
         )DOC")
             .data());
 
@@ -584,6 +686,48 @@ void stim_pybind::pybind_circuit_error_location_methods(
         pybind11::arg("stack_frames"),
         clean_doc_string(R"DOC(
             Creates a stim.CircuitErrorLocation.
+
+            Examples:
+                >>> import stim
+                >>> err = stim.CircuitErrorLocation(
+                ...     tick_offset=1,
+                ...     flipped_pauli_product=(
+                ...         stim.GateTargetWithCoords(
+                ...             gate_target=stim.target_x(0),
+                ...             coords=[],
+                ...         ),
+                ...     ),
+                ...     flipped_measurement=stim.FlippedMeasurement(
+                ...         record_index=None,
+                ...         observable=(),
+                ...     ),
+                ...     instruction_targets=stim.CircuitTargetsInsideInstruction(
+                ...         gate='DEPOLARIZE1',
+                ...         args=[0.001],
+                ...         target_range_start=0,
+                ...         target_range_end=1,
+                ...         targets_in_range=(stim.GateTargetWithCoords(
+                ...             gate_target=0,
+                ...             coords=[],
+                ...         ),)
+                ...     ),
+                ...     stack_frames=(
+                ...         stim.CircuitErrorLocationStackFrame(
+                ...             instruction_offset=2,
+                ...             iteration_index=0,
+                ...             instruction_repetitions_arg=0,
+                ...         ),
+                ...     ),
+                ... )
+                >>> print(err)
+                CircuitErrorLocation {
+                    flipped_pauli_product: X0
+                    Circuit location stack trace:
+                        (after 1 TICKs)
+                        at instruction #3 (DEPOLARIZE1) in the circuit
+                        at target #1 of the instruction
+                        resolving to DEPOLARIZE1(0.001) 0
+                }
         )DOC")
             .data());
     c.def("__repr__", &CircuitErrorLocation_repr);

From fd8c95520941627d82b6453d9654d9c4dfb06020 Mon Sep 17 00:00:00 2001
From: Craig Gidney <craiggidney@google.com>
Date: Tue, 23 Jul 2024 16:31:18 -0700
Subject: [PATCH 06/17] Add some `stim.FlipSimulator` methods and diagram
 niceties (#799)

- Add "rows" argument to `stim.Circuit.diagram` to control layout of
multi-row diagrams
- Add "tick" labels to timeslice diagrams
- Add stim.FlipSimulator.copy
- Add stim.FlipSimulator.reset

Fixes https://github.com/quantumlib/Stim/issues/672
---
 doc/python_api_reference_vDev.md              | 178 +++++++++++++++++-
 doc/stim.pyi                                  | 162 +++++++++++++++-
 glue/python/src/stim/__init__.pyi             | 162 +++++++++++++++-
 src/stim/circuit/circuit.pybind.cc            |  19 +-
 src/stim/cmd/command_diagram.pybind.cc        |  13 +-
 src/stim/cmd/command_diagram.pybind.h         |   1 +
 ...detector_error_model_instruction.pybind.cc |  42 ++++-
 .../detector_slice/detector_slice_set.cc      |  29 +--
 .../detector_slice/detector_slice_set.h       |   2 +-
 .../diagram/timeline/timeline_svg_drawer.cc   |  39 +++-
 .../diagram/timeline/timeline_svg_drawer.h    |   3 +-
 src/stim/mem/bitword_128_sse.h                |   2 +
 src/stim/mem/bitword_256_avx.h                |   2 +
 src/stim/mem/bitword_64.h                     |   2 +
 src/stim/mem/simd_word.test.cc                |  10 +
 src/stim/simulators/frame_simulator.pybind.cc | 114 +++++++++++
 src/stim/stabilizers/pauli_string.pybind.cc   |   7 +
 src/stim/stabilizers/tableau.pybind.cc        |  11 +-
 src/stim/util_top/circuit_vs_amplitudes.cc    |   2 +-
 testdata/anticommuting_detslice.svg           |  10 +-
 testdata/bezier_time_slice.svg                |   4 +-
 testdata/circuit_all_ops_detslice.svg         |  40 ++--
 testdata/circuit_all_ops_timeslice.svg        |  40 ++--
 ...uit_diagram_timeline_svg_chained_loops.svg |  31 ++-
 testdata/detslice-with-ops_surface_code.svg   |  40 ++--
 testdata/observable_slices.svg                |  37 ++--
 .../surface_code_full_time_detector_slice.svg | 106 +++++++----
 testdata/surface_code_time_detector_slice.svg |  31 ++-
 testdata/surface_code_time_slice.svg          |  31 ++-
 29 files changed, 988 insertions(+), 182 deletions(-)

diff --git a/doc/python_api_reference_vDev.md b/doc/python_api_reference_vDev.md
index 145d04ac..4ba62593 100644
--- a/doc/python_api_reference_vDev.md
+++ b/doc/python_api_reference_vDev.md
@@ -186,6 +186,7 @@ API references for stable versions are kept on the [stim github wiki](https://gi
     - [`stim.FlipSimulator.__init__`](#stim.FlipSimulator.__init__)
     - [`stim.FlipSimulator.batch_size`](#stim.FlipSimulator.batch_size)
     - [`stim.FlipSimulator.broadcast_pauli_errors`](#stim.FlipSimulator.broadcast_pauli_errors)
+    - [`stim.FlipSimulator.copy`](#stim.FlipSimulator.copy)
     - [`stim.FlipSimulator.do`](#stim.FlipSimulator.do)
     - [`stim.FlipSimulator.get_detector_flips`](#stim.FlipSimulator.get_detector_flips)
     - [`stim.FlipSimulator.get_measurement_flips`](#stim.FlipSimulator.get_measurement_flips)
@@ -195,6 +196,7 @@ API references for stable versions are kept on the [stim github wiki](https://gi
     - [`stim.FlipSimulator.num_observables`](#stim.FlipSimulator.num_observables)
     - [`stim.FlipSimulator.num_qubits`](#stim.FlipSimulator.num_qubits)
     - [`stim.FlipSimulator.peek_pauli_flips`](#stim.FlipSimulator.peek_pauli_flips)
+    - [`stim.FlipSimulator.reset`](#stim.FlipSimulator.reset)
     - [`stim.FlipSimulator.set_pauli_flip`](#stim.FlipSimulator.set_pauli_flip)
 - [`stim.FlippedMeasurement`](#stim.FlippedMeasurement)
     - [`stim.FlippedMeasurement.__init__`](#stim.FlippedMeasurement.__init__)
@@ -1680,11 +1682,19 @@ def diagram(
 
             Passing `range(A, B)` for a time slice will show the
             operations between tick A and tick B.
-        filter_coords: A set of acceptable coordinate prefixes, or
-            desired stim.DemTargets. For detector slice diagrams, only
-            detectors match one of the filters are included. If no filter
-            is specified, all detectors are included (but no observables).
-            To include an observable, add it as one of the filters.
+        rows: In diagrams that have multiple separate pieces, such as timeslice
+            diagrams and detslice diagrams, this controls how many rows of
+            pieces there will be. If not specified, a number of rows that creates
+            a roughly square layout will be chosen.
+        filter_coords: A list of things to include in the diagram. Different
+            effects depending on the diagram.
+
+            For detslice diagrams, the filter defaults to showing all detectors
+            and no observables. When specified, each list entry can be a collection
+            of floats (detectors whose coordinates start with the same numbers will
+            be included), a stim.DemTarget (specifying a detector or observable
+            to include), a string like "D5" or "L0" specifying a detector or
+            observable to include.
 
     Returns:
         An object whose `__str__` method returns the diagram, so that
@@ -5304,7 +5314,24 @@ def __str__(
 def args_copy(
     self,
 ) -> List[float]:
-    """Returns a copy of the list of numbers parameterizing the instruction (e.g. the probability of an error).
+    """Returns a copy of the list of numbers parameterizing the instruction.
+
+    For example, this would be coordinates of a detector instruction or the
+    probability of an error instruction. The result is a copy, meaning that
+    editing it won't change the instruction's targets or future copies.
+
+    Examples:
+        >>> import stim
+        >>> instruction = stim.DetectorErrorModel('''
+        ...     error(0.125) D0
+        ... ''')[0]
+        >>> instruction.args_copy()
+        [0.125]
+
+        >>> instruction.args_copy() == instruction.args_copy()
+        True
+        >>> instruction.args_copy() is instruction.args_copy()
+        False
     """
 ```
 
@@ -5318,8 +5345,21 @@ def targets_copy(
 ) -> List[Union[int, stim.DemTarget]]:
     """Returns a copy of the instruction's targets.
 
-    (Making a copy is enforced to make it clear that editing the result won't change
-    the instruction's targets.)
+    The result is a copy, meaning that editing it won't change the instruction's
+    targets or future copies.
+
+    Examples:
+        >>> import stim
+        >>> instruction = stim.DetectorErrorModel('''
+        ...     error(0.125) D0 L2
+        ... ''')[0]
+        >>> instruction.targets_copy()
+        [stim.DemTarget('D0'), stim.DemTarget('L2')]
+
+        >>> instruction.targets_copy() == instruction.targets_copy()
+        True
+        >>> instruction.targets_copy() is instruction.targets_copy()
+        False
     """
 ```
 
@@ -7177,6 +7217,83 @@ def broadcast_pauli_errors(
     """
 ```
 
+<a name="stim.FlipSimulator.copy"></a>
+```python
+# stim.FlipSimulator.copy
+
+# (in class stim.FlipSimulator)
+def copy(
+    self,
+    *,
+    copy_rng: bool = False,
+    seed: Optional[int] = None,
+) -> stim.FlipSimulator:
+    """Returns a simulator with the same internal state, except perhaps its prng.
+
+    Args:
+        copy_rng: Defaults to False. When False, the copy's pseudo random number
+            generator is reinitialized with a random seed instead of being a copy
+            of the original simulator's pseudo random number generator. This
+            causes the copy and the original to sample independent randomness,
+            instead of identical randomness, for future random operations. When set
+            to true, the copy will have the exact same pseudo random number
+            generator state as the original, and so will produce identical results
+            if told to do the same noisy operations. This argument is incompatible
+            with the `seed` argument.
+
+        seed: PARTIALLY determines simulation results by deterministically seeding
+            the random number generator.
+
+            Must be None or an integer in range(2**64).
+
+            Defaults to None. When None, the prng state is either copied from the
+            original simulator or reseeded from system entropy, depending on the
+            copy_rng argument.
+
+            When set to an integer, making the exact same series calls on the exact
+            same machine with the exact same version of Stim will produce the exact
+            same simulation results.
+
+            CAUTION: simulation results *WILL NOT* be consistent between versions of
+            Stim. This restriction is present to make it possible to have future
+            optimizations to the random sampling, and is enforced by introducing
+            intentional differences in the seeding strategy from version to version.
+
+            CAUTION: simulation results *MAY NOT* be consistent across machines that
+            differ in the width of supported SIMD instructions. For example, using
+            the same seed on a machine that supports AVX instructions and one that
+            only supports SSE instructions may produce different simulation results.
+
+            CAUTION: simulation results *MAY NOT* be consistent if you vary how the
+            circuit is executed. For example, reordering whether a reset on one
+            qubit happens before or after a reset on another qubit can result in
+            different measurement results being observed starting from the same
+            seed.
+
+    Returns:
+        The copy of the simulator.
+
+    Examples:
+        >>> import stim
+        >>> import numpy as np
+
+        >>> s1 = stim.FlipSimulator(batch_size=256)
+        >>> s1.set_pauli_flip('X', qubit_index=2, instance_index=3)
+        >>> s2 = s1.copy()
+        >>> s2 is s1
+        False
+        >>> s2.peek_pauli_flips() == s1.peek_pauli_flips()
+        True
+
+        >>> s1 = stim.FlipSimulator(batch_size=256)
+        >>> s2 = s1.copy(copy_rng=True)
+        >>> s1.do(stim.Circuit("X_ERROR(0.25) 0 \n M 0"))
+        >>> s2.do(stim.Circuit("X_ERROR(0.25) 0 \n M 0"))
+        >>> np.array_equal(s1.get_measurement_flips(), s2.get_measurement_flips())
+        True
+    """
+```
+
 <a name="stim.FlipSimulator.do"></a>
 ```python
 # stim.FlipSimulator.do
@@ -7613,6 +7730,38 @@ def peek_pauli_flips(
     """
 ```
 
+<a name="stim.FlipSimulator.reset"></a>
+```python
+# stim.FlipSimulator.reset
+
+# (in class stim.FlipSimulator)
+def reset(
+    self,
+) -> None:
+    """Resets the simulator's state, so it can be reused for another simulation.
+
+    This empties the measurement flip history, empties the detector flip history,
+    and zeroes the observable flip state. It also resets all qubits to |0>. If
+    stabilizer randomization is disabled, this zeros all pauli flips data. Otherwise
+    it randomizes all pauli flips to be I or Z with equal probability.
+
+    Examples:
+        >>> import stim
+        >>> sim = stim.FlipSimulator(batch_size=256)
+        >>> sim.do(stim.Circuit("M(0.1) 9"))
+        >>> sim.num_qubits
+        10
+        >>> sim.get_measurement_flips().shape
+        (1, 256)
+
+        >>> sim.reset()
+        >>> sim.num_qubits
+        10
+        >>> sim.get_measurement_flips().shape
+        (0, 256)
+    """
+```
+
 <a name="stim.FlipSimulator.set_pauli_flip"></a>
 ```python
 # stim.FlipSimulator.set_pauli_flip
@@ -9533,6 +9682,13 @@ def __len__(
     self,
 ) -> int:
     """Returns the length the pauli string; the number of qubits it operates on.
+
+    Examples:
+        >>> import stim
+        >>> len(stim.PauliString("XY_ZZ"))
+        5
+        >>> len(stim.PauliString("X0*Z99"))
+        100
     """
 ```
 
@@ -10663,6 +10819,12 @@ def __len__(
     self,
 ) -> int:
     """Returns the number of qubits operated on by the tableau.
+
+    Examples:
+        >>> import stim
+        >>> t = stim.Tableau.from_named_gate("CNOT")
+        >>> len(t)
+        2
     """
 ```
 
diff --git a/doc/stim.pyi b/doc/stim.pyi
index bbd39402..cd232b6a 100644
--- a/doc/stim.pyi
+++ b/doc/stim.pyi
@@ -1084,11 +1084,19 @@ class Circuit:
 
                 Passing `range(A, B)` for a time slice will show the
                 operations between tick A and tick B.
-            filter_coords: A set of acceptable coordinate prefixes, or
-                desired stim.DemTargets. For detector slice diagrams, only
-                detectors match one of the filters are included. If no filter
-                is specified, all detectors are included (but no observables).
-                To include an observable, add it as one of the filters.
+            rows: In diagrams that have multiple separate pieces, such as timeslice
+                diagrams and detslice diagrams, this controls how many rows of
+                pieces there will be. If not specified, a number of rows that creates
+                a roughly square layout will be chosen.
+            filter_coords: A list of things to include in the diagram. Different
+                effects depending on the diagram.
+
+                For detslice diagrams, the filter defaults to showing all detectors
+                and no observables. When specified, each list entry can be a collection
+                of floats (detectors whose coordinates start with the same numbers will
+                be included), a stim.DemTarget (specifying a detector or observable
+                to include), a string like "D5" or "L0" specifying a detector or
+                observable to include.
 
         Returns:
             An object whose `__str__` method returns the diagram, so that
@@ -4145,15 +4153,45 @@ class DemInstruction:
     def args_copy(
         self,
     ) -> List[float]:
-        """Returns a copy of the list of numbers parameterizing the instruction (e.g. the probability of an error).
+        """Returns a copy of the list of numbers parameterizing the instruction.
+
+        For example, this would be coordinates of a detector instruction or the
+        probability of an error instruction. The result is a copy, meaning that
+        editing it won't change the instruction's targets or future copies.
+
+        Examples:
+            >>> import stim
+            >>> instruction = stim.DetectorErrorModel('''
+            ...     error(0.125) D0
+            ... ''')[0]
+            >>> instruction.args_copy()
+            [0.125]
+
+            >>> instruction.args_copy() == instruction.args_copy()
+            True
+            >>> instruction.args_copy() is instruction.args_copy()
+            False
         """
     def targets_copy(
         self,
     ) -> List[Union[int, stim.DemTarget]]:
         """Returns a copy of the instruction's targets.
 
-        (Making a copy is enforced to make it clear that editing the result won't change
-        the instruction's targets.)
+        The result is a copy, meaning that editing it won't change the instruction's
+        targets or future copies.
+
+        Examples:
+            >>> import stim
+            >>> instruction = stim.DetectorErrorModel('''
+            ...     error(0.125) D0 L2
+            ... ''')[0]
+            >>> instruction.targets_copy()
+            [stim.DemTarget('D0'), stim.DemTarget('L2')]
+
+            >>> instruction.targets_copy() == instruction.targets_copy()
+            True
+            >>> instruction.targets_copy() is instruction.targets_copy()
+            False
         """
     @property
     def type(
@@ -5575,6 +5613,76 @@ class FlipSimulator:
             >>> sim.peek_pauli_flips()
             [stim.PauliString("+X_Y"), stim.PauliString("+Z_Y")]
         """
+    def copy(
+        self,
+        *,
+        copy_rng: bool = False,
+        seed: Optional[int] = None,
+    ) -> stim.FlipSimulator:
+        """Returns a simulator with the same internal state, except perhaps its prng.
+
+        Args:
+            copy_rng: Defaults to False. When False, the copy's pseudo random number
+                generator is reinitialized with a random seed instead of being a copy
+                of the original simulator's pseudo random number generator. This
+                causes the copy and the original to sample independent randomness,
+                instead of identical randomness, for future random operations. When set
+                to true, the copy will have the exact same pseudo random number
+                generator state as the original, and so will produce identical results
+                if told to do the same noisy operations. This argument is incompatible
+                with the `seed` argument.
+
+            seed: PARTIALLY determines simulation results by deterministically seeding
+                the random number generator.
+
+                Must be None or an integer in range(2**64).
+
+                Defaults to None. When None, the prng state is either copied from the
+                original simulator or reseeded from system entropy, depending on the
+                copy_rng argument.
+
+                When set to an integer, making the exact same series calls on the exact
+                same machine with the exact same version of Stim will produce the exact
+                same simulation results.
+
+                CAUTION: simulation results *WILL NOT* be consistent between versions of
+                Stim. This restriction is present to make it possible to have future
+                optimizations to the random sampling, and is enforced by introducing
+                intentional differences in the seeding strategy from version to version.
+
+                CAUTION: simulation results *MAY NOT* be consistent across machines that
+                differ in the width of supported SIMD instructions. For example, using
+                the same seed on a machine that supports AVX instructions and one that
+                only supports SSE instructions may produce different simulation results.
+
+                CAUTION: simulation results *MAY NOT* be consistent if you vary how the
+                circuit is executed. For example, reordering whether a reset on one
+                qubit happens before or after a reset on another qubit can result in
+                different measurement results being observed starting from the same
+                seed.
+
+        Returns:
+            The copy of the simulator.
+
+        Examples:
+            >>> import stim
+            >>> import numpy as np
+
+            >>> s1 = stim.FlipSimulator(batch_size=256)
+            >>> s1.set_pauli_flip('X', qubit_index=2, instance_index=3)
+            >>> s2 = s1.copy()
+            >>> s2 is s1
+            False
+            >>> s2.peek_pauli_flips() == s1.peek_pauli_flips()
+            True
+
+            >>> s1 = stim.FlipSimulator(batch_size=256)
+            >>> s2 = s1.copy(copy_rng=True)
+            >>> s1.do(stim.Circuit("X_ERROR(0.25) 0 \n M 0"))
+            >>> s2.do(stim.Circuit("X_ERROR(0.25) 0 \n M 0"))
+            >>> np.array_equal(s1.get_measurement_flips(), s2.get_measurement_flips())
+            True
+        """
     def do(
         self,
         obj: Union[stim.Circuit, stim.CircuitInstruction, stim.CircuitRepeatBlock],
@@ -5948,6 +6056,31 @@ class FlipSimulator:
             >>> sorted(set(str(flips)))  # Should have Zs from stabilizer randomization
             ['+', 'Z', '_']
         """
+    def reset(
+        self,
+    ) -> None:
+        """Resets the simulator's state, so it can be reused for another simulation.
+
+        This empties the measurement flip history, empties the detector flip history,
+        and zeroes the observable flip state. It also resets all qubits to |0>. If
+        stabilizer randomization is disabled, this zeros all pauli flips data. Otherwise
+        it randomizes all pauli flips to be I or Z with equal probability.
+
+        Examples:
+            >>> import stim
+            >>> sim = stim.FlipSimulator(batch_size=256)
+            >>> sim.do(stim.Circuit("M(0.1) 9"))
+            >>> sim.num_qubits
+            10
+            >>> sim.get_measurement_flips().shape
+            (1, 256)
+
+            >>> sim.reset()
+            >>> sim.num_qubits
+            10
+            >>> sim.get_measurement_flips().shape
+            (0, 256)
+        """
     def set_pauli_flip(
         self,
         pauli: Union[str, int],
@@ -7401,6 +7534,13 @@ class PauliString:
         self,
     ) -> int:
         """Returns the length the pauli string; the number of qubits it operates on.
+
+        Examples:
+            >>> import stim
+            >>> len(stim.PauliString("XY_ZZ"))
+            5
+            >>> len(stim.PauliString("X0*Z99"))
+            100
         """
     def __mul__(
         self,
@@ -8306,6 +8446,12 @@ class Tableau:
         self,
     ) -> int:
         """Returns the number of qubits operated on by the tableau.
+
+        Examples:
+            >>> import stim
+            >>> t = stim.Tableau.from_named_gate("CNOT")
+            >>> len(t)
+            2
         """
     def __mul__(
         self,
diff --git a/glue/python/src/stim/__init__.pyi b/glue/python/src/stim/__init__.pyi
index bbd39402..cd232b6a 100644
--- a/glue/python/src/stim/__init__.pyi
+++ b/glue/python/src/stim/__init__.pyi
@@ -1084,11 +1084,19 @@ class Circuit:
 
                 Passing `range(A, B)` for a time slice will show the
                 operations between tick A and tick B.
-            filter_coords: A set of acceptable coordinate prefixes, or
-                desired stim.DemTargets. For detector slice diagrams, only
-                detectors match one of the filters are included. If no filter
-                is specified, all detectors are included (but no observables).
-                To include an observable, add it as one of the filters.
+            rows: In diagrams that have multiple separate pieces, such as timeslice
+                diagrams and detslice diagrams, this controls how many rows of
+                pieces there will be. If not specified, a number of rows that creates
+                a roughly square layout will be chosen.
+            filter_coords: A list of things to include in the diagram. Different
+                effects depending on the diagram.
+
+                For detslice diagrams, the filter defaults to showing all detectors
+                and no observables. When specified, each list entry can be a collection
+                of floats (detectors whose coordinates start with the same numbers will
+                be included), a stim.DemTarget (specifying a detector or observable
+                to include), a string like "D5" or "L0" specifying a detector or
+                observable to include.
 
         Returns:
             An object whose `__str__` method returns the diagram, so that
@@ -4145,15 +4153,45 @@ class DemInstruction:
     def args_copy(
         self,
     ) -> List[float]:
-        """Returns a copy of the list of numbers parameterizing the instruction (e.g. the probability of an error).
+        """Returns a copy of the list of numbers parameterizing the instruction.
+
+        For example, this would be coordinates of a detector instruction or the
+        probability of an error instruction. The result is a copy, meaning that
+        editing it won't change the instruction's targets or future copies.
+
+        Examples:
+            >>> import stim
+            >>> instruction = stim.DetectorErrorModel('''
+            ...     error(0.125) D0
+            ... ''')[0]
+            >>> instruction.args_copy()
+            [0.125]
+
+            >>> instruction.args_copy() == instruction.args_copy()
+            True
+            >>> instruction.args_copy() is instruction.args_copy()
+            False
         """
     def targets_copy(
         self,
     ) -> List[Union[int, stim.DemTarget]]:
         """Returns a copy of the instruction's targets.
 
-        (Making a copy is enforced to make it clear that editing the result won't change
-        the instruction's targets.)
+        The result is a copy, meaning that editing it won't change the instruction's
+        targets or future copies.
+
+        Examples:
+            >>> import stim
+            >>> instruction = stim.DetectorErrorModel('''
+            ...     error(0.125) D0 L2
+            ... ''')[0]
+            >>> instruction.targets_copy()
+            [stim.DemTarget('D0'), stim.DemTarget('L2')]
+
+            >>> instruction.targets_copy() == instruction.targets_copy()
+            True
+            >>> instruction.targets_copy() is instruction.targets_copy()
+            False
         """
     @property
     def type(
@@ -5575,6 +5613,76 @@ class FlipSimulator:
             >>> sim.peek_pauli_flips()
             [stim.PauliString("+X_Y"), stim.PauliString("+Z_Y")]
         """
+    def copy(
+        self,
+        *,
+        copy_rng: bool = False,
+        seed: Optional[int] = None,
+    ) -> stim.FlipSimulator:
+        """Returns a simulator with the same internal state, except perhaps its prng.
+
+        Args:
+            copy_rng: Defaults to False. When False, the copy's pseudo random number
+                generator is reinitialized with a random seed instead of being a copy
+                of the original simulator's pseudo random number generator. This
+                causes the copy and the original to sample independent randomness,
+                instead of identical randomness, for future random operations. When set
+                to true, the copy will have the exact same pseudo random number
+                generator state as the original, and so will produce identical results
+                if told to do the same noisy operations. This argument is incompatible
+                with the `seed` argument.
+
+            seed: PARTIALLY determines simulation results by deterministically seeding
+                the random number generator.
+
+                Must be None or an integer in range(2**64).
+
+                Defaults to None. When None, the prng state is either copied from the
+                original simulator or reseeded from system entropy, depending on the
+                copy_rng argument.
+
+                When set to an integer, making the exact same series calls on the exact
+                same machine with the exact same version of Stim will produce the exact
+                same simulation results.
+
+                CAUTION: simulation results *WILL NOT* be consistent between versions of
+                Stim. This restriction is present to make it possible to have future
+                optimizations to the random sampling, and is enforced by introducing
+                intentional differences in the seeding strategy from version to version.
+
+                CAUTION: simulation results *MAY NOT* be consistent across machines that
+                differ in the width of supported SIMD instructions. For example, using
+                the same seed on a machine that supports AVX instructions and one that
+                only supports SSE instructions may produce different simulation results.
+
+                CAUTION: simulation results *MAY NOT* be consistent if you vary how the
+                circuit is executed. For example, reordering whether a reset on one
+                qubit happens before or after a reset on another qubit can result in
+                different measurement results being observed starting from the same
+                seed.
+
+        Returns:
+            The copy of the simulator.
+
+        Examples:
+            >>> import stim
+            >>> import numpy as np
+
+            >>> s1 = stim.FlipSimulator(batch_size=256)
+            >>> s1.set_pauli_flip('X', qubit_index=2, instance_index=3)
+            >>> s2 = s1.copy()
+            >>> s2 is s1
+            False
+            >>> s2.peek_pauli_flips() == s1.peek_pauli_flips()
+            True
+
+            >>> s1 = stim.FlipSimulator(batch_size=256)
+            >>> s2 = s1.copy(copy_rng=True)
+            >>> s1.do(stim.Circuit("X_ERROR(0.25) 0 \n M 0"))
+            >>> s2.do(stim.Circuit("X_ERROR(0.25) 0 \n M 0"))
+            >>> np.array_equal(s1.get_measurement_flips(), s2.get_measurement_flips())
+            True
+        """
     def do(
         self,
         obj: Union[stim.Circuit, stim.CircuitInstruction, stim.CircuitRepeatBlock],
@@ -5948,6 +6056,31 @@ class FlipSimulator:
             >>> sorted(set(str(flips)))  # Should have Zs from stabilizer randomization
             ['+', 'Z', '_']
         """
+    def reset(
+        self,
+    ) -> None:
+        """Resets the simulator's state, so it can be reused for another simulation.
+
+        This empties the measurement flip history, empties the detector flip history,
+        and zeroes the observable flip state. It also resets all qubits to |0>. If
+        stabilizer randomization is disabled, this zeros all pauli flips data. Otherwise
+        it randomizes all pauli flips to be I or Z with equal probability.
+
+        Examples:
+            >>> import stim
+            >>> sim = stim.FlipSimulator(batch_size=256)
+            >>> sim.do(stim.Circuit("M(0.1) 9"))
+            >>> sim.num_qubits
+            10
+            >>> sim.get_measurement_flips().shape
+            (1, 256)
+
+            >>> sim.reset()
+            >>> sim.num_qubits
+            10
+            >>> sim.get_measurement_flips().shape
+            (0, 256)
+        """
     def set_pauli_flip(
         self,
         pauli: Union[str, int],
@@ -7401,6 +7534,13 @@ class PauliString:
         self,
     ) -> int:
         """Returns the length the pauli string; the number of qubits it operates on.
+
+        Examples:
+            >>> import stim
+            >>> len(stim.PauliString("XY_ZZ"))
+            5
+            >>> len(stim.PauliString("X0*Z99"))
+            100
         """
     def __mul__(
         self,
@@ -8306,6 +8446,12 @@ class Tableau:
         self,
     ) -> int:
         """Returns the number of qubits operated on by the tableau.
+
+        Examples:
+            >>> import stim
+            >>> t = stim.Tableau.from_named_gate("CNOT")
+            >>> len(t)
+            2
         """
     def __mul__(
         self,
diff --git a/src/stim/circuit/circuit.pybind.cc b/src/stim/circuit/circuit.pybind.cc
index 4c870e61..8c801e5c 100644
--- a/src/stim/circuit/circuit.pybind.cc
+++ b/src/stim/circuit/circuit.pybind.cc
@@ -3267,6 +3267,7 @@ void stim_pybind::pybind_circuit_methods(pybind11::module &, pybind11::class_<Ci
         pybind11::arg("type") = "timeline-text",
         pybind11::kw_only(),
         pybind11::arg("tick") = pybind11::none(),
+        pybind11::arg("rows") = pybind11::none(),
         pybind11::arg("filter_coords") = pybind11::none(),
         clean_doc_string(R"DOC(
             @signature def diagram(self, type: str = 'timeline-text', *, tick: Union[None, int, range] = None, filter_coords: Iterable[Union[Iterable[float], stim.DemTarget]] = ((),)) -> 'stim._DiagramHelper':
@@ -3343,11 +3344,19 @@ void stim_pybind::pybind_circuit_methods(pybind11::module &, pybind11::class_<Ci
 
                     Passing `range(A, B)` for a time slice will show the
                     operations between tick A and tick B.
-                filter_coords: A set of acceptable coordinate prefixes, or
-                    desired stim.DemTargets. For detector slice diagrams, only
-                    detectors match one of the filters are included. If no filter
-                    is specified, all detectors are included (but no observables).
-                    To include an observable, add it as one of the filters.
+                rows: In diagrams that have multiple separate pieces, such as timeslice
+                    diagrams and detslice diagrams, this controls how many rows of
+                    pieces there will be. If not specified, a number of rows that creates
+                    a roughly square layout will be chosen.
+                filter_coords: A list of things to include in the diagram. Different
+                    effects depending on the diagram.
+
+                    For detslice diagrams, the filter defaults to showing all detectors
+                    and no observables. When specified, each list entry can be a collection
+                    of floats (detectors whose coordinates start with the same numbers will
+                    be included), a stim.DemTarget (specifying a detector or observable
+                    to include), a string like "D5" or "L0" specifying a detector or
+                    observable to include.
 
             Returns:
                 An object whose `__str__` method returns the diagram, so that
diff --git a/src/stim/cmd/command_diagram.pybind.cc b/src/stim/cmd/command_diagram.pybind.cc
index 64e189d0..3ea12cd4 100644
--- a/src/stim/cmd/command_diagram.pybind.cc
+++ b/src/stim/cmd/command_diagram.pybind.cc
@@ -213,6 +213,7 @@ DiagramHelper stim_pybind::circuit_diagram(
     const Circuit &circuit,
     std::string_view type,
     const pybind11::object &tick,
+    const pybind11::object &rows,
     const pybind11::object &filter_coords_obj) {
     std::vector<CoordFilter> filter_coords;
     try {
@@ -221,6 +222,11 @@ DiagramHelper stim_pybind::circuit_diagram(
         throw std::invalid_argument("filter_coords wasn't an Iterable[stim.DemTarget | Iterable[float]].");
     }
 
+    size_t num_rows = 0;
+    if (!rows.is_none()) {
+        num_rows = pybind11::cast<size_t>(rows);
+    }
+
     uint64_t tick_min;
     uint64_t num_ticks;
     if (tick.is_none()) {
@@ -262,7 +268,7 @@ DiagramHelper stim_pybind::circuit_diagram(
         type == "timeslice" || type == "time-slice") {
         std::stringstream out;
         DiagramTimelineSvgDrawer::make_diagram_write_to(
-            circuit, out, tick_min, num_ticks, DiagramTimelineSvgDrawerMode::SVG_MODE_TIME_SLICE, filter_coords);
+            circuit, out, tick_min, num_ticks, DiagramTimelineSvgDrawerMode::SVG_MODE_TIME_SLICE, filter_coords, num_rows);
         DiagramType d_type =
             type.find("html") != std::string::npos ? DiagramType::DIAGRAM_TYPE_SVG_HTML : DiagramType::DIAGRAM_TYPE_SVG;
         return DiagramHelper{d_type, out.str()};
@@ -270,7 +276,7 @@ DiagramHelper stim_pybind::circuit_diagram(
         type == "detslice-svg" || type == "detslice" || type == "detslice-html" || type == "detslice-svg-html" ||
         type == "detector-slice-svg" || type == "detector-slice") {
         std::stringstream out;
-        DetectorSliceSet::from_circuit_ticks(circuit, tick_min, num_ticks, filter_coords).write_svg_diagram_to(out);
+        DetectorSliceSet::from_circuit_ticks(circuit, tick_min, num_ticks, filter_coords).write_svg_diagram_to(out, num_rows);
         DiagramType d_type =
             type.find("html") != std::string::npos ? DiagramType::DIAGRAM_TYPE_SVG_HTML : DiagramType::DIAGRAM_TYPE_SVG;
         return DiagramHelper{d_type, out.str()};
@@ -284,7 +290,8 @@ DiagramHelper stim_pybind::circuit_diagram(
             tick_min,
             num_ticks,
             DiagramTimelineSvgDrawerMode::SVG_MODE_TIME_DETECTOR_SLICE,
-            filter_coords);
+            filter_coords,
+            num_rows);
         DiagramType d_type =
             type.find("html") != std::string::npos ? DiagramType::DIAGRAM_TYPE_SVG_HTML : DiagramType::DIAGRAM_TYPE_SVG;
         return DiagramHelper{d_type, out.str()};
diff --git a/src/stim/cmd/command_diagram.pybind.h b/src/stim/cmd/command_diagram.pybind.h
index 9e38276f..7c25a628 100644
--- a/src/stim/cmd/command_diagram.pybind.h
+++ b/src/stim/cmd/command_diagram.pybind.h
@@ -42,6 +42,7 @@ DiagramHelper circuit_diagram(
     const stim::Circuit &circuit,
     std::string_view type,
     const pybind11::object &tick,
+    const pybind11::object &rows,
     const pybind11::object &filter_coords_obj);
 
 }  // namespace stim_pybind
diff --git a/src/stim/dem/detector_error_model_instruction.pybind.cc b/src/stim/dem/detector_error_model_instruction.pybind.cc
index dfd98350..a9f0a5ca 100644
--- a/src/stim/dem/detector_error_model_instruction.pybind.cc
+++ b/src/stim/dem/detector_error_model_instruction.pybind.cc
@@ -14,7 +14,6 @@
 
 #include "stim/dem/detector_error_model_instruction.pybind.h"
 
-#include "stim/dem/detector_error_model.pybind.h"
 #include "stim/dem/detector_error_model_target.pybind.h"
 #include "stim/py/base.pybind.h"
 #include "stim/util_bot/str_util.h"
@@ -183,7 +182,29 @@ void stim_pybind::pybind_detector_error_model_instruction_methods(
     c.def(
         "args_copy",
         &ExposedDemInstruction::args_copy,
-        "Returns a copy of the list of numbers parameterizing the instruction (e.g. the probability of an error).");
+        clean_doc_string(R"DOC(
+            @signature def args_copy(self) -> List[float]:
+            Returns a copy of the list of numbers parameterizing the instruction.
+
+            For example, this would be coordinates of a detector instruction or the
+            probability of an error instruction. The result is a copy, meaning that
+            editing it won't change the instruction's targets or future copies.
+
+            Examples:
+                >>> import stim
+                >>> instruction = stim.DetectorErrorModel('''
+                ...     error(0.125) D0
+                ... ''')[0]
+                >>> instruction.args_copy()
+                [0.125]
+
+                >>> instruction.args_copy() == instruction.args_copy()
+                True
+                >>> instruction.args_copy() is instruction.args_copy()
+                False
+        )DOC")
+            .data());
+
     c.def(
         "targets_copy",
         &ExposedDemInstruction::targets_copy,
@@ -191,8 +212,21 @@ void stim_pybind::pybind_detector_error_model_instruction_methods(
             @signature def targets_copy(self) -> List[Union[int, stim.DemTarget]]:
             Returns a copy of the instruction's targets.
 
-            (Making a copy is enforced to make it clear that editing the result won't change
-            the instruction's targets.)
+            The result is a copy, meaning that editing it won't change the instruction's
+            targets or future copies.
+
+            Examples:
+                >>> import stim
+                >>> instruction = stim.DetectorErrorModel('''
+                ...     error(0.125) D0 L2
+                ... ''')[0]
+                >>> instruction.targets_copy()
+                [stim.DemTarget('D0'), stim.DemTarget('L2')]
+
+                >>> instruction.targets_copy() == instruction.targets_copy()
+                True
+                >>> instruction.targets_copy() is instruction.targets_copy()
+                False
         )DOC")
             .data());
     c.def_property_readonly(
diff --git a/src/stim/diagram/detector_slice/detector_slice_set.cc b/src/stim/diagram/detector_slice/detector_slice_set.cc
index abf26b06..ad90760c 100644
--- a/src/stim/diagram/detector_slice/detector_slice_set.cc
+++ b/src/stim/diagram/detector_slice/detector_slice_set.cc
@@ -741,8 +741,7 @@ void _start_two_body_svg_path(
     std::ostream &out,
     const std::function<Coord<2>(uint64_t tick, uint32_t qubit)> &coords,
     uint64_t tick,
-    SpanRef<const GateTarget> terms,
-    std::vector<Coord<2>> &pts_workspace) {
+    SpanRef<const GateTarget> terms) {
     auto a = coords(tick, terms[0].qubit_value());
     auto b = coords(tick, terms[1].qubit_value());
     auto dif = b - a;
@@ -774,7 +773,6 @@ void _start_one_body_svg_path(
     const std::function<Coord<2>(uint64_t tick, uint32_t qubit)> &coords,
     uint64_t tick,
     SpanRef<const GateTarget> terms,
-    std::vector<Coord<2>> &pts_workspace,
     size_t scale) {
     auto c = coords(tick, terms[0].qubit_value());
     out << "<circle";
@@ -793,20 +791,25 @@ void _start_slice_shape_command(
     if (terms.size() > 2) {
         _start_many_body_svg_path(out, coords, tick, terms, pts_workspace);
     } else if (terms.size() == 2) {
-        _start_two_body_svg_path(out, coords, tick, terms, pts_workspace);
+        _start_two_body_svg_path(out, coords, tick, terms);
     } else if (terms.size() == 1) {
-        _start_one_body_svg_path(out, coords, tick, terms, pts_workspace, scale);
+        _start_one_body_svg_path(out, coords, tick, terms, scale);
     }
 }
 
-void DetectorSliceSet::write_svg_diagram_to(std::ostream &out) const {
-    size_t num_cols = (uint64_t)ceil(sqrt((double)num_ticks));
-    size_t num_rows = num_ticks / num_cols;
-    while (num_cols * num_rows < num_ticks) {
-        num_rows++;
-    }
-    while (num_cols * num_rows >= num_ticks + num_rows) {
-        num_cols--;
+void DetectorSliceSet::write_svg_diagram_to(std::ostream &out, size_t num_rows) const {
+    size_t num_cols;
+    if (num_rows == 0) {
+        num_cols = (uint64_t)ceil(sqrt((double)num_ticks));
+        num_rows = num_ticks / num_cols;
+        while (num_cols * num_rows < num_ticks) {
+            num_rows++;
+        }
+        while (num_cols * num_rows >= num_ticks + num_rows) {
+            num_cols--;
+        }
+    } else {
+        num_cols = (num_ticks + num_rows - 1) / num_rows;
     }
 
     auto coordsys = FlattenedCoords::from(*this, 32);
diff --git a/src/stim/diagram/detector_slice/detector_slice_set.h b/src/stim/diagram/detector_slice/detector_slice_set.h
index 76314eba..1fdeffa9 100644
--- a/src/stim/diagram/detector_slice/detector_slice_set.h
+++ b/src/stim/diagram/detector_slice/detector_slice_set.h
@@ -65,7 +65,7 @@ struct DetectorSliceSet {
     std::string str() const;
 
     void write_text_diagram_to(std::ostream &out) const;
-    void write_svg_diagram_to(std::ostream &out) const;
+    void write_svg_diagram_to(std::ostream &out, size_t num_rows = 0) const;
     void write_svg_contents_to(
         std::ostream &out,
         const std::function<Coord<2>(uint32_t qubit)> &unscaled_coords,
diff --git a/src/stim/diagram/timeline/timeline_svg_drawer.cc b/src/stim/diagram/timeline/timeline_svg_drawer.cc
index 59a3ab02..67621fa4 100644
--- a/src/stim/diagram/timeline/timeline_svg_drawer.cc
+++ b/src/stim/diagram/timeline/timeline_svg_drawer.cc
@@ -808,7 +808,8 @@ void DiagramTimelineSvgDrawer::make_diagram_write_to(
     uint64_t tick_slice_start,
     uint64_t tick_slice_num,
     DiagramTimelineSvgDrawerMode mode,
-    SpanRef<const CoordFilter> filter) {
+    SpanRef<const CoordFilter> filter,
+    size_t num_rows) {
     uint64_t circuit_num_ticks = circuit.count_ticks();
     auto circuit_has_ticks = circuit_num_ticks > 0;
     auto num_qubits = circuit.count_qubits();
@@ -825,8 +826,13 @@ void DiagramTimelineSvgDrawer::make_diagram_write_to(
         obj.coord_sys = FlattenedCoords::from(obj.detector_slice_set, GATE_PITCH);
         obj.coord_sys.size.xyz[0] += TIME_SLICE_PADDING * 2;
         obj.coord_sys.size.xyz[1] += TIME_SLICE_PADDING * 2;
-        obj.num_cols = (uint64_t)ceil(sqrt((double)tick_slice_num));
-        obj.num_rows = tick_slice_num / obj.num_cols;
+        if (num_rows == 0) {
+            obj.num_cols = (uint64_t)ceil(sqrt((double)tick_slice_num));
+            obj.num_rows = tick_slice_num / obj.num_cols;
+        } else {
+            obj.num_rows = num_rows;
+            obj.num_cols = (tick_slice_num + num_rows - 1) / num_rows;
+        }
         while (obj.num_cols * obj.num_rows < tick_slice_num) {
             obj.num_rows++;
         }
@@ -855,9 +861,9 @@ void DiagramTimelineSvgDrawer::make_diagram_write_to(
     auto w = obj.m2x(obj.cur_moment) - GATE_PITCH * 0.5f;
     svg_out << R"SVG(<svg viewBox="0 0 )SVG";
     if (mode != DiagramTimelineSvgDrawerMode::SVG_MODE_TIMELINE) {
-        svg_out << obj.coord_sys.size.xyz[0] * ((obj.num_cols - 1) * SLICE_WINDOW_GAP + 1);
-        svg_out << " ";
-        svg_out << obj.coord_sys.size.xyz[1] * ((obj.num_rows - 1) * SLICE_WINDOW_GAP + 1);
+        auto sw = obj.coord_sys.size.xyz[0] * ((obj.num_cols - 1) * SLICE_WINDOW_GAP + 1);
+        auto sh = obj.coord_sys.size.xyz[1] * ((obj.num_rows - 1) * SLICE_WINDOW_GAP + 1);
+        svg_out << sw << " " << sh;
     } else {
         svg_out << w + PADDING;
         svg_out << " ";
@@ -959,15 +965,28 @@ void DiagramTimelineSvgDrawer::make_diagram_write_to(
     if (mode != DiagramTimelineSvgDrawerMode::SVG_MODE_TIMELINE && tick_slice_num > 1) {
         auto k = 0;
         svg_out << "<g id=\"tick_borders\">\n";
-        for (uint64_t col = 0; col < obj.num_cols; col++) {
-            for (uint64_t row = 0; row < obj.num_rows && row * obj.num_cols + col < tick_slice_num; row++) {
+        for (uint64_t row = 0; row < obj.num_rows; row++) {
+            for (uint64_t col = 0; col < obj.num_cols && row * obj.num_cols + col < tick_slice_num; col++) {
                 auto sw = obj.coord_sys.size.xyz[0];
                 auto sh = obj.coord_sys.size.xyz[1];
 
                 std::stringstream id_ss;
+                auto tick = k + tick_slice_start;  // the absolute tick
                 id_ss << "tick_border:" << k;
                 id_ss << ":" << row << "_" << col;
-                id_ss << ":" << k + tick_slice_start;  // the absolute tick
+                id_ss << ":" << tick;
+
+                svg_out << "<text";
+                write_key_val(svg_out, "dominant-baseline", "hanging");
+                write_key_val(svg_out, "text-anchor", "middle");
+                write_key_val(svg_out, "font-family", "serif");
+                write_key_val(svg_out, "font-size", 18);
+                write_key_val(svg_out, "transform", "rotate(90)");
+                write_key_val(svg_out, "x", sh * row * SLICE_WINDOW_GAP + sh / 2);
+                write_key_val(svg_out, "y", -(sw * col * SLICE_WINDOW_GAP + sw - 6));
+                svg_out << ">";
+                svg_out << "Tick " << tick;
+                svg_out << "</text>\n";
 
                 svg_out << "<rect";
                 write_key_val(svg_out, "id", id_ss.str());
@@ -978,6 +997,8 @@ void DiagramTimelineSvgDrawer::make_diagram_write_to(
                 write_key_val(svg_out, "stroke", "black");
                 write_key_val(svg_out, "fill", "none");
                 svg_out << "/>\n";
+
+                k++;
             }
         }
         svg_out << "</g>\n";
diff --git a/src/stim/diagram/timeline/timeline_svg_drawer.h b/src/stim/diagram/timeline/timeline_svg_drawer.h
index 5d0bef1c..cbc35bbe 100644
--- a/src/stim/diagram/timeline/timeline_svg_drawer.h
+++ b/src/stim/diagram/timeline/timeline_svg_drawer.h
@@ -64,7 +64,8 @@ struct DiagramTimelineSvgDrawer {
         uint64_t tick_slice_start,
         uint64_t tick_slice_num,
         DiagramTimelineSvgDrawerMode mode,
-        stim::SpanRef<const CoordFilter> det_coord_filter);
+        stim::SpanRef<const CoordFilter> det_coord_filter,
+        size_t num_rows = 0);
 
     void do_start_repeat(const CircuitTimelineLoopData &loop_data);
     void do_end_repeat(const CircuitTimelineLoopData &loop_data);
diff --git a/src/stim/mem/bitword_128_sse.h b/src/stim/mem/bitword_128_sse.h
index 05aafde8..a84e9875 100644
--- a/src/stim/mem/bitword_128_sse.h
+++ b/src/stim/mem/bitword_128_sse.h
@@ -53,6 +53,8 @@ struct bitword<128> {
     }
     inline bitword<128>(__m128i val) : val(val) {
     }
+    inline bitword<128>(std::array<uint64_t, 2> val) : val{_mm_set_epi64x(val[1], val[0])} {
+    }
     inline bitword<128>(uint64_t val) : val{_mm_set_epi64x(0, val)} {
     }
     inline bitword<128>(int64_t val) : val{_mm_set_epi64x(-(val < 0), val)} {
diff --git a/src/stim/mem/bitword_256_avx.h b/src/stim/mem/bitword_256_avx.h
index 6c35833c..4b5959f9 100644
--- a/src/stim/mem/bitword_256_avx.h
+++ b/src/stim/mem/bitword_256_avx.h
@@ -52,6 +52,8 @@ struct bitword<256> {
     }
     inline bitword<256>(__m256i val) : val(val) {
     }
+    inline bitword<256>(std::array<uint64_t, 4> val) : val{_mm256_set_epi64x(val[3], val[2], val[1], val[0])} {
+    }
     inline bitword<256>(uint64_t val) : val{_mm256_set_epi64x(0, 0, 0, val)} {
     }
     inline bitword<256>(int64_t val) : val{_mm256_set_epi64x(-(val < 0), -(val < 0), -(val < 0), val)} {
diff --git a/src/stim/mem/bitword_64.h b/src/stim/mem/bitword_64.h
index 3f8d588a..4a4a16ec 100644
--- a/src/stim/mem/bitword_64.h
+++ b/src/stim/mem/bitword_64.h
@@ -47,6 +47,8 @@ struct bitword<64> {
 
     inline constexpr bitword<64>() : val{} {
     }
+    inline bitword<64>(std::array<uint64_t, 1> val) : val{val[0]} {
+    }
     inline constexpr bitword<64>(uint64_t v) : val{v} {
     }
     inline constexpr bitword<64>(int64_t v) : val{(uint64_t)v} {
diff --git a/src/stim/mem/simd_word.test.cc b/src/stim/mem/simd_word.test.cc
index 3ab23583..b0777b4c 100644
--- a/src/stim/mem/simd_word.test.cc
+++ b/src/stim/mem/simd_word.test.cc
@@ -149,3 +149,13 @@ TEST_EACH_WORD_SIZE_W(simd_word, ordering, {
     ASSERT_TRUE(!(simd_word<W>(2) < simd_word<W>(2)));
     ASSERT_TRUE(!(simd_word<W>(3) < simd_word<W>(2)));
 })
+
+TEST_EACH_WORD_SIZE_W(simd_word, from_u64_array, {
+    std::array<uint64_t, W / 64> expected;
+    for (size_t k = 0; k < expected.size(); k++) {
+        expected[k] = k * 3 + 1;
+    }
+    simd_word<W> w(expected);
+    std::array<uint64_t, W / 64> actual = w.to_u64_array();
+    ASSERT_EQ(actual, expected);
+})
diff --git a/src/stim/simulators/frame_simulator.pybind.cc b/src/stim/simulators/frame_simulator.pybind.cc
index 05386a4c..ff1a072c 100644
--- a/src/stim/simulators/frame_simulator.pybind.cc
+++ b/src/stim/simulators/frame_simulator.pybind.cc
@@ -870,4 +870,118 @@ void stim_pybind::pybind_frame_simulator_methods(
 
         )DOC")
             .data());
+
+    c.def(
+        "copy",
+        [](const FrameSimulator<MAX_BITWORD_WIDTH> &self, bool copy_rng, pybind11::object &seed) {
+            if (copy_rng && !seed.is_none()) {
+                throw std::invalid_argument("seed and copy_rng are incompatible");
+            }
+
+            FrameSimulator<MAX_BITWORD_WIDTH> copy = self;
+            if (!copy_rng || !seed.is_none()) {
+                copy.rng = make_py_seeded_rng(seed);
+            }
+            return copy;
+        },
+        pybind11::kw_only(),
+        pybind11::arg("copy_rng") = false,
+        pybind11::arg("seed") = pybind11::none(),
+        clean_doc_string(R"DOC(
+            @signature def copy(self, *, copy_rng: bool = False, seed: Optional[int] = None) -> stim.FlipSimulator:
+            Returns a simulator with the same internal state, except perhaps its prng.
+
+            Args:
+                copy_rng: Defaults to False. When False, the copy's pseudo random number
+                    generator is reinitialized with a random seed instead of being a copy
+                    of the original simulator's pseudo random number generator. This
+                    causes the copy and the original to sample independent randomness,
+                    instead of identical randomness, for future random operations. When set
+                    to true, the copy will have the exact same pseudo random number
+                    generator state as the original, and so will produce identical results
+                    if told to do the same noisy operations. This argument is incompatible
+                    with the `seed` argument.
+
+                seed: PARTIALLY determines simulation results by deterministically seeding
+                    the random number generator.
+
+                    Must be None or an integer in range(2**64).
+
+                    Defaults to None. When None, the prng state is either copied from the
+                    original simulator or reseeded from system entropy, depending on the
+                    copy_rng argument.
+
+                    When set to an integer, making the exact same series calls on the exact
+                    same machine with the exact same version of Stim will produce the exact
+                    same simulation results.
+
+                    CAUTION: simulation results *WILL NOT* be consistent between versions of
+                    Stim. This restriction is present to make it possible to have future
+                    optimizations to the random sampling, and is enforced by introducing
+                    intentional differences in the seeding strategy from version to version.
+
+                    CAUTION: simulation results *MAY NOT* be consistent across machines that
+                    differ in the width of supported SIMD instructions. For example, using
+                    the same seed on a machine that supports AVX instructions and one that
+                    only supports SSE instructions may produce different simulation results.
+
+                    CAUTION: simulation results *MAY NOT* be consistent if you vary how the
+                    circuit is executed. For example, reordering whether a reset on one
+                    qubit happens before or after a reset on another qubit can result in
+                    different measurement results being observed starting from the same
+                    seed.
+
+            Returns:
+                The copy of the simulator.
+
+            Examples:
+                >>> import stim
+                >>> import numpy as np
+
+                >>> s1 = stim.FlipSimulator(batch_size=256)
+                >>> s1.set_pauli_flip('X', qubit_index=2, instance_index=3)
+                >>> s2 = s1.copy()
+                >>> s2 is s1
+                False
+                >>> s2.peek_pauli_flips() == s1.peek_pauli_flips()
+                True
+
+                >>> s1 = stim.FlipSimulator(batch_size=256)
+                >>> s2 = s1.copy(copy_rng=True)
+                >>> s1.do(stim.Circuit("X_ERROR(0.25) 0 \n M 0"))
+                >>> s2.do(stim.Circuit("X_ERROR(0.25) 0 \n M 0"))
+                >>> np.array_equal(s1.get_measurement_flips(), s2.get_measurement_flips())
+                True
+        )DOC")
+            .data());
+
+    c.def(
+        "reset",
+        [](FrameSimulator<MAX_BITWORD_WIDTH> &self) {
+            self.reset_all();
+        },
+        clean_doc_string(R"DOC(
+            Resets the simulator's state, so it can be reused for another simulation.
+
+            This empties the measurement flip history, empties the detector flip history,
+            and zeroes the observable flip state. It also resets all qubits to |0>. If
+            stabilizer randomization is disabled, this zeros all pauli flips data. Otherwise
+            it randomizes all pauli flips to be I or Z with equal probability.
+
+            Examples:
+                >>> import stim
+                >>> sim = stim.FlipSimulator(batch_size=256)
+                >>> sim.do(stim.Circuit("M(0.1) 9"))
+                >>> sim.num_qubits
+                10
+                >>> sim.get_measurement_flips().shape
+                (1, 256)
+
+                >>> sim.reset()
+                >>> sim.num_qubits
+                10
+                >>> sim.get_measurement_flips().shape
+                (0, 256)
+        )DOC")
+            .data());
 }
diff --git a/src/stim/stabilizers/pauli_string.pybind.cc b/src/stim/stabilizers/pauli_string.pybind.cc
index 8fd808a3..bcc68ac7 100644
--- a/src/stim/stabilizers/pauli_string.pybind.cc
+++ b/src/stim/stabilizers/pauli_string.pybind.cc
@@ -826,6 +826,13 @@ void stim_pybind::pybind_pauli_string_methods(pybind11::module &m, pybind11::cla
         },
         clean_doc_string(R"DOC(
             Returns the length the pauli string; the number of qubits it operates on.
+
+            Examples:
+                >>> import stim
+                >>> len(stim.PauliString("XY_ZZ"))
+                5
+                >>> len(stim.PauliString("X0*Z99"))
+                100
         )DOC")
             .data());
 
diff --git a/src/stim/stabilizers/tableau.pybind.cc b/src/stim/stabilizers/tableau.pybind.cc
index 7e9a3ba9..6d9849b6 100644
--- a/src/stim/stabilizers/tableau.pybind.cc
+++ b/src/stim/stabilizers/tableau.pybind.cc
@@ -838,7 +838,16 @@ void stim_pybind::pybind_tableau_methods(pybind11::module &m, pybind11::class_<T
         [](const Tableau<MAX_BITWORD_WIDTH> &self) {
             return self.num_qubits;
         },
-        "Returns the number of qubits operated on by the tableau.");
+        clean_doc_string(R"DOC(
+            Returns the number of qubits operated on by the tableau.
+
+            Examples:
+                >>> import stim
+                >>> t = stim.Tableau.from_named_gate("CNOT")
+                >>> len(t)
+                2
+        )DOC")
+            .data());
 
     c.def("__str__", &Tableau<MAX_BITWORD_WIDTH>::str, "Returns a text description.");
 
diff --git a/src/stim/util_top/circuit_vs_amplitudes.cc b/src/stim/util_top/circuit_vs_amplitudes.cc
index 154eabe5..6a56219b 100644
--- a/src/stim/util_top/circuit_vs_amplitudes.cc
+++ b/src/stim/util_top/circuit_vs_amplitudes.cc
@@ -1,6 +1,6 @@
 #include "stim/util_top/circuit_vs_amplitudes.h"
 
-#include "circuit_inverse_unitary.h"
+#include "stim/util_top/circuit_inverse_unitary.h"
 #include "stim/simulators/tableau_simulator.h"
 #include "stim/simulators/vector_simulator.h"
 #include "stim/util_bot/twiddle.h"
diff --git a/testdata/anticommuting_detslice.svg b/testdata/anticommuting_detslice.svg
index 76e9814f..e4502e15 100644
--- a/testdata/anticommuting_detslice.svg
+++ b/testdata/anticommuting_detslice.svg
@@ -42,9 +42,13 @@
 <rect x="508.8" y="240" width="32" height="32" stroke="black" fill="black"/>
 <text dominant-baseline="central" text-anchor="middle" font-family="monospace" font-size="30" x="524.8" y="256" fill="white">M</text>
 <g id="tick_borders">
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="80" y="-282">Tick 0</text>
 <rect id="tick_border:0:0_0:0" x="0" y="0" width="288" height="160" stroke="black" fill="none"/>
-<rect id="tick_border:0:1_0:0" x="0" y="176" width="288" height="160" stroke="black" fill="none"/>
-<rect id="tick_border:0:0_1:0" x="316.8" y="0" width="288" height="160" stroke="black" fill="none"/>
-<rect id="tick_border:0:1_1:0" x="316.8" y="176" width="288" height="160" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="80" y="-598.8">Tick 1</text>
+<rect id="tick_border:1:0_1:1" x="316.8" y="0" width="288" height="160" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="256" y="-282">Tick 2</text>
+<rect id="tick_border:2:1_0:2" x="0" y="176" width="288" height="160" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="256" y="-598.8">Tick 3</text>
+<rect id="tick_border:3:1_1:3" x="316.8" y="176" width="288" height="160" stroke="black" fill="none"/>
 </g>
 </svg>
\ No newline at end of file
diff --git a/testdata/bezier_time_slice.svg b/testdata/bezier_time_slice.svg
index 1f132c86..47291d78 100644
--- a/testdata/bezier_time_slice.svg
+++ b/testdata/bezier_time_slice.svg
@@ -26,7 +26,9 @@
 <circle cx="659.2" cy="80" r="12" stroke="black" fill="white"/>
 <path d="M647.2,80 L671.2,80 M659.2,68 L659.2,92 " stroke="black"/>
 <g id="tick_borders">
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="80" y="-346">Tick 0</text>
 <rect id="tick_border:0:0_0:0" x="0" y="0" width="352" height="160" stroke="black" fill="none"/>
-<rect id="tick_border:0:0_1:0" x="387.2" y="0" width="352" height="160" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="80" y="-733.2">Tick 1</text>
+<rect id="tick_border:1:0_1:1" x="387.2" y="0" width="352" height="160" stroke="black" fill="none"/>
 </g>
 </svg>
\ No newline at end of file
diff --git a/testdata/circuit_all_ops_detslice.svg b/testdata/circuit_all_ops_detslice.svg
index a917974c..6a654cdf 100644
--- a/testdata/circuit_all_ops_detslice.svg
+++ b/testdata/circuit_all_ops_detslice.svg
@@ -561,19 +561,33 @@
 <rect x="1430.4" y="1172.8" width="32" height="32" stroke="black" fill="lightgray"/>
 <text dominant-baseline="central" text-anchor="middle" font-family="monospace" font-size="12" x="1446.4" y="1188.8">Z<tspan baseline-shift="super" font-size="10">rec</tspan></text>
 <g id="tick_borders">
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="168" y="-1178">Tick 0</text>
 <rect id="tick_border:0:0_0:0" x="0" y="0" width="1184" height="336" stroke="black" fill="none"/>
-<rect id="tick_border:0:1_0:0" x="0" y="369.6" width="1184" height="336" stroke="black" fill="none"/>
-<rect id="tick_border:0:2_0:0" x="0" y="739.2" width="1184" height="336" stroke="black" fill="none"/>
-<rect id="tick_border:0:3_0:0" x="0" y="1108.8" width="1184" height="336" stroke="black" fill="none"/>
-<rect id="tick_border:0:0_1:0" x="1302.4" y="0" width="1184" height="336" stroke="black" fill="none"/>
-<rect id="tick_border:0:1_1:0" x="1302.4" y="369.6" width="1184" height="336" stroke="black" fill="none"/>
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-<rect id="tick_border:0:3_1:0" x="1302.4" y="1108.8" width="1184" height="336" stroke="black" fill="none"/>
-<rect id="tick_border:0:0_2:0" x="2604.8" y="0" width="1184" height="336" stroke="black" fill="none"/>
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-<rect id="tick_border:0:1_3:0" x="3907.2" y="369.6" width="1184" height="336" stroke="black" fill="none"/>
-<rect id="tick_border:0:2_3:0" x="3907.2" y="739.2" width="1184" height="336" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="168" y="-2480.4">Tick 1</text>
+<rect id="tick_border:1:0_1:1" x="1302.4" y="0" width="1184" height="336" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="168" y="-3782.8">Tick 2</text>
+<rect id="tick_border:2:0_2:2" x="2604.8" y="0" width="1184" height="336" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="168" y="-5085.2">Tick 3</text>
+<rect id="tick_border:3:0_3:3" x="3907.2" y="0" width="1184" height="336" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="537.6" y="-1178">Tick 4</text>
+<rect id="tick_border:4:1_0:4" x="0" y="369.6" width="1184" height="336" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="537.6" y="-2480.4">Tick 5</text>
+<rect id="tick_border:5:1_1:5" x="1302.4" y="369.6" width="1184" height="336" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="537.6" y="-3782.8">Tick 6</text>
+<rect id="tick_border:6:1_2:6" x="2604.8" y="369.6" width="1184" height="336" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="537.6" y="-5085.2">Tick 7</text>
+<rect id="tick_border:7:1_3:7" x="3907.2" y="369.6" width="1184" height="336" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="907.2" y="-1178">Tick 8</text>
+<rect id="tick_border:8:2_0:8" x="0" y="739.2" width="1184" height="336" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="907.2" y="-2480.4">Tick 9</text>
+<rect id="tick_border:9:2_1:9" x="1302.4" y="739.2" width="1184" height="336" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="907.2" y="-3782.8">Tick 10</text>
+<rect id="tick_border:10:2_2:10" x="2604.8" y="739.2" width="1184" height="336" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="907.2" y="-5085.2">Tick 11</text>
+<rect id="tick_border:11:2_3:11" x="3907.2" y="739.2" width="1184" height="336" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="1276.8" y="-1178">Tick 12</text>
+<rect id="tick_border:12:3_0:12" x="0" y="1108.8" width="1184" height="336" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="1276.8" y="-2480.4">Tick 13</text>
+<rect id="tick_border:13:3_1:13" x="1302.4" y="1108.8" width="1184" height="336" stroke="black" fill="none"/>
 </g>
 </svg>
\ No newline at end of file
diff --git a/testdata/circuit_all_ops_timeslice.svg b/testdata/circuit_all_ops_timeslice.svg
index a917974c..6a654cdf 100644
--- a/testdata/circuit_all_ops_timeslice.svg
+++ b/testdata/circuit_all_ops_timeslice.svg
@@ -561,19 +561,33 @@
 <rect x="1430.4" y="1172.8" width="32" height="32" stroke="black" fill="lightgray"/>
 <text dominant-baseline="central" text-anchor="middle" font-family="monospace" font-size="12" x="1446.4" y="1188.8">Z<tspan baseline-shift="super" font-size="10">rec</tspan></text>
 <g id="tick_borders">
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="168" y="-1178">Tick 0</text>
 <rect id="tick_border:0:0_0:0" x="0" y="0" width="1184" height="336" stroke="black" fill="none"/>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="537.6" y="-3782.8">Tick 6</text>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="907.2" y="-2480.4">Tick 9</text>
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+<rect id="tick_border:11:2_3:11" x="3907.2" y="739.2" width="1184" height="336" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="1276.8" y="-1178">Tick 12</text>
+<rect id="tick_border:12:3_0:12" x="0" y="1108.8" width="1184" height="336" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="1276.8" y="-2480.4">Tick 13</text>
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\ No newline at end of file
diff --git a/testdata/circuit_diagram_timeline_svg_chained_loops.svg b/testdata/circuit_diagram_timeline_svg_chained_loops.svg
index cd3bbbd0..3ef5e37f 100644
--- a/testdata/circuit_diagram_timeline_svg_chained_loops.svg
+++ b/testdata/circuit_diagram_timeline_svg_chained_loops.svg
@@ -46,16 +46,27 @@
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="80" y="-218">Tick 0</text>
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-<rect id="tick_border:0:1_3:0" x="739.2" y="176" width="224" height="160" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="80" y="-464.4">Tick 1</text>
+<rect id="tick_border:1:0_1:1" x="246.4" y="0" width="224" height="160" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="80" y="-710.8">Tick 2</text>
+<rect id="tick_border:2:0_2:2" x="492.8" y="0" width="224" height="160" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="80" y="-957.2">Tick 3</text>
+<rect id="tick_border:3:0_3:3" x="739.2" y="0" width="224" height="160" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="256" y="-218">Tick 4</text>
+<rect id="tick_border:4:1_0:4" x="0" y="176" width="224" height="160" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="256" y="-464.4">Tick 5</text>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="256" y="-710.8">Tick 6</text>
+<rect id="tick_border:6:1_2:6" x="492.8" y="176" width="224" height="160" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="256" y="-957.2">Tick 7</text>
+<rect id="tick_border:7:1_3:7" x="739.2" y="176" width="224" height="160" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="432" y="-218">Tick 8</text>
+<rect id="tick_border:8:2_0:8" x="0" y="352" width="224" height="160" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="432" y="-464.4">Tick 9</text>
+<rect id="tick_border:9:2_1:9" x="246.4" y="352" width="224" height="160" stroke="black" fill="none"/>
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="432" y="-710.8">Tick 10</text>
+<rect id="tick_border:10:2_2:10" x="492.8" y="352" width="224" height="160" stroke="black" fill="none"/>
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 </svg>
\ No newline at end of file
diff --git a/testdata/detslice-with-ops_surface_code.svg b/testdata/detslice-with-ops_surface_code.svg
index e79153bd..6463895f 100644
--- a/testdata/detslice-with-ops_surface_code.svg
+++ b/testdata/detslice-with-ops_surface_code.svg
@@ -1271,19 +1271,33 @@
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 <text dominant-baseline="central" text-anchor="middle" font-family="monospace" font-size="30" x="735.701" y="1775.57">H</text>
 <g id="tick_borders">
+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="215.764" y="-425.529">Tick 0</text>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="215.764" y="-900.211">Tick 1</text>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="215.764" y="-1374.89">Tick 2</text>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="215.764" y="-1849.57">Tick 3</text>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="690.446" y="-1374.89">Tick 6</text>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="690.446" y="-1849.57">Tick 7</text>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="1165.13" y="-425.529">Tick 8</text>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="1165.13" y="-900.211">Tick 9</text>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="1165.13" y="-1374.89">Tick 10</text>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="1639.81" y="-425.529">Tick 12</text>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="1639.81" y="-900.211">Tick 13</text>
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\ No newline at end of file
diff --git a/testdata/observable_slices.svg b/testdata/observable_slices.svg
index 6bff67cb..6d4aa7be 100644
--- a/testdata/observable_slices.svg
+++ b/testdata/observable_slices.svg
@@ -145,18 +145,31 @@
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 <text dominant-baseline="central" text-anchor="middle" font-family="monospace" font-size="30" x="170.51" y="757.341" fill="white">M</text>
 <g id="tick_borders">
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="102.627" y="-520.07">Tick 1</text>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="102.627" y="-795.631">Tick 2</text>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="328.408" y="-795.631">Tick 6</text>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="328.408" y="-1071.19">Tick 7</text>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="554.188" y="-244.51">Tick 8</text>
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+<text dominant-baseline="hanging" text-anchor="middle" font-family="serif" font-size="18" transform="rotate(90)" x="779.968" y="-244.51">Tick 12</text>
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diff --git a/testdata/surface_code_full_time_detector_slice.svg b/testdata/surface_code_full_time_detector_slice.svg
index ace2226d..7e7fdae6 100644
--- a/testdata/surface_code_full_time_detector_slice.svg
+++ b/testdata/surface_code_full_time_detector_slice.svg
@@ -2823,41 +2823,77 @@
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diff --git a/testdata/surface_code_time_detector_slice.svg b/testdata/surface_code_time_detector_slice.svg
index 01fbf938..3bf551e1 100644
--- a/testdata/surface_code_time_detector_slice.svg
+++ b/testdata/surface_code_time_detector_slice.svg
@@ -749,16 +749,27 @@
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diff --git a/testdata/surface_code_time_slice.svg b/testdata/surface_code_time_slice.svg
index 38ede396..1606fe7f 100644
--- a/testdata/surface_code_time_slice.svg
+++ b/testdata/surface_code_time_slice.svg
@@ -373,16 +373,27 @@
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From c0627e2c90dd9598c6ebaf13212786e27c339962 Mon Sep 17 00:00:00 2001
From: Craig Gidney <craiggidney@google.com>
Date: Fri, 26 Jul 2024 18:03:01 -0700
Subject: [PATCH 07/17] Add lassynth to glue directory (#803)

Supporting code for https://arxiv.org/abs/2404.18369

Author: Daniel Tan <bj.tanbochen@gmail.com>
---
 glue/lattice_surgery/README.md                |   39 +
 glue/lattice_surgery/docs/demo.ipynb          |  645 ++++
 glue/lattice_surgery/lassynth/__init__.py     |    2 +
 .../lassynth/lattice_surgery_synthesis.py     |  590 ++++
 .../lassynth/rewrite_passes/__init__.py       |    0
 .../lassynth/rewrite_passes/attach_fixups.py  |   86 +
 .../lassynth/rewrite_passes/color_z.py        |  210 ++
 .../rewrite_passes/remove_unconnected.py      |  152 +
 .../lassynth/sat_synthesis/__init__.py        |    0
 .../sat_synthesis/lattice_surgery_sat.py      | 1842 ++++++++++++
 .../lassynth/tools/__init__.py                |    0
 .../lassynth/tools/verify_stabilizers.py      |   38 +
 .../lassynth/translators/__init__.py          |    1 +
 .../lassynth/translators/gltf_generator.py    | 2622 +++++++++++++++++
 .../translators/networkx_generator.py         |   53 +
 .../lassynth/translators/textfig_generator.py |  217 ++
 .../lassynth/translators/zx_grid_graph.py     |  292 ++
 glue/lattice_surgery/setup.py                 |   28 +
 glue/lattice_surgery/stimzx/__init__.py       |   14 +
 .../stimzx/_external_stabilizer.py            |   90 +
 .../stimzx/_external_stabilizer_test.py       |    7 +
 .../stimzx/_text_diagram_parsing.py           |  178 ++
 .../stimzx/_text_diagram_parsing_test.py      |  149 +
 .../stimzx/_zx_graph_solver.py                |  196 ++
 .../stimzx/_zx_graph_solver_test.py           |  137 +
 25 files changed, 7588 insertions(+)
 create mode 100644 glue/lattice_surgery/README.md
 create mode 100644 glue/lattice_surgery/docs/demo.ipynb
 create mode 100644 glue/lattice_surgery/lassynth/__init__.py
 create mode 100644 glue/lattice_surgery/lassynth/lattice_surgery_synthesis.py
 create mode 100644 glue/lattice_surgery/lassynth/rewrite_passes/__init__.py
 create mode 100644 glue/lattice_surgery/lassynth/rewrite_passes/attach_fixups.py
 create mode 100644 glue/lattice_surgery/lassynth/rewrite_passes/color_z.py
 create mode 100644 glue/lattice_surgery/lassynth/rewrite_passes/remove_unconnected.py
 create mode 100644 glue/lattice_surgery/lassynth/sat_synthesis/__init__.py
 create mode 100644 glue/lattice_surgery/lassynth/sat_synthesis/lattice_surgery_sat.py
 create mode 100644 glue/lattice_surgery/lassynth/tools/__init__.py
 create mode 100644 glue/lattice_surgery/lassynth/tools/verify_stabilizers.py
 create mode 100644 glue/lattice_surgery/lassynth/translators/__init__.py
 create mode 100644 glue/lattice_surgery/lassynth/translators/gltf_generator.py
 create mode 100644 glue/lattice_surgery/lassynth/translators/networkx_generator.py
 create mode 100644 glue/lattice_surgery/lassynth/translators/textfig_generator.py
 create mode 100644 glue/lattice_surgery/lassynth/translators/zx_grid_graph.py
 create mode 100644 glue/lattice_surgery/setup.py
 create mode 100644 glue/lattice_surgery/stimzx/__init__.py
 create mode 100644 glue/lattice_surgery/stimzx/_external_stabilizer.py
 create mode 100644 glue/lattice_surgery/stimzx/_external_stabilizer_test.py
 create mode 100644 glue/lattice_surgery/stimzx/_text_diagram_parsing.py
 create mode 100644 glue/lattice_surgery/stimzx/_text_diagram_parsing_test.py
 create mode 100644 glue/lattice_surgery/stimzx/_zx_graph_solver.py
 create mode 100644 glue/lattice_surgery/stimzx/_zx_graph_solver_test.py

diff --git a/glue/lattice_surgery/README.md b/glue/lattice_surgery/README.md
new file mode 100644
index 00000000..61367480
--- /dev/null
+++ b/glue/lattice_surgery/README.md
@@ -0,0 +1,39 @@
+# Lattice Surgery Subroutine Synthesizer (LaSsynth)
+A lattice surgery subroutine (LaS) is a confined volume with a set of ports.
+Within this volume, lattice surgery merges and splits are performed.
+The function of a LaS is characterized by a set of stabilizers on these ports.
+
+The lattice surgery subroutine synthesizer (LaSsynth) uses SAT/SMT solvers to synthesize LaS given the volume, the ports, and the stabilizers.
+LaSsynth outputs a textual representation of LaS (LaSRe) which is a JSON file with filename extension `.lasre`.
+LaSsynth can also generate 3D modelling files in the [GLTF](https://www.khronos.org/gltf/) format from LaSRe files.
+
+The main ideas of this project is provided in the paper [A SAT Scalpel for Lattice Surgery](http://arxiv.org/abs/2404.18369) by Tan, Niu, and Gidney.
+For files specific to the paper, please refer to [its Zenodo archive](https://zenodo.org/doi/10.5281/zenodo.11051465).
+
+## Installation
+It is recommended to create a virtual Python environment. Once inside the environment, in this directory, `pip install .`
+Apart from LaSsynth, this will install a few packages that we need: 
+  - `z3-solver` version `4.12.1.0`, from pip
+  - `networkx` default version, from pip
+  - `stim` default version, from pip
+  - `stimzx` from files included in sirectory `./stimzx/`. We copied these files from [here](https://github.com/quantumlib/Stim/tree/0fdddef863cfe777f3f2086a092ba99785725c07/glue/zx).
+  - `ipykernel` default version, from pip, to view the demo Jupyter notebook.
+
+We have a dependency [kissat](https://github.com/arminbiere/kissat) which is a SAT solver, not a Python package. 
+It is recommended to install it and find out the directory of the executable `kissat` because we will need it later.
+LaSsynth can be used without Kissat, in which case it just uses `z3-solver`, but on certain cases Kissat can offer big runtime improvements.
+
+## How to use
+See the [demo notebook in the docs directory](docs/demo.ipynb)
+
+## Cite this work
+```bibtex
+@inproceedings{tan-niu-gidney_lattice_surgery,
+    author = {Tan, Daniel Bochen and Niu, Murphy Yuezhen and Gidney, Craig},
+	title = {A {SAT} Scalpel for Lattice Surgery: Representation and Synthesis of Subroutines for Surface-Code Fault-Tolerant Quantum Computing},
+	shorttitle = {A {SAT} Scalpel for Lattice Surgery},
+	booktitle = {2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture ({ISCA})},
+	year = {2024},
+    url = {http://arxiv.org/abs/2404.18369},
+}
+```
\ No newline at end of file
diff --git a/glue/lattice_surgery/docs/demo.ipynb b/glue/lattice_surgery/docs/demo.ipynb
new file mode 100644
index 00000000..36dfe218
--- /dev/null
+++ b/glue/lattice_surgery/docs/demo.ipynb
@@ -0,0 +1,645 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction to the LaSSynth, Lattice Surgery Subroutine Synthesizer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "This Jupyter notebook aims at giving a minimal demo on how to use this software.\n",
+    "The reader needs to know how lattice surgery works to fully understand this notebook.\n",
+    "The most direct reference is [our paper](http://arxiv.org/abs/2404.18369) which, in itself, also provides more pointers to background knowledge references.\n",
+    "There are two we would like to mention here.\n",
+    "- [arXiv:1704.08670](https://arxiv.org/abs/1704.08670) links merging and spliting operations in lattice surgery to ZX calculus.\n",
+    "We leverage this connection a lot in our software.\n",
+    "- [arXiv:1808.02892](https://arxiv.org/abs/1808.02892) is helpful because it works through some examples of composing lattice surgery operations to perform quantum computation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "In what follows, we are assuming a surface code quantum memory with nearest-neighbor connectivity among the qubits (in both the physical and the logical sense).\n",
+    "We perform fault-tolerant quantum computing with lattice surgery between patches of physical qubits.\n",
+    "Some of these patches correspond to logical qubits whereas others can be temporary ancilla during computation.\n",
+    "\n",
+    "Since the logical qubits are in a 2D grid, and there is the time dimension, the compilation problem is laying out operations in a 3D grid to realize certain computation.\n",
+    "We consider only a bounded spacetime and what *can* be realized within the bounds is called a *lattice surgery subroutine* (LaS), because it should be considered as a subroutine in the whole quantum algorithm.\n",
+    "\n",
+    "Because of the connection between lattice surgery and ZX calculus, a LaS can be seen as a ZX diagram with nodes at points in a 3D grid and edges only between nearest neighbors, as seen in the figure below.\n",
+    "If you have worked with ZX calculus, you would know that this ZX diagram is a CNOT.\n",
+    "However, it seems that there are two \"unnecessary\" identity nodes in the middle.\n",
+    "This is because there are other constraints when it comes to realizing the CNOT in a surface code memory.\n",
+    "Our representation of a LaS, the \"pipe diagram\" below, does account for these extra constraints."
+   ]
+  },
+  {
+   "attachments": {
+    "e1d30228-b8e9-4608-942a-e09fe765c155.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Screenshot from 2023-09-14 05-14-00.png](attachment:e1d30228-b8e9-4608-942a-e09fe765c155.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pipe diagrams"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "We use a 3D coordinate system with basis I, J, and K.\n",
+    "We avoid using X, Y, and Z because these letters are also used in ZX calculus for different purposes.\n",
+    "K is the time dimension while I and J are the space dimensions.\n",
+    "\n",
+    "If we want the pipe diagram to look like exactly what happens on chip, we need to draw them in scale, e.g, shown on the right below.\n",
+    "There are four patches of surface codes, and only three are used in the computation.\n",
+    "These three are identified by their coordinates in the I-J plane: (1,0), (1,1), and (0,1) from left to right in the picture.\n",
+    "Between the patches, there are some \"gaps\" which are lines on physical qubits.\n",
+    "We can perform merging and splitting of patches with these gaps.\n",
+    "Since the gap is very narrow compared to the patches, but what happens there are really what decides the computation.\n",
+    "Thus, to see these merging and splitting more clearly, we often stretch the gaps in the pipe diagram, resulting in something like the picture on the left below.\n",
+    "\n",
+    "The unit in all three dimension is the code distance.\n",
+    "So, a patch going through a full QEC cycle will become a cube.\n",
+    "These cubes are sitting at integer points in the I-J-K grid.\n",
+    "Nontrivial logical operations are done by connecting these cubes with pipes.\n",
+    "For example, two cubes connected in the I-J plane correspond to performing merging and splitting of two patches; a cube that has a vertical connection below but not above is a logical measurement; etc.\n",
+    "At this point, we see that the problem of compiling LaS is laying out these cubes and pipes in a limited spacetime."
+   ]
+  },
+  {
+   "attachments": {
+    "dac9ce3a-5960-445f-a5d9-f13e0ac44c80.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Screenshot from 2023-09-14 05-55-57.png](attachment:dac9ce3a-5960-445f-a5d9-f13e0ac44c80.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LaS Specification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are some constraints in the construction of these diagrams, e.g., matching colors at intersection of two pipes, but we are not going to introduce them here.\n",
+    "After all, the purpose of a synthesizer is to let a computer consider those constraints instead of humans.\n",
+    "The reader can refer to our paper, or even to the code in this repo for these constraints later on.\n",
+    "What we are going to detail now is how to specify a problem to the compiler, so that the reader can start using the software."
+   ]
+  },
+  {
+   "attachments": {
+    "4fef384b-1f6b-4712-8dcf-8e3f7b973a2f.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Screenshot from 2023-09-14 06-26-00.png](attachment:4fef384b-1f6b-4712-8dcf-8e3f7b973a2f.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The LaS specification is shown in part b) of the figure above.\n",
+    "`max_i`, `max_j` and `max_k` are the bounds of spacetime.\n",
+    "In our example, they are 2, 2, and 3, which means all the cubes and pipes are within 2x2x3 volume."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_dict = {\n",
+    "  \"max_i\": 2, \"max_j\": 2, \"max_k\": 3\n",
+    "}"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are certain *ports* that connects the LaS to the outside (which makes sense since a subroutine in classical computing always has some arguements and some returns).\n",
+    "In this example, there are two ports on the bottom floor corresponding to the two qubits before the CNOT; then, there is some manipulation of these two qubits implmented with the pipes in the gray box; on the top floor, the two ports are the qubits after going through the CNOT.\n",
+    "\n",
+    "We need to provide three things to specify each port.\n",
+    "Let us look at the port for the output of control qubit in the CNOT indicated in the callout in part a) of the figure above.\n",
+    "In the code block below, it is the third port in `input_dict[\"ports\"]`.\n",
+    "- Its `location` is `[1,0,3]` because that is where the information is going out of the LSS.\n",
+    "- In general, the pipe connecting a port can also be in I, J or K direction.\n",
+    "Additionally, we need another character (`-` or `+`) to indicate the direction from the port to the other parts of the LaS.\n",
+    "In this example, the pipe is in the K direction, and we need to go downward from `[1, 0, 3]` to everything else, so the `direction` of the port is `-K`.\n",
+    "- Finally, surface code patches have a space orientation of the X and Z boundaries indicated by red and blue above.\n",
+    "We provide which one of I, J, and K is orthogonal to the face of Z boundary (blue).\n",
+    "In this example, it is J that is orthogonal to the blue faces, so the `z_basis_direction` of this port is `J`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_dict[\"ports\"] = [\n",
+    "    {\"location\": [1, 0, 0], \"direction\": \"+K\", \"z_basis_direction\": \"J\"},\n",
+    "    {\"location\": [0, 1, 0], \"direction\": \"+K\", \"z_basis_direction\": \"J\"},\n",
+    "    {\"location\": [1, 0, 3], \"direction\": \"-K\", \"z_basis_direction\": \"J\"},\n",
+    "    {\"location\": [0, 1, 3], \"direction\": \"-K\", \"z_basis_direction\": \"J\"},\n",
+    "]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we need to provide the stabilizer constraints on the ports to ensure that the LaS indeed realizes the logical operations we want to perform.\n",
+    "Although intuitively there are input and output ports for the CNOT, in a LaS, there is no inherent distinction between inputs and outputs.\n",
+    "What matters is that the given stabilizers have to match the ordering of the ports.\n",
+    "Our ordering is (control qubit input, target qubit input, control qubit output, target qubit output), so the correct stabilizers are ZIZI, IZZZ, XIXX, and IXIX.\n",
+    "If we change the ordering of the `\"ports\"` list above, we also need to change the stabilizers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_dict[\"stabilizers\"] = [\"Z.Z.\", \".ZZZ\", \"X.XX\", \".X.X\"]\n",
+    "# Note that we use a . for an identity in a stabilizer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Solving LaS"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By now we have finished preparing the specification of the LaS.\n",
+    "We can use our software package `lassynth`, specifically the class `LatticeSurgerySynthesizer` to solve the problem.\n",
+    "When we invoke `solve` method, the synthesizer gives us a solution with respect to a `specification`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<lassynth.lattice_surgery_synthesis.LatticeSurgerySolution at 0x104d68760>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from lassynth import LatticeSurgerySynthesizer\n",
+    "\n",
+    "las_synth = LatticeSurgerySynthesizer()\n",
+    "result = las_synth.solve(specification=input_dict)\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you have noticed, the return value is of a class `LatticeSurgerySolution`.\n",
+    "We implement a few methods for this class to help us further manipulate the solution.\n",
+    "To see the \"raw\" solution, i.e., LaSRe (lattice surgery subroutine representation) in the paper, you can access the `lasre` of this result.\n",
+    "Due to technical reasons, the `ports` here is another encoding compared to the `ports` in the specification.\n",
+    "Intersted readers can refer to comments in the code to understand this encoding, but it is not too important in this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_i': 2,\n",
+       " 'n_j': 2,\n",
+       " 'n_k': 3,\n",
+       " 'n_p': 4,\n",
+       " 'n_s': 4,\n",
+       " 'ports': [{'i': 1, 'j': 0, 'k': 0, 'd': 'K', 'e': '-', 'c': 1},\n",
+       "  {'i': 0, 'j': 1, 'k': 0, 'd': 'K', 'e': '-', 'c': 1},\n",
+       "  {'i': 1, 'j': 0, 'k': 2, 'd': 'K', 'e': '+', 'c': 1},\n",
+       "  {'i': 0, 'j': 1, 'k': 2, 'd': 'K', 'e': '+', 'c': 1}],\n",
+       " 'stabs': [[{'KI': 0, 'KJ': 1},\n",
+       "   {'KI': 0, 'KJ': 0},\n",
+       "   {'KI': 0, 'KJ': 1},\n",
+       "   {'KI': 0, 'KJ': 0}],\n",
+       "  [{'KI': 0, 'KJ': 0},\n",
+       "   {'KI': 0, 'KJ': 1},\n",
+       "   {'KI': 0, 'KJ': 1},\n",
+       "   {'KI': 0, 'KJ': 1}],\n",
+       "  [{'KI': 1, 'KJ': 0},\n",
+       "   {'KI': 0, 'KJ': 0},\n",
+       "   {'KI': 1, 'KJ': 0},\n",
+       "   {'KI': 1, 'KJ': 0}],\n",
+       "  [{'KI': 0, 'KJ': 0},\n",
+       "   {'KI': 1, 'KJ': 0},\n",
+       "   {'KI': 0, 'KJ': 0},\n",
+       "   {'KI': 1, 'KJ': 0}]],\n",
+       " 'port_cubes': [(1, 0, 0), (0, 1, 0), (1, 0, 3), (0, 1, 3)],\n",
+       " 'optional': {},\n",
+       " 'ExistI': [[[0, 1, 0], [0, 1, 0]], [[0, 0, 0], [0, 0, 0]]],\n",
+       " 'ExistJ': [[[0, 0, 1], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]],\n",
+       " 'ExistK': [[[0, 1, 0], [1, 1, 1]], [[1, 1, 1], [1, 1, 0]]],\n",
+       " 'ColorI': [[[0, 1, 1], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]],\n",
+       " 'ColorJ': [[[0, 0, 0], [0, 0, 0]], [[0, 0, 1], [0, 0, 0]]],\n",
+       " 'NodeY': [[[0, 0, 0], [0, 0, 0]], [[0, 0, 0], [1, 0, 1]]],\n",
+       " 'CorrIJ': [[[[0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]],\n",
+       "  [[[0, 1, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]],\n",
+       "  [[[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]]]],\n",
+       " 'CorrIK': [[[[0, 0, 0], [0, 0, 1]], [[0, 0, 0], [0, 0, 0]]],\n",
+       "  [[[0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]],\n",
+       "  [[[0, 1, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]],\n",
+       "  [[[0, 0, 0], [0, 1, 0]], [[0, 0, 0], [0, 0, 0]]]],\n",
+       " 'CorrJK': [[[[0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]],\n",
+       "  [[[0, 0, 1], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]],\n",
+       "  [[[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]]]],\n",
+       " 'CorrJI': [[[[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]]],\n",
+       "  [[[0, 0, 1], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]],\n",
+       "  [[[0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]]],\n",
+       " 'CorrKI': [[[[1, 0, 1], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]],\n",
+       "  [[[1, 0, 1], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]],\n",
+       "  [[[1, 1, 1], [0, 0, 1]], [[1, 1, 1], [0, 0, 0]]],\n",
+       "  [[[0, 0, 0], [1, 1, 1]], [[0, 0, 0], [1, 1, 0]]]],\n",
+       " 'CorrKJ': [[[[0, 0, 1], [0, 0, 0]], [[1, 1, 1], [0, 0, 0]]],\n",
+       "  [[[1, 1, 1], [1, 1, 1]], [[0, 1, 1], [0, 0, 0]]],\n",
+       "  [[[1, 0, 1], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]],\n",
+       "  [[[0, 0, 0], [0, 0, 0]], [[0, 0, 0], [1, 1, 0]]]]}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result.lasre"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Post-process and Output LaS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We provide a few rewrite passes to remove valid but unnecessary structures in the solution, and also color the K-pipes.\n",
+    "These can be applied with the follow call. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = result.after_default_optimizations()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can export the result to a few formats.\n",
+    "The most direct one is to save the LaSRe, which is now just a dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result.save_lasre(\"cnot.lasre.json\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also create a 3D modelling file in the [GLTF](https://www.khronos.org/gltf/) format.\n",
+    "This can be opened in many software, a lot of them are also web-based.\n",
+    "The `attach_axes` flag attaches I (red), J (green), and K (blue) axis to the GLTF."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result.to_3d_model_gltf(\"cnot.gltf\", attach_axes=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Like mentioned previously, the generated LaS can be easily mapped to a ZX-diagram.\n",
+    "We can use this connection to verify our result.\n",
+    "Internally, we construct the ZX-diagram and let [Stim ZX](https://github.com/quantumlib/Stim/tree/main/glue/zx) to derive the stabilizers.\n",
+    "Then, we check whether these stabilizers are commutable with the ones in the specification.\n",
+    "If all are commutable, then our LaS is correct."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "specified:\n",
+      "+Z_Z_\n",
+      "+_ZZZ\n",
+      "+X_XX\n",
+      "+_X_X\n",
+      "==============================================================\n",
+      "resulting:\n",
+      "+X_XX\n",
+      "+Z_Z_\n",
+      "+_X_X\n",
+      "+_ZZZ\n",
+      "==============================================================\n",
+      "specified and resulting stabilizers are equivalent.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result.verify_stabilizers_stimzx(specification=input_dict, print_stabilizers=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using Other SAT solver"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So far, we are using the [Z3 SMT solver](https://github.com/Z3Prover/z3) to do everything.\n",
+    "In our experience, it may be faster to generate an SAT problem with Z3 and solve it using other solvers, like Kissat.\n",
+    "For the user, it is very easy to change: just initiate the `LatticeSurgerySynthesizer` with the directory where Kissat is installed in your system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "las_synth = LatticeSurgerySynthesizer(solver=\"kissat\", kissat_dir=\"\")\n",
+    "# you need to add the kissat dir based on where kissat is on your computer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adding constraints time: 0.207474946975708\n",
+      "CNF generation time: 0.004982948303222656\n",
+      "c ---- [ banner ] ------------------------------------------------------------\n",
+      "c\n",
+      "c Kissat SAT Solver\n",
+      "c \n",
+      "c Copyright (c) 2021-2023 Armin Biere University of Freiburg\n",
+      "c Copyright (c) 2019-2021 Armin Biere Johannes Kepler University Linz\n",
+      "c \n",
+      "c Version 3.1.1 71caafb4d182ced9f76cef45b00f37cc598f2a37\n",
+      "c Apple clang version 15.0.0 (clang-1500.3.9.4) -W -Wall -O3 -DNDEBUG\n",
+      "c Sun May 12 13:03:10 PDT 2024 Darwin MacBook-Pro-2 23.4.0 arm64\n",
+      "c\n",
+      "c ---- [ parsing ] -----------------------------------------------------------\n",
+      "c\n",
+      "c opened and reading DIMACS file:\n",
+      "c\n",
+      "c   cnot.dimacs\n",
+      "c\n",
+      "c parsed 'p cnf 462 2231' header\n",
+      "c closing input after reading 40739 bytes (40 KB)\n",
+      "c finished parsing after 0.00 seconds\n",
+      "c\n",
+      "c ---- [ options ] -----------------------------------------------------------\n",
+      "c\n",
+      "c --seed=916189                    (different from default '0')\n",
+      "c\n",
+      "c ---- [ solving ] -----------------------------------------------------------\n",
+      "c\n",
+      "c  seconds switched conflicts irredundant variables\n",
+      "c         MB reductions redundant    trail   remaining\n",
+      "c          level restarts   binary      glue\n",
+      "c\n",
+      "c *  0.00  2 0 0 0  0   0   0 614 1557 0% 0 402 87%\n",
+      "c {  0.00  2 0 0 0  0   0   0 614 1557 0% 0 402 87%\n",
+      "c i  0.00  2 22 0 0  0  38  23 623 1556 44% 2 398 86%\n",
+      "c i  0.00  2 22 0 0  0  39  23 623 1556 44% 2 397 86%\n",
+      "c }  0.00  2 22 0 0  0  39  23 623 1556 44% 2 397 86%\n",
+      "c 1  0.00  2 22 0 0  0  39  23 623 1556 44% 2 397 86%\n",
+      "c\n",
+      "c ---- [ result ] ------------------------------------------------------------\n",
+      "c\n",
+      "s SATISFIABLE\n",
+      "v 1 -2 -3 -4 5 -6 -7 -8 9 10 -11 -12 -13 -14 -15 -16 -17 -18 -19 -20 -21 -22\n",
+      "v 23 -24 -25 -26 -27 28 -29 -30 -31 -32 33 -34 35 -36 37 38 39 40 41 42 43 44\n",
+      "v 45 46 47 48 -49 50 -51 -52 -53 54 -55 -56 -57 -58 -59 60 -61 62 -63 64 65\n",
+      "v -66 -67 -68 69 -70 71 -72 -73 -74 75 -76 -77 -78 79 -80 -81 -82 -83 -84 85\n",
+      "v 86 -87 -88 -89 90 91 92 93 94 95 -96 -97 -98 99 100 101 -102 -103 -104 -105\n",
+      "v -106 -107 -108 109 110 111 112 113 -114 115 116 117 118 119 120 121 122 -123\n",
+      "v -124 -125 -126 127 128 129 130 131 132 133 134 135 -136 137 -138 139 -140\n",
+      "v 141 142 143 -144 145 -146 147 148 -149 -150 -151 152 153 154 -155 -156 -157\n",
+      "v 158 159 160 -161 -162 163 -164 165 166 -167 168 169 -170 171 172 173 174\n",
+      "v -175 176 177 178 179 180 -181 182 183 184 185 -186 187 188 189 190 191 192\n",
+      "v 193 -194 -195 196 197 198 -199 -200 201 202 -203 204 205 206 207 208 209\n",
+      "v -210 -211 212 213 214 215 216 217 218 219 -220 221 -222 -223 -224 225 226\n",
+      "v 227 228 -229 230 -231 232 -233 234 235 236 -237 -238 239 240 241 242 243\n",
+      "v -244 245 246 247 -248 249 -250 251 -252 -253 254 255 256 257 258 -259 260\n",
+      "v 261 262 263 -264 -265 266 267 268 -269 270 -271 272 273 -274 275 276 277 278\n",
+      "v 279 280 281 282 283 284 -285 286 -287 288 289 290 -291 292 293 -294 -295 296\n",
+      "v 297 -298 299 300 301 302 303 -304 305 -306 307 -308 309 -310 311 312 313\n",
+      "v -314 315 -316 317 318 319 -320 321 322 323 -324 -325 326 327 328 -329 330\n",
+      "v 331 332 333 334 335 336 -337 -338 339 340 341 -342 -343 344 345 346 347 348\n",
+      "v 349 350 351 352 353 354 355 -356 357 -358 359 -360 361 362 363 -364 365 -366\n",
+      "v 367 368 369 -370 371 -372 373 -374 -375 376 -377 378 -379 380 -381 382 -383\n",
+      "v -384 385 386 -387 388 389 390 391 392 393 394 395 -396 -397 398 -399 400\n",
+      "v -401 402 403 -404 405 406 407 408 -409 -410 411 412 413 414 415 -416 -417\n",
+      "v 418 -419 420 421 422 423 424 425 426 427 428 429 430 -431 432 433 434 435\n",
+      "v 436 437 438 439 -440 441 442 443 444 445 446 447 448 -449 450 451 452 453\n",
+      "v 454 455 456 457 -458 459 -460 461 462 0\n",
+      "c\n",
+      "c ---- [ profiling ] ---------------------------------------------------------\n",
+      "c\n",
+      "c           0.00   39.95 %  parse\n",
+      "c           0.00   36.66 %  search\n",
+      "c           0.00   34.35 %  focused\n",
+      "c           0.00    0.00 %  simplify\n",
+      "c =============================================\n",
+      "c           0.00  100.00 %  total\n",
+      "c\n",
+      "c ---- [ statistics ] --------------------------------------------------------\n",
+      "c\n",
+      "c conflicts:                               39            12268.01 per second\n",
+      "c decisions:                              186                4.77 per conflict\n",
+      "c jumped_reasons:                        1002               29 %  propagations\n",
+      "c propagations:                          3417          1074866    per second\n",
+      "c queue_decisions:                        186              100 %  decision\n",
+      "c random_decisions:                         0                0 %  decision\n",
+      "c random_sequences:                         0                0    interval\n",
+      "c score_decisions:                          0                0 %  decision\n",
+      "c switched:                                 0                0    interval\n",
+      "c vivify_checks:                            0                0    per vivify\n",
+      "c vivify_units:                             0                0 %  variables\n",
+      "c\n",
+      "c ---- [ resources ] ---------------------------------------------------------\n",
+      "c\n",
+      "c maximum-resident-set-size:       1828716544 bytes       1744 MB\n",
+      "c process-time:                                              0.00 seconds\n",
+      "c\n",
+      "c ---- [ shutting down ] -----------------------------------------------------\n",
+      "c\n",
+      "c exit 10\n",
+      "kissat runtime: 0.008579015731811523\n",
+      "kissat SAT!\n",
+      "Construct a Z3 SMT model and solve...\n",
+      "elapsed time: 0.021113s\n",
+      "Z3 SAT\n",
+      "Total solving time: 0.04399609565734863\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = las_synth.solve(\n",
+    "    specification=input_dict,\n",
+    "    print_detail=True,\n",
+    "    dimacs_file_name=\"cnot\",\n",
+    "    sat_log_file_name=\"cnot\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We used a few optional arguments above.\n",
+    "`print_detail` will display the output of Kissat on the screen. \n",
+    "`dimacs_file_name` specifies where to store the SAT problem instance in the DIMACS format.\n",
+    "This instance is generated by Z3 and then solved by Kissat.\n",
+    "`sat_log_file_name` saves the output of Kissat, which is basically what you have seen as the output (from `c ---- [ banner ]` to `c exit 10`)."
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.8.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/glue/lattice_surgery/lassynth/__init__.py b/glue/lattice_surgery/lassynth/__init__.py
new file mode 100644
index 00000000..9640f285
--- /dev/null
+++ b/glue/lattice_surgery/lassynth/__init__.py
@@ -0,0 +1,2 @@
+from .lattice_surgery_synthesis import LatticeSurgerySynthesizer
+from .lattice_surgery_synthesis import LatticeSurgerySolution
diff --git a/glue/lattice_surgery/lassynth/lattice_surgery_synthesis.py b/glue/lattice_surgery/lassynth/lattice_surgery_synthesis.py
new file mode 100644
index 00000000..fe83bb23
--- /dev/null
+++ b/glue/lattice_surgery/lassynth/lattice_surgery_synthesis.py
@@ -0,0 +1,590 @@
+"""Two wrapper classes, rewrite passes, and translators."""
+
+import functools
+import itertools
+import json
+import time
+import multiprocessing
+import random
+import networkx
+from typing import Any, Literal, Mapping, Optional, Sequence
+from lassynth.rewrite_passes.attach_fixups import attach_fixups
+from lassynth.rewrite_passes.color_z import color_z
+from lassynth.rewrite_passes.remove_unconnected import remove_unconnected
+from lassynth.sat_synthesis.lattice_surgery_sat import LatticeSurgerySAT
+from lassynth.tools.verify_stabilizers import verify_stabilizers
+from lassynth.translators.gltf_generator import gltf_generator
+from lassynth.translators.textfig_generator import textfig_generator
+from lassynth.translators.zx_grid_graph import ZXGridGraph
+from lassynth.translators.networkx_generator import networkx_generator
+
+
+def check_lasre(lasre: Mapping[str, Any]) -> None:
+    """check aspects of LaSRe other than SMT constraints, i.e., data layout."""
+    if "n_i" not in lasre:
+        raise ValueError(
+            f"upper bound of I dimension, `n_i`, is missing in lasre.")
+    if lasre["n_i"] <= 0:
+        raise ValueError("n_i <= 0.")
+    if "n_j" not in lasre:
+        raise ValueError(
+            f"upper bound of J dimension, `n_j`, is missing in lasre.")
+    if lasre["n_j"] <= 0:
+        raise ValueError("n_j <= 0.")
+    if "n_k" not in lasre:
+        raise ValueError(
+            f"upper bound of K dimension, `n_k`, is missing in lasre.")
+    if lasre["n_k"] <= 0:
+        raise ValueError("n_k <= 0.")
+    if "n_p" not in lasre:
+        raise ValueError(f"number of ports, `n_p`, is missing in lasre.")
+    if lasre["n_p"] <= 0:
+        raise ValueError("n_p <= 0.")
+    if "n_s" not in lasre:
+        raise ValueError(f"number of stabilizers, `n_s`, is missing in lasre.")
+    if lasre["n_s"] < 0:
+        raise ValueError("n_s < 0.")
+    if lasre["n_s"] == 0:
+        print("no stabilizer!")
+
+    if "ports" not in lasre:
+        raise ValueError(f"`ports` is missing in lasre.")
+    if len(lasre["ports"]) != lasre["n_p"]:
+        raise ValueError("number of ports in `ports` is different from `n_p`.")
+    for port in lasre["ports"]:
+        if "i" not in port:
+            raise ValueError(f"location `i` missing from port {port}.")
+        if port["i"] not in range(lasre["n_i"]):
+            raise ValueError(f"i out of range in port {port}.")
+        if "j" not in port:
+            raise ValueError(f"location `j` missing from port {port}.")
+        if port["j"] not in range(lasre["n_j"]):
+            raise ValueError(f"j out of range in port {port}.")
+        if "k" not in port:
+            raise ValueError(f"location `k` missing from port {port}.")
+        if port["k"] not in range(lasre["n_k"]):
+            raise ValueError(f"k out of range in port {port}.")
+        if "d" not in port:
+            raise ValueError(f"direction `d` missing from port {port}.")
+        if port["d"] not in ["I", "J", "K"]:
+            raise ValueError(f"direction not I, J, or K in port {port}.")
+        if "e" not in port:
+            raise ValueError(f"open end `e` missing from port {port}.")
+        if port["e"] not in ["-", "+"]:
+            raise ValueError(f"open end not - or + in port {port}.")
+        if "c" not in port:
+            raise ValueError(f"color `c` missing from port {port}.")
+        if port["c"] not in [0, 1]:
+            raise ValueError(f"color not 0 or 1 in port {port}.")
+
+    if "stabs" not in lasre:
+        raise ValueError(f"`stabs` is missing in lasre.")
+    if len(lasre["stabs"]) != lasre["n_s"]:
+        raise ValueError("number of stabs in `stabs` is different from `n_s`.")
+    for stab in lasre["stabs"]:
+        if len(stab) != lasre["n_p"]:
+            raise ValueError("number of boundary corrsurf is not `n_p`.")
+        for i, corrsurf in enumerate(stab):
+            for (k, v) in corrsurf.items():
+                if lasre["ports"][i]["d"] == "I" and k not in ["IJ", "IK"]:
+                    raise ValueError(f"stabs[{i}] key invalid {stab}.")
+                if lasre["ports"][i]["d"] == "J" and k not in ["JI", "JK"]:
+                    raise ValueError(f"stabs[{i}] key invalid {stab}.")
+                if lasre["ports"][i]["d"] == "K" and k not in ["KI", "KJ"]:
+                    raise ValueError(f"stabs[{i}] key invalid {stab}.")
+                if v not in [0, 1]:
+                    raise ValueError(f"stabs[{i}] value not 0 or 1 {stab}.")
+
+    port_cubes = []
+    for p in lasre["ports"]:
+        # if e=-, (i,j,k); otherwise, +1 in the proper direction
+        if p["e"] == "-":
+            port_cubes.append((p["i"], p["j"], p["k"]))
+        elif p["d"] == "I":
+            port_cubes.append((p["i"] + 1, p["j"], p["k"]))
+        elif p["d"] == "J":
+            port_cubes.append((p["i"], p["j"] + 1, p["k"]))
+        elif p["d"] == "K":
+            port_cubes.append((p["i"], p["j"], p["k"] + 1))
+    lasre["port_cubes"] = port_cubes
+
+    if "optional" not in lasre:
+        lasre["optional"] = {}
+
+    for key in [
+            "NodeY",
+            "ExistI",
+            "ExistJ",
+            "ExistK",
+            "ColorI",
+            "ColorJ",
+    ]:
+        if key not in lasre:
+            raise ValueError(f"`{key}` missing from lasre.")
+        if len(lasre[key]) != lasre["n_i"]:
+            raise ValueError(f"dimension of {key} is wrong.")
+        for tmp in lasre[key]:
+            if len(tmp) != lasre["n_j"]:
+                raise ValueError(f"dimension of {key} is wrong.")
+            for tmptmp in tmp:
+                if len(tmptmp) != lasre["n_k"]:
+                    raise ValueError(f"dimension of {key} is wrong.")
+
+    if lasre["n_s"] > 0:
+        for key in [
+                "CorrIJ",
+                "CorrIK",
+                "CorrJI",
+                "CorrJK",
+                "CorrKI",
+                "CorrKJ",
+        ]:
+            if key not in lasre:
+                raise ValueError(f"`{key}` missing from lasre.")
+            if len(lasre[key]) != lasre["n_s"]:
+                raise ValueError(f"dimension of {key} is wrong.")
+            for tmp in lasre[key]:
+                if len(tmp) != lasre["n_i"]:
+                    raise ValueError(f"dimension of {key} is wrong.")
+                for tmptmp in tmp:
+                    if len(tmptmp) != lasre["n_j"]:
+                        raise ValueError(f"dimension of {key} is wrong.")
+                    for tmptmptmp in tmptmp:
+                        if len(tmptmptmp) != lasre["n_k"]:
+                            raise ValueError(f"dimension of {key} is wrong.")
+
+
+class LatticeSurgerySolution:
+    """A class for the result of synthesizer lattice surgery subroutine. 
+
+    It internally saves an LaSRe (Lattice Surgery Subroutine Representation)
+    and we can apply rewrite passes to it, or use translators to derive
+    other formats of the LaS solution
+    """
+
+    def __init__(
+        self,
+        lasre: Mapping[str, Any],
+    ) -> None:
+        """initialization for LatticeSurgerySubroutine
+
+        Args:
+            lasre (Mapping[str, Any]): LaSRe
+        """
+        check_lasre(lasre)
+        self.lasre = lasre
+
+    def get_depth(self) -> int:
+        """get the depth/height of the LaS in LaSRe.
+
+        Returns:
+            int: depth/height of the LaS in LaSRe
+        """
+        return self.lasre["n_k"]
+
+    def after_removing_disconnected_pieces(self):
+        """remove_unconnected."""
+        return LatticeSurgerySolution(lasre=remove_unconnected(self.lasre))
+
+    def after_color_k_pipes(self):
+        """coloring K pipes."""
+        return LatticeSurgerySolution(lasre=color_z(self.lasre))
+
+    def after_default_optimizations(self):
+        """default optimizations: remove unconnected, and then color K pipes."""
+        solution = LatticeSurgerySolution(lasre=remove_unconnected(self.lasre))
+        solution = LatticeSurgerySolution(lasre=color_z(solution.lasre))
+        return solution
+
+    def after_t_factory_default_optimizations(self):
+        """default optimization for T-factories."""
+        solution = LatticeSurgerySolution(lasre=remove_unconnected(self.lasre))
+        solution = LatticeSurgerySolution(lasre=color_z(solution.lasre))
+        solution = LatticeSurgerySolution(lasre=attach_fixups(solution.lasre))
+        return solution
+
+    def save_lasre(self, file_name: str) -> None:
+        """save the current LaSRe to a file.
+
+        Args:
+            file_name (str): file name including extension to save the LaSRe
+        """
+        with open(file_name, "w") as f:
+            json.dump(self.lasre, f)
+
+    def to_3d_model_gltf(self,
+                         output_file_name: str,
+                         stabilizer: int = -1,
+                         tube_len: float = 2.0,
+                         no_color_z: bool = False,
+                         attach_axes: bool = False,
+                         rm_dir: Optional[str] = None) -> None:
+        """generate gltf file (for 3D modelling).
+
+        Args:
+            output_file_name (str): file name including extension to save gltf
+            stabilizer (int, optional): Defaults to -1 meaning do not draw
+                correlation surfaces. If the value is in [0, n_s),
+                the correlation surfaces corresponding to that stabilizer
+                are drawn and faces in one of the directions are revealed
+                to unveil the correlation surfaces.
+            tube_len (float, optional): Length of the pipe comapred to
+                the cube. Defaults to 2.0.
+            no_color_z (bool, optional): Do not color the K pipes.
+                Defaults to False.
+            attach_axes (bool, optional): attach IJK axes. Defaults to False.
+                If attached, the color coding is I->red, J->green, K->blue.
+            rm_dir (str, optional): the (+|-)(I|J|K) faces to remove.
+                Intended to reveal correlation surfaces. Default to None.
+        """
+        gltf = gltf_generator(
+            self.lasre,
+            stabilizer=stabilizer,
+            tube_len=tube_len,
+            no_color_z=no_color_z,
+            attach_axes=attach_axes,
+            rm_dir=rm_dir if rm_dir else (":+J" if stabilizer >= 0 else None),
+        )
+        with open(output_file_name, "w") as f:
+            json.dump(gltf, f)
+
+    def to_zigxag_url(
+        self,
+        io_spec: Optional[Sequence[str]] = None,
+    ) -> str:
+        """generate a link that leads to a ZigXag figure.
+
+        Args:
+            io_spec (Optional[Sequence[str]], optional): Specify whether
+            each port is an input or an output. Length must be the same
+            with the number of ports. Defaults to None, which means
+            all ports are outputs.
+
+        Returns:
+            str: the ZigXag link
+        """
+        zxgridgraph = ZXGridGraph(self.lasre)
+        return zxgridgraph.to_zigxag_url(io_spec=io_spec)
+
+    def to_text_diagram(self) -> str:
+        """generate the text figure of LaS time slices.
+
+        Returns:
+            str: text figure of the LaS
+        """
+        return textfig_generator(self.lasre)
+
+    def to_networkx_graph(self) -> networkx.Graph:
+        """generate a annotated networkx.Graph correponding to the LaS.
+
+        Returns:
+            networkx.Graph:
+        """
+        return networkx_generator(self.lasre)
+
+    def verify_stabilizers_stimzx(self,
+                                  specification: Mapping[str, Any],
+                                  print_stabilizers: bool = False) -> bool:
+        """verify the stabilizer of the LaS.
+
+        Use StimZX to deduce the stabilizers from the annotated networkx graph.
+        Then use Stim to ensure that this set of stabilizers and the set of
+        stabilizers specified in the input are equivalent.
+
+        Args:
+            specification (Mapping[str, Any]): the LaS specification to verify
+                the current solution against.
+            print_stabilizers (bool, optional): If True, print the two sets of
+                stabilizers. Defaults to False.
+
+        Returns:
+            bool: True if the two sets are equivalent; otherwise False.
+        """
+        paulistrings = [
+            paulistring.replace(".", "_")
+            for paulistring in specification["stabilizers"]
+        ]
+        return verify_stabilizers(
+            paulistrings,
+            self.to_networkx_graph(),
+            print_stabilizers=print_stabilizers,
+        )
+
+
+class LatticeSurgerySynthesizer:
+    """A class to synthesize LaS."""
+
+    def __init__(
+        self,
+        solver: Literal["kissat", "z3"] = "z3",
+        kissat_dir: Optional[str] = None,
+    ) -> None:
+        """initialize.
+
+        Args:
+            solver (Literal["kissat", "z3"], optional): the solver to use.
+                Defaults to "z3". "kissat" is recommended.
+            kissat_dir (Optional[str], optional): directory of the kissat
+                executable. Defaults to None.
+        """
+        self.solver = solver
+        self.kissat_dir = kissat_dir
+
+    def solve(
+        self,
+        specification: Mapping[str, Any],
+        given_arrs: Optional[Mapping[str, Any]] = None,
+        given_vals: Optional[Sequence[Mapping[str, Any]]] = None,
+        print_detail: bool = False,
+        dimacs_file_name: Optional[str] = None,
+        sat_log_file_name: Optional[str] = None,
+    ) -> Optional[LatticeSurgerySolution]:
+        """solve an LaS synthesis problem.
+
+        Args:
+            specification (Mapping[str, Any]): the LaS specification to solve.
+            given_arrs (Optional[Mapping[str, Any]], optional): given array of
+                known values to plug in. Defaults to None.
+            given_vals (Optional[Sequence[Mapping[str, Any]]], optional): given
+                known values to plug in. Defaults to None. Format should be
+                a sequence of dicts. Each one contains three fields: "array",
+                the name of the array, e.g., "ExistI"; "indices", a sequence of
+                the indices, e.g., [0, 0, 0]; and "value", 0 or 1.
+            print_detail (bool, optional): whether to print details in
+                SAT solving. Defaults to False.
+            dimacs_file_name (Optional[str], optional): file to save the
+                DIMACS. Defaults to None.
+            sat_log_file_name (Optional[str], optional): file to save the
+                SAT solver log. Defaults to None.
+
+        Returns:
+            Optional[LatticeSurgerySubroutine]: if the problem is
+                unsatisfiable, this is None; otherwise, a
+                LatticeSurgerySolution initialized by the compiled result.
+        """
+        start_time = time.time()
+        sat_synthesis = LatticeSurgerySAT(
+            input_dict=specification,
+            given_arrs=given_arrs,
+            given_vals=given_vals,
+        )
+        if print_detail:
+            print(f"Adding constraints time: {time.time() - start_time}")
+
+        start_time = time.time()
+        if self.solver == "z3":
+            if_sat = sat_synthesis.check_z3(print_progress=print_detail)
+        else:
+            if_sat = sat_synthesis.check_kissat(
+                dimacs_file_name=dimacs_file_name,
+                sat_log_file_name=sat_log_file_name,
+                print_progress=print_detail,
+                kissat_dir=self.kissat_dir,
+            )
+        if print_detail:
+            print(f"Total solving time: {time.time() - start_time}")
+
+        if if_sat:
+            solver_result = sat_synthesis.get_result()
+            return LatticeSurgerySolution(lasre=solver_result)
+        else:
+            return None
+
+    def optimize_depth(
+        self,
+        specification: Mapping[str, Any],
+        start_depth: Optional[int] = None,
+        print_detail: bool = False,
+        dimacs_file_name_prefix: Optional[str] = None,
+        sat_log_file_name_prefix: Optional[str] = None,
+    ) -> LatticeSurgerySolution:
+        """find the optimal solution in terms of depth/height of the LaS.
+
+    Args:
+        specification (Mapping[str, Any]): the LaS specification to solve.
+        start_depth (int, optional): starting depth of the exploration. If not
+            provided, use the depth given in the specification
+        print_detail (bool, optional): whether to print details in SAT solving.
+            Defaults to False.
+        dimacs_file_name_prefix (Optional[str], optional): file prefix to save
+            the DIMACS. The full file name will contain the specific depth
+            after this prefix. Defaults to None.
+        sat_log_file_name_prefix (Optional[str], optional): file prefix to save
+            the SAT log. The full file name will contain the specific depth
+            after this prefix. Defaults to None.
+        result_file_name_prefix (Optional[str], optional): file prefix to save
+            the variable assignments. The full file name will contain the
+            specific depth after this prefix. Defaults to None.
+        post_optimization (str, optional): optimization to perform when
+            initializing the LatticeSurgerySubroutine object for the result.
+            Defaults to "default".
+
+    Raises:
+        ValueError: starting depth is too low.
+
+    Returns:
+        LatticeSurgerySolution: compiled result with the optimal depth.
+    """
+        self.specification = dict(specification)
+        if start_depth is None:
+            depth = self.specification["max_k"]
+        else:
+            depth = int(start_depth)
+        if depth < 2:
+            raise ValueError("depth too low.")
+
+        checked_depth = {}
+        while True:
+            # the ports on the top floor will still be on the top floor when we
+            # increase the height. This is an assumption. Adapt to your case.
+            for port in self.specification["ports"]:
+                if port["location"][2] == self.specification["max_k"]:
+                    port["location"][2] = depth
+            self.specification["max_k"] = depth
+
+            result = self.solve(
+                specification=self.specification,
+                print_detail=print_detail,
+                dimacs_file_name=dimacs_file_name_prefix +
+                f"_d={depth}" if dimacs_file_name_prefix else None,
+                sat_log_file_name=sat_log_file_name_prefix +
+                f"_d={depth}" if sat_log_file_name_prefix else None,
+            )
+            if result is None:
+                checked_depth[str(depth)] = "UNSAT"
+                if str(depth + 1) in checked_depth:
+                    # since this depth leads to UNSAT, we need to increase
+                    # the depth, but if depth+1 is already checked, we can stop
+                    break
+                else:
+                    depth += 1
+            else:
+                checked_depth[str(depth)] = "SAT"
+                self.sat_result = LatticeSurgerySolution(lasre=result.lasre)
+                if str(depth - 1) in checked_depth:
+                    # since this depth leads to SAT, we need to try decreasing
+                    # the depth, but if depth-1 is already checked, we can stop
+                    break
+                else:
+                    depth -= 1
+
+        return self.sat_result
+
+    def try_one_permutation(
+        self,
+        perm: Sequence[int],
+        specification: Mapping[str, Any],
+        print_detail: bool = False,
+        dimacs_file_name_prefix: Optional[str] = None,
+        sat_log_file_name_prefix: Optional[str] = None,
+    ) -> Optional[LatticeSurgerySolution]:
+        """check if the problem is satisfiable given a port permutation.
+
+        Args:
+            specification (Mapping[str, Any]): the LaS specification to solve.
+            perm (Sequence[int]): the given permutation, which is an integer
+                tuple of length n (n being the number of ports permuted).
+            print_detail (bool, optional): whether to print details in
+                SAT solving. Defaults to False.
+            dimacs_file_name_prefix (Optional[str], optional): file prefix
+                to save the DIMACS. The full file name will contain the
+                specific permutation after this prefix. Defaults to None.
+            sat_log_file_name_prefix (Optional[str], optional): file prefix
+                to save the SAT log. The full file name will contain the
+                specific permutation after this prefix. Defaults to None.
+
+        Returns:
+            Optional[LatticeSurgerySubroutine]: if the problem is
+                unsatisfiable, this is None; otherwise, a
+                LatticeSurgerySolution initialized by the compiled result.
+        """
+
+        # say `perm` is [0,3,2], then `original` is in order, i.e., [0,2,3]
+        # the full permutation is 0,1,2,3 -> 0,1,3,2
+        original = sorted(perm)
+        this_spec = dict(specification)
+        new_ports = []
+        for p, port in enumerate(specification["ports"]):
+            if p not in perm:
+                # the p-th port is not involved in `perm`, e.g., 1 is unchanged
+                new_ports.append(port)
+
+            else:
+                # after the permutation, the index of the p-th port in
+                # specification is the k-th port in `perm` where k is the place
+                # of p in `original`. In this example, when p=0 and 1, nothing
+                # changed. When p=2, we find `place` to be 1, and perm[place]=3
+                # so we attach port_3. When p=3, we end up attach port_2
+                place = original.index(p)
+                new_ports.append(specification["ports"][perm[place]])
+        this_spec["ports"] = new_ports
+
+        result = self.solve(
+            specification=this_spec,
+            print_detail=print_detail,
+            dimacs_file_name=dimacs_file_name_prefix + "_" +
+            perm.__repr__().replace(" ", "")
+            if dimacs_file_name_prefix else None,
+            sat_log_file_name=sat_log_file_name_prefix + "_" +
+            perm.__repr__().replace(" ", "")
+            if sat_log_file_name_prefix else None,
+        )
+        print(f"{perm}: {'SAT' if result else 'UNSAT'}")
+        return result
+
+    def solve_all_port_permutations(
+        self,
+        permute_ports: Sequence[int],
+        parallelism: int = 1,
+        shuffle: bool = True,
+        **kwargs,
+    ) -> Mapping[str, Sequence[Sequence[int]]]:
+        """try all the permutations of given ports, which ones are satisfiable.
+
+        Note that we do not check that the LaS after permuting the ports (we do
+        not permute the stabilizers accordingly) is functionally equivalent.
+        The user should use this method based on their judgement. Also, the
+        number of permutations scales exponentially with the number of ports
+        to permute, so this method can easily take an immense amount of time.
+
+        Args:
+            permute_ports (Sequence[int]): the indices of ports to permute
+            parallelism (int, optional): number of parallel process. Each one
+                try one permutation. A New proess starts when an old one
+                finishes. Defaults to 1.
+            shuffle (bool, optional): whether using a random order to start the
+                processes. Defaults to True.
+            **kwargs: other arguments to `try_one_permutation`.
+
+        Returns:
+            Mapping[str, Sequence[Sequence[int]]]: a dict with two keys.
+                "SAT": [.] a list containing all the satisfiable permutations;
+                "UNSAT": [.] all the unsatisfiable permutations.
+        """
+        perms = list(itertools.permutations(permute_ports))
+        if shuffle:
+            random.shuffle(perms)
+
+        pool = multiprocessing.Pool(parallelism)
+        # issue the job one by one (chuck=1)
+        results = pool.map(
+            functools.partial(
+                self.try_one_permutation,
+                **kwargs,
+            ),
+            perms,
+            chunksize=1,
+        )
+
+        sat_perms = []
+        unsat_perms = []
+        for p, result in enumerate(results):
+            if result is None:
+                unsat_perms.append(perms[p])
+            else:
+                sat_perms.append(perms[p])
+
+        return {
+            "SAT": sat_perms,
+            "UNSAT": unsat_perms,
+        }
diff --git a/glue/lattice_surgery/lassynth/rewrite_passes/__init__.py b/glue/lattice_surgery/lassynth/rewrite_passes/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/glue/lattice_surgery/lassynth/rewrite_passes/attach_fixups.py b/glue/lattice_surgery/lassynth/rewrite_passes/attach_fixups.py
new file mode 100644
index 00000000..cad628f3
--- /dev/null
+++ b/glue/lattice_surgery/lassynth/rewrite_passes/attach_fixups.py
@@ -0,0 +1,86 @@
+"""assuming all T injections requiring fixup are on the top floor.
+The output is also on the top floor"""
+
+from typing import Mapping, Any
+
+
+def attach_fixups(lasre: Mapping[str, Any]) -> Mapping[str, Any]:
+    n_s = lasre["n_s"]
+    n_i = lasre["n_i"]
+    n_j = lasre["n_j"]
+    n_k = lasre["n_k"]
+
+    fixup_locs = []
+    for p in lasre["optional"]["top_fixups"]:
+        fixup_locs.append((lasre["ports"][p]["i"], lasre["ports"][p]["j"]))
+
+    lasre["n_k"] += 2
+    # we add two layers on the top. The lower layer will contain fixups dressed
+    # as Y cubes that is connecting downwards. The upper layer will contain no
+    # new cubes. This corresponds to waiting the machine to apply the fixups
+    # because there is a finite interaction time from the machine knows whether
+    # the injection is T or T^dagger to apply the fixups.
+    for i in range(n_i):
+        for j in range(n_j):
+            if (i, j) in fixup_locs:
+                lasre["NodeY"][i][j].append(1)  # fixup dressed as Y cubes
+                lasre["ExistK"][i][j][n_k - 1] = 1  # connect fixup downwards
+            else:
+                lasre["NodeY"][i][j].append(0)  # no fixup
+                lasre["ExistK"][i][j][n_k - 1] = 0
+            lasre["NodeY"][i][j].append(0)  # the upper layer is empty
+
+            # do not add any new pipes in the added layer
+            for arr in ["ExistI", "ExistJ", "ExistK"]:
+                lasre[arr][i][j].append(0)
+                lasre[arr][i][j].append(0)
+            for arr in ["ColorI", "ColorJ", "ColorKM", "ColorKP"]:
+                lasre[arr][i][j].append(-1)
+                lasre[arr][i][j].append(-1)
+            for s in range(n_s):
+                for arr in [
+                        "CorrIJ", "CorrIK", "CorrJK", "CorrJI", "CorrKI",
+                        "CorrKJ"
+                ]:
+                    lasre[arr][s][i][j].append(0)
+                    lasre[arr][s][i][j].append(0)
+
+    # the output ports need to be extended in the two added layers
+    for port in lasre["ports"]:
+        if "f" in port and port["f"] == "output":
+            ii, jj = port["i"], port["j"]
+            lasre["ExistK"][ii][jj][n_k - 1] = 1
+            lasre["ExistK"][ii][jj][n_k] = 1
+            lasre["ExistK"][ii][jj][n_k + 1] = 1
+            lasre["ColorKM"][ii][jj][n_k] = lasre["ColorKP"][ii][jj][n_k - 1]
+            lasre["ColorKM"][ii][jj][n_k + 1] = lasre["ColorKM"][ii][jj][n_k]
+            lasre["ColorKP"][ii][jj][n_k] = lasre["ColorKM"][ii][jj][n_k]
+            lasre["ColorKP"][ii][jj][n_k + 1] = lasre["ColorKP"][ii][jj][n_k]
+            for s in range(n_s):
+                lasre["CorrKI"][s][ii][jj][n_k] = lasre["CorrKI"][s][ii][jj][
+                    n_k - 1]
+                lasre["CorrKI"][s][ii][jj][n_k +
+                                           1] = lasre["CorrKI"][s][ii][jj][n_k]
+                lasre["CorrKJ"][s][ii][jj][n_k] = lasre["CorrKJ"][s][ii][jj][
+                    n_k - 1]
+                lasre["CorrKJ"][s][ii][jj][n_k +
+                                           1] = lasre["CorrKJ"][s][ii][jj][n_k]
+            port["k"] += 2
+            new_cubes = []
+            for c in lasre["port_cubes"]:
+                if c[0] == port["i"] and c[1] == port["j"]:
+                    new_cubes.append((c[0], c[1], port["k"] + 1))
+                else:
+                    new_cubes.append(c)
+            lasre["port_cubes"] = new_cubes
+
+    t_injections = []
+    for port in lasre["ports"]:
+        if port["f"] == "T":
+            if port["e"] == "+":
+                t_injections.append([port["i"], port["j"], port["k"] + 1])
+            else:
+                t_injections.append([port["i"], port["j"], port["k"]])
+    lasre["optional"]["t_injections"] = t_injections
+
+    return lasre
diff --git a/glue/lattice_surgery/lassynth/rewrite_passes/color_z.py b/glue/lattice_surgery/lassynth/rewrite_passes/color_z.py
new file mode 100644
index 00000000..7c0c956d
--- /dev/null
+++ b/glue/lattice_surgery/lassynth/rewrite_passes/color_z.py
@@ -0,0 +1,210 @@
+"""We do not have ColorZ from the SAT/SMT. Now we color the Z-pipes."""
+
+from typing import Sequence, Mapping, Any, Union, Tuple
+
+
+def if_uncolorK(n_i: int, n_j: int, n_k: int,
+                ExistK: Sequence[Sequence[Sequence[int]]],
+                ColorKP: Sequence[Sequence[Sequence[int]]],
+                ColorKM: Sequence[Sequence[Sequence[int]]]) -> bool:
+    """return whether there are uncolored K-pipes"""
+    for i in range(n_i):
+        for j in range(n_j):
+            for k in range(n_k):
+                if ExistK[i][j][k] and (ColorKP[i][j][k] == -1
+                                        or ColorKM[i][j][k] == -1):
+                    return True
+    return False
+
+
+def in_bound(n_i: int, n_j: int, n_k: int, i: int, j: int, k: int) -> bool:
+    if i in range(n_i) and j in range(n_j) and k in range(n_k):
+        return True
+    return False
+
+
+def propogate_IJcolor(n_i: int, n_j: int, n_k: int,
+                      ExistI: Sequence[Sequence[Sequence[int]]],
+                      ExistJ: Sequence[Sequence[Sequence[int]]],
+                      ExistK: Sequence[Sequence[Sequence[int]]],
+                      ColorI: Sequence[Sequence[Sequence[int]]],
+                      ColorJ: Sequence[Sequence[Sequence[int]]],
+                      ColorKP: Sequence[Sequence[Sequence[int]]],
+                      ColorKM: Sequence[Sequence[Sequence[int]]]) -> None:
+    """propagate the color of I- and J-pipes to their neighbor K-pipes."""
+
+    for i in range(n_i):
+        for j in range(n_j):
+            for k in range(n_k):
+                if ExistK[i][j][k]:
+                    # 4 possible neighbor I/J pipe for the minus end of K-pipe
+                    if in_bound(n_i, n_j, n_k, i - 1, j,
+                                k) and ExistI[i - 1][j][k]:
+                        ColorKM[i][j][k] = 1 - ColorI[i - 1][j][k]
+                    if ExistI[i][j][k]:
+                        ColorKM[i][j][k] = 1 - ColorI[i][j][k]
+                    if in_bound(n_i, n_j, n_k, i, j - 1,
+                                k) and ExistJ[i][j - 1][k]:
+                        ColorKM[i][j][k] = 1 - ColorJ[i][j - 1][k]
+                    if ExistJ[i][j][k]:
+                        ColorKM[i][j][k] = 1 - ColorJ[i][j][k]
+
+                    # 4 possible neighbor I/J pipe for the plus end of K-pipe
+                    if (in_bound(n_i, n_j, n_k, i - 1, j, k + 1)
+                            and ExistI[i - 1][j][k + 1]):
+                        ColorKP[i][j][k] = 1 - ColorI[i - 1][j][k + 1]
+                    if in_bound(n_i, n_j, n_k, i, j,
+                                k + 1) and ExistI[i][j][k + 1]:
+                        ColorKP[i][j][k] = 1 - ColorI[i][j][k + 1]
+                    if (in_bound(n_i, n_j, n_k, i, j - 1, k + 1)
+                            and ExistJ[i][j - 1][k + 1]):
+                        ColorKP[i][j][k] = 1 - ColorJ[i][j - 1][k + 1]
+                    if in_bound(n_i, n_j, n_k, i, j,
+                                k + 1) and ExistJ[i][j][k + 1]:
+                        ColorKP[i][j][k] = 1 - ColorJ[i][j][k + 1]
+
+
+def propogate_Kcolor(n_i: int, n_j: int, n_k: int,
+                     ExistK: Sequence[Sequence[Sequence[int]]],
+                     ColorKP: Sequence[Sequence[Sequence[int]]],
+                     ColorKM: Sequence[Sequence[Sequence[int]]],
+                     NodeY: Sequence[Sequence[Sequence[int]]]) -> bool:
+    """propagate color from colored K-pipes to uncolored K-pipes.
+    If no new color can be assigned, return False; otherwise, return True."""
+
+    did_something = False
+    for i in range(n_i):
+        for j in range(n_j):
+            for k in range(n_k):
+                if ExistK[i][j][k]:
+                    # consider propagate color from below
+                    if in_bound(
+                            n_i, n_j, n_k, i, j, k -
+                            1) and ExistK[i][j][k - 1] and NodeY[i][j][k -
+                                                                       1] == 0:
+                        if ColorKP[i][j][k -
+                                         1] > -1 and ColorKM[i][j][k] == -1:
+                            ColorKM[i][j][k] = ColorKP[i][j][k - 1]
+                            did_something = True
+                    # consider propagate color from above
+                    if in_bound(
+                            n_i, n_j, n_k, i, j, k +
+                            1) and ExistK[i][j][k + 1] and NodeY[i][j][k +
+                                                                       1] == 0:
+                        if ColorKM[i][j][k +
+                                         1] > -1 and ColorKP[i][j][k] == -1:
+                            ColorKP[i][j][k] = ColorKM[i][j][k + 1]
+                            did_something = True
+
+                    # if K-pipe connects a Y Cube, two ends can be colored same
+                    if (NodeY[i][j][k] and ColorKM[i][j][k] == -1
+                            and ColorKP[i][j][k] > -1):
+                        ColorKM[i][j][k] = ColorKP[i][j][k]
+                        did_something = True
+                    if (in_bound(n_i, n_j, n_k, i, j, k + 1)
+                            and NodeY[i][j][k + 1] and ColorKM[i][j][k] > -1
+                            and ColorKP[i][j][k] == -1):
+                        ColorKP[i][j][k] = ColorKM[i][j][k]
+                        did_something = True
+    return did_something
+
+
+def assign_Kcolor(n_i: int, n_j: int, n_k: int,
+                  ExistK: Sequence[Sequence[Sequence[int]]],
+                  ColorKP: Sequence[Sequence[Sequence[int]]],
+                  ColorKM: Sequence[Sequence[Sequence[int]]],
+                  NodeY: Sequence[Sequence[Sequence[int]]]) -> None:
+    """when no color can be deducted by propagating from other K-pipes, we
+    assign some color variables at will. Then, we can continue to propagate."""
+
+    # assign a color by letting the two ends of a K-pipe to be the same
+    for i in range(n_i):
+        for j in range(n_j):
+            for k in range(n_k):
+                if ExistK[i][j][k]:
+                    if ColorKM[i][j][k] > -1 and ColorKP[i][j][k] == -1:
+                        ColorKP[i][j][k] = ColorKM[i][j][k]
+                        break
+    # For K-pipes that have no color at both ends and connects a Y-cube
+    for i in range(n_i):
+        for j in range(n_j):
+            for k in range(n_k):
+                if ExistK[i][j][k]:
+                    if NodeY[i][j][k] and ColorKM[i][j][k] == -1:
+                        ColorKM[i][j][k] = 0
+                        break
+                    if (in_bound(n_i, n_j, n_k, i, j, k + 1)
+                            and NodeY[i][j][k + 1] and ColorKP[i][j][k] == -1):
+                        ColorKP[i][j][k] = 0
+                        break
+
+
+def color_ports(ports: Sequence[Mapping[str, Union[str, int]]],
+                ColorKP: Sequence[Sequence[Sequence[int]]],
+                ColorKM: Sequence[Sequence[Sequence[int]]]) -> None:
+    for port in ports:
+        if port['d'] == 'K':
+            if port['e'] == '+':
+                ColorKP[port['i']][port['j']][port['k']] = port['c']
+            else:
+                ColorKM[port['i']][port['j']][port['k']] = port['c']
+
+
+def color_kp_km(
+    n_i: int,
+    n_j: int,
+    n_k: int,
+    ExistI: Sequence[Sequence[Sequence[int]]],
+    ExistJ: Sequence[Sequence[Sequence[int]]],
+    ExistK: Sequence[Sequence[Sequence[int]]],
+    ColorI: Sequence[Sequence[Sequence[int]]],
+    ColorJ: Sequence[Sequence[Sequence[int]]],
+    ports: Sequence[Mapping[str, Union[str, int]]],
+    NodeY: Sequence[Sequence[Sequence[int]]],
+) -> Tuple[Sequence[Sequence[Sequence[int]]],
+           Sequence[Sequence[Sequence[int]]]]:
+    ColorKP = [[[-1 for _ in range(n_k)] for _ in range(n_j)]
+               for _ in range(n_i)]
+    ColorKM = [[[-1 for _ in range(n_k)] for _ in range(n_j)]
+               for _ in range(n_i)]
+
+    # at ports, the color follows from the port configuration
+    color_ports(ports, ColorKP, ColorKM)
+
+    # propogate the color of I-pipes and J-pipes to their neighboring K-pipes
+    propogate_IJcolor(n_i, n_j, n_k, ExistI, ExistJ, ExistK, ColorI, ColorJ,
+                      ColorKP, ColorKM)
+
+    # the rest of the K-pipes are only neighboring other K-pipes. Until all of
+    # them are colored, we propagate colors of the existing K-pipes. If at one
+    # point, nothing can be implied via propagation, we assign a color at will
+    # and continue. Because of the domain wall operation, we can do this.
+    while if_uncolorK(n_i, n_j, n_k, ExistK, ColorKP, ColorKM):
+        if not propogate_Kcolor(n_i, n_j, n_k, ExistK, ColorKP, ColorKM,
+                                NodeY):
+            assign_Kcolor(n_i, n_j, n_k, ExistK, ColorKP, ColorKM, NodeY)
+    return ColorKP, ColorKM
+
+
+def color_z(lasre: Mapping[str, Any]) -> Mapping[str, Any]:
+    n_i, n_j, n_k = (
+        lasre['n_i'],
+        lasre['n_j'],
+        lasre['n_k'],
+    )
+    ExistI, ColorI, ExistJ, ColorJ, ExistK = (
+        lasre['ExistI'],
+        lasre['ColorI'],
+        lasre['ExistJ'],
+        lasre['ColorJ'],
+        lasre['ExistK'],
+    )
+    NodeY = lasre['NodeY']
+    ports = lasre['ports']
+
+    # for a K-pipe (i,j,k)-(i,j,k+1), ColorKP (plus) is its color at (i,j,k+1)
+    # and ColorKM (minus) is its color at (i,j,k)
+    lasre['ColorKP'], lasre['ColorKM'] = color_kp_km(n_i, n_j, n_k, ExistI,
+                                                     ExistJ, ExistK, ColorI,
+                                                     ColorJ, ports, NodeY)
+    return lasre
diff --git a/glue/lattice_surgery/lassynth/rewrite_passes/remove_unconnected.py b/glue/lattice_surgery/lassynth/rewrite_passes/remove_unconnected.py
new file mode 100644
index 00000000..e983b009
--- /dev/null
+++ b/glue/lattice_surgery/lassynth/rewrite_passes/remove_unconnected.py
@@ -0,0 +1,152 @@
+"""In the generated LaS, there can be some 'floating donuts' not connecting to
+any ports. These objects won't affect the function of the LaS. We remove them.
+"""
+
+from typing import Mapping, Any, Sequence, Union, Tuple
+
+
+def check_cubes(
+    n_i: int, n_j: int, n_k: int, ExistI: Sequence[Sequence[Sequence[int]]],
+    ExistJ: Sequence[Sequence[Sequence[int]]],
+    ExistK: Sequence[Sequence[Sequence[int]]],
+    ports: Sequence[Mapping[str, Union[str, int]]],
+    NodeY: Sequence[Sequence[Sequence[int]]]
+) -> Sequence[Sequence[Sequence[int]]]:
+    # we linearize the cubes, cube at (i,j,k) -> index i*n_j*n_k + j*n_k + k
+    # construct adjancency list of the cubes from the pipes
+    adj = [[] for _ in range(n_i * n_j * n_k)]
+    for i in range(n_i):
+        for j in range(n_j):
+            for k in range(n_k):
+                if ExistI[i][j][k] and i + 1 < n_i:
+                    adj[i * n_j * n_k + j * n_k +
+                        k].append((i + 1) * n_j * n_k + j * n_k + k)
+                    adj[(i + 1) * n_j * n_k + j * n_k +
+                        k].append(i * n_j * n_k + j * n_k + k)
+                if ExistJ[i][j][k] and j + 1 < n_j:
+                    adj[i * n_j * n_k + j * n_k + k].append(i * n_j * n_k +
+                                                            (j + 1) * n_k + k)
+                    adj[i * n_j * n_k + (j + 1) * n_k +
+                        k].append(i * n_j * n_k + j * n_k + k)
+                if ExistK[i][j][k] and k + 1 < n_k:
+                    adj[i * n_j * n_k + j * n_k + k].append(i * n_j * n_k +
+                                                            j * n_k + k + 1)
+                    adj[i * n_j * n_k + j * n_k + k + 1].append(i * n_j * n_k +
+                                                                j * n_k + k)
+
+    # if a cube can reach any of the vips, i.e., open cube for a port
+    vips = [p["i"] * n_j * n_k + p["j"] * n_k + p["k"] for p in ports]
+
+    # first assume all cubes are nonconnected
+    connected_cubes = [[[0 for _ in range(n_k)] for _ in range(n_j)]
+                       for _ in range(n_i)]
+
+    # a Y cube is only effective if it is connected to a cube (i,j,k) that is
+    # connected to ports. In this case, (i,j,k) will be in `connected_cubes`
+    # and all pipes from (i,j,k) will be selected in `check_pipes`, so we can
+    # assume all the Y cubes to be nonconnected for now.
+    y_cubes = [
+        i * n_j * n_k + j * n_k + k for i in range(n_i) for j in range(n_j)
+        for k in range(n_k) if NodeY[i][j][k]
+    ]
+
+    for i in range(n_i):
+        for j in range(n_j):
+            for k in range(n_k):
+                # breadth first search for each cube
+                queue = [
+                    i * n_j * n_k + j * n_k + k,
+                ]
+                if i * n_j * n_k + j * n_k + k in y_cubes:
+                    continue
+                visited = [0 for _ in range(n_i * n_j * n_k)]
+                while len(queue) > 0:
+                    if queue[0] in vips:
+                        connected_cubes[i][j][k] = 1
+                        break
+                    visited[queue[0]] = 1
+                    for v in adj[queue[0]]:
+                        if not visited[v] and v not in y_cubes:
+                            queue.append(v)
+                    queue.pop(0)
+
+    return connected_cubes
+
+
+def check_pipes(
+    n_i: int, n_j: int, n_k: int, ExistI: Sequence[Sequence[Sequence[int]]],
+    ExistJ: Sequence[Sequence[Sequence[int]]],
+    ExistK: Sequence[Sequence[Sequence[int]]],
+    connected_cubes: Sequence[Sequence[Sequence[int]]]
+) -> Tuple[Sequence[Sequence[Sequence[int]]],
+           Sequence[Sequence[Sequence[int]]],
+           Sequence[Sequence[Sequence[int]]]]:
+    EffectI = [[[0 for _ in range(n_k)] for _ in range(n_j)]
+               for _ in range(n_i)]
+    EffectJ = [[[0 for _ in range(n_k)] for _ in range(n_j)]
+               for _ in range(n_i)]
+    EffectK = [[[0 for _ in range(n_k)] for _ in range(n_j)]
+               for _ in range(n_i)]
+    for i in range(n_i):
+        for j in range(n_j):
+            for k in range(n_k):
+                if ExistI[i][j][k] and (connected_cubes[i][j][k] or
+                                        (i + 1 < n_i
+                                         and connected_cubes[i + 1][j][k])):
+                    EffectI[i][j][k] = 1
+                if ExistJ[i][j][k] and (connected_cubes[i][j][k] or
+                                        (j + 1 < n_j
+                                         and connected_cubes[i][j + 1][k])):
+                    EffectJ[i][j][k] = 1
+                if ExistK[i][j][k] and (connected_cubes[i][j][k] or
+                                        (k + 1 < n_k
+                                         and connected_cubes[i][j][k + 1])):
+                    EffectK[i][j][k] = 1
+    return EffectI, EffectJ, EffectK
+
+
+def array3DAnd(
+    arr0: Sequence[Sequence[Sequence[int]]],
+    arr1: Sequence[Sequence[Sequence[int]]]
+) -> Sequence[Sequence[Sequence[int]]]:
+    """taking the AND of two arrays of bits"""
+    a = len(arr0)
+    b = len(arr0[0])
+    c = len(arr0[0][0])
+    arrAnd = [[[0 for _ in range(c)] for _ in range(b)] for _ in range(a)]
+    for i in range(a):
+        for j in range(b):
+            for k in range(c):
+                if arr0[i][j][k] == 1 and arr1[i][j][k] == 1:
+                    arrAnd[i][j][k] = 1
+    return arrAnd
+
+
+def remove_unconnected(lasre: Mapping[str, Any]) -> Mapping[str, Any]:
+    n_i, n_j, n_k = (
+        lasre["n_i"],
+        lasre["n_j"],
+        lasre["n_k"],
+    )
+    ExistI, ExistJ, ExistK, NodeY = (
+        lasre["ExistI"],
+        lasre["ExistJ"],
+        lasre["ExistK"],
+        lasre["NodeY"],
+    )
+    ports = lasre["ports"]
+
+    connected_cubes = check_cubes(n_i, n_j, n_k, ExistI, ExistJ, ExistK, ports,
+                                  NodeY)
+    connectedI, connectedJ, connectedK = check_pipes(n_i, n_j, n_k, ExistI,
+                                                     ExistJ, ExistK,
+                                                     connected_cubes)
+    maskedI, maskedJ, maskedK = (
+        array3DAnd(ExistI, connectedI),
+        array3DAnd(ExistJ, connectedJ),
+        array3DAnd(ExistK, connectedK),
+    )
+    lasre["ExistI"], lasre["ExistJ"], lasre[
+        "ExistK"] = maskedI, maskedJ, maskedK
+
+    return lasre
diff --git a/glue/lattice_surgery/lassynth/sat_synthesis/__init__.py b/glue/lattice_surgery/lassynth/sat_synthesis/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/glue/lattice_surgery/lassynth/sat_synthesis/lattice_surgery_sat.py b/glue/lattice_surgery/lassynth/sat_synthesis/lattice_surgery_sat.py
new file mode 100644
index 00000000..da6bd670
--- /dev/null
+++ b/glue/lattice_surgery/lassynth/sat_synthesis/lattice_surgery_sat.py
@@ -0,0 +1,1842 @@
+"""LatticeSurgerySAT to encode the synthesis problem to SAT/SMT"""
+
+import os
+import subprocess
+import sys
+import tempfile
+import time
+from typing import Any, Mapping, Sequence, Union, Tuple, Optional
+import z3
+
+
+def var_given(
+    data: Mapping[str, Any],
+    arr: str,
+    i: int,
+    j: int,
+    k: int,
+    l: Optional[int] = None,
+) -> bool:
+    """Check whether data[arr][i][j][k]([l]) is given.
+
+    If the given indices are not found, return False; otherwise return True.
+
+    Args:
+        data (Mapping[str, Any]): contain arrays
+        arr (str): ExistI, etc.
+        i (int): first index
+        j (int): second index
+        k (int): third index
+        l (int, optional): optional fourth index. Defaults to None.
+
+    Returns:
+        bool: whether the variable value is given
+
+    Raises:
+        ValueError: found value, but not 0 nor 1 nor -1.
+    """
+
+    if arr not in data:
+        return False
+    if l is None:
+        if data[arr][i][j][k] == -1:
+            return False
+        elif data[arr][i][j][k] != 0 and data[arr][i][j][k] != 1:
+            raise ValueError(f"{arr}[{i}, {j}, {k}] is not 0 nor 1 nor -1.")
+        return True
+    # l is not None, then
+    if data[arr][i][j][k][l] == -1:
+        return False
+    elif data[arr][i][j][k][l] != 0 and data[arr][i][j][k][l] != 1:
+        raise ValueError(f"{arr}[{i}, {j}, {k}, {l}] is not 0 nor 1 nor -1.")
+    return True
+
+
+def port_incident_pipes(
+        port: Mapping[str, Union[str, int]], n_i: int, n_j: int,
+        n_k: int) -> Tuple[Sequence[str], Sequence[Tuple[int, int, int]]]:
+    """Compute the pipes incident to a port.
+
+    A port is an pipe with a open end. The incident pipes of a port are the
+    five other pipes connecting to that end. However, some of these pipes
+    can be out of bound, we just want to compute those that are in bound.
+
+    Args:
+        port (Mapping[str, Union[str,  int]]): the port to consider
+        n_i (int): spatial bound on I direction
+        n_j (int): spatial bound on J direction
+        n_k (int): spatial bound on K direction
+
+    Returns:
+        Tuple[Sequence[str], Sequence[Tuple[int, int, int]]]:
+            Two lists of the same length [0,6): (dirs, coords)
+            dirs: the direction of the incident pipes, can be "I", "J", or "K"
+            coords: the coordinates of the incident pipes, each one is (i,j,k)
+    """
+    coords = []
+    dirs = []
+
+    # first, just consider adjancency without caring about out-of-bound
+    if port["d"] == "I":
+        adj_dirs = ["I", "J", "J", "K", "K"]
+        if port["e"] == "-":  # empty cube is (i,j,k)
+            adj_coords = [
+                (port["i"] - 1, port["j"], port["k"]),  # (i-1,j,k)---(i,j,k)
+                (port["i"], port["j"] - 1, port["k"]),  # (i,j-1,k)---(i,j,k)
+                (port["i"], port["j"], port["k"]),  # (i,j,k)---(i,j+1,k)
+                (port["i"], port["j"], port["k"] - 1),  # (i,j,k-1)---(i,j,k)
+                (port["i"], port["j"], port["k"]),  # (i,j,k)---(i,j,k+1)
+            ]
+        elif port["e"] == "+":  # empty cube is (i+1,j,k)
+            adj_coords = [
+                (port["i"] + 1, port["j"], port["k"]),  # (i+1,j,k)---(i+2,j,k)
+                (port["i"] + 1, port["j"] - 1,
+                 port["k"]),  # (i+1,j-1,k)---(i+1,j,k)
+                (port["i"] + 1, port["j"],
+                 port["k"]),  # (i+1,j,k)---(i+1,j+1,k)
+                (port["i"] + 1, port["j"],
+                 port["k"] - 1),  # (i+1,j,k-1)---(i+1,j,k)
+                (port["i"] + 1, port["j"],
+                 port["k"]),  # (i+1,j,k)---(i+1,j,k+1)
+            ]
+
+    if port["d"] == "J":
+        adj_dirs = ["J", "K", "K", "I", "I"]
+        if port["e"] == "-":
+            adj_coords = [
+                (port["i"], port["j"] - 1, port["k"]),
+                (port["i"], port["j"], port["k"] - 1),
+                (port["i"], port["j"], port["k"]),
+                (port["i"] - 1, port["j"], port["k"]),
+                (port["i"], port["j"], port["k"]),
+            ]
+        elif port["e"] == "+":
+            adj_coords = [
+                (port["i"], port["j"] + 1, port["k"]),
+                (port["i"], port["j"] + 1, port["k"] - 1),
+                (port["i"], port["j"] + 1, port["k"]),
+                (port["i"] - 1, port["j"] + 1, port["k"]),
+                (port["i"], port["j"] + 1, port["k"]),
+            ]
+
+    if port["d"] == "K":
+        adj_dirs = ["K", "I", "I", "J", "J"]
+        if port["e"] == "-":
+            adj_coords = [
+                (port["i"], port["j"], port["k"] - 1),
+                (port["i"] - 1, port["j"], port["k"]),
+                (port["i"], port["j"], port["k"]),
+                (port["i"], port["j"] - 1, port["k"]),
+                (port["i"], port["j"], port["k"]),
+            ]
+        elif port["e"] == "+":
+            adj_coords = [
+                (port["i"], port["j"], port["k"] + 1),
+                (port["i"] - 1, port["j"], port["k"] + 1),
+                (port["i"], port["j"], port["k"] + 1),
+                (port["i"], port["j"] - 1, port["k"] + 1),
+                (port["i"], port["j"], port["k"] + 1),
+            ]
+
+    # only keep the pipes in bound
+    for i, coord in enumerate(adj_coords):
+        if ((coord[0] in range(n_i)) and (coord[1] in range(n_j))
+                and (coord[2] in range(n_k))):
+            coords.append(adj_coords[i])
+            dirs.append(adj_dirs[i])
+
+    return dirs, coords
+
+
+def cnf_even_parity_upto4(eles: Sequence[Any]) -> Any:
+    """Compute the CNF format of parity of up to four Z3 binary variables.
+
+    Args:
+        eles (Sequence[Any]): the binary variables.
+
+    Returns:
+        (Any) the Z3 constraint meaning the parity of the inputs is even.
+
+    Raises:
+        ValueError: number of elements is not 1, 2, 3, or 4.
+    """
+
+    if len(eles) == 1:
+        # 1 var even parity -> this var is false
+        return z3.Not(eles[0])
+
+    elif len(eles) == 2:
+        # 2 vars even pairty -> both True or both False
+        return z3.Or(z3.And(z3.Not(eles[0]), z3.Not(eles[1])),
+                     z3.And(eles[0], eles[1]))
+
+    elif len(eles) == 3:
+        # 3 vars even parity -> all False, or 2 True and 1 False
+        return z3.Or(
+            z3.And(z3.Not(eles[0]), z3.Not(eles[1]), z3.Not(eles[2])),
+            z3.And(eles[0], eles[1], z3.Not(eles[2])),
+            z3.And(eles[0], z3.Not(eles[1]), eles[2]),
+            z3.And(z3.Not(eles[0]), eles[1], eles[2]),
+        )
+
+    elif len(eles) == 4:
+        # 4 vars even parity -> 0, 2, or 4 vars are True
+        return z3.Or(
+            z3.And(z3.Not(eles[0]), z3.Not(eles[1]), z3.Not(eles[2]),
+                   z3.Not(eles[3])),
+            z3.And(z3.Not(eles[0]), z3.Not(eles[1]), eles[2], eles[3]),
+            z3.And(z3.Not(eles[0]), eles[1], z3.Not(eles[2]), eles[3]),
+            z3.And(z3.Not(eles[0]), eles[1], eles[2], z3.Not(eles[3])),
+            z3.And(eles[0], z3.Not(eles[1]), z3.Not(eles[2]), eles[3]),
+            z3.And(eles[0], z3.Not(eles[1]), eles[2], z3.Not(eles[3])),
+            z3.And(eles[0], eles[1], z3.Not(eles[2]), z3.Not(eles[3])),
+            z3.And(eles[0], eles[1], eles[2], eles[3]),
+        )
+
+    else:
+        raise ValueError("This function only supports 1, 2, 3, or 4 vars.")
+
+
+class LatticeSurgerySAT:
+    """class of synthesizing LaSRe using Z3 SMT solver and Kissat SAT solver.
+
+    It encodes a lattice surgery synthesis problem to SAT/SMT and checks
+    whether there is a solution. We are given certain spacetime volume, certain
+    ports, and certain stabilizers. LatticeSurgerySAT encodes the constraints
+    on LaSRe variables such that the resulting variable assignments consist of
+    a valid lattice surgery subroutine with the correct functionality
+    (satisfies all the given stabilizers). LatticeSurgerySAT finds the solution
+    with a SAT/SMT solver.
+    """
+
+    def __init__(
+        self,
+        input_dict: Mapping[str, Any],
+        color_ij: bool = True,
+        given_arrs: Optional[Mapping[str, Any]] = None,
+        given_vals: Optional[Sequence[Mapping[str, Any]]] = None,
+    ) -> None:
+        """initialization of LatticeSurgerySAT.
+
+        Args:
+            input_dict (Mapping[str, Any]): specification of LaS.
+            color_ij (bool, optional): if the color matching constraints of
+                I and J pipes are imposed. Defaults to True. So far, we always
+                impose these constraints.
+            given_arrs (Mapping[str, Any], optional):
+                    Arrays of values to plug in. Defaults to None.
+            given_vals (Sequence[Mapping[str, Any]], optional):
+                Values to plug in. Defaults to None. These values will
+                replace existing values if already set by given_arrs.
+        """
+        self.input_dict = input_dict
+        self.color_ij = color_ij
+        self.goal = z3.Goal()
+        self.process_input(input_dict)
+        self.build_smt_model(given_arrs=given_arrs, given_vals=given_vals)
+
+    def process_input(self, input_dict: Mapping[str, Any]) -> None:
+        """read input specification, mainly translating the info at the ports.
+
+        Args:
+            input_dict (Mapping[str, Any]): LaS specification.
+
+        Raises:
+            ValueError: missing key in input specification.
+            ValueError: some spatial bound <= 0.
+            ValueError: more stabilizers than ports.
+            ValueError: stabilizer length is not the same as
+                the number of ports.
+            ValueError: stabilizer contains things other than I, X, Y, or Z.
+            ValueError: missing key in port.
+            ValueError: port location is not a 3-tuple.
+            ValueError: port direction is not 2-string.
+            ValueError: port sign (which end is dangling) is not - or +.
+            ValueError: port axis is not I, J, or K.
+            ValueError: port location+direction is out of bound.
+            ValueError: port Z basis direction is not I, J, or K, and
+                the same with the pipe.
+            ValueError: forbidden cube location is not a 3-tuple.
+            ValueError: forbiddent cube location is out of bounds.
+        """
+        data = input_dict
+
+        for key in ["max_i", "max_j", "max_k", "ports", "stabilizers"]:
+            if key not in data:
+                raise ValueError(f"missing key {key} in input specification.")
+
+        # load spatial bound, check > 0
+        self.n_i = data["max_i"]
+        self.n_j = data["max_j"]
+        self.n_k = data["max_k"]
+        if min([self.n_i, self.n_j, self.n_k]) <= 0:
+            raise ValueError("max_i or _j or _k <= 0.")
+
+        self.n_p = len(data["ports"])
+        self.n_s = len(data["stabilizers"])
+        # there should be at most as many stabilizers as ports
+        if self.n_s > self.n_p:
+            raise ValueError(
+                f"{self.n_s} stabilizers, too many for {self.n_p} ports.")
+
+        # stabilizers should be paulistrings of length #ports
+        self.paulistrings = [s.replace(".", "I") for s in data["stabilizers"]]
+        for s in self.paulistrings:
+            if len(s) != self.n_p:
+                raise ValueError(
+                    f"len({s}) = {len(s)}, but there are {self.n_p} ports.")
+            for i in range(len(s)):
+                if s[i] not in ["I", "X", "Y", "Z"]:
+                    raise ValueError(
+                        f"{s} has invalid Pauli. I, X, Y, and Z are allowed.")
+
+        # transform port data
+        self.ports = []
+        for port in data["ports"]:
+            for key in ["location", "direction", "z_basis_direction"]:
+                if key not in port:
+                    raise ValueError(f"missing key {key} in port {port}")
+
+            if len(port["location"]) != 3:
+                raise ValueError(f"port location should be 3-tuple {port}.")
+
+            if len(port["direction"]) != 2:
+                raise ValueError(
+                    f"port direction should have 2 characters {port}.")
+            if port["direction"][0] not in ["+", "-"]:
+                raise ValueError(f"port direction with invalid sign {port}.")
+            if port["direction"][1] not in ["I", "J", "K"]:
+                raise ValueError(f"port direction with invalid axis {port}.")
+
+            if port["direction"][0] == "-" and port["direction"][
+                    1] == "I" and (port["location"][0] not in range(
+                        1, self.n_i + 1)):
+                raise ValueError(
+                    f"{port['location']} with direction {port['direction']}"
+                    f" should be in range [1, f{self.n_i+1}).")
+            if port["direction"][0] == "+" and port["direction"][
+                    1] == "I" and (port["location"][0] not in range(
+                        0, self.n_i)):
+                raise ValueError(
+                    f"{port['location']} with direction {port['direction']}"
+                    f" should be in range [0, f{self.n_i}).")
+            if port["direction"][0] == "-" and port["direction"][
+                    1] == "J" and (port["location"][1] not in range(
+                        1, self.n_j + 1)):
+                raise ValueError(
+                    f"{port['location']} with direction {port['direction']}"
+                    f" should be in range [1, {self.n_j+1}).")
+            if port["direction"][0] == "+" and port["direction"][
+                    1] == "J" and (port["location"][1] not in range(
+                        0, self.n_j)):
+                raise ValueError(
+                    f"{port['location']} with direction {port['direction']}"
+                    f" should be in range [0, f{self.n_j}).")
+            if port["direction"][0] == "-" and port["direction"][
+                    1] == "K" and (port["location"][2] not in range(
+                        1, self.n_k + 1)):
+                raise ValueError(
+                    f"{port['location']} with direction {port['direction']}"
+                    f" should be in range [1, f{self.n_k+1}).")
+            if port["direction"][0] == "+" and port["direction"][
+                    1] == "K" and (port["location"][2] not in range(
+                        0, self.n_k)):
+                raise ValueError(
+                    f"{port['location']} with direction {port['direction']}"
+                    f" should be in range [0, f{self.n_k}).")
+
+            # internally, a port is an pipe. This is different from what we
+            # expose to the user: in LaS specification, a port is a cube and
+            # associated with a direction, e.g., cube [i,j,k] and direction
+            # "-K". This means the port should be the pipe connecting (i,j,k)
+            # downwards to the volume of LaS. Thus, that pipe is (i,j,k-1) --
+            # (i,j,k) which by convention is the K-pipe (i,j,k-1).
+            # a port here has fields "i", "j", "k", "d", "e", "f", "c"
+            my_port = {}
+
+            # "i", "j", and "k" are the i,j,k of the pipe
+            my_port["i"], my_port["j"], my_port["k"] = port["location"]
+            if port["direction"][0] == "-":
+                my_port[port["direction"][1].lower()] -= 1
+
+            # "d" is one of I, J, and K, corresponding to the port being an
+            #     I-pipe, J-pipe, or a K-pipe
+            my_port["d"] = port["direction"][1]
+
+            # "e" is the end of the pipe that is open. For example, if the port
+            #     is a K-pipe (i,j,k), then "e"="+" means the cube (i,j,k+1) is
+            #     open; otherwise, "e"="-" means cube (i,j,k) is open.
+            my_port["e"] = "-" if port["direction"][0] == "+" else "+"
+
+            # "c" is the color variable of the pipe corresponding to the port
+            z_dir = port["z_basis_direction"]
+            if z_dir not in ["I", "J", "K"] or z_dir == my_port["d"]:
+                raise ValueError(
+                    f"port with invalid Z basis direction {port}.")
+            if my_port["d"] == "I":
+                my_port["c"] = 0 if z_dir == "J" else 1
+            if my_port["d"] == "J":
+                my_port["c"] = 0 if z_dir == "K" else 1
+            if my_port["d"] == "K":
+                my_port["c"] = 0 if z_dir == "I" else 1
+
+            # "f" is the function of the pipe, e.g., it can say this port is a
+            #     T injection. This field is not used in the SAT synthesis, but
+            #     we keep this info to use in later stages like gltf generation
+            if "function" in port:
+                my_port["f"] = port["function"]
+
+            self.ports.append(my_port)
+
+        # from paulistrings to correlation surfaces
+        self.stabs = self.derive_corr_boundary(self.paulistrings)
+
+        self.optional = {}
+        self.forbidden_cubes = []
+        if "optional" in data:
+            self.optional = data["optional"]
+
+            if "forbidden_cubes" in data["optional"]:
+                for cube in data["optional"]["forbidden_cubes"]:
+                    if len(cube) != 3:
+                        raise ValueError(
+                            f"forbid cube should be 3-tuple {cube}.")
+                    if (cube[0] not in range(self.n_i)
+                            or cube[1] not in range(self.n_j)
+                            or cube[2] not in range(self.n_k)):
+                        raise ValueError(
+                            f"forbidden {cube} out of range "
+                            f"(i,j,k) < ({self.n_i, self.n_j, self.n_k})")
+                    self.forbidden_cubes.append(cube)
+
+        self.get_port_cubes()
+
+    def get_port_cubes(self) -> None:
+        """calculate which cubes are the open cube for the ports.
+        Note that these are *** 3-tuples ***, not lists with 3 elements."""
+        self.port_cubes = []
+        for p in self.ports:
+            # if e=-, (i,j,k); otherwise, +1 in the proper direction
+            if p["e"] == "-":
+                self.port_cubes.append((p["i"], p["j"], p["k"]))
+            elif p["d"] == "I":
+                self.port_cubes.append((p["i"] + 1, p["j"], p["k"]))
+            elif p["d"] == "J":
+                self.port_cubes.append((p["i"], p["j"] + 1, p["k"]))
+            elif p["d"] == "K":
+                self.port_cubes.append((p["i"], p["j"], p["k"] + 1))
+
+    def derive_corr_boundary(
+            self, paulistrings: Sequence[str]
+    ) -> Sequence[Sequence[Mapping[str, int]]]:
+        """derive the boundary correlation surface variable values.
+
+        From the color orientation of the ports and the stabilizers, we can
+        derive which correlation surface variables evaluates to True and which
+        to False at the ports for each stabilizer.
+
+        Args:
+            paulistrings (Sequence[str]): stabilizers as a list of Paulistrings
+
+        Returns:
+            Sequence[Sequence[Mapping[str, int]]]: Outer layer list is the
+            list of stabilizers. Inner layer list is the situation at each port
+            for one specifeic stabilizer. Each port is specified with a
+            dictionary of 2 bits for the 2 correaltion surfaces.
+        """
+        stabs = []
+        for paulistring in paulistrings:
+            corr = []
+            for p in range(self.n_p):
+                if paulistring[p] == "I":
+                    # I -> no corr surf should be present
+                    if self.ports[p]["d"] == "I":
+                        corr.append({"IJ": 0, "IK": 0})
+                    if self.ports[p]["d"] == "J":
+                        corr.append({"JI": 0, "JK": 0})
+                    if self.ports[p]["d"] == "K":
+                        corr.append({"KI": 0, "KJ": 0})
+
+                if paulistring[p] == "Y":
+                    # Y -> both corr surf should be present
+                    if self.ports[p]["d"] == "I":
+                        corr.append({"IJ": 1, "IK": 1})
+                    if self.ports[p]["d"] == "J":
+                        corr.append({"JI": 1, "JK": 1})
+                    if self.ports[p]["d"] == "K":
+                        corr.append({"KI": 1, "KJ": 1})
+
+                if paulistring[p] == "X":
+                    # X -> only corr surf touching red faces
+                    if self.ports[p]["d"] == "I":
+                        if self.ports[p]["c"]:
+                            corr.append({"IJ": 1, "IK": 0})
+                        else:
+                            corr.append({"IJ": 0, "IK": 1})
+                    if self.ports[p]["d"] == "J":
+                        if self.ports[p]["c"]:
+                            corr.append({"JI": 0, "JK": 1})
+                        else:
+                            corr.append({"JI": 1, "JK": 0})
+                    if self.ports[p]["d"] == "K":
+                        if self.ports[p]["c"]:
+                            corr.append({"KI": 1, "KJ": 0})
+                        else:
+                            corr.append({"KI": 0, "KJ": 1})
+
+                if paulistring[p] == "Z":
+                    # Z -> only corr surf touching blue faces
+                    if self.ports[p]["d"] == "I":
+                        if not self.ports[p]["c"]:
+                            corr.append({"IJ": 1, "IK": 0})
+                        else:
+                            corr.append({"IJ": 0, "IK": 1})
+                    if self.ports[p]["d"] == "J":
+                        if not self.ports[p]["c"]:
+                            corr.append({"JI": 0, "JK": 1})
+                        else:
+                            corr.append({"JI": 1, "JK": 0})
+                    if self.ports[p]["d"] == "K":
+                        if not self.ports[p]["c"]:
+                            corr.append({"KI": 1, "KJ": 0})
+                        else:
+                            corr.append({"KI": 0, "KJ": 1})
+            stabs.append(corr)
+        return stabs
+
+    def build_smt_model(
+        self,
+        given_arrs: Optional[Mapping[str, Any]] = None,
+        given_vals: Optional[Sequence[Mapping[str, Any]]] = None,
+    ) -> None:
+        """build the SMT model with variables and constraints.
+
+            Args:
+                given_arrs (Mapping[str, Any], optional):
+                    Arrays of values to plug in. Defaults to None.
+                given_vals (Sequence[Mapping[str, Any]], optional):
+                    Values to plug in. Defaults to None. These values will
+                    replace existing values if already set by given_arrs.
+            """
+        self.define_vars()
+        if given_arrs is not None:
+            self.plugin_arrs(given_arrs)
+        if given_vals is not None:
+            self.plugin_vals(given_vals)
+
+        # baseline order of constraint sets, '...' menas name in the paper
+
+        # validity constraints that directly set variables values
+        self.constraint_forbid_cube()
+        self.constraint_port()  # 'no fanouts'
+        self.constraint_connect_outside()  # 'no unexpected ports'
+
+        # more complex validity constraints involving boolean logic
+        self.constraint_timelike_y()  # 'time-like Y cubes'
+        self.constraint_no_deg1()  # 'no degree-1 non-Y cubes'
+        if self.color_ij:
+            # 'matching colors at passthroughs' and '... at turns'
+            self.constraint_ij_color()
+        self.constraint_3d_corner()  # 'no 3D corners'
+
+        # simpler functionality constraints
+        self.constraint_corr_ports()  # 'stabilizer as boundary conditions'
+        self.constraint_corr_y()  # 'both or non at Y cubes'
+
+        # more complex functionality constraints
+        # 'all or no orthogonal surfaces at non-Y cubes:
+        self.constraint_corr_perp()
+        # 'even parity of parallel surfaces at non-Y cubes':
+        self.constraint_corr_para()
+
+    def define_vars(self) -> None:
+        """define the variables in Z3 into self.vars."""
+        self.vars = {
+            "ExistI":
+            [[[z3.Bool(f"ExistI({i},{j},{k})") for k in range(self.n_k)]
+              for j in range(self.n_j)] for i in range(self.n_i)],
+            "ExistJ":
+            [[[z3.Bool(f"ExistJ({i},{j},{k})") for k in range(self.n_k)]
+              for j in range(self.n_j)] for i in range(self.n_i)],
+            "ExistK":
+            [[[z3.Bool(f"ExistK({i},{j},{k})") for k in range(self.n_k)]
+              for j in range(self.n_j)] for i in range(self.n_i)],
+            "NodeY":
+            [[[z3.Bool(f"NodeY({i},{j},{k})") for k in range(self.n_k)]
+              for j in range(self.n_j)] for i in range(self.n_i)],
+            "CorrIJ":
+            [[[[z3.Bool(f"CorrIJ({s},{i},{j},{k})") for k in range(self.n_k)]
+               for j in range(self.n_j)] for i in range(self.n_i)]
+             for s in range(self.n_s)],
+            "CorrIK":
+            [[[[z3.Bool(f"CorrIK({s},{i},{j},{k})") for k in range(self.n_k)]
+               for j in range(self.n_j)] for i in range(self.n_i)]
+             for s in range(self.n_s)],
+            "CorrJK":
+            [[[[z3.Bool(f"CorrJK({s},{i},{j},{k})") for k in range(self.n_k)]
+               for j in range(self.n_j)] for i in range(self.n_i)]
+             for s in range(self.n_s)],
+            "CorrJI":
+            [[[[z3.Bool(f"CorrJI({s},{i},{j},{k})") for k in range(self.n_k)]
+               for j in range(self.n_j)] for i in range(self.n_i)]
+             for s in range(self.n_s)],
+            "CorrKI":
+            [[[[z3.Bool(f"CorrKI({s},{i},{j},{k})") for k in range(self.n_k)]
+               for j in range(self.n_j)] for i in range(self.n_i)]
+             for s in range(self.n_s)],
+            "CorrKJ":
+            [[[[z3.Bool(f"CorrKJ({s},{i},{j},{k})") for k in range(self.n_k)]
+               for j in range(self.n_j)] for i in range(self.n_i)]
+             for s in range(self.n_s)],
+        }
+
+        if self.color_ij:
+            self.vars["ColorI"] = [[[
+                z3.Bool(f"ColorI({i},{j},{k})") for k in range(self.n_k)
+            ] for j in range(self.n_j)] for i in range(self.n_i)]
+            self.vars["ColorJ"] = [[[
+                z3.Bool(f"ColorJ({i},{j},{k})") for k in range(self.n_k)
+            ] for j in range(self.n_j)] for i in range(self.n_i)]
+
+    def plugin_arrs(self, data: Mapping[str, Any]) -> None:
+        """plug in the given arrays of values.
+
+        Args:
+            data (Mapping[str, Any]): contains gieven values.
+
+        Raises:
+            ValueError: data contains an invalid array name.
+            ValueError: array given has wrong dimensions.
+        """
+
+        for key in data:
+            if key in [
+                    "NodeY",
+                    "ExistI",
+                    "ExistJ",
+                    "ExistK",
+                    "ColorI",
+                    "ColorJ",
+            ]:
+                if len(data[key]) != self.n_i:
+                    raise ValueError(f"dimension of {key} is wrong.")
+                for tmp in data[key]:
+                    if len(tmp) != self.n_j:
+                        raise ValueError(f"dimension of {key} is wrong.")
+                    for tmptmp in tmp:
+                        if len(tmptmp) != self.n_k:
+                            raise ValueError(f"dimension of {key} is wrong.")
+            elif key in [
+                    "CorrIJ",
+                    "CorrIK",
+                    "CorrJI",
+                    "CorrJK",
+                    "CorrKI",
+                    "CorrKJ",
+            ]:
+                if len(data[key]) != self.n_s:
+                    raise ValueError(f"dimension of {key} is wrong.")
+                for tmp in data[key]:
+                    if len(tmp) != self.n_i:
+                        raise ValueError(f"dimension of {key} is wrong.")
+                    for tmptmp in tmp:
+                        if len(tmptmp) != self.n_j:
+                            raise ValueError(f"dimension of {key} is wrong.")
+                        for tmptmptmp in tmptmp:
+                            if len(tmptmptmp) != self.n_k:
+                                raise ValueError(
+                                    f"dimension of {key} is wrong.")
+            else:
+                raise ValueError(f"{key} is not a valid array name")
+
+        arrs = [
+            "NodeY",
+            "ExistI",
+            "ExistJ",
+            "ExistK",
+        ]
+        if self.color_ij:
+            arrs += ["ColorI", "ColorJ"]
+
+        for s in range(self.n_s):
+            for i in range(self.n_i):
+                for j in range(self.n_j):
+                    for k in range(self.n_k):
+                        if s == 0:  # Exist, Node, and Color vars
+                            for arr in arrs:
+                                if var_given(data, arr, i, j, k):
+                                    self.goal.add(
+                                        self.vars[arr][i][j][k]
+                                        if data[arr][i][j][k] ==
+                                        1 else z3.Not(self.vars[arr][i][j][k]))
+                        # Corr vars
+                        for arr in [
+                                "CorrIJ",
+                                "CorrIK",
+                                "CorrJI",
+                                "CorrJK",
+                                "CorrKI",
+                                "CorrKJ",
+                        ]:
+                            if var_given(data, arr, s, i, j, k):
+                                self.goal.add(
+                                    self.vars[arr][s][i][j][k]
+                                    if data[arr][s][i][j][k] ==
+                                    1 else z3.Not(self.vars[arr][s][i][j][k]))
+
+    def plugin_vals(self, data_set: Sequence[Mapping[str, Any]]):
+        """plug in the given values
+
+        Args:
+            data (Sequence[Mapping[str, Any]]): given values as a sequence
+            of dicts. Each one contains three fields: "array", the name of
+            the array, e.g., "ExistI"; "indices", a sequence of the indices;
+            and "value".
+
+        Raises:
+            ValueError: given_vals missing a field.
+            ValueError: array name is not valid.
+            ValueError: indices dimension for certain array is wrong.
+            ValueError: index value out of bound.
+            ValueError: given value is neither 0 nor 1.
+        """
+        for data in data_set:
+            for key in ["array", "indices", "value"]:
+                if key not in data:
+                    raise ValueError(f"{key} is not in given val")
+            if data["array"] not in [
+                    "NodeY",
+                    "ExistI",
+                    "ExistJ",
+                    "ExistK",
+                    "ColorI",
+                    "ColorJ",
+                    "CorrIJ",
+                    "CorrIK",
+                    "CorrJI",
+                    "CorrJK",
+                    "CorrKI",
+                    "CorrKJ",
+            ]:
+                raise ValueError(f"{data['array']} is not a valid array.")
+            if data["array"] in [
+                    "NodeY",
+                    "ExistI",
+                    "ExistJ",
+                    "ExistK",
+                    "ColorI",
+                    "ColorJ",
+            ]:
+                if len(data["indices"] != 3):
+                    raise ValueError(f"Need 3 indices for {data['array']}.")
+                if data["indices"][0] not in range(self.n_i):
+                    raise ValueError(f"i index out of range")
+                if data["indices"][1] not in range(self.n_j):
+                    raise ValueError(f"j index out of range")
+                if data["indices"][2] not in range(self.n_k):
+                    raise ValueError(f"k index out of range")
+
+            if data["array"] in [
+                    "CorrIJ",
+                    "CorrIK",
+                    "CorrJI",
+                    "CorrJK",
+                    "CorrKI",
+                    "CorrKJ",
+            ]:
+                if len(data["indices"] != 4):
+                    raise ValueError(f"Need 4 indices for {data['array']}.")
+                if data["indices"][0] not in range(self.n_s):
+                    raise ValueError(f"s index out of range")
+                if data["indices"][1] not in range(self.n_i):
+                    raise ValueError(f"i index out of range")
+                if data["indices"][2] not in range(self.n_j):
+                    raise ValueError(f"j index out of range")
+                if data["indices"][3] not in range(self.n_k):
+                    raise ValueError(f"k index out of range")
+
+            if data["value"] not in [0, 1]:
+                raise ValueError("Given value can only be 0 or 1.")
+
+            (arr, idx) = data["array"], data["indices"]
+            if arr.startswith("Corr"):
+                s, i, j, k = idx
+                if data["value"] == 1:
+                    self.goal.add(self.vars[arr][s][i][j][k])
+                else:
+                    self.goal.add(z3.Not(self.vars[arr][s][i][j][k]))
+            else:
+                i, j, k = idx
+                if data["value"] == 1:
+                    self.goal.add(self.vars[arr][i][j][k])
+                else:
+                    self.goal.add(z3.Not(self.vars[arr][i][j][k]))
+
+    def constraint_forbid_cube(self) -> None:
+        """forbid a list of cubes."""
+        for cube in self.forbidden_cubes:
+            (i, j, k) = cube[0], cube[1], cube[2]
+            self.goal.add(z3.Not(self.vars["NodeY"][i][j][k]))
+            if i > 0:
+                self.goal.add(z3.Not(self.vars["ExistI"][i - 1][j][k]))
+            self.goal.add(z3.Not(self.vars["ExistI"][i][j][k]))
+            if j > 0:
+                self.goal.add(z3.Not(self.vars["ExistJ"][i][j - 1][k]))
+            self.goal.add(z3.Not(self.vars["ExistJ"][i][j][k]))
+            if k > 0:
+                self.goal.add(z3.Not(self.vars["ExistK"][i][j][k - 1]))
+            self.goal.add(z3.Not(self.vars["ExistK"][i][j][k]))
+
+    def constraint_port(self) -> None:
+        """some pipes must exist and some must not depending on the ports."""
+        for port in self.ports:
+            # the pipe specified by the port exists
+            self.goal.add(self.vars[f"Exist{port['d']}"][port["i"]][port["j"]][
+                port["k"]])
+            # if I- or J-pipe exist, set the color value too to the given one
+            if self.color_ij:
+                if port["d"] != "K":
+                    if port["c"] == 1:
+                        self.goal.add(self.vars[f"Color{port['d']}"][port["i"]]
+                                      [port["j"]][port["k"]])
+                    else:
+                        self.goal.add(
+                            z3.Not(self.vars[f"Color{port['d']}"][port["i"]][
+                                port["j"]][port["k"]]))
+
+            # collect the pipes touching the port to forbid them
+            dirs, coords = port_incident_pipes(port, self.n_i, self.n_j,
+                                               self.n_k)
+            for i, coord in enumerate(coords):
+                self.goal.add(
+                    z3.Not(self.vars[f"Exist{dirs[i]}"][coord[0]][coord[1]][
+                        coord[2]]))
+
+    def constraint_connect_outside(self) -> None:
+        """no pipe should cross the spatial bound except for ports."""
+        for i in range(self.n_i):
+            for j in range(self.n_j):
+                # consider K-pipes crossing K-bound and not a port
+                if (i, j, self.n_k) not in self.port_cubes:
+                    self.goal.add(
+                        z3.Not(self.vars["ExistK"][i][j][self.n_k - 1]))
+        for i in range(self.n_i):
+            for k in range(self.n_k):
+                if (i, self.n_j, k) not in self.port_cubes:
+                    self.goal.add(
+                        z3.Not(self.vars["ExistJ"][i][self.n_j - 1][k]))
+        for j in range(self.n_j):
+            for k in range(self.n_k):
+                if (self.n_i, j, k) not in self.port_cubes:
+                    self.goal.add(
+                        z3.Not(self.vars["ExistI"][self.n_i - 1][j][k]))
+
+    def constraint_timelike_y(self) -> None:
+        """forbid all I- and J- pipes to Y cubes."""
+        for i in range(self.n_i):
+            for j in range(self.n_j):
+                for k in range(self.n_k):
+                    if (i, j, k) not in self.port_cubes:
+                        self.goal.add(
+                            z3.Implies(
+                                self.vars["NodeY"][i][j][k],
+                                z3.Not(self.vars["ExistI"][i][j][k]),
+                            ))
+                        self.goal.add(
+                            z3.Implies(
+                                self.vars["NodeY"][i][j][k],
+                                z3.Not(self.vars["ExistJ"][i][j][k]),
+                            ))
+                        if i - 1 >= 0:
+                            self.goal.add(
+                                z3.Implies(
+                                    self.vars["NodeY"][i][j][k],
+                                    z3.Not(self.vars["ExistI"][i - 1][j][k]),
+                                ))
+                        if j - 1 >= 0:
+                            self.goal.add(
+                                z3.Implies(
+                                    self.vars["NodeY"][i][j][k],
+                                    z3.Not(self.vars["ExistJ"][i][j - 1][k]),
+                                ))
+
+    def constraint_ij_color(self) -> None:
+        """color matching for I- and J-pipes."""
+        for i in range(self.n_i):
+            for j in range(self.n_j):
+                for k in range(self.n_k):
+                    if i >= 1 and j >= 1:
+                        # (i-1,j,k)-(i,j,k) and (i,j-1,k)-(i,j,k)
+                        self.goal.add(
+                            z3.Implies(
+                                z3.And(
+                                    self.vars["ExistI"][i - 1][j][k],
+                                    self.vars["ExistJ"][i][j - 1][k],
+                                ),
+                                z3.Or(
+                                    z3.And(
+                                        self.vars["ColorI"][i - 1][j][k],
+                                        z3.Not(self.vars["ColorJ"][i][j -
+                                                                      1][k]),
+                                    ),
+                                    z3.And(
+                                        z3.Not(self.vars["ColorI"][i -
+                                                                   1][j][k]),
+                                        self.vars["ColorJ"][i][j - 1][k],
+                                    ),
+                                ),
+                            ))
+
+                    if i >= 1:
+                        # (i-1,j,k)-(i,j,k) and (i,j,k)-(i,j+1,k)
+                        self.goal.add(
+                            z3.Implies(
+                                z3.And(
+                                    self.vars["ExistI"][i - 1][j][k],
+                                    self.vars["ExistJ"][i][j][k],
+                                ),
+                                z3.Or(
+                                    z3.And(
+                                        self.vars["ColorI"][i - 1][j][k],
+                                        z3.Not(self.vars["ColorJ"][i][j][k]),
+                                    ),
+                                    z3.And(
+                                        z3.Not(self.vars["ColorI"][i -
+                                                                   1][j][k]),
+                                        self.vars["ColorJ"][i][j][k],
+                                    ),
+                                ),
+                            ))
+                        # (i-1,j,k)-(i,j,k) and (i,j,k)-(i+1,j,k)
+                        self.goal.add(
+                            z3.Implies(
+                                z3.And(
+                                    self.vars["ExistI"][i - 1][j][k],
+                                    self.vars["ExistI"][i][j][k],
+                                ),
+                                z3.Or(
+                                    z3.And(
+                                        self.vars["ColorI"][i - 1][j][k],
+                                        self.vars["ColorI"][i][j][k],
+                                    ),
+                                    z3.And(
+                                        z3.Not(self.vars["ColorI"][i -
+                                                                   1][j][k]),
+                                        z3.Not(self.vars["ColorI"][i][j][k]),
+                                    ),
+                                ),
+                            ))
+
+                    if j >= 1:
+                        # (i,j,k)-(i+1,j,k) and (i,j-1,k)-(i,j,k)
+                        self.goal.add(
+                            z3.Implies(
+                                z3.And(
+                                    self.vars["ExistI"][i][j][k],
+                                    self.vars["ExistJ"][i][j - 1][k],
+                                ),
+                                z3.Or(
+                                    z3.And(
+                                        self.vars["ColorI"][i][j][k],
+                                        z3.Not(self.vars["ColorJ"][i][j -
+                                                                      1][k]),
+                                    ),
+                                    z3.And(
+                                        z3.Not(self.vars["ColorI"][i][j][k]),
+                                        self.vars["ColorJ"][i][j - 1][k],
+                                    ),
+                                ),
+                            ))
+                        # (i,j-1,k)-(i,j,k) and (i,j,k)-(i,j+1,k)
+                        self.goal.add(
+                            z3.Implies(
+                                z3.And(
+                                    self.vars["ExistJ"][i][j - 1][k],
+                                    self.vars["ExistJ"][i][j][k],
+                                ),
+                                z3.Or(
+                                    z3.And(
+                                        self.vars["ColorJ"][i][j - 1][k],
+                                        self.vars["ColorJ"][i][j][k],
+                                    ),
+                                    z3.And(
+                                        z3.Not(self.vars["ColorJ"][i][j -
+                                                                      1][k]),
+                                        z3.Not(self.vars["ColorJ"][i][j][k]),
+                                    ),
+                                ),
+                            ))
+
+                    # (i,j,k)-(i+1,j,k) and (i,j,k)-(i,j+1,k)
+                    self.goal.add(
+                        z3.Implies(
+                            z3.And(self.vars["ExistI"][i][j][k],
+                                   self.vars["ExistJ"][i][j][k]),
+                            z3.Or(
+                                z3.And(
+                                    self.vars["ColorI"][i][j][k],
+                                    z3.Not(self.vars["ColorJ"][i][j][k]),
+                                ),
+                                z3.And(
+                                    z3.Not(self.vars["ColorI"][i][j][k]),
+                                    self.vars["ColorJ"][i][j][k],
+                                ),
+                            ),
+                        ))
+
+    def constraint_3d_corner(self) -> None:
+        """at least in one direction, both pipes nonexist."""
+        for i in range(self.n_i):
+            for j in range(self.n_j):
+                for k in range(self.n_k):
+                    i_pipes = [
+                        self.vars["ExistI"][i][j][k],
+                    ]
+                    if i - 1 >= 0:
+                        i_pipes.append(self.vars["ExistI"][i - 1][j][k])
+                    j_pipes = [
+                        self.vars["ExistJ"][i][j][k],
+                    ]
+                    if j - 1 >= 0:
+                        j_pipes.append(self.vars["ExistJ"][i][j - 1][k])
+                    k_pipes = [
+                        self.vars["ExistK"][i][j][k],
+                    ]
+                    if k - 1 >= 0:
+                        k_pipes.append(self.vars["ExistK"][i][j][k - 1])
+
+                    # at least one of the three terms is true. The first term
+                    # is that both I-pipes connecting to (i,j,k) do not exist.
+                    self.goal.add(
+                        z3.Or(
+                            z3.Not(z3.Or(i_pipes)),
+                            z3.Not(z3.Or(j_pipes)),
+                            z3.Not(z3.Or(k_pipes)),
+                        ))
+
+    def constraint_no_deg1(self) -> None:
+        """forbid degree-1 X or Z cubes by considering incident pipes."""
+        for i in range(self.n_i):
+            for j in range(self.n_j):
+                for k in range(self.n_k):
+                    for d in ["I", "J", "K"]:
+                        for e in ["-", "+"]:
+                            cube = {"I": i, "J": j, "K": k}
+                            cube[d] += 1 if e == "+" else 0
+
+                            # construct fake ports to get incident pipes
+                            p0 = {
+                                "i": i,
+                                "j": j,
+                                "k": k,
+                                "d": d,
+                                "e": e,
+                                "c": 0
+                            }
+                            found_p0 = False
+                            for port in self.ports:
+                                if (i == port["i"] and j == port["j"]
+                                        and k == port["k"] and d == port["d"]):
+                                    found_p0 = True
+
+                            # only non-port pipes need to consider
+                            if (not found_p0 and cube["I"] < self.n_i
+                                    and cube["J"] < self.n_j
+                                    and cube["K"] < self.n_k):
+                                # only cubes inside bound need to consider
+                                dirs, coords = port_incident_pipes(
+                                    p0, self.n_i, self.n_j, self.n_k)
+                                pipes = [
+                                    self.vars[f"Exist{dirs[l]}"][coord[0]][
+                                        coord[1]][coord[2]]
+                                    for l, coord in enumerate(coords)
+                                ]
+                                # if the cube is not Y and the pipe exist, then
+                                # at least one of its incident pipes exists.
+                                self.goal.add(
+                                    z3.Implies(
+                                        z3.And(
+                                            z3.Not(
+                                                self.vars["NodeY"][cube["I"]][
+                                                    cube["J"]][cube["K"]]),
+                                            self.vars[f"Exist{d}"][i][j][k],
+                                        ),
+                                        z3.Or(pipes),
+                                    ))
+
+    def constraint_corr_ports(self) -> None:
+        """plug in the correlation surface values at the ports."""
+        for s, stab in enumerate(self.stabs):
+            for p, corrs in enumerate(stab):
+                for k, v in corrs.items():
+                    if v == 1:
+                        self.goal.add(
+                            self.vars[f"Corr{k}"][s][self.ports[p]["i"]][
+                                self.ports[p]["j"]][self.ports[p]["k"]])
+                    else:
+                        self.goal.add(
+                            z3.Not(self.vars[f"Corr{k}"][s][self.ports[p]["i"]]
+                                   [self.ports[p]["j"]][self.ports[p]["k"]]))
+
+    def constraint_corr_y(self) -> None:
+        """correlation surfaces at Y-cubes should both exist or nonexist."""
+        for s in range(self.n_s):
+            for i in range(self.n_i):
+                for j in range(self.n_j):
+                    for k in range(self.n_k):
+                        self.goal.add(
+                            z3.Or(
+                                z3.Not(self.vars["NodeY"][i][j][k]),
+                                z3.Or(
+                                    z3.And(
+                                        self.vars["CorrKI"][s][i][j][k],
+                                        self.vars["CorrKJ"][s][i][j][k],
+                                    ),
+                                    z3.And(
+                                        z3.Not(
+                                            self.vars["CorrKI"][s][i][j][k]),
+                                        z3.Not(
+                                            self.vars["CorrKJ"][s][i][j][k]),
+                                    ),
+                                ),
+                            ))
+                        if k - 1 >= 0:
+                            self.goal.add(
+                                z3.Or(
+                                    z3.Not(self.vars["NodeY"][i][j][k]),
+                                    z3.Or(
+                                        z3.And(
+                                            self.vars["CorrKI"][s][i][j][k -
+                                                                         1],
+                                            self.vars["CorrKJ"][s][i][j][k -
+                                                                         1],
+                                        ),
+                                        z3.And(
+                                            z3.Not(self.vars["CorrKI"][s][i][j]
+                                                   [k - 1]),
+                                            z3.Not(self.vars["CorrKJ"][s][i][j]
+                                                   [k - 1]),
+                                        ),
+                                    ),
+                                ))
+
+    def constraint_corr_perp(self) -> None:
+        """for corr surf perpendicular to normal vector, all or none exists."""
+        for s in range(self.n_s):
+            for i in range(self.n_i):
+                for j in range(self.n_j):
+                    for k in range(self.n_k):
+                        if (i, j, k) not in self.port_cubes:
+                            # only consider X or Z spider
+                            # if normal is K meaning meaning both
+                            # (i,j,k)-(i,j,k+1) and (i,j,k)-(i,j,k-1) are
+                            # out of range, or in range but nonexistent
+                            normal = z3.And(
+                                z3.Not(self.vars["NodeY"][i][j][k]),
+                                z3.Not(self.vars["ExistK"][i][j][k]),
+                            )
+                            if k - 1 >= 0:
+                                normal = z3.And(
+                                    normal,
+                                    z3.Not(self.vars["ExistK"][i][j][k - 1]))
+
+                            # for other pipes, we need to build an intermediate
+                            # expression for (i,j,k)-(i+1,j,k) and
+                            # (i,j,k)-(i,j+1,k), built expression meaning
+                            # the pipe is nonexistent or exist and has
+                            # the correlation surface perpendicular to
+                            # the normal vector in them.
+                            no_pipe_or_with_corr = [
+                                z3.Or(
+                                    z3.Not(self.vars["ExistI"][i][j][k]),
+                                    self.vars["CorrIJ"][s][i][j][k],
+                                ),
+                                z3.Or(
+                                    z3.Not(self.vars["ExistJ"][i][j][k]),
+                                    self.vars["CorrJI"][s][i][j][k],
+                                ),
+                            ]
+
+                            # for (i,j,k)-(i+1,j,k) and (i,j,k)-(i,j+1,k),
+                            # build expression meaning the pipe is nonexistent
+                            # or exist and does not have the correlation
+                            # surface perpendicular to the normal vector.
+                            no_pipe_or_no_corr = [
+                                z3.Or(
+                                    z3.Not(self.vars["ExistI"][i][j][k]),
+                                    z3.Not(self.vars["CorrIJ"][s][i][j][k]),
+                                ),
+                                z3.Or(
+                                    z3.Not(self.vars["ExistJ"][i][j][k]),
+                                    z3.Not(self.vars["CorrJI"][s][i][j][k]),
+                                ),
+                            ]
+
+                            if i - 1 >= 0:
+                                # add (i-1,j,k)-(i,j,k) to the expression
+                                no_pipe_or_with_corr.append(
+                                    z3.Or(
+                                        z3.Not(self.vars["ExistI"][i -
+                                                                   1][j][k]),
+                                        self.vars["CorrIJ"][s][i - 1][j][k],
+                                    ))
+                                no_pipe_or_no_corr.append(
+                                    z3.Or(
+                                        z3.Not(self.vars["ExistI"][i -
+                                                                   1][j][k]),
+                                        z3.Not(
+                                            self.vars["CorrIJ"][s][i -
+                                                                   1][j][k]),
+                                    ))
+
+                            if j - 1 >= 0:
+                                # add (i,j-1,k)-(i,j,k) to the expression
+                                no_pipe_or_with_corr.append(
+                                    z3.Or(
+                                        z3.Not(self.vars["ExistJ"][i][j -
+                                                                      1][k]),
+                                        self.vars["CorrJI"][s][i][j - 1][k],
+                                    ))
+                                no_pipe_or_no_corr.append(
+                                    z3.Or(
+                                        z3.Not(self.vars["ExistJ"][i][j -
+                                                                      1][k]),
+                                        z3.Not(
+                                            self.vars["CorrJI"][s][i][j -
+                                                                      1][k]),
+                                    ))
+
+                            # if normal vector is K, then in all its
+                            # incident pipes that exist all correlation surface
+                            # in I-J plane exist or nonexist
+                            self.goal.add(
+                                z3.Implies(
+                                    normal,
+                                    z3.Or(
+                                        z3.And(no_pipe_or_with_corr),
+                                        z3.And(no_pipe_or_no_corr),
+                                    ),
+                                ))
+
+                            # if normal is I
+                            normal = z3.And(
+                                z3.Not(self.vars["NodeY"][i][j][k]),
+                                z3.Not(self.vars["ExistI"][i][j][k]),
+                            )
+                            if i - 1 >= 0:
+                                normal = z3.And(
+                                    normal,
+                                    z3.Not(self.vars["ExistI"][i - 1][j][k]))
+                            no_pipe_or_with_corr = [
+                                z3.Or(
+                                    z3.Not(self.vars["ExistJ"][i][j][k]),
+                                    self.vars["CorrJK"][s][i][j][k],
+                                ),
+                                z3.Or(
+                                    z3.Not(self.vars["ExistK"][i][j][k]),
+                                    self.vars["CorrKJ"][s][i][j][k],
+                                ),
+                            ]
+                            no_pipe_or_no_corr = [
+                                z3.Or(
+                                    z3.Not(self.vars["ExistJ"][i][j][k]),
+                                    z3.Not(self.vars["CorrJK"][s][i][j][k]),
+                                ),
+                                z3.Or(
+                                    z3.Not(self.vars["ExistK"][i][j][k]),
+                                    z3.Not(self.vars["CorrKJ"][s][i][j][k]),
+                                ),
+                            ]
+                            if j - 1 >= 0:
+                                no_pipe_or_with_corr.append(
+                                    z3.Or(
+                                        z3.Not(self.vars["ExistJ"][i][j -
+                                                                      1][k]),
+                                        self.vars["CorrJK"][s][i][j - 1][k],
+                                    ))
+                                no_pipe_or_no_corr.append(
+                                    z3.Or(
+                                        z3.Not(self.vars["ExistJ"][i][j -
+                                                                      1][k]),
+                                        z3.Not(
+                                            self.vars["CorrJK"][s][i][j -
+                                                                      1][k]),
+                                    ))
+                            if k - 1 >= 0:
+                                no_pipe_or_with_corr.append(
+                                    z3.Or(
+                                        z3.Not(self.vars["ExistK"][i][j][k -
+                                                                         1]),
+                                        self.vars["CorrKJ"][s][i][j][k - 1],
+                                    ))
+                                no_pipe_or_no_corr.append(
+                                    z3.Or(
+                                        z3.Not(self.vars["ExistK"][i][j][k -
+                                                                         1]),
+                                        z3.Not(
+                                            self.vars["CorrKJ"][s][i][j][k -
+                                                                         1]),
+                                    ))
+                            self.goal.add(
+                                z3.Implies(
+                                    normal,
+                                    z3.Or(
+                                        z3.And(no_pipe_or_with_corr),
+                                        z3.And(no_pipe_or_no_corr),
+                                    ),
+                                ))
+
+                            # if normal is J
+                            normal = z3.And(
+                                z3.Not(self.vars["NodeY"][i][j][k]),
+                                z3.Not(self.vars["ExistJ"][i][j][k]),
+                            )
+                            if j - 1 >= 0:
+                                normal = z3.And(
+                                    normal,
+                                    z3.Not(self.vars["ExistJ"][i][j - 1][k]))
+                            no_pipe_or_with_corr = [
+                                z3.Or(
+                                    z3.Not(self.vars["ExistI"][i][j][k]),
+                                    self.vars["CorrIK"][s][i][j][k],
+                                ),
+                                z3.Or(
+                                    z3.Not(self.vars["ExistK"][i][j][k]),
+                                    self.vars["CorrKI"][s][i][j][k],
+                                ),
+                            ]
+                            no_pipe_or_no_corr = [
+                                z3.Or(
+                                    z3.Not(self.vars["ExistI"][i][j][k]),
+                                    z3.Not(self.vars["CorrIK"][s][i][j][k]),
+                                ),
+                                z3.Or(
+                                    z3.Not(self.vars["ExistK"][i][j][k]),
+                                    z3.Not(self.vars["CorrKI"][s][i][j][k]),
+                                ),
+                            ]
+                            if i - 1 >= 0:
+                                no_pipe_or_with_corr.append(
+                                    z3.Or(
+                                        z3.Not(self.vars["ExistI"][i -
+                                                                   1][j][k]),
+                                        self.vars["CorrIK"][s][i - 1][j][k],
+                                    ))
+                                no_pipe_or_no_corr.append(
+                                    z3.Or(
+                                        z3.Not(self.vars["ExistI"][i -
+                                                                   1][j][k]),
+                                        z3.Not(
+                                            self.vars["CorrIK"][s][i -
+                                                                   1][j][k]),
+                                    ))
+                            if k - 1 >= 0:
+                                no_pipe_or_with_corr.append(
+                                    z3.Or(
+                                        z3.Not(self.vars["ExistK"][i][j][k -
+                                                                         1]),
+                                        self.vars["CorrKI"][s][i][j][k - 1],
+                                    ))
+                                no_pipe_or_no_corr.append(
+                                    z3.Or(
+                                        z3.Not(self.vars["ExistK"][i][j][k -
+                                                                         1]),
+                                        z3.Not(
+                                            self.vars["CorrKI"][s][i][j][k -
+                                                                         1]),
+                                    ))
+                            self.goal.add(
+                                z3.Implies(
+                                    normal,
+                                    z3.Or(
+                                        z3.And(no_pipe_or_with_corr),
+                                        z3.And(no_pipe_or_no_corr),
+                                    ),
+                                ))
+
+    def constraint_corr_para(self) -> None:
+        """for corr surf parallel to the normal , even number of them exist."""
+        for s in range(self.n_s):
+            for i in range(self.n_i):
+                for j in range(self.n_j):
+                    for k in range(self.n_k):
+                        if (i, j, k) not in self.port_cubes:
+                            # only X or Z spiders, if normal is K
+                            normal = z3.And(
+                                z3.Not(self.vars["NodeY"][i][j][k]),
+                                z3.Not(self.vars["ExistK"][i][j][k]),
+                            )
+                            if k - 1 >= 0:
+                                normal = z3.And(
+                                    normal,
+                                    z3.Not(self.vars["ExistK"][i][j][k - 1]))
+
+                            # unlike in constraint_corr_perp, we only care
+                            # about the cases where the pipe exists and the
+                            # correlation surface parallel to K also is present
+                            # so we build intermediate expressions as below
+                            pipe_with_corr = [
+                                z3.And(
+                                    self.vars["ExistI"][i][j][k],
+                                    self.vars["CorrIK"][s][i][j][k],
+                                ),
+                                z3.And(
+                                    self.vars["ExistJ"][i][j][k],
+                                    self.vars["CorrJK"][s][i][j][k],
+                                ),
+                            ]
+
+                            # add (i-1,j,k)-(i,j,k) to the expression
+                            if i - 1 >= 0:
+                                pipe_with_corr.append(
+                                    z3.And(
+                                        self.vars["ExistI"][i - 1][j][k],
+                                        self.vars["CorrIK"][s][i - 1][j][k],
+                                    ))
+
+                            # add (i,j-1,k)-(i,j,k) to the expression
+                            if j - 1 >= 0:
+                                pipe_with_corr.append(
+                                    z3.And(
+                                        self.vars["ExistJ"][i][j - 1][k],
+                                        self.vars["CorrJK"][s][i][j - 1][k],
+                                    ))
+
+                            # parity of the expressions must be even
+                            self.goal.add(
+                                z3.Implies(
+                                    normal,
+                                    cnf_even_parity_upto4(pipe_with_corr)))
+
+                            # if normal is I
+                            normal = z3.And(
+                                z3.Not(self.vars["NodeY"][i][j][k]),
+                                z3.Not(self.vars["ExistI"][i][j][k]),
+                            )
+                            if i - 1 >= 0:
+                                normal = z3.And(
+                                    normal,
+                                    z3.Not(self.vars["ExistI"][i - 1][j][k]))
+                            pipe_with_corr = [
+                                z3.And(
+                                    self.vars["ExistJ"][i][j][k],
+                                    self.vars["CorrJI"][s][i][j][k],
+                                ),
+                                z3.And(
+                                    self.vars["ExistK"][i][j][k],
+                                    self.vars["CorrKI"][s][i][j][k],
+                                ),
+                            ]
+                            if j - 1 >= 0:
+                                pipe_with_corr.append(
+                                    z3.And(
+                                        self.vars["ExistJ"][i][j - 1][k],
+                                        self.vars["CorrJI"][s][i][j - 1][k],
+                                    ))
+                            if k - 1 >= 0:
+                                pipe_with_corr.append(
+                                    z3.And(
+                                        self.vars["ExistK"][i][j][k - 1],
+                                        self.vars["CorrKI"][s][i][j][k - 1],
+                                    ))
+                            self.goal.add(
+                                z3.Implies(
+                                    normal,
+                                    cnf_even_parity_upto4(pipe_with_corr)))
+
+                            # if normal is J
+                            normal = z3.And(
+                                z3.Not(self.vars["NodeY"][i][j][k]),
+                                z3.Not(self.vars["ExistJ"][i][j][k]),
+                            )
+                            if j - 1 >= 0:
+                                normal = z3.And(
+                                    normal,
+                                    z3.Not(self.vars["ExistJ"][i][j - 1][k]))
+                            pipe_with_corr = [
+                                z3.And(
+                                    self.vars["ExistI"][i][j][k],
+                                    self.vars["CorrIJ"][s][i][j][k],
+                                ),
+                                z3.And(
+                                    self.vars["ExistK"][i][j][k],
+                                    self.vars["CorrKJ"][s][i][j][k],
+                                ),
+                            ]
+                            if i - 1 >= 0:
+                                pipe_with_corr.append(
+                                    z3.And(
+                                        self.vars["ExistI"][i - 1][j][k],
+                                        self.vars["CorrIJ"][s][i - 1][j][k],
+                                    ))
+                            if k - 1 >= 0:
+                                pipe_with_corr.append(
+                                    z3.And(
+                                        self.vars["ExistK"][i][j][k - 1],
+                                        self.vars["CorrKJ"][s][i][j][k - 1],
+                                    ))
+                            self.goal.add(
+                                z3.Implies(
+                                    normal,
+                                    cnf_even_parity_upto4(pipe_with_corr)))
+
+    def check_z3(self, print_progress: bool = True) -> bool:
+        """check whether the built goal in self.goal is satisfiable.
+
+        Args:
+            print_progress (bool, optional): if print out the progress made.
+
+        Returns:
+            bool: True if SAT, False if UNSAT
+        """
+        if print_progress:
+            print("Construct a Z3 SMT model and solve...")
+        start_time = time.time()
+        self.solver = z3.Solver()
+        self.solver.add(self.goal)
+        ifsat = self.solver.check()
+        elapsed = time.time() - start_time
+        if print_progress:
+            print("elapsed time: {:2f}s".format(elapsed))
+        if ifsat == z3.sat:
+            if print_progress:
+                print("Z3 SAT")
+            return True
+        else:
+            if print_progress:
+                print("Z3 UNSAT")
+            return False
+
+    def check_kissat(
+        self,
+        kissat_dir: str,
+        dimacs_file_name: Optional[str] = None,
+        sat_log_file_name: Optional[str] = None,
+        print_progress: bool = True,
+    ) -> bool:
+        """check whether there is a solution with Kissat
+
+        Args:
+            kissat_dir (str): directory containing an executable named kissat
+            dimacs_file_name (str, optional): Defaults to None. Then, the
+                dimacs file is in a tmp directory. If specified, the dimacs
+                will be saved to that path.
+            sat_log_file_name (str, optional): Defaults to None. Then, the
+                sat log file is in a tmp directory. If specified, the sat log
+                will be saved to that path.
+            print_progress (bool, optional): whether print the SAT solving
+                process on screen. Defaults to True.
+
+        Raises:
+            ValueError: kissat_dir is not a directory
+            ValueError: there is no executable kissat in kissat_dir
+            ValueError: the return code to kissat is neither SAT nor UNSAT
+
+        Returns:
+            bool: True if SAT, False if UNSAT
+        """
+        if not os.path.isdir(kissat_dir):
+            raise ValueError(f"{kissat_dir} is not a directory.")
+        if kissat_dir.endswith("/"):
+            solver_cmd = kissat_dir + "kissat"
+        else:
+            solver_cmd = kissat_dir + "/kissat"
+        if not os.path.isfile(solver_cmd):
+            raise ValueError(f"There is no kissat in {kissat_dir}.")
+
+        if_solved = False
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            # open a tmp directory as workspace
+
+            # dimacs and sat log are either in the tmp dir, or as user specify
+            tmp_dimacs_file_name = (dimacs_file_name + ".dimacs" if dimacs_file_name else
+                                    tmp_dir + "/tmp.dimacs")
+            tmp_sat_log_file_name = (sat_log_file_name + ".kissat" if sat_log_file_name
+                                     else tmp_dir + "/tmp.sat")
+
+            if self.write_cnf(tmp_dimacs_file_name,
+                              print_progress=print_progress):
+                # continue if the CNF is non-trivial, i.e., write_cnf is True
+                kissat_start_time = time.time()
+
+                with open(tmp_sat_log_file_name, "w") as log:
+                    # use tmp_sat_log_file_name to record stdout of kissat
+
+                    kissat_return_code = -100
+                    # -100 if the return code is not updated later on.
+
+                    import random
+                    with subprocess.Popen(
+                        [
+                            solver_cmd, f'--seed={random.randrange(1000000)}',
+                            tmp_dimacs_file_name
+                        ],
+                            stdout=subprocess.PIPE,
+                            bufsize=1,
+                            universal_newlines=True,
+                    ) as run_kissat:
+                        for line in run_kissat.stdout:
+                            log.write(line)
+                            if print_progress:
+                                sys.stdout.write(line)
+                        get_return_code = run_kissat.communicate()[0]
+                        kissat_return_code = run_kissat.returncode
+
+                if kissat_return_code == 10:
+                    # 10 means SAT in Kissat
+                    if_solved = True
+                    if print_progress:
+                        print(
+                            f"kissat runtime: {time.time()-kissat_start_time}")
+                        print("kissat SAT!")
+
+                    # we read the Kissat solution from the SAT log, then, plug
+                    # those into the Z3 model and solved inside Z3 again.
+                    # The reason is that Z3 did some simplification of the
+                    # problem so not every variable appear in the DIMACS given
+                    # to Kissat. We still need to know their value.
+                    result = self.read_kissat_result(
+                        tmp_dimacs_file_name,
+                        tmp_sat_log_file_name,
+                    )
+                    self.plugin_arrs(result)
+                    self.check_z3(print_progress)
+
+                elif kissat_return_code == 20:
+                    if print_progress:
+                        print(f"{solver_cmd} UNSAT")
+
+                elif kissat_return_code == -100:
+                    print("Did not get Kissat return code.")
+
+                else:
+                    raise ValueError(
+                        f"Kissat return code {kissat_return_code} is neither"
+                        " SAT nor UNSAT. Maybe you should add print_process="
+                        "True to enable the Kissat printout message to see "
+                        "what is going on.")
+        # closing the tmp directory, the files and itself are removed
+
+        return if_solved
+
+    def write_cnf(self,
+                  output_file_name: str,
+                  print_progress: bool = False) -> bool:
+        """generate CNF for the problem.
+
+        Args:
+            output_file_name (str): file to write CNF.
+
+        Returns:
+            bool: False if the CNF is trivial, True otherwise
+        """
+        cnf_start_time = time.time()
+        simplified = z3.Tactic("simplify")(self.goal)[0]
+        simplified = z3.Tactic("propagate-values")(simplified)[0]
+        cnf = z3.Tactic("tseitin-cnf")(simplified)[0]
+        dimacs = cnf.dimacs()
+        if print_progress:
+            print(f"CNF generation time: {time.time() - cnf_start_time}")
+
+        with open(output_file_name, "w") as output_f:
+            output_f.write(cnf.dimacs())
+        if dimacs.startswith("p cnf 1 1"):
+            print("Generated CNF is trivial meaning z3 concludes the instance"
+                  " UNSAT during simplification.")
+            return False
+        else:
+            return True
+
+    def read_kissat_result(self, dimacs_file: str,
+                           result_file: str) -> Mapping[str, Any]:
+        """read result from external SAT solver
+
+        Args:
+            dimacs_file (str):
+            result_file (str): log from Kissat containing SAT assignments
+
+        Raises:
+            ValueError: in the dimacs file, the last lines are comments that
+                records the mapping from SAT variable indices to the variable
+                names in Z3. If the coordinates in this name is incorrect.
+
+        Returns:
+            Mapping[str, Any]: variable assignment in arrays. All the one with
+                a corresponding SAT variable are read off from the SAT log.
+                The others are left with -1.
+        """
+        results = {
+            "ExistI": [[[-1 for _ in range(self.n_k)] for _ in range(self.n_j)]
+                       for _ in range(self.n_i)],
+            "ExistJ": [[[-1 for _ in range(self.n_k)] for _ in range(self.n_j)]
+                       for _ in range(self.n_i)],
+            "ExistK": [[[-1 for _ in range(self.n_k)] for _ in range(self.n_j)]
+                       for _ in range(self.n_i)],
+            "ColorI": [[[-1 for _ in range(self.n_k)] for _ in range(self.n_j)]
+                       for _ in range(self.n_i)],
+            "ColorJ": [[[-1 for _ in range(self.n_k)] for _ in range(self.n_j)]
+                       for _ in range(self.n_i)],
+            "NodeY": [[[-1 for _ in range(self.n_k)] for _ in range(self.n_j)]
+                      for _ in range(self.n_i)],
+            "CorrIJ": [[[[-1 for _ in range(self.n_k)]
+                         for _ in range(self.n_j)] for _ in range(self.n_i)]
+                       for _ in range(self.n_s)],
+            "CorrIK": [[[[-1 for _ in range(self.n_k)]
+                         for _ in range(self.n_j)] for _ in range(self.n_i)]
+                       for _ in range(self.n_s)],
+            "CorrJK": [[[[-1 for _ in range(self.n_k)]
+                         for _ in range(self.n_j)] for _ in range(self.n_i)]
+                       for _ in range(self.n_s)],
+            "CorrJI": [[[[-1 for _ in range(self.n_k)]
+                         for _ in range(self.n_j)] for _ in range(self.n_i)]
+                       for _ in range(self.n_s)],
+            "CorrKI": [[[[-1 for _ in range(self.n_k)]
+                         for _ in range(self.n_j)] for _ in range(self.n_i)]
+                       for _ in range(self.n_s)],
+            "CorrKJ": [[[[-1 for _ in range(self.n_k)]
+                         for _ in range(self.n_j)] for _ in range(self.n_i)]
+                       for _ in range(self.n_s)],
+        }
+
+        # in this file, the assigments are lines starting with "v" like
+        # v -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ...
+        # the vars starts from 1 and - means it's False; otherwise, it's True
+        # we scan through all these lines, and save the assignments to `sat`
+        with open(result_file, "r") as f:
+            sat_output = f.readlines()
+        sat = {}
+        for line in sat_output:
+            if line.startswith("v"):
+                assignments = line[1:].strip().split(" ")
+                for assignment in assignments:
+                    tmp = int(assignment)
+                    if tmp < 0:
+                        sat[str(-tmp)] = 0
+                    elif tmp > 0:
+                        sat[str(tmp)] = 1
+
+        # in the dimacs generated by Z3, there are lines starting with "c" like
+        # c 8804 CorrIJ(1,0,3,4)    or    c 60053 k!44404
+        # which records the mapping from our variables to variables in dimacs
+        # the ones starting with k! are added in the translation, we don"t care
+        with open(dimacs_file, "r") as f:
+            dimacs = f.readlines()
+        for line in dimacs:
+            if line.startswith("c"):
+                _, index, name = line.strip().split(" ")
+                if name.startswith((
+                        "NodeY",
+                        "ExistI",
+                        "ExistJ",
+                        "ExistK",
+                        "ColorI",
+                        "ColorJ",
+                        "CorrIJ",
+                        "CorrIK",
+                        "CorrJI",
+                        "CorrJK",
+                        "CorrKI",
+                        "CorrKJ",
+                )):
+                    arr, tmp = name[:-1].split("(")
+                    coords = [int(num) for num in tmp.split(",")]
+                    if len(coords) == 3:
+                        results[arr][coords[0]][coords[1]][
+                            coords[2]] = sat[index]
+                    elif len(coords) == 4:
+                        results[arr][coords[0]][coords[1]][coords[2]][
+                            coords[3]] = sat[index]
+                    else:
+                        raise ValueError("number of coord should be 3 or 4!")
+
+        return results
+
+    def get_result(self) -> Mapping[str, Any]:
+        """get the variable values.
+
+            Returns:
+                Mapping[str, Any]: output in the LaSRe format
+        """
+        model = self.solver.model()
+        data = {
+            "n_i":
+            self.n_i,
+            "n_j":
+            self.n_j,
+            "n_k":
+            self.n_k,
+            "n_p":
+            self.n_p,
+            "n_s":
+            self.n_s,
+            "ports":
+            self.ports,
+            "stabs":
+            self.stabs,
+            "port_cubes":
+            self.port_cubes,
+            "optional":
+            self.optional,
+            "ExistI": [[[-1 for _ in range(self.n_k)] for _ in range(self.n_j)]
+                       for _ in range(self.n_i)],
+            "ExistJ": [[[-1 for _ in range(self.n_k)] for _ in range(self.n_j)]
+                       for _ in range(self.n_i)],
+            "ExistK": [[[-1 for _ in range(self.n_k)] for _ in range(self.n_j)]
+                       for _ in range(self.n_i)],
+            "ColorI": [[[-1 for _ in range(self.n_k)] for _ in range(self.n_j)]
+                       for _ in range(self.n_i)],
+            "ColorJ": [[[-1 for _ in range(self.n_k)] for _ in range(self.n_j)]
+                       for _ in range(self.n_i)],
+            "NodeY": [[[-1 for _ in range(self.n_k)] for _ in range(self.n_j)]
+                      for _ in range(self.n_i)],
+            "CorrIJ": [[[[-1 for _ in range(self.n_k)]
+                         for _ in range(self.n_j)] for _ in range(self.n_i)]
+                       for _ in range(self.n_s)],
+            "CorrIK": [[[[-1 for _ in range(self.n_k)]
+                         for _ in range(self.n_j)] for _ in range(self.n_i)]
+                       for _ in range(self.n_s)],
+            "CorrJK": [[[[-1 for _ in range(self.n_k)]
+                         for _ in range(self.n_j)] for _ in range(self.n_i)]
+                       for _ in range(self.n_s)],
+            "CorrJI": [[[[-1 for _ in range(self.n_k)]
+                         for _ in range(self.n_j)] for _ in range(self.n_i)]
+                       for _ in range(self.n_s)],
+            "CorrKI": [[[[-1 for _ in range(self.n_k)]
+                         for _ in range(self.n_j)] for _ in range(self.n_i)]
+                       for _ in range(self.n_s)],
+            "CorrKJ": [[[[-1 for _ in range(self.n_k)]
+                         for _ in range(self.n_j)] for _ in range(self.n_i)]
+                       for _ in range(self.n_s)],
+        }
+        arrs = [
+            "NodeY",
+            "ExistI",
+            "ExistJ",
+            "ExistK",
+        ]
+        if self.color_ij:
+            arrs += [
+                "ColorI",
+                "ColorJ",
+            ]
+        for s in range(self.n_s):
+            for i in range(self.n_i):
+                for j in range(self.n_j):
+                    for k in range(self.n_k):
+                        if s == 0:  # Exist, Node, and Color vars
+                            for arr in arrs:
+                                data[arr][i][j][k] = (
+                                    1 if model[self.vars[arr][i][j][k]] else 0)
+
+                        # Corr vars
+                        for arr in [
+                                "CorrIJ",
+                                "CorrIK",
+                                "CorrJI",
+                                "CorrJK",
+                                "CorrKI",
+                                "CorrKJ",
+                        ]:
+                            data[arr][s][i][j][k] = (
+                                1 if model[self.vars[arr][s][i][j][k]] else 0)
+        return data
diff --git a/glue/lattice_surgery/lassynth/tools/__init__.py b/glue/lattice_surgery/lassynth/tools/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/glue/lattice_surgery/lassynth/tools/verify_stabilizers.py b/glue/lattice_surgery/lassynth/tools/verify_stabilizers.py
new file mode 100644
index 00000000..25fba0b8
--- /dev/null
+++ b/glue/lattice_surgery/lassynth/tools/verify_stabilizers.py
@@ -0,0 +1,38 @@
+from typing import Sequence
+import stim
+import stimzx
+
+
+def verify_stabilizers(
+    specified_paulistrings: Sequence[str],
+    result_networkx,
+    print_stabilizers: bool = False,
+) -> bool:
+    result_stabilizers = [
+        stab.output
+        for stab in stimzx.zx_graph_to_external_stabilizers(result_networkx)
+    ]
+    specified_stabilizers = [
+        stim.PauliString(paulistring) for paulistring in specified_paulistrings
+    ]
+    if print_stabilizers:
+        print("specified:")
+        for s in specified_stabilizers:
+            print(s)
+        print("==============================================================")
+        print("resulting:")
+        for s in result_stabilizers:
+            print(s)
+        print("==============================================================")
+
+    for s in result_stabilizers:
+        for ss in specified_stabilizers:
+            if not ss.commutes(s):
+                print(f"result stabilizer {s} not commuting with "
+                      f"specified stabilizer {ss}")
+                if print_stabilizers:
+                    print("specified and resulting stabilizers not equivalent")
+                return False
+    if print_stabilizers:
+        print("specified and resulting stabilizers are equivalent.")
+    return True
diff --git a/glue/lattice_surgery/lassynth/translators/__init__.py b/glue/lattice_surgery/lassynth/translators/__init__.py
new file mode 100644
index 00000000..e93e0abc
--- /dev/null
+++ b/glue/lattice_surgery/lassynth/translators/__init__.py
@@ -0,0 +1 @@
+from lassynth.translators.zx_grid_graph import ZXGridGraph
diff --git a/glue/lattice_surgery/lassynth/translators/gltf_generator.py b/glue/lattice_surgery/lassynth/translators/gltf_generator.py
new file mode 100644
index 00000000..20ce69be
--- /dev/null
+++ b/glue/lattice_surgery/lassynth/translators/gltf_generator.py
@@ -0,0 +1,2622 @@
+"""generating a 3D modelling file in gltf format from our LaSRe."""
+
+import json
+from typing import Any, Mapping, Optional, Sequence, Tuple
+
+# constants
+SQ2 = 0.707106769085  # square root of 2
+THICKNESS = 0.01  # half separation of front and back sides of each face
+AXESTHICKNESS = 0.1
+
+def float_to_little_endian_hex(f):
+    from struct import pack
+
+    # Pack the float into a binary string using the little-endian format
+    binary_data = pack("<f", f)
+
+    # Convert the binary string to a hexadecimal string
+    hex_string = binary_data.hex()
+
+    return hex_string
+
+
+def hex_to_bin(s):
+    from codecs import encode, decode
+
+    s = encode(decode(s, "hex"), "base64").decode()
+    s = str(s).replace("\n", "")
+    return "data:application/octet-stream;base64," + s
+
+
+def base_gen(
+    tubelen: float = 2.0,
+) -> Mapping[str, Any]:
+    """generate basic gltf contents, i.e., independent from the LaS
+
+    Args:
+        tubelen (float, optional): ratio of the length of the pipe with respect
+            to the length of a cube. Defaults to 2.0.
+
+    Returns:
+        Mapping[str, Any]: gltf with everything here.
+    """
+    # floats as hex, little endian
+    floats = {
+        "0": "00000000",
+        "1": "0000803F",
+        "-1": "000080BF",
+        "0.5": "0000003F",
+        "0.45": "6666E63E",
+    }
+    floats["tube"] = float_to_little_endian_hex(tubelen)
+    floats["+SQ2"] = float_to_little_endian_hex(SQ2)
+    floats["-SQ2"] = float_to_little_endian_hex(-SQ2)
+    floats["+T"] = float_to_little_endian_hex(THICKNESS)
+    floats["-T"] = float_to_little_endian_hex(-THICKNESS)
+    floats["1-T"] = float_to_little_endian_hex(1 - THICKNESS)
+    floats["T-1"] = float_to_little_endian_hex(THICKNESS - 1)
+    floats["0.5+T"] = float_to_little_endian_hex(0.5 + THICKNESS)
+    floats["0.5-T"] = float_to_little_endian_hex(0.5 - THICKNESS)
+
+    # integers as hex
+    ints = ["0000", "0100", "0200", "0300", "0400", "0500", "0600", "0700"]
+
+    gltf = {
+        "asset": {
+            "generator": "LaSRe CodeGen by Daniel Bochen Tan",
+            "version": "2.0"
+        },
+        "scene": 0,
+        "scenes": [{
+            "name": "Scene",
+            "nodes": [0]
+        }],
+        "nodes": [{
+            "name": "Lattice Surgery Subroutine",
+            "children": []
+        }],
+    }
+    gltf["accessors"] = []
+    gltf["buffers"] = []
+    gltf["bufferViews"] = []
+
+    # materials are the colors. baseColorFactor is (R, G, B, alpha)
+    gltf["materials"] = [
+        {
+            "name": "0-blue",
+            "pbrMetallicRoughness": {
+                "baseColorFactor": [0, 0, 1, 1]
+            },
+            "doubleSided": False,
+        },
+        {
+            "name": "1-red",
+            "pbrMetallicRoughness": {
+                "baseColorFactor": [1, 0, 0, 1]
+            },
+            "doubleSided": False,
+        },
+        {
+            "name": "2-green",
+            "pbrMetallicRoughness": {
+                "baseColorFactor": [0, 1, 0, 1]
+            },
+            "doubleSided": False,
+        },
+        {
+            "name": "3-gray",
+            "pbrMetallicRoughness": {
+                "baseColorFactor": [0.5, 0.5, 0.5, 1]
+            },
+            "doubleSided": False,
+        },
+        {
+            "name": "4-cyan.3",
+            "pbrMetallicRoughness": {
+                "baseColorFactor": [0, 1, 1, 0.3]
+            },
+            "doubleSided": False,
+            "alphaMode": "BLEND",
+        },
+        {
+            "name": "5-black",
+            "pbrMetallicRoughness": {
+                "baseColorFactor": [0, 0, 0, 1]
+            },
+            "doubleSided": False,
+        },
+        {
+            "name": "6-yellow",
+            "pbrMetallicRoughness": {
+                "baseColorFactor": [1, 1, 0, 1]
+            },
+            "doubleSided": False,
+        },
+        {
+            "name": "7-white",
+            "pbrMetallicRoughness": {
+                "baseColorFactor": [1, 1, 1, 1]
+            },
+            "doubleSided": False,
+        },
+    ]
+
+    # for a 3D coordinate (X,Y,Z), the convention of VEC3 in GLTF is (X,Z,-Y)
+    # below are the data we store into the embedded binary in the GLTF.
+    # For each data, we create a buffer, there is one and only one bufferView
+    # for this buffer, and there is one and only one accessor for this
+    # bufferView. This is for simplicity. So in what follows, we always gather
+    # the string corresponding to the data, whether they're a list of floats or
+    # a list of integers. Then, we append a buffer, a bufferView, and an
+    # accessor to the GLTF. This part is quite machinary.
+
+    # GLTF itself support doubleside color in materials, but this can lead to
+    # problems when converting to other formats. So, for each face of a cube or
+    # a pipe, we will make it two sides, front and back. The POSITION of
+    # vertices of these two are shifted on the Z axis by 2*THICKNESS. Since we
+    # need their color to both facing outside, the back side require opposite
+    # NORMAL vectors, and the index order needs to be reversed. We begin with
+    # definition for the front sides.
+
+    # 0, positions of square: [(+T,+T,-T),(1-T,+T,-T),(+T,1-T,-T),(1-T,1-T,-T)]
+    s = (floats["+T"] + floats["-T"] + floats["-T"] + floats["1-T"] +
+         floats["-T"] + floats["-T"] + floats["+T"] + floats["-T"] +
+         floats["T-1"] + floats["1-T"] + floats["-T"] + floats["T-1"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 0,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 0,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4,
+        "max": [1 - THICKNESS, -THICKNESS, -THICKNESS],
+        "min": [THICKNESS, -THICKNESS, THICKNESS - 1],
+    })
+
+    # 1, positions of rectangle: [(0,0,-T),(L,0,-T),(0,1,-T),(L,1,-T)]
+    s = (floats["0"] + floats["-T"] + floats["0"] + floats["tube"] +
+         floats["-T"] + floats["0"] + floats["0"] + floats["-T"] +
+         floats["-1"] + floats["tube"] + floats["-T"] + floats["-1"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 1,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 1,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4,
+        "max": [tubelen, -THICKNESS, 0],
+        "min": [0, -THICKNESS, -1],
+    })
+
+    # 2, normals of rect/sqr: (0,0,-1)*4
+    s = (floats["0"] + floats["-1"] + floats["0"] + floats["0"] +
+         floats["-1"] + floats["0"] + floats["0"] + floats["-1"] +
+         floats["0"] + floats["0"] + floats["-1"] + floats["0"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 2,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 2,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4
+    })
+
+    # 3, vertices of rect/sqr: [1,0,3, 3,0,2]
+    s = ints[1] + ints[0] + ints[3] + ints[3] + ints[0] + ints[2]
+    gltf["buffers"].append({"byteLength": 12, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 3,
+        "byteLength": 12,
+        "byteOffset": 0,
+        "target": 34963
+    })
+    gltf["accessors"].append({
+        "bufferView": 3,
+        "componentType": 5123,
+        "type": "SCALAR",
+        "count": 6
+    })
+
+    # 4, positions of tilted rect: [(0,0,1/2+T),(1/2,0,+T),(0,1,1/2+T),(1/2,1,+T)]
+    s = (floats["0"] + floats["0.5+T"] + floats["0"] + floats["0.5"] +
+         floats["+T"] + floats["0"] + floats["0"] + floats["0.5+T"] +
+         floats["-1"] + floats["0.5"] + floats["+T"] + floats["-1"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 4,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 4,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4,
+        "max": [0.5, 0.5 + THICKNESS, 0],
+        "min": [0, THICKNESS, -1],
+    })
+
+    # 5, normals of tilted rect: (-sqrt(2)/2, 0, -sqrt(2)/2)*4
+    s = (floats["-SQ2"] + floats["-SQ2"] + floats["0"] + floats["-SQ2"] +
+         floats["-SQ2"] + floats["0"] + floats["-SQ2"] + floats["-SQ2"] +
+         floats["0"] + floats["-SQ2"] + floats["-SQ2"] + floats["0"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 5,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 5,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4
+    })
+
+    # 6, positions of Hadamard rectangle: [(0,0,-T),(15/32L,0,-T),(15/32L,1,-T),
+    # (15/32L,1,-T),(17/32L,0,-T),(17/32L,1,-T),(L,0,-T),(L,1,-T)]
+    floats["left"] = float_to_little_endian_hex(tubelen * 15 / 32)
+    floats["right"] = float_to_little_endian_hex(tubelen * 17 / 32)
+    s = (floats["0"] + floats["-T"] + floats["0"] + floats["left"] +
+         floats["-T"] + floats["0"] + floats["0"] + floats["-T"] +
+         floats["-1"] + floats["left"] + floats["-T"] + floats["-1"] +
+         floats["right"] + floats["-T"] + floats["0"] + floats["right"] +
+         floats["-T"] + floats["-1"] + floats["tube"] + floats["-T"] +
+         floats["0"] + floats["tube"] + floats["-T"] + floats["-1"])
+    gltf["buffers"].append({"byteLength": 96, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 6,
+        "byteLength": 96,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 6,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 8,
+        "max": [tubelen, -THICKNESS, 0],
+        "min": [0, -THICKNESS, -1],
+    })
+
+    # 7, normals of Hadamard rect (0,0,-1)*8
+    s = (floats["0"] + floats["-1"] + floats["0"] + floats["0"] +
+         floats["-1"] + floats["0"] + floats["0"] + floats["-1"] +
+         floats["0"] + floats["0"] + floats["-1"] + floats["0"] + floats["0"] +
+         floats["-1"] + floats["0"] + floats["0"] + floats["-1"] +
+         floats["0"] + floats["0"] + floats["-1"] + floats["0"] + floats["0"] +
+         floats["-1"] + floats["0"])
+    gltf["buffers"].append({"byteLength": 96, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 7,
+        "byteLength": 96,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 7,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 8
+    })
+
+    # 8, vertices of middle rect in Hadamard rect: [4,1,5, 5,1,3]
+    s = ints[4] + ints[1] + ints[5] + ints[5] + ints[1] + ints[3]
+    gltf["buffers"].append({"byteLength": 12, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 8,
+        "byteLength": 12,
+        "byteOffset": 0,
+        "target": 34963
+    })
+    gltf["accessors"].append({
+        "bufferView": 8,
+        "componentType": 5123,
+        "type": "SCALAR",
+        "count": 6
+    })
+
+    # 9, vertices of upper rect in Hadamard rect: [6,4,7, 7,4,5]
+    s = ints[6] + ints[4] + ints[7] + ints[7] + ints[4] + ints[5]
+    gltf["buffers"].append({"byteLength": 12, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 9,
+        "byteLength": 12,
+        "byteOffset": 0,
+        "target": 34963
+    })
+    gltf["accessors"].append({
+        "bufferView": 9,
+        "componentType": 5123,
+        "type": "SCALAR",
+        "count": 6
+    })
+
+    # 10, vertices of lines around a face: [0,1, 0,2, 2,3, 3,1]
+    # GLTF supports drawing lines, but there may be a problem converting to
+    # other formats. We have thus not used these data.
+    s = (ints[0] + ints[1] + ints[0] + ints[2] + ints[2] + ints[3] + ints[3] +
+         ints[1])
+    gltf["buffers"].append({"byteLength": 16, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 10,
+        "byteLength": 16,
+        "byteOffset": 0,
+        "target": 34963
+    })
+    gltf["accessors"].append({
+        "bufferView": 10,
+        "componentType": 5123,
+        "type": "SCALAR",
+        "count": 8
+    })
+
+    # 11, positions of half-distance rectangle: [(0,0,-T),(0.45,0,-T),(0,1,-T),(0.45,1,-T)]
+    s = (floats["0"] + floats["-T"] + floats["0"] + floats["0.45"] +
+         floats["-T"] + floats["0"] + floats["0"] + floats["-T"] +
+         floats["-1"] + floats["0.45"] + floats["-T"] + floats["-1"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 11,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 11,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4,
+        "max": [0.45, -THICKNESS, 0],
+        "min": [0, -THICKNESS, -1],
+    })
+
+    # 12, backside, positions of square: [(+T,+T,+T),(1-T,+T,+T),(+T,1-T,+T),(1-T,1-T,+T)]
+    s = (floats["+T"] + floats["+T"] + floats["-T"] + floats["1-T"] +
+         floats["+T"] + floats["-T"] + floats["+T"] + floats["+T"] +
+         floats["T-1"] + floats["1-T"] + floats["+T"] + floats["T-1"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 12,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 12,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4,
+        "max": [1 - THICKNESS, THICKNESS, -THICKNESS],
+        "min": [THICKNESS, THICKNESS, THICKNESS - 1],
+    })
+
+    # 13, backside, normals of rect/sqr: (0,0,1)*4
+    s = (floats["0"] + floats["1"] + floats["0"] + floats["0"] + floats["1"] +
+         floats["0"] + floats["0"] + floats["1"] + floats["0"] + floats["0"] +
+         floats["1"] + floats["0"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 13,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 13,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4
+    })
+
+    # For the cubes, we want to draw black lines around its boundaries to help
+    # people identify them visually. However, drawing lines in GLTF may become
+    # a problem when converting to other formats. Here, we define super thin
+    # rectangles at the boundaries of squares which will be seen as lines.
+
+    # 14, frontside, positions of edge 0: [(+T,0,-T),(1-T,0,-T),(+T,+T,-T),(1-T,+T,-T)]
+    s = (floats["+T"] + floats["-T"] + floats["0"] + floats["1-T"] +
+         floats["-T"] + floats["0"] + floats["+T"] + floats["-T"] +
+         floats["-T"] + floats["1-T"] + floats["-T"] + floats["-T"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 14,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 14,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4,
+        "max": [1 - THICKNESS, -THICKNESS, 0],
+        "min": [THICKNESS, -THICKNESS, -THICKNESS],
+    })
+
+    # 15, frontside, positions of edge 1: [(1-T,+T,-T),(1,+T,-T),(1-T,1-T,-T),(1,1-T,-T)]
+    s = (floats["1-T"] + floats["-T"] + floats["-T"] + floats["1"] +
+         floats["-T"] + floats["-T"] + floats["1-T"] + floats["-T"] +
+         floats["T-1"] + floats["1"] + floats["-T"] + floats["T-1"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 15,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 15,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4,
+        "max": [1, -THICKNESS, -THICKNESS],
+        "min": [1 - THICKNESS, -THICKNESS, THICKNESS - 1],
+    })
+
+    # 16, frontside, positions of edge 2: [(0,+T,-T),(+T,+T,-T),(0,1-T,-T),(+T,1-T,-T)]
+    s = (floats["0"] + floats["-T"] + floats["-T"] + floats["+T"] +
+         floats["-T"] + floats["-T"] + floats["0"] + floats["-T"] +
+         floats["T-1"] + floats["+T"] + floats["-T"] + floats["T-1"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 16,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 16,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4,
+        "max": [THICKNESS, -THICKNESS, -THICKNESS],
+        "min": [0, -THICKNESS, THICKNESS - 1],
+    })
+
+    # 17, frontside, positions of edge 3: [(+T,1-T,-T),(1-T,1-T,-T),(+T,1,-T),(1-T,1,-T)]
+    s = (floats["+T"] + floats["-T"] + floats["T-1"] + floats["1-T"] +
+         floats["-T"] + floats["T-1"] + floats["+T"] + floats["-T"] +
+         floats["-1"] + floats["1-T"] + floats["-T"] + floats["-1"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 17,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 17,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4,
+        "max": [1 - THICKNESS, -THICKNESS, THICKNESS - 1],
+        "min": [THICKNESS, -THICKNESS, -1],
+    })
+
+    # 18, backside, positions of edge 0: [(+T,0,+T),(1-T,0,+T),(+T,+T,+T),(1-T,+T,+T)]
+    s = (floats["+T"] + floats["+T"] + floats["0"] + floats["1-T"] +
+         floats["+T"] + floats["0"] + floats["+T"] + floats["+T"] +
+         floats["-T"] + floats["1-T"] + floats["+T"] + floats["-T"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 18,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 18,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4,
+        "max": [1 - THICKNESS, THICKNESS, 0],
+        "min": [THICKNESS, THICKNESS, -THICKNESS],
+    })
+
+    # 19, backside, positions of edge 1: [(1-T,+T,+T),(1,+T,+T),(1-T,1-T,+T),(1,1-T,+T)]
+    s = (floats["1-T"] + floats["+T"] + floats["-T"] + floats["1"] +
+         floats["+T"] + floats["-T"] + floats["1-T"] + floats["+T"] +
+         floats["T-1"] + floats["1"] + floats["+T"] + floats["T-1"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 19,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 19,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4,
+        "max": [1, THICKNESS, -THICKNESS],
+        "min": [1 - THICKNESS, THICKNESS, THICKNESS - 1],
+    })
+
+    # 20, backside, positions of edge 2: [(0,+T,+T),(+T,+T,+T),(0,1-T,+T),(+T,1-T,+T)]
+    s = (floats["0"] + floats["+T"] + floats["-T"] + floats["+T"] +
+         floats["+T"] + floats["-T"] + floats["0"] + floats["+T"] +
+         floats["T-1"] + floats["+T"] + floats["+T"] + floats["T-1"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 20,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 20,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4,
+        "max": [THICKNESS, THICKNESS, -THICKNESS],
+        "min": [0, THICKNESS, THICKNESS - 1],
+    })
+
+    # 21, backside, positions of edge 3: [(+T,1-T,+T),(1-T,1-T,+T),(+T,1,+T),(1-T,1,+T)]
+    s = (floats["+T"] + floats["+T"] + floats["T-1"] + floats["1-T"] +
+         floats["+T"] + floats["T-1"] + floats["+T"] + floats["+T"] +
+         floats["-1"] + floats["1-T"] + floats["+T"] + floats["-1"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 21,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 21,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4,
+        "max": [1 - THICKNESS, THICKNESS, THICKNESS - 1],
+        "min": [THICKNESS, THICKNESS, -1],
+    })
+
+    # 22, backside vertices of rect/sqr: [1,3,0, 3,2,0]
+    s = ints[1] + ints[3] + ints[0] + ints[3] + ints[2] + ints[0]
+    gltf["buffers"].append({"byteLength": 12, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 22,
+        "byteLength": 12,
+        "byteOffset": 0,
+        "target": 34963
+    })
+    gltf["accessors"].append({
+        "bufferView": 22,
+        "componentType": 5123,
+        "type": "SCALAR",
+        "count": 6
+    })
+
+    # 23, backside, positions of rectangle: [(0,0,+T),(L,0,+T),(0,1,+T),(L,1,+T)]
+    s = (floats["0"] + floats["+T"] + floats["0"] + floats["tube"] +
+         floats["+T"] + floats["0"] + floats["0"] + floats["+T"] +
+         floats["-1"] + floats["tube"] + floats["+T"] + floats["-1"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 23,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 23,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4,
+        "max": [tubelen, THICKNESS, 0],
+        "min": [0, THICKNESS, -1],
+    })
+
+    # 24, backside, positions of half-distance rectangle: [(0,0,+T),(0.45,0,+T),(0,1,+T),(0.45,1,+T)]
+    s = (floats["0"] + floats["+T"] + floats["0"] + floats["0.45"] +
+         floats["+T"] + floats["0"] + floats["0"] + floats["+T"] +
+         floats["-1"] + floats["0.45"] + floats["+T"] + floats["-1"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 24,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 24,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4,
+        "max": [0.45, THICKNESS, 0],
+        "min": [0, THICKNESS, -1],
+    })
+
+    # 25, backside, positions of Hadamard rectangle: [(0,0,+T),(15/32L,0,+T),(15/32L,1,+T),
+    # (15/32L,1,+T),(17/32L,0,+T),(17/32L,1,+T),(L,0,+T),(L,1,+T)]
+    floats["left"] = float_to_little_endian_hex(tubelen * 15 / 32)
+    floats["right"] = float_to_little_endian_hex(tubelen * 17 / 32)
+    s = (floats["0"] + floats["+T"] + floats["0"] + floats["left"] +
+         floats["+T"] + floats["0"] + floats["0"] + floats["+T"] +
+         floats["-1"] + floats["left"] + floats["+T"] + floats["-1"] +
+         floats["right"] + floats["+T"] + floats["0"] + floats["right"] +
+         floats["+T"] + floats["-1"] + floats["tube"] + floats["+T"] +
+         floats["0"] + floats["tube"] + floats["+T"] + floats["-1"])
+    gltf["buffers"].append({"byteLength": 96, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 25,
+        "byteLength": 96,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 25,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 8,
+        "max": [tubelen, THICKNESS, 0],
+        "min": [0, THICKNESS, -1],
+    })
+
+    # 26, backside, normals of Hadamard rect (0,0,1)*8
+    s = (floats["0"] + floats["1"] + floats["0"] + floats["0"] + floats["1"] +
+         floats["0"] + floats["0"] + floats["1"] + floats["0"] + floats["0"] +
+         floats["1"] + floats["0"] + floats["0"] + floats["1"] + floats["0"] +
+         floats["0"] + floats["1"] + floats["0"] + floats["0"] + floats["1"] +
+         floats["0"] + floats["0"] + floats["1"] + floats["0"])
+    gltf["buffers"].append({"byteLength": 96, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 26,
+        "byteLength": 96,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 26,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 8
+    })
+
+    # 27, backside, vertices of middle rect in Hadamard rect: [4,5,1, 5,3,1]
+    s = ints[4] + ints[5] + ints[1] + ints[5] + ints[3] + ints[1]
+    gltf["buffers"].append({"byteLength": 12, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 27,
+        "byteLength": 12,
+        "byteOffset": 0,
+        "target": 34963
+    })
+    gltf["accessors"].append({
+        "bufferView": 27,
+        "componentType": 5123,
+        "type": "SCALAR",
+        "count": 6
+    })
+
+    # 28, backside, vertices of upper rect in Hadamard rect: [6,7,4, 7,5,4]
+    s = ints[6] + ints[7] + ints[4] + ints[7] + ints[5] + ints[4]
+    gltf["buffers"].append({"byteLength": 12, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 28,
+        "byteLength": 12,
+        "byteOffset": 0,
+        "target": 34963
+    })
+    gltf["accessors"].append({
+        "bufferView": 28,
+        "componentType": 5123,
+        "type": "SCALAR",
+        "count": 6
+    })
+
+    # 29, backside, positions of tilted rect: [(0,0,1/2+T),(1/2,0,+T),(0,1,1/2+T),(1/2,1,+T)]
+    s = (floats["0"] + floats["0.5-T"] + floats["0"] + floats["0.5"] +
+         floats["-T"] + floats["0"] + floats["0"] + floats["0.5-T"] +
+         floats["-1"] + floats["0.5"] + floats["-T"] + floats["-1"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 29,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 29,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4,
+        "max": [0.5, 0.5 - THICKNESS, 0],
+        "min": [0, -THICKNESS, -1],
+    })
+
+    # 30, backside, normals of tilted rect: (sqrt(2)/2, 0, sqrt(2)/2)*4
+    s = (floats["+SQ2"] + floats["+SQ2"] + floats["0"] + floats["+SQ2"] +
+         floats["+SQ2"] + floats["0"] + floats["+SQ2"] + floats["+SQ2"] +
+         floats["0"] + floats["+SQ2"] + floats["+SQ2"] + floats["0"])
+    gltf["buffers"].append({"byteLength": 48, "uri": hex_to_bin(s)})
+    gltf["bufferViews"].append({
+        "buffer": 30,
+        "byteLength": 48,
+        "byteOffset": 0,
+        "target": 34962
+    })
+    gltf["accessors"].append({
+        "bufferView": 30,
+        "componentType": 5126,
+        "type": "VEC3",
+        "count": 4
+    })
+
+    # finished creating the binary
+
+    # Now we create meshes. These are the real constructors of the 3D diagram.
+    # a mesh can contain multiple primitives. A primitive can be defined by a
+    # set of vertices POSITION, their NORMAL vectors, the order of going around
+    # these vertices, and the color (material) for the triangles defined by
+    # going around the vertices. `mode:4` means color these triangles.
+    gltf["meshes"] = [
+        {
+            "name":
+            "0-square-blue",
+            "primitives": [
+                # front side
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 0
+                    },
+                    "indices": 3,
+                    "material": 0,
+                    "mode": 4,
+                },
+                # back side
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 12
+                    },
+                    "indices": 22,
+                    "material": 0,
+                    "mode": 4,
+                },
+                # front side edge 0
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 14
+                    },
+                    "indices": 3,
+                    "material": 5,
+                    "mode": 4,
+                },
+                # front side edge 1
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 15
+                    },
+                    "indices": 3,
+                    "material": 5,
+                    "mode": 4,
+                },
+                # front side edge 2
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 16
+                    },
+                    "indices": 3,
+                    "material": 5,
+                    "mode": 4,
+                },
+                # front side edge 3
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 17
+                    },
+                    "indices": 3,
+                    "material": 5,
+                    "mode": 4,
+                },
+                # back side edge 0
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 18
+                    },
+                    "indices": 22,
+                    "material": 5,
+                    "mode": 4,
+                },
+                # back side edge 1
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 19
+                    },
+                    "indices": 22,
+                    "material": 5,
+                    "mode": 4,
+                },
+                # back side edge 2
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 20
+                    },
+                    "indices": 22,
+                    "material": 5,
+                    "mode": 4,
+                },
+                # back side edge 3
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 21
+                    },
+                    "indices": 22,
+                    "material": 5,
+                    "mode": 4,
+                },
+            ],
+        },
+        {
+            "name":
+            "1-square-red",
+            "primitives": [
+                # front side
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 0
+                    },
+                    "indices": 3,
+                    "material": 1,
+                    "mode": 4,
+                },
+                # back side
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 12
+                    },
+                    "indices": 22,
+                    "material": 1,
+                    "mode": 4,
+                },
+                # front side edge 0
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 14
+                    },
+                    "indices": 3,
+                    "material": 5,
+                    "mode": 4,
+                },
+                # front side edge 1
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 15
+                    },
+                    "indices": 3,
+                    "material": 5,
+                    "mode": 4,
+                },
+                # front side edge 2
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 16
+                    },
+                    "indices": 3,
+                    "material": 5,
+                    "mode": 4,
+                },
+                # front side edge 3
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 17
+                    },
+                    "indices": 3,
+                    "material": 5,
+                    "mode": 4,
+                },
+                # back side edge 0
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 18
+                    },
+                    "indices": 22,
+                    "material": 5,
+                    "mode": 4,
+                },
+                # back side edge 1
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 19
+                    },
+                    "indices": 22,
+                    "material": 5,
+                    "mode": 4,
+                },
+                # back side edge 2
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 20
+                    },
+                    "indices": 22,
+                    "material": 5,
+                    "mode": 4,
+                },
+                # back side edge 3
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 21
+                    },
+                    "indices": 22,
+                    "material": 5,
+                    "mode": 4,
+                },
+            ],
+        },
+        {
+            "name":
+            "2-square-gray",
+            "primitives": [
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 0
+                    },
+                    "indices": 3,
+                    "material": 3,
+                    "mode": 4,
+                },
+                # back side
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 12
+                    },
+                    "indices": 22,
+                    "material": 3,
+                    "mode": 4,
+                },
+            ],
+        },
+        {
+            "name":
+            "3-square-green",
+            "primitives": [
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 0
+                    },
+                    "indices": 3,
+                    "material": 2,
+                    "mode": 4,
+                },  # back side
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 12
+                    },
+                    "indices": 22,
+                    "material": 2,
+                    "mode": 4,
+                },
+            ],
+        },
+        {
+            "name":
+            "4-rectangle-blue",
+            "primitives": [
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 1
+                    },
+                    "indices": 3,
+                    "material": 0,
+                    "mode": 4,
+                },
+                # backside
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 23
+                    },
+                    "indices": 22,
+                    "material": 0,
+                    "mode": 4,
+                }
+            ],
+        },
+        {
+            "name":
+            "5-rectangle-red",
+            "primitives": [
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 1
+                    },
+                    "indices": 3,
+                    "material": 1,
+                    "mode": 4,
+                },
+                # backside
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 23
+                    },
+                    "indices": 22,
+                    "material": 1,
+                    "mode": 4,
+                }
+            ],
+        },
+        {
+            "name":
+            "6-rectangle-gray",
+            "primitives": [
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 1
+                    },
+                    "indices": 3,
+                    "material": 3,
+                    "mode": 4,
+                },
+                # backside
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 23
+                    },
+                    "indices": 22,
+                    "material": 3,
+                    "mode": 4,
+                }
+            ],
+        },
+        {
+            "name":
+            "7-rectangle-red/yellow/blue",
+            "primitives": [
+                {
+                    "attributes": {
+                        "NORMAL": 7,
+                        "POSITION": 6
+                    },
+                    "indices": 3,
+                    "material": 1,
+                    "mode": 4,
+                },
+                {
+                    "attributes": {
+                        "NORMAL": 7,
+                        "POSITION": 6
+                    },
+                    "indices": 8,
+                    "material": 6,
+                    "mode": 4,
+                },
+                {
+                    "attributes": {
+                        "NORMAL": 7,
+                        "POSITION": 6
+                    },
+                    "indices": 9,
+                    "material": 0,
+                    "mode": 4,
+                },
+                # backside
+                {
+                    "attributes": {
+                        "NORMAL": 26,
+                        "POSITION": 25
+                    },
+                    "indices": 22,
+                    "material": 1,
+                    "mode": 4,
+                },
+                {
+                    "attributes": {
+                        "NORMAL": 26,
+                        "POSITION": 25
+                    },
+                    "indices": 27,
+                    "material": 6,
+                    "mode": 4,
+                },
+                {
+                    "attributes": {
+                        "NORMAL": 26,
+                        "POSITION": 25
+                    },
+                    "indices": 28,
+                    "material": 0,
+                    "mode": 4,
+                },
+            ],
+        },
+        {
+            "name":
+            "8-rectangle-blue/yellow/red",
+            "primitives": [
+                {
+                    "attributes": {
+                        "NORMAL": 7,
+                        "POSITION": 6
+                    },
+                    "indices": 3,
+                    "material": 0,
+                    "mode": 4,
+                },
+                {
+                    "attributes": {
+                        "NORMAL": 7,
+                        "POSITION": 6
+                    },
+                    "indices": 8,
+                    "material": 6,
+                    "mode": 4,
+                },
+                {
+                    "attributes": {
+                        "NORMAL": 7,
+                        "POSITION": 6
+                    },
+                    "indices": 9,
+                    "material": 1,
+                    "mode": 4,
+                },
+                # backside
+                {
+                    "attributes": {
+                        "NORMAL": 26,
+                        "POSITION": 25
+                    },
+                    "indices": 22,
+                    "material": 0,
+                    "mode": 4,
+                },
+                {
+                    "attributes": {
+                        "NORMAL": 26,
+                        "POSITION": 25
+                    },
+                    "indices": 27,
+                    "material": 6,
+                    "mode": 4,
+                },
+                {
+                    "attributes": {
+                        "NORMAL": 26,
+                        "POSITION": 25
+                    },
+                    "indices": 28,
+                    "material": 1,
+                    "mode": 4,
+                },
+            ],
+        },
+        {
+            "name":
+            "9-square-cyan.3",
+            "primitives": [
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 0
+                    },
+                    "indices": 3,
+                    "material": 4,
+                    "mode": 4,
+                },  # back side
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 12
+                    },
+                    "indices": 22,
+                    "material": 4,
+                    "mode": 4,
+                },
+            ],
+        },
+        {
+            "name":
+            "10-rectangle-cyan.3",
+            "primitives": [
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 1
+                    },
+                    "indices": 3,
+                    "material": 4,
+                    "mode": 4,
+                },
+                # backside
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 23
+                    },
+                    "indices": 22,
+                    "material": 4,
+                    "mode": 4,
+                }
+            ],
+        },
+        {
+            "name":
+            "11-tilted-cyan.3",
+            "primitives": [
+                {
+                    "attributes": {
+                        "NORMAL": 5,
+                        "POSITION": 4
+                    },
+                    "indices": 3,
+                    "material": 4,
+                    "mode": 4,
+                },
+                # backside
+                {
+                    "attributes": {
+                        "NORMAL": 30,
+                        "POSITION": 29
+                    },
+                    "indices": 22,
+                    "material": 4,
+                    "mode": 4,
+                },
+            ],
+        },
+        {
+            "name":
+            "12-half-distance-rectangle-green",
+            "primitives": [
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 11
+                    },
+                    "indices": 3,
+                    "material": 2,
+                    "mode": 4,
+                },
+                # back side
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 24
+                    },
+                    "indices": 22,
+                    "material": 2,
+                    "mode": 4,
+                },
+            ],
+        },
+        {
+            "name":
+            "13-square-black",
+            "primitives": [
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 0
+                    },
+                    "indices": 3,
+                    "material": 5,
+                    "mode": 4,
+                },  # back side
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 12
+                    },
+                    "indices": 22,
+                    "material": 5,
+                    "mode": 4,
+                },
+            ],
+        },
+        {
+            "name":
+            "14-half-distance-rectangle-black",
+            "primitives": [
+                {
+                    "attributes": {
+                        "NORMAL": 2,
+                        "POSITION": 11
+                    },
+                    "indices": 3,
+                    "material": 5,
+                    "mode": 4,
+                },
+                # back side
+                {
+                    "attributes": {
+                        "NORMAL": 13,
+                        "POSITION": 24
+                    },
+                    "indices": 22,
+                    "material": 5,
+                    "mode": 4,
+                },
+            ],
+        },
+    ]
+
+    return gltf
+
+
+def axes_gen(SEP: float, max_i: int, max_j: int,
+             max_k: int) -> Sequence[Mapping[str, Any]]:
+    rectangles = []
+
+    # I axis, red
+    rectangles += [
+        {
+            "name": f"axisI:-K",
+            "mesh": 5,
+            "translation": [-0.5, -0.5, 0.5],
+            "scale": [SEP * max_i / (SEP - 1), AXESTHICKNESS, AXESTHICKNESS],
+        },
+        {
+            "name": f"axisI:+K",
+            "mesh": 5,
+            "translation": [-0.5, -0.5 + AXESTHICKNESS, 0.5],
+            "scale": [SEP * max_i / (SEP - 1), AXESTHICKNESS, AXESTHICKNESS],
+        },
+        {
+            "name": f"axisI:-J",
+            "mesh": 5,
+            "translation": [-0.5, -0.5, 0.5],
+            "rotation": [SQ2, 0, 0, SQ2],
+            "scale": [SEP * max_i / (SEP - 1), AXESTHICKNESS, AXESTHICKNESS],
+        },
+        {
+            "name": f"axisI:+J",
+            "mesh": 5,
+            "translation": [-0.5, -0.5, 0.5 - AXESTHICKNESS],
+            "rotation": [SQ2, 0, 0, SQ2],
+            "scale": [SEP * max_i / (SEP - 1), AXESTHICKNESS, AXESTHICKNESS],
+        },
+    ]
+
+    # J axis, green
+    rectangles += [
+        {
+            "name": f"axisJ:-K",
+            "rotation": [0, SQ2, 0, SQ2],
+            "translation": [-0.5 + AXESTHICKNESS, -0.5, 0.5],
+            "mesh": 3,
+            "scale": [SEP * max_j, AXESTHICKNESS, AXESTHICKNESS],
+        },
+        {
+            "name": f"axisJ:+K",
+            "rotation": [0, SQ2, 0, SQ2],
+            "translation": [
+                -0.5 + AXESTHICKNESS,
+                -0.5 + AXESTHICKNESS,
+                0.5,
+            ],
+            "mesh": 3,
+            "scale": [SEP * max_j, AXESTHICKNESS, AXESTHICKNESS],
+        },
+        {
+            "name": f"axisJ:-I",
+            "rotation": [0.5, 0.5, -0.5, 0.5],
+            "translation": [-0.5, -0.5, 0.5],
+            "mesh": 3,
+            "scale": [SEP * max_j, AXESTHICKNESS, AXESTHICKNESS],
+        },
+        {
+            "name": f"axisJ:+I",
+            "rotation": [0.5, 0.5, -0.5, 0.5],
+            "translation": [-0.5 + AXESTHICKNESS, -0.5, 0.5],
+            "mesh": 3,
+            "scale": [SEP * max_j, AXESTHICKNESS, AXESTHICKNESS],
+        },
+    ]
+
+    # K axis, blue
+    rectangles += [
+        {
+            "name": f"axisK:-I",
+            "mesh": 4,
+            "rotation": [0, 0, SQ2, SQ2],
+            "translation": [-0.5, -0.5 + AXESTHICKNESS, 0.5],
+            "scale": [SEP * max_k / (SEP - 1), AXESTHICKNESS, AXESTHICKNESS],
+        },
+        {
+            "name": f"axisK:+I",
+            "mesh": 4,
+            "rotation": [0, 0, SQ2, SQ2],
+            "translation": [-0.5 + AXESTHICKNESS, -0.5 + AXESTHICKNESS, 0.5],
+            "scale": [SEP * max_k / (SEP - 1), AXESTHICKNESS, AXESTHICKNESS],
+        },
+        {
+            "name": f"axisK:-J",
+            "mesh": 4,
+            "rotation": [0.5, 0.5, 0.5, 0.5],
+            "translation": [-0.5 + AXESTHICKNESS, -0.5 + AXESTHICKNESS, 0.5],
+            "scale": [SEP * max_k / (SEP - 1), AXESTHICKNESS, AXESTHICKNESS],
+        },
+        {
+            "name":
+            f"axisK:+J",
+            "mesh":
+            4,
+            "rotation": [0.5, 0.5, 0.5, 0.5],
+            "translation": [
+                -0.5 + AXESTHICKNESS,
+                -0.5 + AXESTHICKNESS,
+                0.5 - AXESTHICKNESS,
+            ],
+            "scale": [SEP * max_k / (SEP - 1), AXESTHICKNESS, AXESTHICKNESS],
+        },
+    ]
+
+    return rectangles
+
+
+def tube_gen(SEP: float, loc: Tuple[int, int, int], dir: str, color: int,
+             stabilizer: int, corr: Tuple[int, int], noColor: bool,
+             rm_dir: str) -> Sequence[Mapping[str, Any]]:
+    """compute the GLTF nodes for a pipe. This can include its four faces and
+    correlation surface inside, minus the face to remove specified by rm_dir. 
+
+    Args:
+        SEP (float): the distance, e.g., from I-pipe(i,j,k) to I-pipe(i+1,j,k).
+        loc (Tuple[int, int, int]): 3D coordinate of the pipe.
+        dir (str): direction of the pipe, "I", "J", or "K".
+        color (int): color variable of the pipe, can be -1(unknown), 0, or 1.
+        stabilizer (int): index of the stabilizer.
+        corr (Tuple[int, int]): two bits for two possible corr surface inside.
+        noColor (bool): K-pipe are not colored if this is True.
+        rm_dir (str): the direction of face to remove. if a stabilier is shown.
+
+    Returns:
+        Sequence[Mapping[str, Any]]: list of constructed GLTF nodes, typically
+            4 or 5 contiguous nodes in the list corredpond to one pipe.
+    """
+    rectangles = []
+    if dir == "I":
+        rectangles = [
+            {
+                "name": f"edgeI{loc}:-K",
+                "mesh": 4 if color else 5,
+                "translation": [1 + SEP * loc[0], SEP * loc[2], -SEP * loc[1]],
+            },
+            {
+                "name": f"edgeI{loc}:+K",
+                "mesh": 4 if color else 5,
+                "translation":
+                [1 + SEP * loc[0], 1 + SEP * loc[2], -SEP * loc[1]],
+            },
+            {
+                "name": f"edgeI{loc}:-J",
+                "mesh": 5 if color else 4,
+                "translation": [1 + SEP * loc[0], SEP * loc[2], -SEP * loc[1]],
+                "rotation": [SQ2, 0, 0, SQ2],
+            },
+            {
+                "name": f"edgeI{loc}:+J",
+                "mesh": 5 if color else 4,
+                "translation":
+                [1 + SEP * loc[0], SEP * loc[2], -1 - SEP * loc[1]],
+                "rotation": [SQ2, 0, 0, SQ2],
+            },
+        ]
+        if corr[0]:
+            rectangles.append({
+                "name":
+                f"edgeI{loc}:CorrIJ",
+                "mesh":
+                10,
+                "translation": [
+                    1 + SEP * loc[0],
+                    0.5 + SEP * loc[2],
+                    -SEP * loc[1],
+                ],
+            })
+        if corr[1]:
+            rectangles.append({
+                "name":
+                f"edgeI{loc}:CorrIK",
+                "mesh":
+                10,
+                "translation": [
+                    1 + SEP * loc[0],
+                    SEP * loc[2],
+                    -0.5 - SEP * loc[1],
+                ],
+                "rotation": [SQ2, 0, 0, SQ2],
+            })
+    elif dir == "J":
+        rectangles = [
+            {
+                "name": f"edgeJ{loc}:-K",
+                "rotation": [0, SQ2, 0, SQ2],
+                "translation":
+                [1 + SEP * loc[0], SEP * loc[2], -1 - SEP * loc[1]],
+                "mesh": 5 if color else 4,
+            },
+            {
+                "name":
+                f"edgeJ{loc}:+K",
+                "rotation": [0, SQ2, 0, SQ2],
+                "translation": [
+                    1 + SEP * loc[0],
+                    1 + SEP * loc[2],
+                    -1 - SEP * loc[1],
+                ],
+                "mesh":
+                5 if color else 4,
+            },
+            {
+                "name": f"edgeJ{loc}:-I",
+                "rotation": [0.5, 0.5, -0.5, 0.5],
+                "translation": [SEP * loc[0], SEP * loc[2], -1 - SEP * loc[1]],
+                "mesh": 4 if color else 5,
+            },
+            {
+                "name": f"edgeJ{loc}:+I",
+                "rotation": [0.5, 0.5, -0.5, 0.5],
+                "translation":
+                [1 + SEP * loc[0], SEP * loc[2], -1 - SEP * loc[1]],
+                "mesh": 4 if color else 5,
+            },
+        ]
+        if corr[0]:
+            rectangles.append({
+                "name":
+                f"edgeJ{loc}:CorrJK",
+                "mesh":
+                10,
+                "rotation": [0.5, 0.5, -0.5, 0.5],
+                "translation": [
+                    0.5 + SEP * loc[0],
+                    SEP * loc[2],
+                    -1 - SEP * loc[1],
+                ],
+            })
+        if corr[1]:
+            rectangles.append({
+                "name":
+                f"edgeJ{loc}:CorrJI",
+                "mesh":
+                10,
+                "rotation": [0, SQ2, 0, SQ2],
+                "translation": [
+                    1 + SEP * loc[0],
+                    0.5 + SEP * loc[2],
+                    -1 - SEP * loc[1],
+                ],
+            })
+
+    elif dir == "K":
+        colorKM = color // 7
+        colorKP = color % 7
+        rectangles = [
+            {
+                "name": f"edgeJ{loc}:-I",
+                "mesh": 6,
+                "rotation": [0, 0, SQ2, SQ2],
+                "translation": [SEP * loc[0], 1 + SEP * loc[2], -SEP * loc[1]],
+            },
+            {
+                "name": f"edgeJ{loc}:+I",
+                "mesh": 6,
+                "rotation": [0, 0, SQ2, SQ2],
+                "translation":
+                [1 + SEP * loc[0], 1 + SEP * loc[2], -SEP * loc[1]],
+            },
+            {
+                "name": f"edgeK{loc}:-J",
+                "mesh": 6,
+                "rotation": [0.5, 0.5, 0.5, 0.5],
+                "translation":
+                [1 + SEP * loc[0], 1 + SEP * loc[2], -SEP * loc[1]],
+            },
+            {
+                "name":
+                f"edgeJ{loc}:+J",
+                "mesh":
+                6,
+                "rotation": [0.5, 0.5, 0.5, 0.5],
+                "translation": [
+                    1 + SEP * loc[0],
+                    1 + SEP * loc[2],
+                    -1 - SEP * loc[1],
+                ],
+            },
+        ]
+        if not noColor:
+            if colorKM == 0 and colorKP == 0:
+                rectangles[0]["mesh"] = 4
+                rectangles[1]["mesh"] = 4
+                rectangles[2]["mesh"] = 5
+                rectangles[3]["mesh"] = 5
+            if colorKM == 1 and colorKP == 1:
+                rectangles[0]["mesh"] = 5
+                rectangles[1]["mesh"] = 5
+                rectangles[2]["mesh"] = 4
+                rectangles[3]["mesh"] = 4
+            if colorKM == 1 and colorKP == 0:
+                rectangles[0]["mesh"] = 7
+                rectangles[1]["mesh"] = 7
+                rectangles[2]["mesh"] = 8
+                rectangles[3]["mesh"] = 8
+            if colorKM == 0 and colorKP == 1:
+                rectangles[0]["mesh"] = 8
+                rectangles[1]["mesh"] = 8
+                rectangles[2]["mesh"] = 7
+                rectangles[3]["mesh"] = 7
+
+        if corr[0]:
+            rectangles.append({
+                "name":
+                f"edgeK{loc}:CorrKI",
+                "mesh":
+                10,
+                "rotation": [0.5, 0.5, 0.5, 0.5],
+                "translation": [
+                    1 + SEP * loc[0],
+                    1 + SEP * loc[2],
+                    -0.5 - SEP * loc[1],
+                ],
+            })
+        if corr[1]:
+            rectangles.append({
+                "name":
+                f"edgeK{loc}:CorrKJ",
+                "mesh":
+                10,
+                "rotation": [0, 0, SQ2, SQ2],
+                "translation": [
+                    0.5 + SEP * loc[0],
+                    1 + SEP * loc[2],
+                    -SEP * loc[1],
+                ],
+            })
+
+    rectangles = [rect for rect in rectangles if rm_dir not in rect["name"]]
+    if stabilizer == -1:
+        rectangles = [
+            rect for rect in rectangles if "Corr" not in rect["name"]
+        ]
+    return rectangles
+
+
+def cube_gen(
+    SEP: float,
+    loc: Tuple[int, int, int],
+    exists: Mapping[str, int],
+    colors: Mapping[str, int],
+    stabilizer: int,
+    corr: Mapping[str, Tuple[int, int]],
+    noColor: bool,
+    rm_dir: str,
+) -> Sequence[Mapping[str, Any]]:
+    """compute the GLTF nodes for a cube. This can include its faces and
+    correlation surface inside, minus the face to remove specified by rm_dir. 
+
+    Args:
+        SEP (float): the distance, e.g., from cube(i,j,k) to cube(i+1,j,k).
+        loc (Tuple[int, int, int]): 3D coordinate of the pipe.
+        exists (Mapping[str, int]): whether there is a pipe in the 6 directions
+            to this cube. (+|-)(I|J|K).
+        colors (Mapping[str, int]): color variable of the pipe, can be
+            -1(unknown), 0, or 1.
+        stabilizer (int): index of the stabilizer.
+        corr (Mapping[str, Tuple[int, int]]): two bits for two possible
+            correlation surface inside a pipe. These info for all 6 pipes.
+        noColor (bool): K-pipe are not colored if this is True.
+        rm_dir (str): the direction of face to remove. if a stabilier is shown.
+
+    Returns:
+        Sequence[Mapping[str, Any]]: list of constructed GLTF nodes.
+    """
+    squares = []
+    for face in ["-K", "+K"]:
+        if exists[face] == 0:
+            squares.append({
+                "name":
+                f"spider{loc}:{face}",
+                "mesh":
+                2,
+                "translation": [
+                    SEP * loc[0],
+                    (1 if face == "+K" else 0) + SEP * loc[2],
+                    -SEP * loc[1],
+                ],
+            })
+            for dir in ["+I", "-I", "+J", "-J"]:
+                if exists[dir]:
+                    if dir == "+I" or dir == "-I":
+                        if colors[dir] == 1:
+                            squares[-1]["mesh"] = 0
+                        else:
+                            squares[-1]["mesh"] = 1
+                    else:
+                        if colors[dir] == 0:
+                            squares[-1]["mesh"] = 0
+                        else:
+                            squares[-1]["mesh"] = 1
+                    break
+    for face in ["-I", "+I"]:
+        if exists[face] == 0:
+            squares.append({
+                "name":
+                f"spider{loc}:{face}",
+                "mesh":
+                2,
+                "translation": [
+                    (1 if face == "+I" else 0) + SEP * loc[0],
+                    SEP * loc[2],
+                    -SEP * loc[1],
+                ],
+                "rotation": [0, 0, SQ2, SQ2],
+            })
+            for dir in ["+J", "-J", "+K", "-K"]:
+                if exists[dir]:
+                    if dir == "+J" or dir == "-J":
+                        if colors[dir] == 1:
+                            squares[-1]["mesh"] = 0
+                        else:
+                            squares[-1]["mesh"] = 1
+                    elif not noColor:
+                        if colors[dir] == 1:
+                            squares[-1]["mesh"] = 1
+                        elif colors[dir] == 0:
+                            squares[-1]["mesh"] = 0
+    for face in ["-J", "+J"]:
+        if exists[face] == 0:
+            squares.append({
+                "name":
+                f"spider{loc}:{face}",
+                "mesh":
+                2,
+                "translation": [
+                    1 + SEP * loc[0],
+                    SEP * loc[2],
+                    (-1 if face == "+J" else 0) - SEP * loc[1],
+                ],
+                "rotation": [0.5, 0.5, 0.5, 0.5],
+            })
+            for dir in ["+I", "-I", "+K", "-K"]:
+                if exists[dir]:
+                    if dir == "+I" or dir == "-I":
+                        if colors[dir] == 0:
+                            squares[-1]["mesh"] = 0
+                        else:
+                            squares[-1]["mesh"] = 1
+                    elif not noColor:
+                        if colors[dir] == 1:
+                            squares[-1]["mesh"] = 0
+                        elif colors[dir] == 0:
+                            squares[-1]["mesh"] = 1
+
+    degree = sum([v for (k, v) in exists.items()])
+    normal = {"I": 0, "J": 0, "K": 0}
+    if exists["-I"] == 0 and exists["+I"] == 0:
+        normal["I"] = 1
+    if exists["-J"] == 0 and exists["+J"] == 0:
+        normal["J"] = 1
+    if exists["-K"] == 0 and exists["+K"] == 0:
+        normal["K"] = 1
+    if degree > 1:
+        if (exists["-I"] and exists["+I"] and exists["-J"] == 0
+                and exists["+J"] == 0 and exists["-K"] == 0
+                and exists["+K"] == 0):
+            if corr["-I"][0]:
+                squares.append({
+                    "name":
+                    f"spider{loc}:Corr",
+                    "mesh":
+                    9,
+                    "translation": [
+                        SEP * loc[0],
+                        0.5 + SEP * loc[2],
+                        -SEP * loc[1],
+                    ],
+                })
+            if corr["-I"][1]:
+                squares.append({
+                    "name":
+                    f"spider{loc}:Corr",
+                    "mesh":
+                    9,
+                    "translation": [
+                        1 + SEP * loc[0],
+                        SEP * loc[2],
+                        -0.5 - SEP * loc[1],
+                    ],
+                    "rotation": [0.5, 0.5, 0.5, 0.5],
+                })
+        elif (exists["-I"] == 0 and exists["+I"] == 0 and exists["-J"]
+              and exists["+J"] and exists["-K"] == 0 and exists["+K"] == 0):
+            if corr["-J"][0]:
+                squares.append({
+                    "name":
+                    f"spider{loc}:Corr",
+                    "mesh":
+                    9,
+                    "translation": [
+                        0.5 + SEP * loc[0],
+                        SEP * loc[2],
+                        -SEP * loc[1],
+                    ],
+                    "rotation": [0, 0, SQ2, SQ2],
+                })
+            if corr["-J"][1]:
+                squares.append({
+                    "name":
+                    f"spider{loc}:Corr",
+                    "mesh":
+                    9,
+                    "translation": [
+                        SEP * loc[0],
+                        0.5 + SEP * loc[2],
+                        -SEP * loc[1],
+                    ],
+                })
+        elif (exists["-I"] == 0 and exists["+I"] == 0 and exists["-J"] == 0
+              and exists["+J"] == 0 and exists["-K"] and exists["+K"]):
+            if corr["-K"][0]:
+                squares.append({
+                    "name":
+                    f"spider{loc}:Corr",
+                    "mesh":
+                    9,
+                    "translation": [
+                        1 + SEP * loc[0],
+                        SEP * loc[2],
+                        -0.5 - SEP * loc[1],
+                    ],
+                    "rotation": [0.5, 0.5, 0.5, 0.5],
+                })
+            if corr["-K"][1]:
+                squares.append({
+                    "name":
+                    f"spider{loc}:Corr",
+                    "mesh":
+                    9,
+                    "translation": [
+                        0.5 + SEP * loc[0],
+                        SEP * loc[2],
+                        -SEP * loc[1],
+                    ],
+                    "rotation": [0, 0, SQ2, SQ2],
+                })
+        else:
+            if normal["I"]:
+                if corr["-J"][0] or corr["+J"][0] or corr["-K"][1] or corr[
+                        "+K"][1]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        9,
+                        "translation": [
+                            0.5 + SEP * loc[0],
+                            SEP * loc[2],
+                            -SEP * loc[1],
+                        ],
+                        "rotation": [0, 0, SQ2, SQ2],
+                    })
+
+                if corr["-J"][1] and corr["+J"][1] and corr["-K"][0] and corr[
+                        "+K"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation": [
+                            SEP * loc[0],
+                            SEP * loc[2],
+                            -1 - SEP * loc[1],
+                        ],
+                        "rotation": [0, -SQ2, 0, SQ2],
+                    })
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation": [
+                            SEP * loc[0],
+                            0.5 + SEP * loc[2],
+                            -0.5 - SEP * loc[1],
+                        ],
+                        "rotation": [0, -SQ2, 0, SQ2],
+                    })
+                elif corr["-J"][1] and corr["+J"][1]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        9,
+                        "translation": [
+                            SEP * loc[0],
+                            0.5 + SEP * loc[2],
+                            -SEP * loc[1],
+                        ],
+                    })
+                elif corr["-K"][0] and corr["+K"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        9,
+                        "translation": [
+                            1 + SEP * loc[0],
+                            SEP * loc[2],
+                            -0.5 - SEP * loc[1],
+                        ],
+                        "rotation": [0.5, 0.5, 0.5, 0.5],
+                    })
+                elif corr["-J"][1] and corr["-K"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation": [
+                            1 + SEP * loc[0],
+                            SEP * loc[2],
+                            -SEP * loc[1],
+                        ],
+                        "rotation": [0, SQ2, 0, SQ2],
+                    })
+                elif corr["+J"][1] and corr["+K"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation": [
+                            1 + SEP * loc[0],
+                            0.5 + SEP * loc[2],
+                            -0.5 - SEP * loc[1],
+                        ],
+                        "rotation": [0, SQ2, 0, SQ2],
+                    })
+                elif corr["+J"][1] and corr["-K"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation": [
+                            SEP * loc[0],
+                            SEP * loc[2],
+                            -1 - SEP * loc[1],
+                        ],
+                        "rotation": [0, -SQ2, 0, SQ2],
+                    })
+                elif corr["-J"][1] and corr["+K"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation": [
+                            SEP * loc[0],
+                            0.5 + SEP * loc[2],
+                            -0.5 - SEP * loc[1],
+                        ],
+                        "rotation": [0, -SQ2, 0, SQ2],
+                    })
+            elif normal["J"]:
+                if corr["-K"][0] or corr["+K"][0] or corr["-I"][1] or corr[
+                        "+I"][1]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        9,
+                        "translation": [
+                            1 + SEP * loc[0],
+                            SEP * loc[2],
+                            -0.5 - SEP * loc[1],
+                        ],
+                        "rotation": [0.5, 0.5, 0.5, 0.5],
+                    })
+
+                if corr["-K"][1] and corr["+K"][1] and corr["-I"][0] and corr[
+                        "+I"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation": [
+                            0.5 + SEP * loc[0],
+                            0.5 + SEP * loc[2],
+                            -SEP * loc[1],
+                        ],
+                        "rotation": [0, 0, SQ2, SQ2],
+                    })
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation": [
+                            1 + SEP * loc[0],
+                            SEP * loc[2],
+                            -SEP * loc[1],
+                        ],
+                        "rotation": [0, 0, SQ2, SQ2],
+                    })
+                elif corr["-K"][1] and corr["+K"][1]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        9,
+                        "translation": [
+                            0.5 + SEP * loc[0],
+                            SEP * loc[2],
+                            -SEP * loc[1],
+                        ],
+                        "rotation": [0, 0, SQ2, SQ2],
+                    })
+                elif corr["-I"][0] and corr["+I"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        9,
+                        "translation": [
+                            SEP * loc[0],
+                            0.5 + SEP * loc[2],
+                            -SEP * loc[1],
+                        ],
+                    })
+                elif corr["-K"][1] and corr["-I"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation":
+                        [SEP * loc[0], SEP * loc[2], -SEP * loc[1]],
+                    })
+                elif corr["+K"][1] and corr["+I"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation": [
+                            0.5 + SEP * loc[0],
+                            0.5 + SEP * loc[2],
+                            -SEP * loc[1],
+                        ],
+                    })
+                elif corr["+K"][1] and corr["-I"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation": [
+                            0.5 + SEP * loc[0],
+                            0.5 + SEP * loc[2],
+                            -SEP * loc[1],
+                        ],
+                        "rotation": [0, 0, SQ2, SQ2],
+                    })
+                elif corr["-K"][1] and corr["+I"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation": [
+                            1 + SEP * loc[0],
+                            SEP * loc[2],
+                            -SEP * loc[1],
+                        ],
+                        "rotation": [0, 0, SQ2, SQ2],
+                    })
+            else:
+                if corr["-I"][0] or corr["+I"][0] or corr["-J"][1] or corr[
+                        "+J"][1]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        9,
+                        "translation": [
+                            SEP * loc[0],
+                            0.5 + SEP * loc[2],
+                            -SEP * loc[1],
+                        ],
+                    })
+
+                if corr["-I"][1] and corr["+I"][1] and corr["-J"][0] and corr[
+                        "+J"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation": [
+                            0.5 + SEP * loc[0],
+                            SEP * loc[2],
+                            -0.5 - SEP * loc[1],
+                        ],
+                        "rotation": [SQ2, 0, 0, SQ2],
+                    })
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation": [
+                            SEP * loc[0],
+                            SEP * loc[2],
+                            -1 - SEP * loc[1],
+                        ],
+                        "rotation": [SQ2, 0, 0, SQ2],
+                    })
+                elif corr["-I"][1] and corr["+I"][1]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        9,
+                        "translation": [
+                            1 + SEP * loc[0],
+                            SEP * loc[2],
+                            -0.5 - SEP * loc[1],
+                        ],
+                        "rotation": [0.5, 0.5, 0.5, 0.5],
+                    })
+                elif corr["-J"][0] and corr["+J"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        9,
+                        "translation": [
+                            0.5 + SEP * loc[0],
+                            SEP * loc[2],
+                            -SEP * loc[1],
+                        ],
+                        "rotation": [0, 0, SQ2, SQ2],
+                    })
+                elif corr["-I"][1] and corr["-J"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation": [
+                            SEP * loc[0],
+                            1 + SEP * loc[2],
+                            -SEP * loc[1],
+                        ],
+                        "rotation": [-SQ2, 0, 0, SQ2],
+                    })
+                elif corr["+I"][1] and corr["+J"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation": [
+                            0.5 + SEP * loc[0],
+                            1 + SEP * loc[2],
+                            -0.5 - SEP * loc[1],
+                        ],
+                        "rotation": [-SQ2, 0, 0, SQ2],
+                    })
+                elif corr["+I"][1] and corr["-J"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation": [
+                            0.5 + SEP * loc[0],
+                            SEP * loc[2],
+                            -0.5 - SEP * loc[1],
+                        ],
+                        "rotation": [SQ2, 0, 0, SQ2],
+                    })
+                elif corr["-I"][1] and corr["+J"][0]:
+                    squares.append({
+                        "name":
+                        f"spider{loc}:Corr",
+                        "mesh":
+                        11,
+                        "translation": [
+                            SEP * loc[0],
+                            SEP * loc[2],
+                            -1 - SEP * loc[1],
+                        ],
+                        "rotation": [SQ2, 0, 0, SQ2],
+                    })
+
+    squares = [sqar for sqar in squares if rm_dir not in sqar["name"]]
+    if stabilizer == -1:
+        squares = [sqar for sqar in squares if "Corr" not in sqar["name"]]
+    return squares
+
+
+def special_gen(
+    SEP: float,
+    loc: Tuple[int, int, int],
+    exists: Mapping[str, int],
+    type: str,
+    stabilizer: int,
+    rm_dir: str,
+) -> Sequence[Mapping[str, Any]]:
+    """compute the GLTF nodes for special cubes. Currently Ycube and Tinjection 
+
+    Args:
+        SEP (float): the distance, e.g., from cube(i,j,k) to cube(i+1,j,k).
+        loc (Tuple[int, int, int]): 3D coordinate of the pipe.
+        exists (Mapping[str, int]): whether there is a pipe in the 6 directions
+            to this cube. (+|-)(I|J|K).
+        stabilizer (int): index of the stabilizer.
+        noColor (bool): K-pipe are not colored if this is True.
+        rm_dir (str): the direction of face to remove. if a stabilier is shown.
+
+    Returns:
+        Sequence[Mapping[str, Any]]: list of constructed GLTF nodes.
+    """
+    if type == "Y":
+        square_mesh = 3
+        half_dist_mesh = 12
+    elif type == "T":
+        square_mesh = 13
+        half_dist_mesh = 14
+    else:
+        square_mesh = -1
+        half_dist_mesh = -1
+
+    shapes = []
+    if exists["+K"]:
+        # need connect to top
+        shapes.append({
+            "name":
+            f"spider{loc}:top:-K",
+            "mesh":
+            square_mesh,
+            "translation": [
+                SEP * loc[0],
+                0.55 + SEP * loc[2],
+                -SEP * loc[1],
+            ],
+        })
+        shapes.append({
+            "name":
+            f"spider{loc}:top:-I",
+            "mesh":
+            half_dist_mesh,
+            "rotation": [0, 0, SQ2, SQ2],
+            "translation": [SEP * loc[0], 0.55 + SEP * loc[2], -SEP * loc[1]],
+        })
+        shapes.append({
+            "name":
+            f"spider{loc}:top:+I",
+            "mesh":
+            half_dist_mesh,
+            "rotation": [0, 0, SQ2, SQ2],
+            "translation":
+            [1 + SEP * loc[0], 0.55 + SEP * loc[2], -SEP * loc[1]],
+        })
+        shapes.append({
+            "name":
+            f"spider{loc}:top:-J",
+            "mesh":
+            half_dist_mesh,
+            "rotation": [0.5, 0.5, 0.5, 0.5],
+            "translation":
+            [1 + SEP * loc[0], 0.55 + SEP * loc[2], -SEP * loc[1]],
+        })
+        shapes.append({
+            "name":
+            f"spider{loc}:top:+J",
+            "mesh":
+            half_dist_mesh,
+            "rotation": [0.5, 0.5, 0.5, 0.5],
+            "translation": [
+                1 + SEP * loc[0],
+                0.55 + SEP * loc[2],
+                -SEP * loc[1] - 1,
+            ],
+        })
+
+    if exists["-K"]:
+        # need connect to bottom
+        shapes.append({
+            "name":
+            f"spider{loc}:bottom:+K",
+            "mesh":
+            square_mesh,
+            "translation": [
+                SEP * loc[0],
+                0.45 + SEP * loc[2],
+                -SEP * loc[1],
+            ],
+        })
+        shapes.append({
+            "name":
+            f"spider{loc}:bottom:-I",
+            "mesh":
+            half_dist_mesh,
+            "rotation": [0, 0, SQ2, SQ2],
+            "translation": [SEP * loc[0], SEP * loc[2], -SEP * loc[1]],
+        })
+        shapes.append({
+            "name":
+            f"spider{loc}:bottom:+I",
+            "mesh":
+            half_dist_mesh,
+            "rotation": [0, 0, SQ2, SQ2],
+            "translation": [1 + SEP * loc[0], SEP * loc[2], -SEP * loc[1]],
+        })
+        shapes.append({
+            "name":
+            f"spider{loc}:bottom:-J",
+            "mesh":
+            half_dist_mesh,
+            "rotation": [0.5, 0.5, 0.5, 0.5],
+            "translation": [1 + SEP * loc[0], SEP * loc[2], -SEP * loc[1]],
+        })
+        shapes.append({
+            "name":
+            f"spider{loc}:bottom:+J",
+            "mesh":
+            half_dist_mesh,
+            "rotation": [0.5, 0.5, 0.5, 0.5],
+            "translation": [
+                1 + SEP * loc[0],
+                SEP * loc[2],
+                -SEP * loc[1] - 1,
+            ],
+        })
+
+    shapes = [shp for shp in shapes if rm_dir not in shp["name"]]
+    return shapes
+
+
+def gltf_generator(lasre: Mapping[str, Any],
+                   stabilizer: int = -1,
+                   tube_len: float = 2.0,
+                   no_color_z: bool = False,
+                   attach_axes: bool = False,
+                   rm_dir: Optional[str] = None) -> Mapping[str, Any]:
+    """generate gltf in a dict and write to a json file with extension .gltf
+
+    Args:
+        lasre (Mapping[str, Any]): LaSRe of the LaS.
+        stabilizer (int, optional): index of the stabilizer. The correlation
+            surfaces corresponding to it will be drawn. Defaults to -1, which
+            means do not draw any correlation surfaces.
+        tube_len (float, optional): ratio of the length of the pipes compared
+            to the length of the cubes. Defaults to 2.0.
+        no_color_z (bool, optional): do not color the Z-pipes. Defaults to
+            False, which means by default Z-pipes are colored.
+        attach_axes (bool, optional): attach an IJK axes. Defaults to False.
+        rm_dir (str, optional): the direction of faces to remove to reveal
+            the correlation surfaces. Defaults to None.
+
+    Raises:
+        ValueError: rm_dir is not any one of :(+|-)(I|J|K)
+        ValueError: the index of stabilizer is not -1 nor in [0, n_stabilizer)
+
+    Returns:
+        Mapping[str, Any]: the constructed gltf in a dict.
+    """
+    s, tubelen, noColor = (
+        stabilizer,
+        tube_len,
+        no_color_z,
+    )
+    if rm_dir is None:
+        rm_dir = ":II"
+    elif rm_dir not in [":+I", ":-I", ":+J", ":-J", ":+K", ":-K"]:
+        raise ValueError("rm_dir is not one of :+I, :-I, :+J, :-J, :+K, :-K")
+
+    gltf = base_gen(tubelen)
+
+    i_bound = lasre["n_i"]
+    j_bound = lasre["n_j"]
+    k_bound = lasre["n_k"]
+    NodeY = lasre["NodeY"]
+    ExistI = lasre["ExistI"]
+    ColorI = lasre["ColorI"]
+    ExistJ = lasre["ExistJ"]
+    ColorJ = lasre["ColorJ"]
+    ExistK = lasre["ExistK"]
+    if "CorrIJ" in lasre:
+        CorrIJ = lasre["CorrIJ"]
+        CorrIK = lasre["CorrIK"]
+        CorrJI = lasre["CorrJI"]
+        CorrJK = lasre["CorrJK"]
+        CorrKI = lasre["CorrKI"]
+        CorrKJ = lasre["CorrKJ"]
+        s_bound = len(CorrIJ)
+    if "ColorKP" not in lasre:
+        ColorKP = [[[-1 for _ in range(k_bound)] for _ in range(j_bound)]
+                   for _ in range(i_bound)]
+    else:
+        ColorKP = lasre["ColorKP"]
+    if "ColorKM" not in lasre:
+        ColorKM = [[[-1 for _ in range(k_bound)] for _ in range(j_bound)]
+                   for _ in range(i_bound)]
+    else:
+        ColorKM = lasre["ColorKM"]
+    port_cubes = lasre["port_cubes"]
+    t_injections = (lasre["optional"]["t_injections"]
+                    if "t_injections" in lasre["optional"] else [])
+
+    if s < -1 or (s_bound > 0 and s not in range(-1, s_bound)):
+        raise ValueError(f"No such stabilizer index {s}.")
+
+    for i in range(i_bound):
+        for j in range(j_bound):
+            for k in range(k_bound):
+                if ExistI[i][j][k]:
+                    gltf["nodes"] += tube_gen(
+                        tubelen + 1.0,
+                        (i, j, k),
+                        "I",
+                        ColorI[i][j][k],
+                        s,
+                        (CorrIJ[s][i][j][k],
+                         CorrIK[s][i][j][k]) if s_bound else (0, 0),
+                        noColor,
+                        rm_dir,
+                    )
+                if ExistJ[i][j][k]:
+                    gltf["nodes"] += tube_gen(
+                        tubelen + 1.0,
+                        (i, j, k),
+                        "J",
+                        ColorJ[i][j][k],
+                        s,
+                        (CorrJK[s][i][j][k],
+                         CorrJI[s][i][j][k]) if s_bound else (0, 0),
+                        noColor,
+                        rm_dir,
+                    )
+                if ExistK[i][j][k]:
+                    gltf["nodes"] += tube_gen(
+                        tubelen + 1.0,
+                        (i, j, k),
+                        "K",
+                        7 * ColorKM[i][j][k] + ColorKP[i][j][k],
+                        s,
+                        (CorrKI[s][i][j][k],
+                         CorrKJ[s][i][j][k]) if s_bound else (0, 0),
+                        noColor,
+                        rm_dir,
+                    )
+
+    for i in range(i_bound):
+        for j in range(j_bound):
+            for k in range(k_bound):
+                exists = {"-I": 0, "+I": 0, "-K": 0, "+K": 0, "-J": 0, "+J": 0}
+                colors = {}
+                corr = {
+                    "-I": (0, 0),
+                    "+I": (0, 0),
+                    "-J": (0, 0),
+                    "+J": (0, 0),
+                    "-K": (0, 0),
+                    "+K": (0, 0),
+                }
+                if i > 0 and ExistI[i - 1][j][k]:
+                    exists["-I"] = 1
+                    colors["-I"] = ColorI[i - 1][j][k]
+                    corr["-I"] = (CorrIJ[s][i - 1][j][k],
+                                  CorrIK[s][i - 1][j][k]) if s_bound else (0,
+                                                                           0)
+                if ExistI[i][j][k]:
+                    exists["+I"] = 1
+                    colors["+I"] = ColorI[i][j][k]
+                    corr["+I"] = (CorrIJ[s][i][j][k],
+                                  CorrIK[s][i][j][k]) if s_bound else (0, 0)
+                if j > 0 and ExistJ[i][j - 1][k]:
+                    exists["-J"] = 1
+                    colors["-J"] = ColorJ[i][j - 1][k]
+                    corr["-J"] = (CorrJK[s][i][j - 1][k],
+                                  CorrJI[s][i][j - 1][k]) if s_bound else (0,
+                                                                           0)
+                if ExistJ[i][j][k]:
+                    exists["+J"] = 1
+                    colors["+J"] = ColorJ[i][j][k]
+                    corr["+J"] = (CorrJK[s][i][j][k],
+                                  CorrJI[s][i][j][k]) if s_bound else (0, 0)
+                if k > 0 and ExistK[i][j][k - 1]:
+                    exists["-K"] = 1
+                    colors["-K"] = ColorKP[i][j][k - 1]
+                    corr["-K"] = (CorrKI[s][i][j][k - 1],
+                                  CorrKJ[s][i][j][k - 1]) if s_bound else (0,
+                                                                           0)
+                if ExistK[i][j][k]:
+                    exists["+K"] = 1
+                    colors["+K"] = ColorKM[i][j][k]
+                    corr["+K"] = (CorrKI[s][i][j][k],
+                                  CorrKJ[s][i][j][k]) if s_bound else (0, 0)
+                if sum([v for (k, v) in exists.items()]) > 0:
+                    if (i, j, k) not in port_cubes:
+                        if NodeY[i][j][k]:
+                            gltf["nodes"] += special_gen(
+                                tubelen + 1.0,
+                                (i, j, k),
+                                exists,
+                                "Y",
+                                s,
+                                rm_dir,
+                            )
+                        else:
+                            gltf["nodes"] += cube_gen(
+                                tubelen + 1.0,
+                                (i, j, k),
+                                exists,
+                                colors,
+                                s,
+                                corr,
+                                noColor,
+                                rm_dir,
+                            )
+                    elif [i, j, k] in t_injections:
+                        gltf["nodes"] += special_gen(
+                            tubelen + 1.0,
+                            (i, j, k),
+                            exists,
+                            "T",
+                            s,
+                            rm_dir,
+                        )
+
+    if attach_axes:
+        gltf["nodes"] += axes_gen(tube_len + 1.0, i_bound, j_bound, k_bound)
+
+    gltf["nodes"][0]["children"] = list(range(1, len(gltf["nodes"])))
+
+    return gltf
diff --git a/glue/lattice_surgery/lassynth/translators/networkx_generator.py b/glue/lattice_surgery/lassynth/translators/networkx_generator.py
new file mode 100644
index 00000000..4003f75c
--- /dev/null
+++ b/glue/lattice_surgery/lassynth/translators/networkx_generator.py
@@ -0,0 +1,53 @@
+"""generate a annotated networkx.Graph corresponding to the LaS."""
+
+import networkx
+from lassynth.translators import ZXGridGraph
+import stimzx
+from typing import Mapping, Any
+
+
+def networkx_generator(lasre: Mapping[str, Any]) -> networkx.Graph:
+    n_i, n_j, n_k = lasre["n_i"], lasre["n_j"], lasre["n_k"]
+    port_cubes = lasre["port_cubes"]
+    zxgridgraph = ZXGridGraph(lasre)
+    edges = zxgridgraph.edges
+    nodes = zxgridgraph.nodes
+
+    zx_nx_graph = networkx.Graph()
+    type_to_str = {"X": "X", "Z": "Z", "Pi": "in", "Po": "out", "I": "X"}
+    cnt = 0
+    for (i, j, k) in port_cubes:
+        node = nodes[i][j][k]
+        zx_nx_graph.add_node(cnt, value=stimzx.ZxType(type_to_str[node.type]))
+        node.node_id = cnt
+        cnt += 1
+
+    for i in range(n_i + 1):
+        for j in range(n_j + 1):
+            for k in range(n_k + 1):
+                node = nodes[i][j][k]
+                if node.type not in ["N", "Po", "Pi"]:
+                    zx_nx_graph.add_node(cnt,
+                                         value=stimzx.ZxType(
+                                             type_to_str[node.type]))
+                    node.node_id = cnt
+                    cnt += 1
+                if node.y_tail_minus:
+                    zx_nx_graph.add_node(cnt, value=stimzx.ZxType("Z", 1))
+                    zx_nx_graph.add_edge(node.node_id, cnt)
+                    cnt += 1
+                if node.y_tail_plus:
+                    zx_nx_graph.add_node(cnt, value=stimzx.ZxType("Z", 3))
+                    zx_nx_graph.add_edge(node.node_id, cnt)
+                    cnt += 1
+
+    for edge in edges:
+        if edge.type != "h":
+            zx_nx_graph.add_edge(edge.node0.node_id, edge.node1.node_id)
+        else:
+            zx_nx_graph.add_node(cnt, value=stimzx.ZxType("H"))
+            zx_nx_graph.add_edge(cnt, edge.node0.node_id)
+            zx_nx_graph.add_edge(cnt, edge.node1.node_id)
+            cnt += 1
+
+    return zx_nx_graph
diff --git a/glue/lattice_surgery/lassynth/translators/textfig_generator.py b/glue/lattice_surgery/lassynth/translators/textfig_generator.py
new file mode 100644
index 00000000..1024b46f
--- /dev/null
+++ b/glue/lattice_surgery/lassynth/translators/textfig_generator.py
@@ -0,0 +1,217 @@
+"""Generate text figures of 2D time slices of the LaS."""
+
+from lassynth.translators import ZXGridGraph
+
+
+class TextLayer:
+    pad_i = 1
+    pad_j = 1
+    sep_i = 4
+    sep_j = 4
+
+    def __init__(self, zx_graph: ZXGridGraph, k: int, if_middle: bool) -> None:
+        self.n_i, self.n_j, self.n_k = (
+            zx_graph.n_i,
+            zx_graph.n_j,
+            zx_graph.n_k,
+        )
+        self.chars = [[
+            " " for _ in range(2 * TextLayer.pad_i +
+                               (self.n_i - 1) * TextLayer.sep_i + 1)
+        ] + ["\n"] for _ in range(2 * TextLayer.pad_j +
+                                  (self.n_j - 1) * TextLayer.sep_j + 1)]
+        if if_middle:
+            self.compute_middle(zx_graph, k)
+        else:
+            self.compute_normal(zx_graph, k)
+
+    def set_char(self, j: int, i: int, character):
+        self.chars[j][i] = character
+
+    def compute_normal(self, zx_graph: ZXGridGraph, k: int):
+        """a normal layer corresponds to a layer of cubes in LaS, e.g., 
+             /   /
+            X   X 
+            |  /  
+            |     
+            |/    
+            Z   . 
+           /      
+        There are 2x2 tiles of surface codes. The bottom right one is not being
+        used, represented by a `.`; the top right one is identity in because
+        it has degree 2, but our convention is that these spiders have type `X`
+        The top left one is like that, too. The bottom left is a Z-spider with
+        three edges, which is non trivial. `-` and `|` I-pipes and J-pipes.
+        `/` are K-pipes. The `/` on the bottom left corner of a spider connects
+        to the previous moment. The `/` on the top right corner of a spider
+        connects to the next moment.
+
+        Args:
+            zx_graph (ZXGridGraph):
+            k (int): the height of this layer.
+        """
+        for i in range(self.n_i):
+            for j in range(self.n_j):
+                spider = zx_graph.nodes[i][j][k]
+
+                if spider.type in ["N", "Pi", "Po"]:
+                    self.set_char(
+                        TextLayer.pad_j + j * TextLayer.sep_j,
+                        TextLayer.pad_i + i * TextLayer.sep_i,
+                        ".",
+                    )
+                    continue
+                elif spider.type == "I":
+                    self.set_char(
+                        TextLayer.pad_j + j * TextLayer.sep_j,
+                        TextLayer.pad_i + i * TextLayer.sep_i,
+                        "X",
+                    )
+                else:
+                    self.set_char(
+                        TextLayer.pad_j + j * TextLayer.sep_j,
+                        TextLayer.pad_i + i * TextLayer.sep_i,
+                        spider.type,
+                    )
+
+                # I pipes
+                if spider.exists["+I"]:
+                    for offset in range(1, TextLayer.sep_i):
+                        self.set_char(
+                            TextLayer.pad_j + j * TextLayer.sep_j,
+                            TextLayer.pad_i + i * TextLayer.sep_i + offset,
+                            "-",
+                        )
+
+                # J pipes
+                if spider.exists["+J"]:
+                    for offset in range(1, TextLayer.sep_i):
+                        self.set_char(
+                            TextLayer.pad_j + j * TextLayer.sep_j + offset,
+                            TextLayer.pad_i + i * TextLayer.sep_i,
+                            "|",
+                        )
+
+                # K pipes
+                if spider.exists["+K"]:
+                    self.set_char(
+                        TextLayer.pad_j + j * TextLayer.sep_j - 1,
+                        TextLayer.pad_i + i * TextLayer.sep_i + 1,
+                        "/",
+                    )
+                if spider.exists["-K"]:
+                    self.set_char(
+                        TextLayer.pad_j + j * TextLayer.sep_j + 1,
+                        TextLayer.pad_i + i * TextLayer.sep_i - 1,
+                        "/",
+                    )
+
+    def compute_middle(self, zx_graph: ZXGridGraph, k: int):
+        """a middle layer is either a Hadmard edge or a normal edge, e.g.,
+                 /
+            .   X 
+               /  
+                
+             /    
+            H   . 
+           /      
+        These layers cannot have `-` or `|`. It only has `/` which are K-pipes.
+        The node is either `H` meaning the edge is a Hadamard edge, or `X`
+        meaning the edge is a normal edge. We use `X` for identity here.
+
+        Args:
+            zx_graph (ZXGridGraph): 
+            k (int): the height of this layer. There is a middle layer after a 
+                normal layer.
+        """
+        for i in range(self.n_i):
+            for j in range(self.n_j):
+                self.set_char(
+                    TextLayer.pad_j + j * TextLayer.sep_j,
+                    TextLayer.pad_i + i * TextLayer.sep_i,
+                    ".",
+                )
+                spider = zx_graph.nodes[i][j][k]
+                color_sum = -1
+                if k == self.n_k - 1:
+                    try:
+                        for port in zx_graph.lasre["ports"]:
+                            if (port["i"], port["j"], port["k"]) == (i, j, k):
+                                color_sum = port["c"] + spider.colors["+K"]
+                                break
+                    except ValueError:
+                        print(
+                            f"KPipe({i},{j},{k}) connect outside but not port."
+                        )
+                else:
+                    upper_spider = zx_graph.nodes[i][j][k + 1]
+                    if spider.exists["+K"] == 1 and upper_spider.exists[
+                            "-K"] == 1:
+                        color_sum = spider.colors["+K"] + upper_spider.colors[
+                            "-K"]
+                    if spider.exists["+K"] == 0 and upper_spider.exists[
+                            "-K"] == 1:
+                        try:
+                            for port in zx_graph.lasre["ports"]:
+                                if (port["i"], port["j"], port["k"]) == (i, j,
+                                                                         k):
+                                    color_sum = port[
+                                        "c"] + upper_spider.colors["-K"]
+                                    break
+                        except ValueError:
+                            print(f"KPipe({i},{j},{k})- should be a port.")
+                    if spider.exists["+K"] == 1 and upper_spider.exists[
+                            "-K"] == 0:
+                        try:
+                            for port in zx_graph.lasre["ports"]:
+                                if (port["i"], port["j"],
+                                        port["k"]) == (i, j, k + 1):
+                                    color_sum = port["c"] + spider.colors["+K"]
+                                    break
+                        except ValueError:
+                            print(f"KPipe({i},{j},{k + 1})- should be a port")
+
+                if color_sum != -1:
+                    self.set_char(
+                        TextLayer.pad_j + j * TextLayer.sep_j - 1,
+                        TextLayer.pad_i + i * TextLayer.sep_i + 1,
+                        "/",
+                    )
+                    self.set_char(
+                        TextLayer.pad_j + j * TextLayer.sep_j + 1,
+                        TextLayer.pad_i + i * TextLayer.sep_i - 1,
+                        "/",
+                    )
+                    if color_sum == 1:
+                        self.set_char(
+                            TextLayer.pad_j + j * TextLayer.sep_j,
+                            TextLayer.pad_i + i * TextLayer.sep_i,
+                            "H",
+                        )
+                    else:
+                        self.set_char(
+                            TextLayer.pad_j + j * TextLayer.sep_j,
+                            TextLayer.pad_i + i * TextLayer.sep_i,
+                            "X",
+                        )
+
+    def get_text(self):
+        text = ""
+        for j in range(2 * TextLayer.pad_j + (self.n_j - 1) * TextLayer.sep_j +
+                       1):
+            for i in range(2 * TextLayer.pad_i +
+                           (self.n_i - 1) * TextLayer.sep_i + 1):
+                text += self.chars[j][i]
+            text += "\n"
+        return text
+
+
+def textfig_generator(lasre: dict):
+    text = "======================================\n"
+    zx_graph = ZXGridGraph(lasre)
+    for k in range(lasre["n_k"] - 1, -1, -1):
+        text += TextLayer(zx_graph, k, True).get_text()
+        text += "======================================\n"
+        text += TextLayer(zx_graph, k, False).get_text()
+        text += "======================================\n"
+    return text
diff --git a/glue/lattice_surgery/lassynth/translators/zx_grid_graph.py b/glue/lattice_surgery/lassynth/translators/zx_grid_graph.py
new file mode 100644
index 00000000..4586e6df
--- /dev/null
+++ b/glue/lattice_surgery/lassynth/translators/zx_grid_graph.py
@@ -0,0 +1,292 @@
+"""Classes ZXGridEdge, ZXGridSpider, and ZXGridGraph. ZXGridGraph is a graph
+where nodes are the cubes in LaS and edges are pipes in LaS.
+"""
+
+from typing import Any, Mapping, Sequence, Tuple, Optional
+
+
+class ZXGridNode:
+
+    def __init__(self, coord3: Tuple[int, int, int],
+                 connectivity: Mapping[str, Mapping[str, int]]) -> None:
+        """initialize ZXGridNode for a cube in the LaS.
+
+        self.type: type of ZX spider, 'N': no spider, 'X'/'Z': X/Z-spider,
+            'S': Y cube, 'I': identity, 'Pi': input port, 'Po': output port. 
+        self.i/j/k: 3D corrdinates of the cube in the LaS.
+        self.exists is a dictionary with six keys corresponding to whether a
+            pipe exist in the six directions to a cube in the LaS.
+        self.colors are the colors of these possible pipes.
+        self.y_tail_plus: if this node connects a Y on the top.
+        self.y_tail_minus: if this node connects a Y on the bottom.
+        
+        Args:
+            coord3 (Tuple[int, int, int]): 3D coordinate of the cube.
+            connectivity (Mapping[str, Mapping[str, int]]): contains exists
+                and colors of the six possible pipes to a cube
+        """
+        self.i, self.j, self.k = coord3
+        self.y_tail_plus = False
+        self.y_tail_minus = False
+        self.node_id = -1
+        self.exists = connectivity["exists"]
+        self.colors = connectivity["colors"]
+        self.compute_type()
+
+    def compute_type(self) -> None:
+        """decide the type of a ZXGridNoe
+        
+        Raises:
+            ValueError: node has degree=1, which should be forbidden earlier.
+            ValueError: node has degree>4, which should be forbidden earlier.
+        """
+        deg = sum([v for (k, v) in self.exists.items()])
+        if deg == 0:
+            self.type = "N"
+            return
+        elif deg == 1:
+            raise ValueError("There should not be deg-1 Z or X spiders.")
+        elif deg == 2:
+            self.type = "I"
+        elif deg >= 5:
+            raise ValueError("deg > 4: 3D corner exists")
+        else:  # degree = 3 or 4
+            if self.exists["-I"] == 0 and self.exists["+I"] == 0:
+                if self.exists["-J"]:
+                    if self.colors["-J"] == 0:
+                        self.type = "X"
+                    else:
+                        self.type = "Z"
+                else:  # must exist +J
+                    if self.colors["+J"] == 0:
+                        self.type = "X"
+                    else:
+                        self.type = "Z"
+
+            if self.exists["-J"] == 0 and self.exists["+J"] == 0:
+                if self.exists["-I"]:
+                    if self.colors["-I"] == 0:
+                        self.type = "Z"
+                    else:
+                        self.type = "X"
+                else:  # must exist +I
+                    if self.colors["+I"] == 0:
+                        self.type = "Z"
+                    else:
+                        self.type = "X"
+
+            if self.exists["-K"] == 0 and self.exists["+K"] == 0:
+                if self.exists["-I"]:
+                    if self.colors["-I"] == 0:
+                        self.type = "X"
+                    else:
+                        self.type = "Z"
+                else:  # must exist +I
+                    if self.colors["+I"] == 0:
+                        self.type = "X"
+                    else:
+                        self.type = "Z"
+
+    def zigxag_xy(self, n_j: int) -> Tuple[int, int]:
+        return (self.k * (n_j + 2) + self.j, -(n_j + 1) * self.i + self.j)
+
+    def zigxag_str(self, n_j: int) -> str:
+        zigxag_type = {
+            'Z': '@',
+            'X': 'O',
+            'S': 's',
+            'W': 'w',
+            'I': 'O',
+            'Pi': 'in',
+            'Po': 'out',
+        }
+        (x, y) = self.zigxag_xy(n_j)
+        return str(-y) + ',' + str(-x) + ',' + str(zigxag_type[self.type])
+
+
+class ZXGridEdge:
+
+    def __init__(self, if_h: bool, node0: ZXGridNode,
+                 node1: ZXGridNode) -> None:
+        """initialize ZXGridEdge for a pipe in the LaS.
+        
+        Args:
+            if_h (bool): if this edge is a Hadamard edge.
+            node0 (ZXGridNode): one end of the edge.
+            node1 (ZXGridNode): the other end of the edge.
+
+        Raises:
+            ValueError: the two spiders are the same.
+            ValueError: the two spiders are not neighbors.
+        """
+
+        dist = abs(node0.i - node1.i) + abs(node0.j - node1.j) + abs(node0.k -
+                                                                     node1.k)
+        if dist == 0:
+            raise ValueError(f"{node0} and {node1} are the same.")
+        if dist > 1:
+            raise ValueError(f"{node0} and {node1} are not neighbors.")
+        self.node0, self.node1 = node0, node1
+        self.type = "h" if if_h else "-"
+
+    def zigxag_str(self, n_j: int) -> str:
+        (xa, ya) = self.node0.zigxag_xy(n_j)
+        (xb, yb) = self.node1.zigxag_xy(n_j)
+        return (str(-ya) + ',' + str(-xa) + ',' + str(-yb) + ',' + str(-xb) +
+                ',' + self.type)
+
+
+class ZXGridGraph:
+
+    def __init__(self, lasre: Mapping[str, Any]) -> None:
+        self.lasre = lasre
+        self.n_i, self.n_j, self.n_k = (
+            lasre["n_i"],
+            lasre["n_j"],
+            lasre["n_k"],
+        )
+        self.nodes = [[[
+            ZXGridNode((i, j, k), self.gather_cube_connectivity(i, j, k))
+            for k in range(self.n_k + 1)
+        ] for j in range(self.n_j + 1)] for i in range(self.n_i + 1)]
+        for (i, j, k) in self.lasre["port_cubes"]:
+            self.nodes[i][j][k].type = 'Po'
+        self.append_y_tails()
+        self.edges = []
+        self.derive_edges()
+
+    def gather_cube_connectivity(self, i: int, j: int,
+                                 k: int) -> Mapping[str, Mapping[str, int]]:
+        # exists and colors for no cube
+        exists = {"-I": 0, "+I": 0, "-K": 0, "+K": 0, "-J": 0, "+J": 0}
+        colors = {
+            "-I": -1,
+            "+I": -1,
+            "-K": -1,
+            "+K": -1,
+            "-J": -1,
+            "+J": -1,
+        }
+        if i in range(self.n_i) and j in range(self.n_j) and k in range(
+                self.n_k) and ((i, j, k) not in self.lasre["port_cubes"]) and (
+                    self.lasre["NodeY"][i][j][k] == 0):
+            if i > 0 and self.lasre["ExistI"][i - 1][j][k]:
+                exists["-I"] = 1
+                colors["-I"] = self.lasre["ColorI"][i - 1][j][k]
+            if self.lasre["ExistI"][i][j][k]:
+                exists["+I"] = 1
+                colors["+I"] = self.lasre["ColorI"][i][j][k]
+            if j > 0 and self.lasre["ExistJ"][i][j - 1][k]:
+                exists["-J"] = 1
+                colors["-J"] = self.lasre["ColorJ"][i][j - 1][k]
+            if self.lasre["ExistJ"][i][j][k]:
+                exists["+J"] = 1
+                colors["+J"] = self.lasre["ColorJ"][i][j][k]
+            if k > 0 and self.lasre["ExistK"][i][j][k - 1]:
+                exists["-K"] = 1
+                colors["-K"] = self.lasre["ColorKP"][i][j][k - 1]
+            if self.lasre["ExistK"][i][j][k]:
+                exists["+K"] = 1
+                colors["+K"] = self.lasre["ColorKM"][i][j][k]
+        return {"exists": exists, "colors": colors}
+
+    def append_y_tails(self) -> None:
+        for i in range(self.n_i):
+            for j in range(self.n_j):
+                for k in range(self.n_k):
+                    if self.lasre["NodeY"][i][j][k]:
+                        if (k - 1 >= 0 and self.lasre["ExistK"][i][j][k - 1]
+                                and (not self.lasre["NodeY"][i][j][k - 1])):
+                            self.nodes[i][j][k - 1].y_tail_plus = True
+                        if (k + 1 < self.n_k and self.lasre["ExistK"][i][j][k]
+                                and (not self.lasre["NodeY"][i][j][k + 1])):
+                            self.nodes[i][j][k + 1].y_tail_minus = True
+
+    def derive_edges(self):
+        valid_types = ["Z", "X", "S", "I", "Pi", "Po"]
+        for i in range(self.n_i):
+            for j in range(self.n_j):
+                for k in range(self.n_k):
+                    if (self.lasre["ExistI"][i][j][k] == 1
+                            and self.nodes[i][j][k].type in valid_types
+                            and self.nodes[i + 1][j][k].type in valid_types):
+                        self.edges.append(
+                            ZXGridEdge(0, self.nodes[i][j][k],
+                                       self.nodes[i + 1][j][k]))
+
+                    if (self.lasre["ExistJ"][i][j][k] == 1
+                            and self.nodes[i][j][k].type in valid_types
+                            and self.nodes[i][j + 1][k].type in valid_types):
+                        self.edges.append(
+                            ZXGridEdge(0, self.nodes[i][j][k],
+                                       self.nodes[i][j + 1][k]))
+
+                    if (self.lasre["ExistK"][i][j][k] == 1
+                            and self.nodes[i][j][k].type in valid_types
+                            and self.nodes[i][j][k + 1].type in valid_types):
+                        self.edges.append(
+                            ZXGridEdge(
+                                abs(self.lasre["ColorKM"][i][j][k] -
+                                    self.lasre["ColorKP"][i][j][k]),
+                                self.nodes[i][j][k],
+                                self.nodes[i][j][k + 1],
+                            ))
+
+    def to_zigxag_url(self, io_spec: Optional[Sequence[str]] = None) -> str:
+        """generate a url for ZigXag
+
+        Args:
+            io_spec (Sequence[str], optional): specify whether each port is an
+                input port or an output port. 
+
+        Raises:
+            ValueError: len(io_spec) is not the same with the number of ports.
+
+        Returns:
+            str: zigxag url
+        """
+        if io_spec is not None:
+            if len(io_spec) != len(self.lasre["port_cubes"]):
+                raise ValueError(
+                    f"io_spec has length {len(io_spec)} but there are "
+                    f"{len(self.lasre['port_cubes'])} ports.")
+            for w, (i, j, k) in enumerate(self.lasre["port_cubes"]):
+                self.nodes[i][j][k].type = io_spec[w]
+
+        valid_types = ["Z", "X", "S", "W", "I", "Pi", "Po"]
+        nodes_str = ""
+        first = True
+        for i in range(self.n_i + 1):
+            for j in range(self.n_j + 1):
+                for k in range(self.n_k + 1):
+                    if self.nodes[i][j][k].type in valid_types:
+                        if not first:
+                            nodes_str += ";"
+                        nodes_str += self.nodes[i][j][k].zigxag_str(self.n_j)
+                        first = False
+
+        edges_str = ""
+        for i, edge in enumerate(self.edges):
+            if i > 0:
+                edges_str += ";"
+            edges_str += edge.zigxag_str(self.n_j)
+
+        # add nodes and edges for Y cubes
+        for i in range(self.n_i):
+            for j in range(self.n_j):
+                for k in range(self.n_k):
+                    (x, y) = self.nodes[i][j][k].zigxag_xy(self.n_j)
+                    if self.nodes[i][j][k].y_tail_plus:
+                        nodes_str += (";" + str(x + self.n_j - j) + "," +
+                                      str(y) + ",s")
+                        edges_str += (";" + str(x + self.n_j - j) + "," +
+                                      str(y) + "," + str(x) + "," + str(y) +
+                                      ",-")
+                    if self.nodes[i][j][k].y_tail_minus:
+                        nodes_str += (";" + str(x - j - 1) + "," + str(y) +
+                                      ",s")
+                        edges_str += (";" + str(x - j - 1) + "," + str(y) +
+                                      "," + str(x) + "," + str(y) + ",-")
+
+        zigxag_str = "https://algassert.com/zigxag#" + nodes_str + ":" + edges_str
+        return zigxag_str
diff --git a/glue/lattice_surgery/setup.py b/glue/lattice_surgery/setup.py
new file mode 100644
index 00000000..008b51bb
--- /dev/null
+++ b/glue/lattice_surgery/setup.py
@@ -0,0 +1,28 @@
+from setuptools import find_packages, setup
+
+with open('README.md', encoding='UTF-8') as f:
+    long_description = f.read()
+
+__version__ = '0.1.0'
+
+setup(
+    name='LaSsynth',
+    version=__version__,
+    author='',
+    author_email='',
+    url='',
+    license='Apache 2',
+    packages=find_packages(),
+    description='Lattice Surgery Subroutine Synthesizer',
+    long_description=long_description,
+    long_description_content_type='text/markdown',
+    python_requires='>=3.6.0',
+    data_files=['README.md'],
+    install_requires=[
+        'z3-solver==4.12.1.0',
+        'stim',
+        'networkx',
+        'ipykernel',
+    ],
+    tests_require=['pytest', 'python3-distutils'],
+)
diff --git a/glue/lattice_surgery/stimzx/__init__.py b/glue/lattice_surgery/stimzx/__init__.py
new file mode 100644
index 00000000..93489c42
--- /dev/null
+++ b/glue/lattice_surgery/stimzx/__init__.py
@@ -0,0 +1,14 @@
+__version__ = '1.12.dev0'
+from ._external_stabilizer import (
+    ExternalStabilizer,
+)
+
+from ._text_diagram_parsing import (
+    text_diagram_to_networkx_graph,
+)
+
+from ._zx_graph_solver import (
+    zx_graph_to_external_stabilizers,
+    text_diagram_to_zx_graph,
+    ZxType,
+)
diff --git a/glue/lattice_surgery/stimzx/_external_stabilizer.py b/glue/lattice_surgery/stimzx/_external_stabilizer.py
new file mode 100644
index 00000000..1363fed4
--- /dev/null
+++ b/glue/lattice_surgery/stimzx/_external_stabilizer.py
@@ -0,0 +1,90 @@
+from typing import List, Any
+
+import stim
+
+
+class ExternalStabilizer:
+    """An input-to-output relationship enforced by a stabilizer circuit."""
+
+    def __init__(self, *, input: stim.PauliString, output: stim.PauliString):
+        self.input = input
+        self.output = output
+
+    @staticmethod
+    def from_dual(dual: stim.PauliString, num_inputs: int) -> 'ExternalStabilizer':
+        sign = dual.sign
+
+        # Transpose input. Ys get negated.
+        for k in range(num_inputs):
+            if dual[k] == 2:
+                sign *= -1
+
+        return ExternalStabilizer(
+            input=dual[:num_inputs],
+            output=dual[num_inputs:],
+        )
+
+    @staticmethod
+    def canonicals_from_duals(duals: List[stim.PauliString], num_inputs: int) -> List['ExternalStabilizer']:
+        if not duals:
+            return []
+        duals = [e.copy() for e in duals]
+        num_qubits = len(duals[0])
+        num_outputs = num_qubits - num_inputs
+        id_out = stim.PauliString(num_outputs)
+
+        # Pivot on output qubits, to potentially isolate input-only stabilizers.
+        _eliminate_stabilizers(duals, range(num_inputs, num_qubits))
+
+        # Separate input-only stabilizers from the rest.
+        input_only_stabilizers = []
+        output_using_stabilizers = []
+        for dual in duals:
+            if dual[num_inputs:] == id_out:
+                input_only_stabilizers.append(dual)
+            else:
+                output_using_stabilizers.append(dual)
+
+        # Separately canonicalize the output-using and input-only stabilizers.
+        _eliminate_stabilizers(output_using_stabilizers, range(num_qubits))
+        _eliminate_stabilizers(input_only_stabilizers, range(num_inputs))
+
+        duals = input_only_stabilizers + output_using_stabilizers
+        return [ExternalStabilizer.from_dual(e, num_inputs) for e in duals]
+
+    def __mul__(self, other: 'ExternalStabilizer') -> 'ExternalStabilizer':
+        return ExternalStabilizer(input=other.input * self.input, output=self.output * other.output)
+
+    def __str__(self) -> str:
+        return str(self.input) + ' -> ' + str(self.output)
+
+    def __eq__(self, other: Any) -> bool:
+        if not isinstance(other, ExternalStabilizer):
+            return NotImplemented
+        return self.output == other.output and self.input == other.input
+
+    def __ne__(self, other: Any) -> bool:
+        return not self == other
+
+    def __repr__(self):
+        return f'stimzx.ExternalStabilizer(input={self.input!r}, output={self.output!r})'
+
+
+def _eliminate_stabilizers(stabilizers: List[stim.PauliString], elimination_indices: range):
+    """Performs partial Gaussian elimination on the list of stabilizers."""
+    min_pivot = 0
+    for q in elimination_indices:
+        for b in [1, 3]:
+            for pivot in range(min_pivot, len(stabilizers)):
+                p = stabilizers[pivot][q]
+                if p == 2 or p == b:
+                    break
+            else:
+                continue
+            for k, stabilizer in enumerate(stabilizers):
+                p = stabilizer[q]
+                if k != pivot and (p == 2 or p == b):
+                    stabilizer *= stabilizers[pivot]
+            if min_pivot != pivot:
+                stabilizers[min_pivot], stabilizers[pivot] = stabilizers[pivot], stabilizers[min_pivot]
+            min_pivot += 1
diff --git a/glue/lattice_surgery/stimzx/_external_stabilizer_test.py b/glue/lattice_surgery/stimzx/_external_stabilizer_test.py
new file mode 100644
index 00000000..b0e940b4
--- /dev/null
+++ b/glue/lattice_surgery/stimzx/_external_stabilizer_test.py
@@ -0,0 +1,7 @@
+import stim
+import stimzx
+
+
+def test_repr():
+    e = stimzx.ExternalStabilizer(input=stim.PauliString("XX"), output=stim.PauliString("Y"))
+    assert eval(repr(e), {'stimzx': stimzx, 'stim': stim}) == e
diff --git a/glue/lattice_surgery/stimzx/_text_diagram_parsing.py b/glue/lattice_surgery/stimzx/_text_diagram_parsing.py
new file mode 100644
index 00000000..36c1068a
--- /dev/null
+++ b/glue/lattice_surgery/stimzx/_text_diagram_parsing.py
@@ -0,0 +1,178 @@
+import re
+from typing import Dict, Tuple, TypeVar, List, Set, Callable
+
+import networkx as nx
+
+K = TypeVar("K")
+
+
+def text_diagram_to_networkx_graph(text_diagram: str, *, value_func: Callable[[str], K] = str) -> nx.MultiGraph:
+    r"""Converts a text diagram into a networkx multi graph.
+
+    Args:
+        text_diagram: An ascii text diagram of the graph, linking nodes together with edges. Edges can be horizontal
+            (-), vertical (|), diagonal (/\), crossing (+), or changing direction (*). Nodes can be alphanumeric with
+            parentheses. It is assumed that all text is shown with a fixed-width font.
+        value_func: An optional transformation to apply to the node text in order to get the node's value. Otherwise
+            the node's value is just its text.
+
+    Example:
+
+        >>> import stimzx
+        >>> import networkx as nx
+        >>> actual = stimzx.text_diagram_to_networkx_graph(r'''
+        ...
+        ...           A
+        ...           |
+        ...    NODE1--+--NODE2----------*
+        ...           |     |          /
+        ...           B     |         /
+        ...                 *------NODE4
+        ...
+        ... ''')
+        >>> expected = nx.MultiGraph()
+        >>> expected.add_node(0, value='A')
+        >>> expected.add_node(1, value='NODE1')
+        >>> expected.add_node(2, value='NODE2')
+        >>> expected.add_node(3, value='B')
+        >>> expected.add_node(4, value='NODE4')
+        >>> _ = expected.add_edge(0, 3)
+        >>> _ = expected.add_edge(1, 2)
+        >>> _ = expected.add_edge(2, 4)
+        >>> _ = expected.add_edge(2, 4)
+        >>> nx.testing.assert_graphs_equal(actual, expected)
+
+    Returns:
+        A networkx multi graph containing the graph from the text diagram. Nodes in the graph are integers (the ordering
+        of nodes is in the natural string ordering from left to right then top to bottom in the diagram), and have a
+        "value" attribute containing either the node's string from the diagram or else a function of that string if
+        value_func was specified.
+    """
+    char_map = _text_to_char_map(text_diagram)
+    node_ids, nodes = _find_nodes(char_map, value_func)
+    edges = _find_all_edges(char_map, node_ids)
+    result = nx.MultiGraph()
+    for k, v in enumerate(nodes):
+        result.add_node(k, value=v)
+    for a, b in edges:
+        result.add_edge(a, b)
+    return result
+
+
+def _text_to_char_map(text: str) -> Dict[complex, str]:
+    char_map = {}
+    x = 0
+    y = 0
+    for c in text:
+        if c == '\n':
+            x = 0
+            y += 1
+            continue
+        if c != ' ':
+            char_map[x + 1j*y] = c
+        x += 1
+    return char_map
+
+
+DIR_TO_CHARS = {
+    -1 - 1j: '\\',
+    0 - 1j: '|+',
+    1 - 1j: '/',
+    -1: '-+',
+    1: '-+',
+    -1 + 1j: '/',
+    1j: '|+',
+    1 + 1j: '\\',
+}
+
+CHAR_TO_DIR = {
+    '\\': 1 + 1j,
+    '-': 1,
+    '|': 1j,
+    '/': -1 + 1j,
+}
+
+
+def _find_all_edges(char_map: Dict[complex, str], terminal_map: Dict[complex, K]) -> List[Tuple[K, K]]:
+    edges = []
+    already_travelled = set()
+    for xy, c in char_map.items():
+        x = int(xy.real)
+        y = int(xy.imag)
+        if xy in terminal_map or xy in already_travelled or c in '*+':
+            continue
+        already_travelled.add(xy)
+        dxy = CHAR_TO_DIR.get(c)
+        if dxy is None:
+            raise ValueError(f"Character {x+1} ('{c}') in line {y+1} isn't part in a node or an edge")
+        n1 = _find_end_of_edge(xy + dxy, dxy, char_map, terminal_map, already_travelled)
+        n2 = _find_end_of_edge(xy - dxy, -dxy, char_map, terminal_map, already_travelled)
+        edges.append((n2, n1))
+    return edges
+
+
+def _find_end_of_edge(xy: complex, dxy: complex, char_map: Dict[complex, str], terminal_map: Dict[complex, K], already_travelled: Set[complex]):
+    while True:
+        c = char_map[xy]
+        if xy in terminal_map:
+            return terminal_map[xy]
+
+        if c != '+':
+            if xy in already_travelled:
+                raise ValueError("Edge used twice.")
+            already_travelled.add(xy)
+
+        next_deltas: List[complex] = []
+        if c == '*':
+            for dx2 in [-1, 0, 1]:
+                for dy2 in [-1, 0, 1]:
+                    dxy2 = dx2 + dy2 * 1j
+                    c2 = char_map.get(xy + dxy2)
+                    if dxy2 != 0 and dxy2 != -dxy and c2 is not None and c2 in DIR_TO_CHARS[dxy2]:
+                        next_deltas.append(dxy2)
+            if len(next_deltas) != 1:
+                raise ValueError(f"Edge junction ('*') at character {int(xy.real)+1}$ in line {int(xy.imag)+1} doesn't have exactly 2 legs.")
+            dxy, = next_deltas
+        else:
+            expected = DIR_TO_CHARS[dxy]
+            if c not in expected:
+                raise ValueError(f"Dangling edge at character {int(xy.real)+1} in line {int(xy.imag)+1} travelling dx=${int(dxy.real)},dy={int(dxy.imag)}.")
+        xy += dxy
+
+
+def _find_nodes(char_map: Dict[complex, str], value_func: Callable[[str], K]) -> Tuple[Dict[complex, int], List[K]]:
+    node_ids = {}
+    nodes = []
+
+    node_chars = re.compile("^[a-zA-Z0-9()]$")
+    next_node_id = 0
+
+    for xy, lead_char in char_map.items():
+        if xy in node_ids:
+            continue
+        if not node_chars.match(lead_char):
+            continue
+
+        n = 0
+        nested = 0
+        full_name = ''
+        while True:
+            c = char_map.get(xy + n, ' ')
+            if c == ' ' and nested > 0:
+                raise ValueError("Label ended before ')' to go with '(' was found.")
+            if nested == 0 and not node_chars.match(c):
+                break
+            full_name += c
+            if c == '(':
+                nested += 1
+            elif c == ')':
+                nested -= 1
+            n += 1
+
+        nodes.append(value_func(full_name))
+        node_id = next_node_id
+        next_node_id += 1
+        for k in range(n):
+            node_ids[xy + k] = node_id
+
+    return node_ids, nodes
diff --git a/glue/lattice_surgery/stimzx/_text_diagram_parsing_test.py b/glue/lattice_surgery/stimzx/_text_diagram_parsing_test.py
new file mode 100644
index 00000000..eef8e900
--- /dev/null
+++ b/glue/lattice_surgery/stimzx/_text_diagram_parsing_test.py
@@ -0,0 +1,149 @@
+import networkx as nx
+import pytest
+from ._text_diagram_parsing import _find_nodes, _text_to_char_map, _find_end_of_edge, _find_all_edges, text_diagram_to_networkx_graph
+
+
+def test_text_to_char_map():
+    assert _text_to_char_map("""
+ABC DEF
+G    
+ HI
+    """) == {
+        0 + 1j: 'A',
+        1 + 1j: 'B',
+        2 + 1j: 'C',
+        4 + 1j: 'D',
+        5 + 1j: 'E',
+        6 + 1j: 'F',
+        0 + 2j: 'G',
+        1 + 3j: 'H',
+        2 + 3j: 'I',
+    }
+
+
+def test_find_nodes():
+    assert _find_nodes(_text_to_char_map(''), lambda e: e) == ({}, [])
+    with pytest.raises(ValueError, match="base 10"):
+        _find_nodes(_text_to_char_map('NOTANINT'), int)
+    with pytest.raises(ValueError, match=r"ended before '\)'"):
+        _find_nodes(_text_to_char_map('X(run_off'), str)
+    assert _find_nodes(_text_to_char_map('X'), str) == (
+        {
+            0j: 0,
+        },
+        ['X'],
+    )
+    assert _find_nodes(_text_to_char_map('\n   X'), str) == (
+        {
+            3 + 1j: 0,
+        },
+        ['X'],
+    )
+    assert _find_nodes(_text_to_char_map('X(pi)'), str) == (
+        {
+            0: 0,
+            1: 0,
+            2: 0,
+            3: 0,
+            4: 0,
+        },
+        ['X(pi)'],
+    )
+    assert _find_nodes(_text_to_char_map('X--Z'), str) == (
+        {
+            0: 0,
+            3: 1,
+        },
+        ['X', 'Z'],
+    )
+    assert _find_nodes(_text_to_char_map("""
+X--*
+  /
+ Z
+"""), str) == (
+        {
+            1j: 0,
+            1 + 3j: 1,
+        },
+        ['X', 'Z'],
+    )
+    assert _find_nodes(_text_to_char_map("""
+X(pi)--Z
+"""), str) == (
+        {
+            0 + 1j: 0,
+            1 + 1j: 0,
+            2 + 1j: 0,
+            3 + 1j: 0,
+            4 + 1j: 0,
+            7 + 1j: 1,
+        },
+        ["X(pi)", "Z"],
+    )
+
+
+def test_find_end_of_edge():
+    c = _text_to_char_map(r"""
+1--------*
+          \    2      |
+     5     \      *--++-*
+            *-----+-* |/
+                  | | /
+                  2 |/
+                    *
+    """)
+    terminal = {1: 'ONE', 18 + 6j: 'TWO'}
+    seen = set()
+    assert _find_end_of_edge(1 + 1j, 1, c, terminal, seen) == 'TWO'
+    assert len(seen) == 31
+
+
+def test_find_all_edges():
+    c = _text_to_char_map(r"""
+X---Z      H----X(pi/2)
+          /
+       Z(pi/2)
+    """)
+    node_ids, _ = _find_nodes(c, str)
+    assert _find_all_edges(c, node_ids) == [
+        (0, 1),
+        (2, 3),
+        (2, 4),
+    ]
+
+
+def test_from_text_diagram():
+    actual = text_diagram_to_networkx_graph("""
+in---Z---H---------out
+     |
+in---X---Z(-pi/2)---out
+    """)
+    expected = nx.MultiGraph()
+    expected.add_node(0, value='in'),
+    expected.add_node(1, value='Z'),
+    expected.add_node(2, value='H'),
+    expected.add_node(3, value='out'),
+    expected.add_node(4, value='in'),
+    expected.add_node(5, value='X'),
+    expected.add_node(6, value='Z(-pi/2)'),
+    expected.add_node(7, value='out'),
+    expected.add_edge(0, 1)
+    expected.add_edge(1, 2)
+    expected.add_edge(2, 3)
+    expected.add_edge(1, 5)
+    expected.add_edge(4, 5)
+    expected.add_edge(5, 6)
+    expected.add_edge(6, 7)
+    nx.testing.assert_graphs_equal(actual, expected)
+
+    actual = text_diagram_to_networkx_graph("""
+        Z-*
+        | |
+        X-*
+    """)
+    expected = nx.MultiGraph()
+    expected.add_node(0, value='Z')
+    expected.add_node(1, value='X')
+    expected.add_edge(0, 1)
+    expected.add_edge(0, 1)
+    nx.testing.assert_graphs_equal(actual, expected)
diff --git a/glue/lattice_surgery/stimzx/_zx_graph_solver.py b/glue/lattice_surgery/stimzx/_zx_graph_solver.py
new file mode 100644
index 00000000..ff5bd091
--- /dev/null
+++ b/glue/lattice_surgery/stimzx/_zx_graph_solver.py
@@ -0,0 +1,196 @@
+from typing import Dict, Tuple, List, Any, Union
+import stim
+import networkx as nx
+
+from ._text_diagram_parsing import text_diagram_to_networkx_graph
+from ._external_stabilizer import ExternalStabilizer
+
+
+class ZxType:
+    """Data describing a ZX node."""
+
+    def __init__(self, kind: str, quarter_turns: int = 0):
+        self.kind = kind
+        self.quarter_turns = quarter_turns
+
+    def __eq__(self, other):
+        if not isinstance(other, ZxType):
+            return NotImplemented
+        return self.kind == other.kind and self.quarter_turns == other.quarter_turns
+
+    def __ne__(self, other):
+        return not self == other
+
+    def __hash__(self):
+        return hash((ZxType, self.kind, self.quarter_turns))
+
+    def __repr__(self):
+        return f'ZxType(kind={self.kind!r}, quarter_turns={self.quarter_turns!r})'
+
+
+ZX_TYPES = {
+    "X": ZxType("X"),
+    "X(pi/2)": ZxType("X", 1),
+    "X(pi)": ZxType("X", 2),
+    "X(-pi/2)": ZxType("X", 3),
+    "Z": ZxType("Z"),
+    "Z(pi/2)": ZxType("Z", 1),
+    "Z(pi)": ZxType("Z", 2),
+    "Z(-pi/2)": ZxType("Z", 3),
+    "H": ZxType("H"),
+    "in": ZxType("in"),
+    "out": ZxType("out"),
+}
+
+
+def text_diagram_to_zx_graph(text_diagram: str) -> nx.MultiGraph:
+    """Converts an ASCII text diagram into a ZX graph (represented as a networkx MultiGraph).
+
+    Supported node types:
+        "X": X spider with angle set to 0.
+        "Z": Z spider with angle set to 0.
+        "X(pi/2)": X spider with angle set to pi/2.
+        "X(pi)": X spider with angle set to pi.
+        "X(-pi/2)": X spider with angle set to -pi/2.
+        "Z(pi/2)": X spider with angle set to pi/2.
+        "Z(pi)": X spider with angle set to pi.
+        "Z(-pi/2)": X spider with angle set to -pi/2.
+        "H": Hadamard node. Must have degree 2.
+        "in": Input node. Must have degree 1.
+        "out": Output node. Must have degree 1.
+
+    Args:
+        text_diagram: A text diagram containing ZX nodes (e.g. "X(pi)") and edges (e.g. "------") connecting them.
+
+    Example:
+        >>> import stimzx
+        >>> import networkx
+        >>> actual: networkx.MultiGraph = stimzx.text_diagram_to_zx_graph(r'''
+        ...     in----X------out
+        ...           |
+        ...     in---Z(pi)---out
+        ... ''')
+        >>> expected = networkx.MultiGraph()
+        >>> expected.add_node(0, value=stimzx.ZxType("in"))
+        >>> expected.add_node(1, value=stimzx.ZxType("X"))
+        >>> expected.add_node(2, value=stimzx.ZxType("out"))
+        >>> expected.add_node(3, value=stimzx.ZxType("in"))
+        >>> expected.add_node(4, value=stimzx.ZxType("Z", quarter_turns=2))
+        >>> expected.add_node(5, value=stimzx.ZxType("out"))
+        >>> _ = expected.add_edge(0, 1)
+        >>> _ = expected.add_edge(1, 2)
+        >>> _ = expected.add_edge(1, 4)
+        >>> _ = expected.add_edge(3, 4)
+        >>> _ = expected.add_edge(4, 5)
+        >>> networkx.testing.assert_graphs_equal(actual, expected)
+
+    Returns:
+        A networkx MultiGraph containing the nodes and edges from the diagram. Nodes are numbered 0, 1, 2, etc in
+            reading ordering from the diagram, and have a "value" attribute of type `stimzx.ZxType`.
+    """
+    return text_diagram_to_networkx_graph(text_diagram, value_func=ZX_TYPES.__getitem__)
+
+
+def _reduced_zx_graph(graph: Union[nx.Graph, nx.MultiGraph]) -> nx.Graph:
+    """Return an equivalent graph without self edges or repeated edges."""
+    reduced_graph = nx.Graph()
+    odd_parity_edges = set()
+    for n1, n2 in graph.edges():
+        if n1 == n2:
+            continue
+        odd_parity_edges ^= {frozenset([n1, n2])}
+    for n, value in graph.nodes('value'):
+        reduced_graph.add_node(n, value=value)
+    for n1, n2 in odd_parity_edges:
+        reduced_graph.add_edge(n1, n2)
+    return reduced_graph
+
+
+def zx_graph_to_external_stabilizers(graph: Union[nx.Graph, nx.MultiGraph]) -> List[ExternalStabilizer]:
+    """Computes the external stabilizers of a ZX graph; generators of Paulis that leave it unchanged including sign.
+
+    Args:
+        graph: A non-contradictory connected ZX graph with nodes annotated by 'type' and optionally by 'angle'.
+            Allowed types are 'x', 'z', 'h', and 'out'.
+            Allowed angles are multiples of `math.pi/2`. Only 'x' and 'z' node types can have angles.
+            'out' nodes must have degree 1.
+            'h' nodes must have degree 2.
+
+    Returns:
+        A list of canonicalized external stabilizer generators for the graph.
+    """
+
+    graph = _reduced_zx_graph(graph)
+    sim = stim.TableauSimulator()
+
+    # Interpret each edge as a cup producing an EPR pair.
+    # - The qubits of the EPR pair fly away from the center of the edge, towards their respective nodes.
+    # - The qubit keyed by (a, b) is the qubit heading towards b from the edge between a and b.
+    qubit_ids: Dict[Tuple[Any, Any], int] = {}
+    for n1, n2 in graph.edges:
+        qubit_ids[(n1, n2)] = len(qubit_ids)
+        qubit_ids[(n2, n1)] = len(qubit_ids)
+        sim.h(qubit_ids[(n1, n2)])
+        sim.cnot(qubit_ids[(n1, n2)], qubit_ids[(n2, n1)])
+
+    # Interpret each internal node as a family of post-selected parity measurements.
+    for n, node_type in graph.nodes('value'):
+        if node_type.kind in 'XZ':
+            # Surround X type node with Hadamards so it can be handled as if it were Z type.
+            if node_type.kind == 'X':
+                for neighbor in graph.neighbors(n):
+                    sim.h(qubit_ids[(neighbor, n)])
+        elif node_type.kind == 'H':
+            # Hadamard one input so the H node can be handled as if it were Z type.
+            neighbor, _ = graph.neighbors(n)
+            sim.h(qubit_ids[(neighbor, n)])
+        elif node_type.kind in ['out', 'in']:
+            continue  # Don't measure qubits leaving the system.
+        else:
+            raise ValueError(f"Unknown node type {node_type!r}")
+
+        # Handle Z type node.
+        # - Postselects the ZZ observable over each pair of incoming qubits.
+        # - Postselects the (S**quarter_turns X S**-quarter_turns)XX..X observable over all incoming qubits.
+        neighbors = [n2 for n2 in graph.neighbors(n) if n2 != n]
+        center = qubit_ids[(neighbors[0], n)]  # Pick one incoming qubit to be the common control for the others.
+        # Handle node angle using a phasing operation.
+        [id, sim.s, sim.z, sim.s_dag][node_type.quarter_turns](center)
+        # Use multi-target CNOT and Hadamard to transform postselected observables into single-qubit Z observables.
+        for n2 in neighbors[1:]:
+            sim.cnot(center, qubit_ids[(n2, n)])
+        sim.h(center)
+        # Postselect the observables.
+        for n2 in neighbors:
+            _pseudo_postselect(sim, qubit_ids[(n2, n)])
+
+    # Find output qubits.
+    in_nodes = sorted(n for n, value in graph.nodes('value') if value.kind == 'in')
+    out_nodes = sorted(n for n, value in graph.nodes('value') if value.kind == 'out')
+    ext_nodes = in_nodes + out_nodes
+    out_qubits = []
+    for out in ext_nodes:
+        (neighbor,) = graph.neighbors(out)
+        out_qubits.append(qubit_ids[(neighbor, out)])
+
+    # Remove qubits corresponding to non-external edges.
+    for i, q in enumerate(out_qubits):
+        sim.swap(q, len(qubit_ids) + i)
+    for i, q in enumerate(out_qubits):
+        sim.swap(i, len(qubit_ids) + i)
+    sim.set_num_qubits(len(out_qubits))
+
+    # Stabilizers of the simulator state are the external stabilizers of the graph.
+    dual_stabilizers = sim.canonical_stabilizers()
+    return ExternalStabilizer.canonicals_from_duals(dual_stabilizers, len(in_nodes))
+
+
+def _pseudo_postselect(sim: stim.TableauSimulator, target: int):
+    """Pretend to postselect by using classical feedback to consistently get into the measurement-was-false state."""
+    measurement_result, kickback = sim.measure_kickback(target)
+    if kickback is not None:
+        for qubit, pauli in enumerate(kickback):
+            feedback_op = [None, sim.cnot, sim.cy, sim.cz][pauli]
+            if feedback_op is not None:
+                feedback_op(stim.target_rec(-1), qubit)
+    assert kickback is not None or not measurement_result, "Impossible postselection. Graph contained a contradiction."
diff --git a/glue/lattice_surgery/stimzx/_zx_graph_solver_test.py b/glue/lattice_surgery/stimzx/_zx_graph_solver_test.py
new file mode 100644
index 00000000..9db02fdf
--- /dev/null
+++ b/glue/lattice_surgery/stimzx/_zx_graph_solver_test.py
@@ -0,0 +1,137 @@
+from typing import List
+
+import stim
+
+from ._zx_graph_solver import zx_graph_to_external_stabilizers, text_diagram_to_zx_graph, ExternalStabilizer
+
+
+def test_disconnected():
+    assert zx_graph_to_external_stabilizers(text_diagram_to_zx_graph("""
+        in---X    X---out
+    """)) == [
+        ExternalStabilizer(input=stim.PauliString("Z"), output=stim.PauliString("_")),
+        ExternalStabilizer(input=stim.PauliString("_"), output=stim.PauliString("Z")),
+    ]
+    assert zx_graph_to_external_stabilizers(text_diagram_to_zx_graph("""
+        in---Z---out
+             |
+             X
+    """)) == [
+        ExternalStabilizer(input=stim.PauliString("Z"), output=stim.PauliString("_")),
+        ExternalStabilizer(input=stim.PauliString("_"), output=stim.PauliString("Z")),
+    ]
+    assert zx_graph_to_external_stabilizers(text_diagram_to_zx_graph("""
+        in---Z---X---out
+             |   |
+             *---*
+    """)) == [
+        ExternalStabilizer(input=stim.PauliString("X"), output=stim.PauliString("_")),
+        ExternalStabilizer(input=stim.PauliString("_"), output=stim.PauliString("Z")),
+    ]
+
+
+def test_cnot():
+    assert zx_graph_to_external_stabilizers(text_diagram_to_zx_graph("""
+        in---X---out
+             |
+        in---Z---out
+    """)) == external_stabilizers_of_circuit(stim.Circuit("CNOT 1 0"))
+
+    assert zx_graph_to_external_stabilizers(text_diagram_to_zx_graph("""
+        in---Z---out
+             |
+        in---X---out
+    """)) == external_stabilizers_of_circuit(stim.Circuit("CNOT 0 1"))
+
+
+def test_cz():
+    assert zx_graph_to_external_stabilizers(text_diagram_to_zx_graph("""
+        in---Z---out
+             |
+             H
+             |
+        in---Z---out
+    """)) == external_stabilizers_of_circuit(stim.Circuit("CZ 0 1"))
+
+
+def test_s():
+    assert zx_graph_to_external_stabilizers(text_diagram_to_zx_graph("""
+        in---Z(pi/2)---out
+    """)) == external_stabilizers_of_circuit(stim.Circuit("S 0"))
+
+
+def test_s_dag():
+    assert zx_graph_to_external_stabilizers(text_diagram_to_zx_graph("""
+        in---Z(-pi/2)---out
+    """)) == external_stabilizers_of_circuit(stim.Circuit("S_DAG 0"))
+
+
+def test_sqrt_x():
+    assert zx_graph_to_external_stabilizers(text_diagram_to_zx_graph("""
+        in---X(pi/2)---out
+    """)) == external_stabilizers_of_circuit(stim.Circuit("SQRT_X 0"))
+
+
+def test_sqrt_x_sqrt_x():
+    assert zx_graph_to_external_stabilizers(text_diagram_to_zx_graph("""
+        in---X(pi/2)---X(pi/2)---out
+    """)) == external_stabilizers_of_circuit(stim.Circuit("X 0"))
+
+
+def test_sqrt_z_sqrt_z():
+    assert zx_graph_to_external_stabilizers(text_diagram_to_zx_graph("""
+        in---Z(pi/2)---Z(pi/2)---out
+    """)) == external_stabilizers_of_circuit(stim.Circuit("Z 0"))
+
+
+def test_sqrt_x_dag():
+    assert zx_graph_to_external_stabilizers(text_diagram_to_zx_graph("""
+        in---X(-pi/2)---out
+    """)) == external_stabilizers_of_circuit(stim.Circuit("SQRT_X_DAG 0"))
+
+
+def test_x():
+    assert zx_graph_to_external_stabilizers(text_diagram_to_zx_graph("""
+        in---X(pi)---out
+    """)) == external_stabilizers_of_circuit(stim.Circuit("X 0"))
+
+
+def test_z():
+    assert zx_graph_to_external_stabilizers(text_diagram_to_zx_graph("""
+        in---Z(pi)---out
+    """)) == external_stabilizers_of_circuit(stim.Circuit("Z 0"))
+
+
+def test_id():
+    assert zx_graph_to_external_stabilizers(text_diagram_to_zx_graph("""
+        in---X---Z---out
+    """)) == external_stabilizers_of_circuit(stim.Circuit("I 0"))
+
+
+def test_s_state_distill():
+    assert zx_graph_to_external_stabilizers(text_diagram_to_zx_graph(r"""
+                        *                  *---------------Z--------------------Z-------Z(pi/2)
+                       / \                 |               |                    |
+                *-----*   *------------Z---+---------------+---Z----------------+-------Z(pi/2)
+                |                      |   |               |   |                |
+                X---X---Z(pi/2)        X---X---Z(pi/2)     X---X---Z(pi/2)      X---X---Z(pi/2)
+                |   |                  |                   |                    |   |
+                *---+------------------Z-------------------+--------------------+---Z---Z(pi/2)
+                    |                                      |                    |
+           in-------Z--------------------------------------Z-------------------Z(pi)--------out
+    """)) == external_stabilizers_of_circuit(stim.Circuit("S 0"))
+
+
+def external_stabilizers_of_circuit(circuit: stim.Circuit) -> List[ExternalStabilizer]:
+    n = circuit.num_qubits
+    s = stim.TableauSimulator()
+    s.do(circuit)
+    t = s.current_inverse_tableau()**-1
+    stabilizers = []
+    for k in range(n):
+        p = [0] * n
+        p[k] = 1
+        stabilizers.append(stim.PauliString(p) + t.x_output(k))
+        p[k] = 3
+        stabilizers.append(stim.PauliString(p) + t.z_output(k))
+    return [ExternalStabilizer.from_dual(e, circuit.num_qubits) for e in stabilizers]

From b4ec946f2f11aef63783cbf3273da0a03ca007ed Mon Sep 17 00:00:00 2001
From: Craig Gidney <craiggidney@google.com>
Date: Mon, 29 Jul 2024 15:59:49 -0700
Subject: [PATCH 08/17] Fix `HERALDED_PAULI_CHANNEL_1` permuting X/Y/Z error
 argument components (#805)

- :foreheadslap:
- autoformat
- Add `stim::circuit_to_dem` to the C++ API for easier conversions with
named arguments via a struct
---
 file_lists/test_files                         |   1 +
 src/stim.h                                    |   1 +
 src/stim/cmd/command_diagram.pybind.cc        |  11 +-
 .../dem/detector_error_model_target.pybind.cc |   1 -
 src/stim/gates/gates.cc                       | 200 +++++++++---------
 src/stim/gates/gates.test.cc                  |   8 +-
 src/stim/simulators/error_analyzer.cc         |  25 ++-
 src/stim/simulators/error_analyzer.h          |   3 +-
 src/stim/simulators/error_analyzer.test.cc    | 117 ++++------
 src/stim/simulators/matched_error.pybind.cc   |   3 +-
 src/stim/util_top/circuit_to_dem.h            |  30 +++
 src/stim/util_top/circuit_to_dem.test.cc      | 115 ++++++++++
 src/stim/util_top/circuit_vs_amplitudes.cc    |   2 +-
 13 files changed, 326 insertions(+), 191 deletions(-)
 create mode 100644 src/stim/util_top/circuit_to_dem.h
 create mode 100644 src/stim/util_top/circuit_to_dem.test.cc

diff --git a/file_lists/test_files b/file_lists/test_files
index 9de5d724..b57d0093 100644
--- a/file_lists/test_files
+++ b/file_lists/test_files
@@ -82,6 +82,7 @@ src/stim/util_bot/twiddle.test.cc
 src/stim/util_top/circuit_flow_generators.test.cc
 src/stim/util_top/circuit_inverse_qec.test.cc
 src/stim/util_top/circuit_inverse_unitary.test.cc
+src/stim/util_top/circuit_to_dem.test.cc
 src/stim/util_top/circuit_to_detecting_regions.test.cc
 src/stim/util_top/circuit_vs_amplitudes.test.cc
 src/stim/util_top/circuit_vs_tableau.test.cc
diff --git a/src/stim.h b/src/stim.h
index adc845a5..e04fcdba 100644
--- a/src/stim.h
+++ b/src/stim.h
@@ -108,6 +108,7 @@
 #include "stim/util_top/circuit_flow_generators.h"
 #include "stim/util_top/circuit_inverse_qec.h"
 #include "stim/util_top/circuit_inverse_unitary.h"
+#include "stim/util_top/circuit_to_dem.h"
 #include "stim/util_top/circuit_to_detecting_regions.h"
 #include "stim/util_top/circuit_vs_amplitudes.h"
 #include "stim/util_top/circuit_vs_tableau.h"
diff --git a/src/stim/cmd/command_diagram.pybind.cc b/src/stim/cmd/command_diagram.pybind.cc
index 3ea12cd4..8f35dda0 100644
--- a/src/stim/cmd/command_diagram.pybind.cc
+++ b/src/stim/cmd/command_diagram.pybind.cc
@@ -268,7 +268,13 @@ DiagramHelper stim_pybind::circuit_diagram(
         type == "timeslice" || type == "time-slice") {
         std::stringstream out;
         DiagramTimelineSvgDrawer::make_diagram_write_to(
-            circuit, out, tick_min, num_ticks, DiagramTimelineSvgDrawerMode::SVG_MODE_TIME_SLICE, filter_coords, num_rows);
+            circuit,
+            out,
+            tick_min,
+            num_ticks,
+            DiagramTimelineSvgDrawerMode::SVG_MODE_TIME_SLICE,
+            filter_coords,
+            num_rows);
         DiagramType d_type =
             type.find("html") != std::string::npos ? DiagramType::DIAGRAM_TYPE_SVG_HTML : DiagramType::DIAGRAM_TYPE_SVG;
         return DiagramHelper{d_type, out.str()};
@@ -276,7 +282,8 @@ DiagramHelper stim_pybind::circuit_diagram(
         type == "detslice-svg" || type == "detslice" || type == "detslice-html" || type == "detslice-svg-html" ||
         type == "detector-slice-svg" || type == "detector-slice") {
         std::stringstream out;
-        DetectorSliceSet::from_circuit_ticks(circuit, tick_min, num_ticks, filter_coords).write_svg_diagram_to(out, num_rows);
+        DetectorSliceSet::from_circuit_ticks(circuit, tick_min, num_ticks, filter_coords)
+            .write_svg_diagram_to(out, num_rows);
         DiagramType d_type =
             type.find("html") != std::string::npos ? DiagramType::DIAGRAM_TYPE_SVG_HTML : DiagramType::DIAGRAM_TYPE_SVG;
         return DiagramHelper{d_type, out.str()};
diff --git a/src/stim/dem/detector_error_model_target.pybind.cc b/src/stim/dem/detector_error_model_target.pybind.cc
index 43b442e6..ce5ca16f 100644
--- a/src/stim/dem/detector_error_model_target.pybind.cc
+++ b/src/stim/dem/detector_error_model_target.pybind.cc
@@ -28,7 +28,6 @@ pybind11::class_<ExposedDemTarget> stim_pybind::pybind_detector_error_model_targ
 
 void stim_pybind::pybind_detector_error_model_target_methods(
     pybind11::module &m, pybind11::class_<ExposedDemTarget> &c) {
-
     c.def(
         pybind11::init([](const pybind11::object &arg) -> ExposedDemTarget {
             if (pybind11::isinstance<ExposedDemTarget>(arg)) {
diff --git a/src/stim/gates/gates.cc b/src/stim/gates/gates.cc
index 09f46adf..2ee52a2b 100644
--- a/src/stim/gates/gates.cc
+++ b/src/stim/gates/gates.cc
@@ -47,110 +47,110 @@ GateDataMap::GateDataMap() {
 
 GateType Gate::hadamard_conjugated(bool ignoring_sign) const {
     switch (id) {
-    case GateType::DETECTOR:
-    case GateType::OBSERVABLE_INCLUDE:
-    case GateType::TICK:
-    case GateType::QUBIT_COORDS:
-    case GateType::SHIFT_COORDS:
-    case GateType::MPAD:
-    case GateType::H:
-    case GateType::DEPOLARIZE1:
-    case GateType::DEPOLARIZE2:
-    case GateType::Y_ERROR:
-    case GateType::I:
-    case GateType::Y:
-    case GateType::SQRT_YY:
-    case GateType::SQRT_YY_DAG:
-    case GateType::MYY:
-    case GateType::SWAP:
-        return id;
+        case GateType::DETECTOR:
+        case GateType::OBSERVABLE_INCLUDE:
+        case GateType::TICK:
+        case GateType::QUBIT_COORDS:
+        case GateType::SHIFT_COORDS:
+        case GateType::MPAD:
+        case GateType::H:
+        case GateType::DEPOLARIZE1:
+        case GateType::DEPOLARIZE2:
+        case GateType::Y_ERROR:
+        case GateType::I:
+        case GateType::Y:
+        case GateType::SQRT_YY:
+        case GateType::SQRT_YY_DAG:
+        case GateType::MYY:
+        case GateType::SWAP:
+            return id;
 
-    case GateType::MY:
-    case GateType::MRY:
-    case GateType::RY:
-    case GateType::YCY:
-        return ignoring_sign ? id : GateType::NOT_A_GATE;
+        case GateType::MY:
+        case GateType::MRY:
+        case GateType::RY:
+        case GateType::YCY:
+            return ignoring_sign ? id : GateType::NOT_A_GATE;
 
-    case GateType::ISWAP:
-    case GateType::CZSWAP:
-    case GateType::ISWAP_DAG:
-        return GateType::NOT_A_GATE;
+        case GateType::ISWAP:
+        case GateType::CZSWAP:
+        case GateType::ISWAP_DAG:
+            return GateType::NOT_A_GATE;
 
-    case GateType::XCY:
-        return ignoring_sign ? GateType::CY : GateType::NOT_A_GATE;
-    case GateType::CY:
-        return ignoring_sign ? GateType::XCY : GateType::NOT_A_GATE;
-    case GateType::YCX:
-        return ignoring_sign ? GateType::YCZ : GateType::NOT_A_GATE;
-    case GateType::YCZ:
-        return ignoring_sign ? GateType::YCX : GateType::NOT_A_GATE;
-    case GateType::C_XYZ:
-        return ignoring_sign ? GateType::C_ZYX : GateType::NOT_A_GATE;
-    case GateType::C_ZYX:
-        return ignoring_sign ? GateType::C_XYZ : GateType::NOT_A_GATE;
-    case GateType::H_XY:
-        return ignoring_sign ? GateType::H_YZ : GateType::NOT_A_GATE;
-    case GateType::H_YZ:
-        return ignoring_sign ? GateType::H_XY : GateType::NOT_A_GATE;
+        case GateType::XCY:
+            return ignoring_sign ? GateType::CY : GateType::NOT_A_GATE;
+        case GateType::CY:
+            return ignoring_sign ? GateType::XCY : GateType::NOT_A_GATE;
+        case GateType::YCX:
+            return ignoring_sign ? GateType::YCZ : GateType::NOT_A_GATE;
+        case GateType::YCZ:
+            return ignoring_sign ? GateType::YCX : GateType::NOT_A_GATE;
+        case GateType::C_XYZ:
+            return ignoring_sign ? GateType::C_ZYX : GateType::NOT_A_GATE;
+        case GateType::C_ZYX:
+            return ignoring_sign ? GateType::C_XYZ : GateType::NOT_A_GATE;
+        case GateType::H_XY:
+            return ignoring_sign ? GateType::H_YZ : GateType::NOT_A_GATE;
+        case GateType::H_YZ:
+            return ignoring_sign ? GateType::H_XY : GateType::NOT_A_GATE;
 
-    case GateType::X:
-        return GateType::Z;
-    case GateType::Z:
-        return GateType::X;
-    case GateType::SQRT_Y:
-        return GateType::SQRT_Y_DAG;
-    case GateType::SQRT_Y_DAG:
-        return GateType::SQRT_Y;
-    case GateType::MX:
-        return GateType::M;
-    case GateType::M:
-        return GateType::MX;
-    case GateType::MRX:
-        return GateType::MR;
-    case GateType::MR:
-        return GateType::MRX;
-    case GateType::RX:
-        return GateType::R;
-    case GateType::R:
-        return GateType::RX;
-    case GateType::XCX:
-        return GateType::CZ;
-    case GateType::XCZ:
-        return GateType::CX;
-    case GateType::CX:
-        return GateType::XCZ;
-    case GateType::CZ:
-        return GateType::XCX;
-    case GateType::X_ERROR:
-        return GateType::Z_ERROR;
-    case GateType::Z_ERROR:
-        return GateType::X_ERROR;
-    case GateType::SQRT_X:
-        return GateType::S;
-    case GateType::SQRT_X_DAG:
-        return GateType::S_DAG;
-    case GateType::S:
-        return GateType::SQRT_X;
-    case GateType::S_DAG:
-        return GateType::SQRT_X_DAG;
-    case GateType::SQRT_XX:
-        return GateType::SQRT_ZZ;
-    case GateType::SQRT_XX_DAG:
-        return GateType::SQRT_ZZ_DAG;
-    case GateType::SQRT_ZZ:
-        return GateType::SQRT_XX;
-    case GateType::SQRT_ZZ_DAG:
-        return GateType::SQRT_XX_DAG;
-    case GateType::CXSWAP:
-        return GateType::SWAPCX;
-    case GateType::SWAPCX:
-        return GateType::CXSWAP;
-    case GateType::MXX:
-        return GateType::MZZ;
-    case GateType::MZZ:
-        return GateType::MXX;
-    default:
-        return GateType::NOT_A_GATE;
+        case GateType::X:
+            return GateType::Z;
+        case GateType::Z:
+            return GateType::X;
+        case GateType::SQRT_Y:
+            return GateType::SQRT_Y_DAG;
+        case GateType::SQRT_Y_DAG:
+            return GateType::SQRT_Y;
+        case GateType::MX:
+            return GateType::M;
+        case GateType::M:
+            return GateType::MX;
+        case GateType::MRX:
+            return GateType::MR;
+        case GateType::MR:
+            return GateType::MRX;
+        case GateType::RX:
+            return GateType::R;
+        case GateType::R:
+            return GateType::RX;
+        case GateType::XCX:
+            return GateType::CZ;
+        case GateType::XCZ:
+            return GateType::CX;
+        case GateType::CX:
+            return GateType::XCZ;
+        case GateType::CZ:
+            return GateType::XCX;
+        case GateType::X_ERROR:
+            return GateType::Z_ERROR;
+        case GateType::Z_ERROR:
+            return GateType::X_ERROR;
+        case GateType::SQRT_X:
+            return GateType::S;
+        case GateType::SQRT_X_DAG:
+            return GateType::S_DAG;
+        case GateType::S:
+            return GateType::SQRT_X;
+        case GateType::S_DAG:
+            return GateType::SQRT_X_DAG;
+        case GateType::SQRT_XX:
+            return GateType::SQRT_ZZ;
+        case GateType::SQRT_XX_DAG:
+            return GateType::SQRT_ZZ_DAG;
+        case GateType::SQRT_ZZ:
+            return GateType::SQRT_XX;
+        case GateType::SQRT_ZZ_DAG:
+            return GateType::SQRT_XX_DAG;
+        case GateType::CXSWAP:
+            return GateType::SWAPCX;
+        case GateType::SWAPCX:
+            return GateType::CXSWAP;
+        case GateType::MXX:
+            return GateType::MZZ;
+        case GateType::MZZ:
+            return GateType::MXX;
+        default:
+            return GateType::NOT_A_GATE;
     }
 }
 
diff --git a/src/stim/gates/gates.test.cc b/src/stim/gates/gates.test.cc
index edafb88f..1fe5c6b3 100644
--- a/src/stim/gates/gates.test.cc
+++ b/src/stim/gates/gates.test.cc
@@ -22,8 +22,8 @@
 #include "stim/simulators/tableau_simulator.h"
 #include "stim/util_bot/str_util.h"
 #include "stim/util_bot/test_util.test.h"
-#include "stim/util_top/has_flow.h"
 #include "stim/util_top/circuit_flow_generators.h"
+#include "stim/util_top/has_flow.h"
 
 using namespace stim;
 
@@ -375,8 +375,10 @@ TEST(gate_data, hadamard_conjugated_vs_flow_generators_of_two_qubit_gates) {
             GateType actual_s = g.hadamard_conjugated(false);
             GateType actual_u = g.hadamard_conjugated(true);
             bool found = std::find(other_us.begin(), other_us.end(), actual_u) != other_us.end();
-            EXPECT_EQ(actual_s, expected_s) << "signed " << g.name << " -> " << GATE_DATA[actual_s].name << " != " << GATE_DATA[expected_s].name;
-            EXPECT_TRUE(found) << "unsigned " << g.name << " -> " << GATE_DATA[actual_u].name << " not in " << GATE_DATA[other_us[0]].name;
+            EXPECT_EQ(actual_s, expected_s)
+                << "signed " << g.name << " -> " << GATE_DATA[actual_s].name << " != " << GATE_DATA[expected_s].name;
+            EXPECT_TRUE(found) << "unsigned " << g.name << " -> " << GATE_DATA[actual_u].name << " not in "
+                               << GATE_DATA[other_us[0]].name;
         }
     }
 }
diff --git a/src/stim/simulators/error_analyzer.cc b/src/stim/simulators/error_analyzer.cc
index 823caeb0..47f393be 100644
--- a/src/stim/simulators/error_analyzer.cc
+++ b/src/stim/simulators/error_analyzer.cc
@@ -307,7 +307,7 @@ void ErrorAnalyzer::undo_MZ_with_context(const CircuitInstruction &dat, const ch
 }
 
 void ErrorAnalyzer::undo_HERALDED_ERASE(const CircuitInstruction &dat) {
-    check_can_approximate_disjoint("HERALDED_ERASE", dat.args);
+    check_can_approximate_disjoint("HERALDED_ERASE", dat.args, false);
     double p = dat.args[0] * 0.25;
     double i = std::max(0.0, 1.0 - 4 * p);
 
@@ -327,7 +327,7 @@ void ErrorAnalyzer::undo_HERALDED_ERASE(const CircuitInstruction &dat) {
 }
 
 void ErrorAnalyzer::undo_HERALDED_PAULI_CHANNEL_1(const CircuitInstruction &dat) {
-    check_can_approximate_disjoint("HERALDED_PAULI_CHANNEL_1", dat.args);
+    check_can_approximate_disjoint("HERALDED_PAULI_CHANNEL_1", dat.args, true);
     double hi = dat.args[0];
     double hx = dat.args[1];
     double hy = dat.args[2];
@@ -341,7 +341,7 @@ void ErrorAnalyzer::undo_HERALDED_PAULI_CHANNEL_1(const CircuitInstruction &dat)
         SparseXorVec<DemTarget> &herald_symptoms = tracker.rec_bits[tracker.num_measurements_in_past];
         if (accumulate_errors) {
             add_error_combinations<3>(
-                {i, 0, 0, 0, hi, hx, hy, hz},
+                {i, 0, 0, 0, hi, hz, hx, hy},
                 {tracker.xs[q].range(), tracker.zs[q].range(), herald_symptoms.range()},
                 true);
         }
@@ -750,7 +750,7 @@ void ErrorAnalyzer::correlated_error_block(const std::vector<CircuitInstruction>
         add_composite_error(dats[0].args[0], dats[0].targets);
         return;
     }
-    check_can_approximate_disjoint("ELSE_CORRELATED_ERROR", {});
+    check_can_approximate_disjoint("ELSE_CORRELATED_ERROR", {}, false);
 
     double remaining_p = 1;
     for (size_t k = dats.size(); k--;) {
@@ -820,7 +820,18 @@ void ErrorAnalyzer::undo_ELSE_CORRELATED_ERROR(const CircuitInstruction &dat) {
     }
 }
 
-void ErrorAnalyzer::check_can_approximate_disjoint(const char *op_name, SpanRef<const double> probabilities) const {
+void ErrorAnalyzer::check_can_approximate_disjoint(
+    const char *op_name, SpanRef<const double> probabilities, bool allow_single_component) const {
+    if (allow_single_component) {
+        size_t num_specified = 0;
+        for (double p : probabilities) {
+            num_specified += p > 0;
+        }
+        if (num_specified <= 1) {
+            return;
+        }
+    }
+
     if (approximate_disjoint_errors_threshold == 0) {
         std::stringstream msg;
         msg << "Encountered the operation " << op_name
@@ -854,7 +865,7 @@ void ErrorAnalyzer::undo_PAULI_CHANNEL_1(const CircuitInstruction &dat) {
     double iz;
     bool is_independent = try_disjoint_to_independent_xyz_errors_approx(dx, dy, dz, &ix, &iy, &iz);
     if (!is_independent) {
-        check_can_approximate_disjoint("PAULI_CHANNEL_1", dat.args);
+        check_can_approximate_disjoint("PAULI_CHANNEL_1", dat.args, true);
         ix = dx;
         iy = dy;
         iz = dz;
@@ -875,7 +886,7 @@ void ErrorAnalyzer::undo_PAULI_CHANNEL_1(const CircuitInstruction &dat) {
 }
 
 void ErrorAnalyzer::undo_PAULI_CHANNEL_2(const CircuitInstruction &dat) {
-    check_can_approximate_disjoint("PAULI_CHANNEL_2", dat.args);
+    check_can_approximate_disjoint("PAULI_CHANNEL_2", dat.args, true);
 
     std::array<double, 16> probabilities;
     for (size_t k = 0; k < 15; k++) {
diff --git a/src/stim/simulators/error_analyzer.h b/src/stim/simulators/error_analyzer.h
index f55178f1..38c14420 100644
--- a/src/stim/simulators/error_analyzer.h
+++ b/src/stim/simulators/error_analyzer.h
@@ -329,7 +329,8 @@ struct ErrorAnalyzer {
     void undo_MXX_disjoint_controls_segment(const CircuitInstruction &inst);
     void undo_MYY_disjoint_controls_segment(const CircuitInstruction &inst);
     void undo_MZZ_disjoint_controls_segment(const CircuitInstruction &inst);
-    void check_can_approximate_disjoint(const char *op_name, SpanRef<const double> probabilities) const;
+    void check_can_approximate_disjoint(
+        const char *op_name, SpanRef<const double> probabilities, bool allow_single_component) const;
     void add_composite_error(double probability, SpanRef<const GateTarget> targets);
     void correlated_error_block(const std::vector<CircuitInstruction> &dats);
 };
diff --git a/src/stim/simulators/error_analyzer.test.cc b/src/stim/simulators/error_analyzer.test.cc
index 1666d96c..3e6b26c9 100644
--- a/src/stim/simulators/error_analyzer.test.cc
+++ b/src/stim/simulators/error_analyzer.test.cc
@@ -23,6 +23,7 @@
 #include "stim/mem/simd_word.test.h"
 #include "stim/simulators/frame_simulator.h"
 #include "stim/util_bot/test_util.test.h"
+#include "stim/util_top/circuit_to_dem.h"
 
 using namespace stim;
 
@@ -3491,17 +3492,13 @@ TEST(ErrorAnalyzer, heralded_erase_conditional_division) {
 }
 
 TEST(ErrorAnalyzer, heralded_erase) {
-    ErrorAnalyzer::circuit_to_detector_error_model(
-        Circuit("HERALDED_ERASE(0.25) 0"), false, false, false, 0.3, false, false);
+    circuit_to_dem(Circuit("HERALDED_ERASE(0.25) 0"), {.approximate_disjoint_errors_threshold = 0.3});
     ASSERT_THROW(
-        {
-            ErrorAnalyzer::circuit_to_detector_error_model(
-                Circuit("HERALDED_ERASE(0.25) 0"), false, false, false, 0.2, false, false);
-        },
+        { circuit_to_dem(Circuit("HERALDED_ERASE(0.25) 0"), {.approximate_disjoint_errors_threshold = 0.2}); },
         std::invalid_argument);
 
     ASSERT_EQ(
-        ErrorAnalyzer::circuit_to_detector_error_model(
+        circuit_to_dem(
             Circuit(R"CIRCUIT(
                 MZZ 0 1
                 MXX 0 1
@@ -3512,12 +3509,7 @@ TEST(ErrorAnalyzer, heralded_erase) {
                 DETECTOR rec[-2] rec[-5]
                 DETECTOR rec[-3]
             )CIRCUIT"),
-            false,
-            false,
-            false,
-            1.0,
-            false,
-            false),
+            {.approximate_disjoint_errors_threshold = 1}),
         DetectorErrorModel(R"DEM(
             error(0.0625) D0 D1 D2
             error(0.0625) D0 D2
@@ -3526,7 +3518,7 @@ TEST(ErrorAnalyzer, heralded_erase) {
         )DEM"));
 
     ASSERT_EQ(
-        ErrorAnalyzer::circuit_to_detector_error_model(
+        circuit_to_dem(
             Circuit(R"CIRCUIT(
                 MPP X10*X11*X20*X21
                 MPP Z11*Z12*Z21*Z22
@@ -3543,12 +3535,7 @@ TEST(ErrorAnalyzer, heralded_erase) {
                 DETECTOR rec[-4] rec[-9]
                 DETECTOR rec[-5]
             )CIRCUIT"),
-            true,
-            false,
-            false,
-            1.0,
-            false,
-            false),
+            {.decompose_errors = true, .approximate_disjoint_errors_threshold = 1}),
         DetectorErrorModel(R"DEM(
             error(0.0625) D0 D3 ^ D1 D2 ^ D4
             error(0.0625) D0 D3 ^ D4
@@ -3557,7 +3544,7 @@ TEST(ErrorAnalyzer, heralded_erase) {
         )DEM"));
 
     ASSERT_EQ(
-        ErrorAnalyzer::circuit_to_detector_error_model(
+        circuit_to_dem(
             Circuit(R"CIRCUIT(
                 M 0
                 HERALDED_ERASE(0.25) 9 0 9 9 9
@@ -3565,19 +3552,14 @@ TEST(ErrorAnalyzer, heralded_erase) {
                 DETECTOR rec[-1] rec[-7]
                 DETECTOR rec[-5]
             )CIRCUIT"),
-            false,
-            false,
-            false,
-            1.0,
-            false,
-            false),
+            {.approximate_disjoint_errors_threshold = 1}),
         DetectorErrorModel(R"DEM(
             error(0.125) D0 D1
             error(0.125) D1
         )DEM"));
 
     ASSERT_EQ(
-        ErrorAnalyzer::circuit_to_detector_error_model(
+        circuit_to_dem(
             Circuit(R"CIRCUIT(
                 MPAD 0
                 MPAD 0
@@ -3594,12 +3576,7 @@ TEST(ErrorAnalyzer, heralded_erase) {
                 DETECTOR rec[-4] rec[-9]
                 DETECTOR rec[-5]
             )CIRCUIT"),
-            true,
-            false,
-            false,
-            1.0,
-            false,
-            false),
+            {.decompose_errors = true, .approximate_disjoint_errors_threshold = 1}),
         DetectorErrorModel(R"DEM(
             error(0.0625) D0 ^ D1 ^ D4
             error(0.0625) D0 ^ D4
@@ -3626,54 +3603,44 @@ TEST(ErrorAnalyzer, heralded_pauli_channel_1) {
         },
         std::invalid_argument);
 
-    ASSERT_TRUE(ErrorAnalyzer::circuit_to_detector_error_model(
+    ASSERT_TRUE(circuit_to_dem(
                     Circuit(R"CIRCUIT(
-                MZZ 0 1
-                MXX 0 1
-                HERALDED_PAULI_CHANNEL_1(0.01, 0.02, 0.03, 0.04) 0
-                MZZ 0 1
-                MXX 0 1
-                DETECTOR rec[-1] rec[-4]
-                DETECTOR rec[-2] rec[-5]
-                DETECTOR rec[-3]
-            )CIRCUIT"),
-                    false,
-                    false,
-                    false,
-                    1.0,
-                    false,
-                    false)
+                        MZZ 0 1
+                        MXX 0 1
+                        HERALDED_PAULI_CHANNEL_1(0.01, 0.02, 0.03, 0.04) 0
+                        MZZ 0 1
+                        MXX 0 1
+                        DETECTOR rec[-1] rec[-4]
+                        DETECTOR rec[-2] rec[-5]
+                        DETECTOR rec[-3]
+                    )CIRCUIT"),
+                    {.approximate_disjoint_errors_threshold = 1})
                     .approx_equals(
                         DetectorErrorModel(R"DEM(
-            error(0.04) D0 D1 D2
-            error(0.02) D0 D2
-            error(0.03) D1 D2
-            error(0.01) D2
-        )DEM"),
+                            error(0.03) D0 D1 D2
+                            error(0.04) D0 D2
+                            error(0.02) D1 D2
+                            error(0.01) D2
+                        )DEM"),
                         1e-6));
 
-    ASSERT_TRUE(ErrorAnalyzer::circuit_to_detector_error_model(
+    ASSERT_TRUE(circuit_to_dem(
                     Circuit(R"CIRCUIT(
-                MZZ 0 1
-                MXX 0 1
-                HERALDED_PAULI_CHANNEL_1(0.01, 0.02, 0.03, 0.04) 0
-                MZZ 0 1
-                MXX 0 1
-                DETECTOR
-                DETECTOR rec[-2] rec[-5]
-                DETECTOR rec[-3]
-            )CIRCUIT"),
-                    false,
-                    false,
-                    false,
-                    1.0,
-                    false,
-                    false)
+                        MZZ 0 1
+                        MXX 0 1
+                        HERALDED_PAULI_CHANNEL_1(0.01, 0.02, 0.03, 0.1) 0
+                        MZZ 0 1
+                        MXX 0 1
+                        DETECTOR
+                        DETECTOR rec[-2] rec[-5]
+                        DETECTOR rec[-3]
+                    )CIRCUIT"),
+                    {.approximate_disjoint_errors_threshold = 1})
                     .approx_equals(
                         DetectorErrorModel(R"DEM(
-            error(0.07) D1 D2
-            error(0.03) D2
-            detector D0
-        )DEM"),
+                            error(0.05) D1 D2
+                            error(0.11) D2
+                            detector D0
+                        )DEM"),
                         1e-6));
 }
diff --git a/src/stim/simulators/matched_error.pybind.cc b/src/stim/simulators/matched_error.pybind.cc
index 984c74c4..a85efef3 100644
--- a/src/stim/simulators/matched_error.pybind.cc
+++ b/src/stim/simulators/matched_error.pybind.cc
@@ -383,7 +383,8 @@ void stim_pybind::pybind_flipped_measurement_methods(
     });
     c.def(
         pybind11::init(
-            [](const pybind11::object &measurement_record_index, const pybind11::object &measured_observable) -> FlippedMeasurement {
+            [](const pybind11::object &measurement_record_index,
+               const pybind11::object &measured_observable) -> FlippedMeasurement {
                 uint64_t u;
                 if (measurement_record_index.is_none()) {
                     u = UINT64_MAX;
diff --git a/src/stim/util_top/circuit_to_dem.h b/src/stim/util_top/circuit_to_dem.h
new file mode 100644
index 00000000..1ffcf4a1
--- /dev/null
+++ b/src/stim/util_top/circuit_to_dem.h
@@ -0,0 +1,30 @@
+#ifndef _STIM_UTIL_TOP_CIRCUIT_TO_DEM_H
+#define _STIM_UTIL_TOP_CIRCUIT_TO_DEM_H
+
+#include "stim/simulators/error_analyzer.h"
+
+namespace stim {
+
+struct DemOptions {
+    bool decompose_errors = false;
+    bool flatten_loops = true;
+    bool allow_gauge_detectors = false;
+    double approximate_disjoint_errors_threshold = 0;
+    bool ignore_decomposition_failures = false;
+    bool block_decomposition_from_introducing_remnant_edges = false;
+};
+
+inline DetectorErrorModel circuit_to_dem(const Circuit &circuit, DemOptions options = {}) {
+    return ErrorAnalyzer::circuit_to_detector_error_model(
+        circuit,
+        options.decompose_errors,
+        !options.flatten_loops,
+        options.allow_gauge_detectors,
+        options.approximate_disjoint_errors_threshold,
+        options.ignore_decomposition_failures,
+        options.block_decomposition_from_introducing_remnant_edges);
+}
+
+}  // namespace stim
+
+#endif
diff --git a/src/stim/util_top/circuit_to_dem.test.cc b/src/stim/util_top/circuit_to_dem.test.cc
new file mode 100644
index 00000000..371717a6
--- /dev/null
+++ b/src/stim/util_top/circuit_to_dem.test.cc
@@ -0,0 +1,115 @@
+#include "stim/util_top/circuit_to_dem.h"
+
+#include "gtest/gtest.h"
+
+using namespace stim;
+
+TEST(circuit_to_dem, heralded_noise_basis) {
+    ASSERT_EQ(
+        circuit_to_dem(Circuit(R"CIRCUIT(
+            MXX 0 1
+            MZZ 0 1
+            HERALDED_PAULI_CHANNEL_1(0.25, 0, 0, 0) 0
+            MXX 0 1
+            MZZ 0 1
+            DETECTOR(2) rec[-3]
+            DETECTOR(3) rec[-2] rec[-5]
+            DETECTOR(5) rec[-1] rec[-4]
+        )CIRCUIT")),
+        DetectorErrorModel(R"DEM(
+            error(0.25) D0
+            detector(2) D0
+            detector(3) D1
+            detector(5) D2
+        )DEM"));
+
+    ASSERT_EQ(
+        circuit_to_dem(Circuit(R"CIRCUIT(
+            MXX 0 1
+            MZZ 0 1
+            HERALDED_PAULI_CHANNEL_1(0, 0.25, 0, 0) 0
+            MXX 0 1
+            MZZ 0 1
+            DETECTOR(2) rec[-3]
+            DETECTOR(3) rec[-2] rec[-5]
+            DETECTOR(5) rec[-1] rec[-4]
+        )CIRCUIT")),
+        DetectorErrorModel(R"DEM(
+            error(0.25) D0 D2
+            detector(2) D0
+            detector(3) D1
+            detector(5) D2
+        )DEM"));
+
+    ASSERT_EQ(
+        circuit_to_dem(Circuit(R"CIRCUIT(
+            MXX 0 1
+            MZZ 0 1
+            HERALDED_PAULI_CHANNEL_1(0, 0, 0.25, 0) 0
+            MXX 0 1
+            MZZ 0 1
+            DETECTOR(2) rec[-3]
+            DETECTOR(3) rec[-2] rec[-5]
+            DETECTOR(5) rec[-1] rec[-4]
+        )CIRCUIT")),
+        DetectorErrorModel(R"DEM(
+            error(0.25) D0 D1 D2
+            detector(2) D0
+            detector(3) D1
+            detector(5) D2
+        )DEM"));
+
+    ASSERT_EQ(
+        circuit_to_dem(Circuit(R"CIRCUIT(
+            MXX 0 1
+            MZZ 0 1
+            HERALDED_PAULI_CHANNEL_1(0, 0, 0, 0.25) 0
+            MXX 0 1
+            MZZ 0 1
+            DETECTOR(2) rec[-3]
+            DETECTOR(3) rec[-2] rec[-5]
+            DETECTOR(5) rec[-1] rec[-4]
+        )CIRCUIT")),
+        DetectorErrorModel(R"DEM(
+            error(0.25) D0 D1
+            detector(2) D0
+            detector(3) D1
+            detector(5) D2
+        )DEM"));
+
+    ASSERT_EQ(
+        circuit_to_dem(
+            Circuit(R"CIRCUIT(
+            MXX 0 1
+            MZZ 0 1
+            HERALDED_PAULI_CHANNEL_1(0.125, 0, 0.25, 0) 0
+            MXX 0 1
+            MZZ 0 1
+            DETECTOR(2) rec[-3]
+            DETECTOR(3) rec[-2] rec[-5]
+            DETECTOR(5) rec[-1] rec[-4]
+        )CIRCUIT"),
+            {.approximate_disjoint_errors_threshold = 1}),
+        DetectorErrorModel(R"DEM(
+            error(0.125) D0
+            error(0.25) D0 D1 D2
+            detector(2) D0
+            detector(3) D1
+            detector(5) D2
+        )DEM"));
+
+    ASSERT_THROW(
+        {
+            circuit_to_dem(Circuit(R"CIRCUIT(
+            MXX 0 1
+            MZZ 0 1
+            HERALDED_PAULI_CHANNEL_1(0.125, 0, 0.25, 0) 0
+            MXX 0 1
+            MZZ 0 1
+            DETECTOR(2) rec[-3]
+            DETECTOR(3) rec[-2] rec[-5]
+            DETECTOR(5) rec[-1] rec[-4]
+        )CIRCUIT"));
+        },
+        std::invalid_argument);
+}
diff --git a/src/stim/util_top/circuit_vs_amplitudes.cc b/src/stim/util_top/circuit_vs_amplitudes.cc
index 6a56219b..75bfeba9 100644
--- a/src/stim/util_top/circuit_vs_amplitudes.cc
+++ b/src/stim/util_top/circuit_vs_amplitudes.cc
@@ -1,9 +1,9 @@
 #include "stim/util_top/circuit_vs_amplitudes.h"
 
-#include "stim/util_top/circuit_inverse_unitary.h"
 #include "stim/simulators/tableau_simulator.h"
 #include "stim/simulators/vector_simulator.h"
 #include "stim/util_bot/twiddle.h"
+#include "stim/util_top/circuit_inverse_unitary.h"
 
 using namespace stim;
 

From 81c925a68c2d4c2007aa4d008c89299b496ef785 Mon Sep 17 00:00:00 2001
From: Craig Gidney <craiggidney@google.com>
Date: Tue, 30 Jul 2024 01:00:36 -0700
Subject: [PATCH 09/17] Create documentation listing papers with stim files
 attached (#802)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

## 2021

- [arXiv:2103.02202](https://arxiv.org/abs/2103.02202) → [Ancillary
files of "Stim: a fast stabilizer circuit
simulator"](https://arxiv.org/src/2103.02202v3/anc)
- [arXiv:2108.10457](https://arxiv.org/abs/2108.10457) → [Ancillary
files of "A Fault-Tolerant Honeycomb
Memory"](https://arxiv.org/src/2108.10457v2/anc)

## 2022

- [arXiv:2202.11845](https://arxiv.org/abs/2202.11845) → [Data for
"Benchmarking the Planar Honeycomb
Code"](https://zenodo.org/records/7072889)
- [arXiv:2204.13834](https://arxiv.org/abs/2204.13834) → [Data for
"Stability Experiments: The Overlooked Dual of Memory
Experiments"](https://zenodo.org/records/6859486)
- [arXiv:2206.12780](https://arxiv.org/abs/2206.12780) → [Data for "A
Pair Measurement Surface Code on
Pentagons"](https://zenodo.org/records/6626417)
- [arXiv:2207.06431](https://arxiv.org/abs/2207.06431) → [Data for
"Suppressing quantum errors by scaling a surface code logical
qubit"](https://zenodo.org/records/6804040)
- [arXiv:2209.08552](https://arxiv.org/abs/2209.08552) → [Parallel
window decoding enables scalable fault tolerant quantum
computation](https://doi.org/10.5281/zenodo.8422904)

## 2023

- [arXiv:2302.02192](https://arxiv.org/abs/2302.02192) → [Data for
"Relaxing Hardware Requirements for Surface Code Circuits using
Time-dynamics"](https://zenodo.org/records/7587578)
- [arXiv:2302.07395](https://arxiv.org/abs/2302.07395) → [Data for
"Inplace Access to the Surface Code Y
Basis"](https://zenodo.org/records/7487893)
- [arXiv:2302.12292](https://arxiv.org/abs/2302.12292) → [Data for
"Cleaner magic states with hook
injection"](https://zenodo.org/records/7575030)
- [arXiv:2305.12046](https://arxiv.org/abs/2305.12046) → [Data for "Less
Bacon More Threshold"](https://zenodo.org/records/7901729)
- [arXiv:2307.10147](https://arxiv.org/abs/2307.10147) → [Stim circuits
for "Tangling schedules eases hardware connectivity requirements for
quantum error correction"
manuscript](https://zenodo.org/records/8391674)
- [arXiv:2308.03750](https://arxiv.org/abs/2308.03750) → [Ancillary data
for the paper "Constructions and performance of hyperbolic and
semi-hyperbolic Floquet codes"
](https://github.com/oscarhiggott/hyperbolic-floquet-data)
- [arXiv:2312.04522](https://arxiv.org/abs/2312.04522) → [Data for
"Yoked Surface Codes"](https://zenodo.org/records/10277397)
- [arXiv:2312.08813](https://arxiv.org/abs/2312.08813) → [Data for "New
circuits and an open source decoder for the
colorcode"](https://zenodo.org/records/10375289)
- [arXiv:2312.11605](https://arxiv.org/abs/2312.11605) → [Stim circuits
and collected data for "Error-corrected Hadamard gate simulated at the
circuit level" manuscript](https://zenodo.org/records/10391116)

## 2024

- [arXiv:2405.15854](https://arxiv.org/abs/2405.15854) → [Stim circuits
for 'Accommodating Fabrication Defects on Floquet Codes with Minimal
Hardware Requirements' manuscript](https://zenodo.org/records/11241876)
---
 doc/circuit_data_references.md | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)
 create mode 100644 doc/circuit_data_references.md

diff --git a/doc/circuit_data_references.md b/doc/circuit_data_references.md
new file mode 100644
index 00000000..3e26d485
--- /dev/null
+++ b/doc/circuit_data_references.md
@@ -0,0 +1,28 @@
+## 2021
+
+- [arXiv:2103.02202](https://arxiv.org/abs/2103.02202) → [Ancillary files of "Stim: a fast stabilizer circuit simulator"](https://arxiv.org/src/2103.02202v3/anc)
+- [arXiv:2108.10457](https://arxiv.org/abs/2108.10457) → [Ancillary files of "A Fault-Tolerant Honeycomb Memory"](https://arxiv.org/src/2108.10457v2/anc)
+
+## 2022
+
+- [arXiv:2202.11845](https://arxiv.org/abs/2202.11845) → [Data for "Benchmarking the Planar Honeycomb Code"](https://zenodo.org/records/7072889)
+- [arXiv:2204.13834](https://arxiv.org/abs/2204.13834) → [Data for "Stability Experiments: The Overlooked Dual of Memory Experiments"](https://zenodo.org/records/6859486)
+- [arXiv:2206.12780](https://arxiv.org/abs/2206.12780) → [Data for "A Pair Measurement Surface Code on Pentagons"](https://zenodo.org/records/6626417)
+- [arXiv:2207.06431](https://arxiv.org/abs/2207.06431) → [Data for "Suppressing quantum errors by scaling a surface code logical qubit"](https://zenodo.org/records/6804040)
+- [arXiv:2209.08552](https://arxiv.org/abs/2209.08552) → [Parallel window decoding enables scalable fault tolerant quantum computation](https://doi.org/10.5281/zenodo.8422904)
+
+## 2023
+
+- [arXiv:2302.02192](https://arxiv.org/abs/2302.02192) → [Data for "Relaxing Hardware Requirements for Surface Code Circuits using Time-dynamics"](https://zenodo.org/records/7587578)
+- [arXiv:2302.07395](https://arxiv.org/abs/2302.07395) → [Data for "Inplace Access to the Surface Code Y Basis"](https://zenodo.org/records/7487893)
+- [arXiv:2302.12292](https://arxiv.org/abs/2302.12292) → [Data for "Cleaner magic states with hook injection"](https://zenodo.org/records/7575030)
+- [arXiv:2305.12046](https://arxiv.org/abs/2305.12046) → [Data for "Less Bacon More Threshold"](https://zenodo.org/records/7901729)
+- [arXiv:2307.10147](https://arxiv.org/abs/2307.10147) → [Stim circuits for "Tangling schedules eases hardware connectivity requirements for quantum error correction" manuscript](https://zenodo.org/records/8391674)
+- [arXiv:2308.03750](https://arxiv.org/abs/2308.03750) → [Ancillary data for the paper "Constructions and performance of hyperbolic and semi-hyperbolic Floquet codes" ](https://github.com/oscarhiggott/hyperbolic-floquet-data)
+- [arXiv:2312.04522](https://arxiv.org/abs/2312.04522) → [Data for "Yoked Surface Codes"](https://zenodo.org/records/10277397)
+- [arXiv:2312.08813](https://arxiv.org/abs/2312.08813) → [Data for "New circuits and an open source decoder for the colorcode"](https://zenodo.org/records/10375289)
+- [arXiv:2312.11605](https://arxiv.org/abs/2312.11605) → [Stim circuits and collected data for "Error-corrected Hadamard gate simulated at the circuit level" manuscript](https://zenodo.org/records/10391116)
+
+## 2024
+
+- [arXiv:2405.15854](https://arxiv.org/abs/2405.15854) → [Stim circuits for 'Accommodating Fabrication Defects on Floquet Codes with Minimal Hardware Requirements' manuscript](https://zenodo.org/records/11241876)

From f66de616f09c50ac77267e7e6f392d8eb677923f Mon Sep 17 00:00:00 2001
From: Craig Gidney <craiggidney@google.com>
Date: Wed, 31 Jul 2024 00:04:40 -0700
Subject: [PATCH 10/17] Improve dem anticommutation error message to suggest
 making a diagram (#807)

- Also give `_DiagramHelper` a repr that explains what it is and how to get at its contents
- Also compress DETECTOR and OBSERVABLE_INCLUDE names in crumble URLs
---
 src/stim/cmd/command_analyze_errors.test.cc  |   6 +-
 src/stim/cmd/command_diagram.pybind.cc       |  25 ++
 src/stim/simulators/error_analyzer.cc        |  34 ++-
 src/stim/simulators/error_analyzer.test.cc   | 288 +++++++++----------
 src/stim/stabilizers/pauli_string_ref.inl    |  18 +-
 src/stim/util_top/export_crumble_url.cc      |   2 +
 src/stim/util_top/export_crumble_url.test.cc |   4 +-
 7 files changed, 207 insertions(+), 170 deletions(-)

diff --git a/src/stim/cmd/command_analyze_errors.test.cc b/src/stim/cmd/command_analyze_errors.test.cc
index 875e6cc6..dfb5c61a 100644
--- a/src/stim/cmd/command_analyze_errors.test.cc
+++ b/src/stim/cmd/command_analyze_errors.test.cc
@@ -97,7 +97,11 @@ DETECTOR rec[-2]
 [stderr=)OUTPUT"
              "\x1B"
              R"OUTPUT([31mThe circuit contains non-deterministic detectors.
-(To allow non-deterministic detectors, use the `allow_gauge_detectors` option.)
+
+To make an SVG picture of the problem, you can use the python API like this:
+    your_circuit.diagram('detslice-with-ops-svg', tick=range(0, 5), filter_coords=['D0', 'D1', ])
+or the command line API like this:
+    stim diagram --in your_circuit_file.stim --type detslice-with-ops-svg --tick 0:5 --filter_coords D0:D1 > output_image.svg
 
 This was discovered while analyzing a Z-basis reset (R) on:
     qubit 0
diff --git a/src/stim/cmd/command_diagram.pybind.cc b/src/stim/cmd/command_diagram.pybind.cc
index 8f35dda0..9ef8b45a 100644
--- a/src/stim/cmd/command_diagram.pybind.cc
+++ b/src/stim/cmd/command_diagram.pybind.cc
@@ -125,6 +125,31 @@ void stim_pybind::pybind_diagram_methods(pybind11::module &m, pybind11::class_<D
     c.def("_repr_pretty_", [](const DiagramHelper &self, pybind11::object p, pybind11::object cycle) -> void {
         pybind11::getattr(p, "text")(self.content);
     });
+    c.def("__repr__", [](const DiagramHelper &self) -> std::string {
+        std::stringstream ss;
+        ss << "<A stim._DiagramHelper containing ";
+        switch (self.type) {
+            case DiagramType::DIAGRAM_TYPE_GLTF:
+                ss << "a GLTF 3d model";
+                break;
+            case DiagramType::DIAGRAM_TYPE_SVG:
+                ss << "an SVG image";
+                break;
+            case DiagramType::DIAGRAM_TYPE_TEXT:
+                ss << "text";
+                break;
+            case DiagramType::DIAGRAM_TYPE_HTML:
+                ss << "an HTML document";
+                break;
+            case DiagramType::DIAGRAM_TYPE_SVG_HTML:
+                ss << "an HTML SVG image viewer";
+                break;
+            default:
+                ss << "???";
+        }
+        ss << " that will display inline in Jupyter notebooks. Use 'str' or 'print' to access the contents as text.>";
+        return ss.str();
+    });
     c.def("__str__", [](const DiagramHelper &self) -> pybind11::object {
         if (self.type == DiagramType::DIAGRAM_TYPE_SVG_HTML) {
             return diagram_as_html(self);
diff --git a/src/stim/simulators/error_analyzer.cc b/src/stim/simulators/error_analyzer.cc
index 47f393be..7edb4431 100644
--- a/src/stim/simulators/error_analyzer.cc
+++ b/src/stim/simulators/error_analyzer.cc
@@ -414,12 +414,32 @@ void ErrorAnalyzer::check_for_gauge(
     has_detectors &= !allow_gauge_detectors;
     if (has_observables) {
         error_msg << "The circuit contains non-deterministic observables.\n";
-        error_msg << "(Error analysis requires deterministic observables.)\n";
     }
     if (has_detectors) {
         error_msg << "The circuit contains non-deterministic detectors.\n";
-        error_msg << "(To allow non-deterministic detectors, use the `allow_gauge_detectors` option.)\n";
     }
+    size_t range_start = num_ticks_in_past - std::min((size_t)num_ticks_in_past, size_t{5});
+    size_t range_end = num_ticks_in_past + 5;
+    error_msg << "\nTo make an SVG picture of the problem, you can use the python API like this:\n    ";
+    error_msg << "your_circuit.diagram('detslice-with-ops-svg'";
+    error_msg << ", tick=range(" << range_start << ", " << range_end << ")";
+    error_msg << ", filter_coords=[";
+    for (auto d : potential_gauge) {
+        error_msg << "'" << d << "', ";
+    }
+    error_msg << "])";
+    error_msg << "\nor the command line API like this:\n    ";
+    error_msg << "stim diagram --in your_circuit_file.stim";
+    error_msg << " --type detslice-with-ops-svg";
+    error_msg << " --tick " << range_start << ":" << range_end;
+    error_msg << " --filter_coords ";
+    for (size_t k = 0; k < potential_gauge.size(); k++) {
+        if (k) {
+            error_msg << ':';
+        }
+        error_msg << potential_gauge.sorted_items[k];
+    }
+    error_msg << " > output_image.svg\n";
 
     std::map<uint64_t, std::vector<double>> qubit_coords_map;
     if (current_circuit_being_analyzed != nullptr) {
@@ -1492,7 +1512,7 @@ void ErrorAnalyzer::do_global_error_decomposition_pass() {
                           "`--ignore_decomposition_failures` to `stim analyze_errors`.";
                     if (block_decomposition_from_introducing_remnant_edges) {
                         ss << "\n\nNote: `block_decomposition_from_introducing_remnant_edges` is ON.\n";
-                        ss << "Turning it off may prevent this error.\n";
+                        ss << "Turning it off may prevent this error.";
                     }
                     throw std::invalid_argument(ss.str());
                 }
@@ -1538,12 +1558,16 @@ void ErrorAnalyzer::add_error_combinations(
                             std::stringstream message;
                             message
                                 << "An error case in a composite error exceeded the max supported number of symptoms "
-                                   "(<=15). ";
+                                   "(<=15).";
                             message << "\nThe " << std::to_string(s)
                                     << " basis error cases (e.g. X, Z) used to form the combined ";
                             message << "error cases (e.g. Y = X*Z) are:\n";
                             for (size_t k2 = 0; k2 < s; k2++) {
-                                message << std::to_string(k2) << ": " << comma_sep_workaround(basis_errors[k2]) << "\n";
+                                message << std::to_string(k2) << ":";
+                                if (!basis_errors[k2].empty()) {
+                                    message << ' ';
+                                }
+                                message << comma_sep_workaround(basis_errors[k2]) << "\n";
                             }
                             throw std::invalid_argument(message.str());
                         }
diff --git a/src/stim/simulators/error_analyzer.test.cc b/src/stim/simulators/error_analyzer.test.cc
index 3e6b26c9..c8d7d49c 100644
--- a/src/stim/simulators/error_analyzer.test.cc
+++ b/src/stim/simulators/error_analyzer.test.cc
@@ -1,17 +1,3 @@
-// Copyright 2021 Google LLC
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
 #include "stim/simulators/error_analyzer.h"
 
 #include <regex>
@@ -2349,28 +2335,19 @@ TEST(ErrorAnalyzer, noisy_measurement_mrz) {
 }
 
 template <typename TEx>
-std::string check_catch(std::string expected_substring, std::function<void(void)> func) {
+std::string expect_catch_message(std::function<void(void)> func) {
     try {
         func();
-        return "Expected an exception with message '" + expected_substring + "', but no exception was thrown.";
-    } catch (const TEx &ex) {
-        std::string s = ex.what();
-        if (s.find(expected_substring) == std::string::npos) {
-            return "Didn't find '" + expected_substring + "' in '" + std::string(ex.what()) + "'.";
-        }
+        EXPECT_TRUE(false) << "Function didn't throw an exception.";
         return "";
+    } catch (const TEx &ex) {
+        return ex.what();
     }
 }
 
 TEST(ErrorAnalyzer, context_clues_for_errors) {
     ASSERT_EQ(
-        "",
-        check_catch<std::invalid_argument>(
-            "Can't analyze over-mixing DEPOLARIZE1 errors (probability > 3/4).\n"
-            "\n"
-            "Circuit stack trace:\n"
-            "    at instruction #2 [which is DEPOLARIZE1(1) 0]",
-            [&] {
+        expect_catch_message<std::invalid_argument>([&](){
                 ErrorAnalyzer::circuit_to_detector_error_model(
                     Circuit(R"CIRCUIT(
                 X 0
@@ -2382,33 +2359,28 @@ TEST(ErrorAnalyzer, context_clues_for_errors) {
                     0.0,
                     false,
                     true);
-            }));
+        }),
+        "Can't analyze over-mixing DEPOLARIZE1 errors (probability > 3/4).\n"
+        "\n"
+        "Circuit stack trace:\n"
+        "    at instruction #2 [which is DEPOLARIZE1(1) 0]");
 
     ASSERT_EQ(
-        "",
-        check_catch<std::invalid_argument>(
-            "Can't analyze over-mixing DEPOLARIZE1 errors (probability > 3/4).\n"
-            "\n"
-            "Circuit stack trace:\n"
-            "    at instruction #3 [which is a REPEAT 500 block]\n"
-            "    at block's instruction #1 [which is DEPOLARIZE1(1) 0]",
-            [&] {
-                ErrorAnalyzer::circuit_to_detector_error_model(
-                    Circuit(R"CIRCUIT(
+        expect_catch_message<std::invalid_argument>([&](){
+            circuit_to_dem(Circuit(R"CIRCUIT(
                 X 0
                 Y 1
                 REPEAT 500 {
                     DEPOLARIZE1(1) 0
                 }
                 Z 3
-            )CIRCUIT"),
-                    false,
-                    false,
-                    false,
-                    0.0,
-                    false,
-                    true);
-            }));
+            )CIRCUIT"), {.block_decomposition_from_introducing_remnant_edges = true});
+        }),
+        "Can't analyze over-mixing DEPOLARIZE1 errors (probability > 3/4).\n"
+        "\n"
+        "Circuit stack trace:\n"
+        "    at instruction #3 [which is a REPEAT 500 block]\n"
+        "    at block's instruction #1 [which is DEPOLARIZE1(1) 0]");
 }
 
 TEST(ErrorAnalyzer, too_many_symptoms) {
@@ -2436,9 +2408,22 @@ TEST(ErrorAnalyzer, too_many_symptoms) {
         DETECTOR rec[-1]
         DETECTOR rec[-1]
     )CIRCUIT");
-    ASSERT_EQ("", check_catch<std::invalid_argument>("max supported number of symptoms", [&] {
-                  ErrorAnalyzer::circuit_to_detector_error_model(symptoms_20, true, false, false, 0.0, false, true);
-              }));
+
+    ASSERT_EQ(
+        expect_catch_message<std::invalid_argument>([&](){
+            circuit_to_dem(
+                symptoms_20,
+                {.decompose_errors = true, .block_decomposition_from_introducing_remnant_edges = true});
+        }),
+        R"MSG(An error case in a composite error exceeded the max supported number of symptoms (<=15).
+The 2 basis error cases (e.g. X, Z) used to form the combined error cases (e.g. Y = X*Z) are:
+0:
+1: D0, D1, D2, D3, D4, D5, D6, D7, D8, D9, D10, D11, D12, D13, D14, D15, D16, D17, D18, D19
+
+
+Circuit stack trace:
+    at instruction #1 [which is DEPOLARIZE1(0.001) 0])MSG");
+
     ASSERT_EQ(
         ErrorAnalyzer::circuit_to_detector_error_model(symptoms_20, false, false, false, 0.0, false, true),
         DetectorErrorModel(R"model(
@@ -2447,57 +2432,63 @@ TEST(ErrorAnalyzer, too_many_symptoms) {
 }
 
 TEST(ErrorAnalyzer, decompose_error_failures) {
-    ASSERT_EQ("", check_catch<std::invalid_argument>("failed to decompose is 'D0, D1, D2'", [] {
-                  ErrorAnalyzer::circuit_to_detector_error_model(
-                      Circuit(R"CIRCUIT(
+    ASSERT_EQ(
+        expect_catch_message<std::invalid_argument>([&](){
+            circuit_to_dem(Circuit(R"CIRCUIT(
                 DEPOLARIZE1(0.001) 0
                 M 0
                 DETECTOR rec[-1]
                 DETECTOR rec[-1]
                 DETECTOR rec[-1]
-            )CIRCUIT"),
-                      true,
-                      false,
-                      false,
-                      0.0,
-                      false,
-                      true);
-              }));
-
-    ASSERT_EQ("", check_catch<std::invalid_argument>("decompose errors into graphlike components", [] {
-                  ErrorAnalyzer::circuit_to_detector_error_model(
-                      Circuit(R"CIRCUIT(
+            )CIRCUIT"), {.decompose_errors = true, .block_decomposition_from_introducing_remnant_edges = true});
+        }),
+        R"MSG(Failed to decompose errors into graphlike components with at most two symptoms.
+The error component that failed to decompose is 'D0, D1, D2'.
+
+In Python, you can ignore this error by passing `ignore_decomposition_failures=True` to `stim.Circuit.detector_error_model(...)`.
+From the command line, you can ignore this error by passing the flag `--ignore_decomposition_failures` to `stim analyze_errors`.
+
+Note: `block_decomposition_from_introducing_remnant_edges` is ON.
+Turning it off may prevent this error.)MSG");
+
+    ASSERT_EQ(
+        expect_catch_message<std::invalid_argument>([&](){
+            circuit_to_dem(Circuit(R"CIRCUIT(
                 X_ERROR(0.001) 0
                 M 0
                 DETECTOR rec[-1]
                 DETECTOR rec[-1]
                 DETECTOR rec[-1]
-            )CIRCUIT"),
-                      true,
-                      false,
-                      false,
-                      0.0,
-                      false,
-                      true);
-              }));
-
-    ASSERT_EQ("", check_catch<std::invalid_argument>("failed to decompose is 'D0, D1, D2, L5'", [] {
-                  ErrorAnalyzer::circuit_to_detector_error_model(
-                      Circuit(R"CIRCUIT(
+            )CIRCUIT"), {.decompose_errors = true, .block_decomposition_from_introducing_remnant_edges = true});
+        }),
+        R"MSG(Failed to decompose errors into graphlike components with at most two symptoms.
+The error component that failed to decompose is 'D0, D1, D2'.
+
+In Python, you can ignore this error by passing `ignore_decomposition_failures=True` to `stim.Circuit.detector_error_model(...)`.
+From the command line, you can ignore this error by passing the flag `--ignore_decomposition_failures` to `stim analyze_errors`.
+
+Note: `block_decomposition_from_introducing_remnant_edges` is ON.
+Turning it off may prevent this error.)MSG");
+
+    ASSERT_EQ(
+        expect_catch_message<std::invalid_argument>([&](){
+            circuit_to_dem(Circuit(R"CIRCUIT(
                 X_ERROR(0.001) 0
                 M 0
                 DETECTOR rec[-1]
                 DETECTOR rec[-1]
                 DETECTOR rec[-1]
                 OBSERVABLE_INCLUDE(5) rec[-1]
-            )CIRCUIT"),
-                      true,
-                      false,
-                      false,
-                      0.0,
-                      false,
-                      true);
-              }));
+            )CIRCUIT"), {.decompose_errors = true, .block_decomposition_from_introducing_remnant_edges = true});
+        }),
+        R"MSG(Failed to decompose errors into graphlike components with at most two symptoms.
+The error component that failed to decompose is 'D0, D1, D2, L5'.
+
+In Python, you can ignore this error by passing `ignore_decomposition_failures=True` to `stim.Circuit.detector_error_model(...)`.
+From the command line, you can ignore this error by passing the flag `--ignore_decomposition_failures` to `stim analyze_errors`.
+
+Note: `block_decomposition_from_introducing_remnant_edges` is ON.
+Turning it off may prevent this error.)MSG");
 }
 
 TEST(ErrorAnalyzer, other_error_decomposition_fallback) {
@@ -2846,10 +2837,31 @@ TEST(ErrorAnalyzer, mpp_ordering) {
 TEST(ErrorAnalyzer, anticommuting_observable_error_message_help) {
     for (size_t folding = 0; folding < 2; folding++) {
         ASSERT_EQ(
-            "",
-            check_catch<std::invalid_argument>(
-                R"ERROR(The circuit contains non-deterministic observables.
-(Error analysis requires deterministic observables.)
+            expect_catch_message<std::invalid_argument>([&](){
+                circuit_to_dem(
+                    Circuit(R"CIRCUIT(
+                        QUBIT_COORDS(1, 2, 3) 0
+                        RX 2
+                        REPEAT 10 {
+                            REPEAT 20 {
+                                C_XYZ 0
+                                R 1
+                                M 1
+                                DETECTOR rec[-1]
+                                TICK
+                            }
+                        }
+                        M 0 2
+                        OBSERVABLE_INCLUDE(0) rec[-1] rec[-2]
+                    )CIRCUIT"),
+                    {.flatten_loops = folding != 1});
+            }),
+            R"ERROR(The circuit contains non-deterministic observables.
+
+To make an SVG picture of the problem, you can use the python API like this:
+    your_circuit.diagram('detslice-with-ops-svg', tick=range(0, 5), filter_coords=['L0', ])
+or the command line API like this:
+    stim diagram --in your_circuit_file.stim --type detslice-with-ops-svg --tick 0:5 --filter_coords L0 > output_image.svg
 
 This was discovered while analyzing an X-basis reset (RX) on:
     qubit 2
@@ -2863,39 +2875,42 @@ The backward-propagating error sensitivity for L0 was:
 
 Circuit stack trace:
     during TICK layer #1 of 201
-    at instruction #2 [which is RX 2])ERROR",
-                [&] {
-                    ErrorAnalyzer::circuit_to_detector_error_model(
-                        Circuit(R"CIRCUIT(
-                            QUBIT_COORDS(1, 2, 3) 0
-                            RX 2
-                            REPEAT 10 {
-                                REPEAT 20 {
-                                    C_XYZ 0
-                                    R 1
-                                    M 1
-                                    DETECTOR rec[-1]
-                                    TICK
-                                }
-                            }
-                            M 0 2
-                            OBSERVABLE_INCLUDE(0) rec[-1] rec[-2]
-                        )CIRCUIT"),
-                        false,
-                        folding == 1,
-                        false,
-                        0.0,
-                        false,
-                        true);
-                }));
+    at instruction #2 [which is RX 2])ERROR");
 
         ASSERT_EQ(
-            "",
-            check_catch<std::invalid_argument>(
-                R"ERROR(The circuit contains non-deterministic observables.
-(Error analysis requires deterministic observables.)
+            expect_catch_message<std::invalid_argument>([&](){
+                circuit_to_dem(Circuit(R"CIRCUIT(
+                    TICK
+                    SHIFT_COORDS(1000, 2000)
+                    M 0 1
+                    REPEAT 100 {
+                        RX 0
+                        DETECTOR rec[-1]
+                        TICK
+                    }
+                    REPEAT 200 {
+                        TICK
+                    }
+                    REPEAT 100 {
+                        M 0 1
+                        SHIFT_COORDS(0, 100)
+                        DETECTOR(1, 2, 3) rec[-1] rec[-3]
+                        DETECTOR(4, 5, 6) rec[-2] rec[-4]
+                        OBSERVABLE_INCLUDE(0) rec[-1] rec[-2] rec[-3] rec[-4]
+                        TICK
+                    }
+                    REPEAT 1000 {
+                        TICK
+                    }
+                )CIRCUIT"), {.flatten_loops = folding != 1});
+            }),
+            R"ERROR(The circuit contains non-deterministic observables.
 The circuit contains non-deterministic detectors.
-(To allow non-deterministic detectors, use the `allow_gauge_detectors` option.)
+
+To make an SVG picture of the problem, you can use the python API like this:
+    your_circuit.diagram('detslice-with-ops-svg', tick=range(95, 105), filter_coords=['D101', 'L0', ])
+or the command line API like this:
+    stim diagram --in your_circuit_file.stim --type detslice-with-ops-svg --tick 95:105 --filter_coords D101:L0 > output_image.svg
 
 This was discovered while analyzing an X-basis reset (RX) on:
     qubit 0
@@ -2914,40 +2929,7 @@ The backward-propagating error sensitivity for L0 was:
 Circuit stack trace:
     during TICK layer #101 of 1402
     at instruction #4 [which is a REPEAT 100 block]
-    at block's instruction #1 [which is RX 0])ERROR",
-                [&] {
-                    ErrorAnalyzer::circuit_to_detector_error_model(
-                        Circuit(R"CIRCUIT(
-                            TICK
-                            SHIFT_COORDS(1000, 2000)
-                            M 0 1
-                            REPEAT 100 {
-                                RX 0
-                                DETECTOR rec[-1]
-                                TICK
-                            }
-                            REPEAT 200 {
-                                TICK
-                            }
-                            REPEAT 100 {
-                                M 0 1
-                                SHIFT_COORDS(0, 100)
-                                DETECTOR(1, 2, 3) rec[-1] rec[-3]
-                                DETECTOR(4, 5, 6) rec[-2] rec[-4]
-                                OBSERVABLE_INCLUDE(0) rec[-1] rec[-2] rec[-3] rec[-4]
-                                TICK
-                            }
-                            REPEAT 1000 {
-                                TICK
-                            }
-                        )CIRCUIT"),
-                        false,
-                        folding == 1,
-                        false,
-                        0.0,
-                        false,
-                        true);
-                }));
+    at block's instruction #1 [which is RX 0])ERROR");
     }
 }
 
diff --git a/src/stim/stabilizers/pauli_string_ref.inl b/src/stim/stabilizers/pauli_string_ref.inl
index 1be812e7..7fcd5f65 100644
--- a/src/stim/stabilizers/pauli_string_ref.inl
+++ b/src/stim/stabilizers/pauli_string_ref.inl
@@ -989,9 +989,9 @@ template <bool reverse_order>
 void PauliStringRef<W>::do_SWAP(const CircuitInstruction &inst) {
     const auto &targets = inst.targets;
     assert((targets.size() & 1) == 0);
-    for (size_t k = 0; k < inst.targets.size(); k += 2) {
-        size_t k2 = reverse_order ? inst.targets.size() - 2 - k : k;
-        size_t q1 = inst.targets[k2].data, q2 = inst.targets[k2 + 1].data;
+    for (size_t k = 0; k < targets.size(); k += 2) {
+        size_t k2 = reverse_order ? targets.size() - 2 - k : k;
+        size_t q1 = targets[k2].data, q2 = targets[k2 + 1].data;
         zs[q1].swap_with(zs[q2]);
         xs[q1].swap_with(xs[q2]);
     }
@@ -1195,9 +1195,9 @@ template <bool reverse_order>
 void PauliStringRef<W>::do_XCZ(const CircuitInstruction &inst) {
     const auto &targets = inst.targets;
     assert((targets.size() & 1) == 0);
-    for (size_t k = 0; k < inst.targets.size(); k += 2) {
-        size_t k2 = reverse_order ? inst.targets.size() - 2 - k : k;
-        size_t q1 = inst.targets[k2].data, q2 = inst.targets[k2 + 1].data;
+    for (size_t k = 0; k < targets.size(); k += 2) {
+        size_t k2 = reverse_order ? targets.size() - 2 - k : k;
+        size_t q1 = targets[k2].data, q2 = targets[k2 + 1].data;
         do_single_cx(inst, q2, q1);
     }
 }
@@ -1238,9 +1238,9 @@ template <bool reverse_order>
 void PauliStringRef<W>::do_YCZ(const CircuitInstruction &inst) {
     const auto &targets = inst.targets;
     assert((targets.size() & 1) == 0);
-    for (size_t k = 0; k < inst.targets.size(); k += 2) {
-        size_t k2 = reverse_order ? inst.targets.size() - 2 - k : k;
-        size_t q1 = inst.targets[k2].data, q2 = inst.targets[k2 + 1].data;
+    for (size_t k = 0; k < targets.size(); k += 2) {
+        size_t k2 = reverse_order ? targets.size() - 2 - k : k;
+        size_t q1 = targets[k2].data, q2 = targets[k2 + 1].data;
         do_single_cy(inst, q2, q1);
     }
 }
diff --git a/src/stim/util_top/export_crumble_url.cc b/src/stim/util_top/export_crumble_url.cc
index fcb9765c..dab4bd92 100644
--- a/src/stim/util_top/export_crumble_url.cc
+++ b/src/stim/util_top/export_crumble_url.cc
@@ -7,6 +7,8 @@ std::string stim::export_crumble_url(const Circuit &circuit) {
     std::string_view s_view = s;
     std::vector<std::pair<std::string_view, std::string_view>> replace_rules{
         {"QUBIT_COORDS", "Q"},
+        {"DETECTOR", "DT"},
+        {"OBSERVABLE_INCLUDE", "OI"},
         {", ", ","},
         {") ", ")"},
         {"    ", ""},
diff --git a/src/stim/util_top/export_crumble_url.test.cc b/src/stim/util_top/export_crumble_url.test.cc
index 29061aac..e1436cb9 100644
--- a/src/stim/util_top/export_crumble_url.test.cc
+++ b/src/stim/util_top/export_crumble_url.test.cc
@@ -91,8 +91,8 @@ TEST(export_crumble, all_operations) {
         "X_ERROR(0.1)0;"
         "MR(0.01)0;"
         "SHIFT_COORDS(1,2,3);"
-        "DETECTOR(1,2,3)rec[-1];"
-        "OBSERVABLE_INCLUDE(0)rec[-1];"
+        "DT(1,2,3)rec[-1];"
+        "OI(0)rec[-1];"
         "MPAD_0_1_0;"
         "TICK;"
         "MRX_!0;"

From 51b29e339b9828a51e57e7fdc615e3d81656d1a2 Mon Sep 17 00:00:00 2001
From: Craig Gidney <craiggidney@google.com>
Date: Fri, 2 Aug 2024 18:55:54 -0700
Subject: [PATCH 11/17] Fix rounding errors breaking buffer sizes of large gltf
 models (#810)

- Add uint64_t/int64_t types to the JsonObj class so that sizes never
touch floats
- In match graph diagrams, color nodes with obs-crossing boundary edges
in red instead of in black
---
 src/stim/diagram/basic_3d_diagram.cc          |   2 +-
 src/stim/diagram/gate_data_3d.cc              |  27 +--
 src/stim/diagram/gltf.cc                      |  12 +-
 src/stim/diagram/gltf.h                       |  49 +++--
 .../diagram/graph/match_graph_3d_drawer.cc    |  17 +-
 src/stim/diagram/json_obj.cc                  | 168 +++++++++++-------
 src/stim/diagram/json_obj.h                   |  20 ++-
 testdata/circuit_all_ops_3d.gltf              |   2 +-
 testdata/classical_feedback.gltf              |   2 +-
 testdata/collapsing.gltf                      |   2 +-
 testdata/detector_pseudo_targets.gltf         |   2 +-
 testdata/empty_match_graph.gltf               |   2 +-
 testdata/lattice_surgery_cnot.gltf            |   2 +-
 testdata/match_graph_no_coords.gltf           |   2 +-
 testdata/match_graph_repetition_code.gltf     |   2 +-
 testdata/match_graph_surface_code.gltf        |   2 +-
 testdata/measurement_looping.gltf             |   2 +-
 testdata/noise_gates_1.gltf                   |   2 +-
 testdata/noise_gates_2.gltf                   |   2 +-
 testdata/noise_gates_3.gltf                   |   2 +-
 testdata/repeat.gltf                          |   2 +-
 testdata/repetition_code.gltf                 |   2 +-
 testdata/single_qubits_gates.gltf             |   2 +-
 testdata/surface_code.gltf                    |   2 +-
 testdata/tick.gltf                            |   2 +-
 testdata/two_qubits_gates.gltf                |   2 +-
 26 files changed, 194 insertions(+), 139 deletions(-)

diff --git a/src/stim/diagram/basic_3d_diagram.cc b/src/stim/diagram/basic_3d_diagram.cc
index b9cc590b..5baa916b 100644
--- a/src/stim/diagram/basic_3d_diagram.cc
+++ b/src/stim/diagram/basic_3d_diagram.cc
@@ -60,7 +60,7 @@ GltfScene Basic3dDiagram::to_gltf_scene() const {
     });
 
     auto buf_purple_scattered_lines = std::shared_ptr<GltfBuffer<3>>(new GltfBuffer<3>{
-        {"buf_blue_scattered_lines"},
+        {"buf_purple_scattered_lines"},
         purple_line_data,
     });
 
diff --git a/src/stim/diagram/gate_data_3d.cc b/src/stim/diagram/gate_data_3d.cc
index 6571747c..e260479b 100644
--- a/src/stim/diagram/gate_data_3d.cc
+++ b/src/stim/diagram/gate_data_3d.cc
@@ -342,7 +342,7 @@ std::pair<std::string_view, std::shared_ptr<GltfMesh>> make_z_control_mesh() {
     return {"Z_CONTROL", mesh};
 }
 
-std::pair<std::string_view, std::shared_ptr<GltfMesh>> make_detector_mesh() {
+std::pair<std::string_view, std::shared_ptr<GltfMesh>> make_detector_mesh(bool excited) {
     auto circle = make_circle_loop(8, CONTROL_RADIUS, true);
     auto circle2 = make_circle_loop(8, CONTROL_RADIUS, true);
     auto circle3 = make_circle_loop(8, CONTROL_RADIUS, true);
@@ -354,44 +354,44 @@ std::pair<std::string_view, std::shared_ptr<GltfMesh>> make_detector_mesh() {
         std::swap(e.xyz[0], e.xyz[1]);
         std::swap(e.xyz[1], e.xyz[2]);
     }
-    auto black_material = std::shared_ptr<GltfMaterial>(new GltfMaterial{
-        {"black"},
-        {0, 0, 0, 1},
+    auto material = std::shared_ptr<GltfMaterial>(new GltfMaterial{
+        {excited ? "det_red" : "det_black"},
+        {excited ? 1.0f : 0.0f, excited ? 0.5f : 0.0f, excited ? 0.5f : 0.0f, 1},
         1,
         1,
         true,
         nullptr,
     });
     auto disc_interior = std::shared_ptr<GltfPrimitive>(new GltfPrimitive{
-        {"detector_primitive_circle_interior"},
+        {excited ? "excited_detector_primitive_circle_interior" : "detector_primitive_circle_interior"},
         GL_TRIANGLE_FAN,
         circle,
         nullptr,
-        black_material,
+        material,
     });
     auto disc_interior2 = std::shared_ptr<GltfPrimitive>(new GltfPrimitive{
-        {"detector_primitive_circle_interior_2"},
+        {excited ? "excited_detector_primitive_circle_interior_2" : "detector_primitive_circle_interior_2"},
         GL_TRIANGLE_FAN,
         circle2,
         nullptr,
-        black_material,
+        material,
     });
     auto disc_interior3 = std::shared_ptr<GltfPrimitive>(new GltfPrimitive{
-        {"detector_primitive_circle_interior_3"},
+        {excited ? "excited_detector_primitive_circle_interior_3" : "detector_primitive_circle_interior_3"},
         GL_TRIANGLE_FAN,
         circle3,
         nullptr,
-        black_material,
+        material,
     });
     auto mesh = std::shared_ptr<GltfMesh>(new GltfMesh{
-        {"mesh_DETECTOR"},
+        {excited ? "mesh_EXCITED_DETECTOR" : "mesh_DETECTOR"},
         {
             disc_interior,
             disc_interior2,
             disc_interior3,
         },
     });
-    return {"DETECTOR", mesh};
+    return {excited ? "EXCITED_DETECTOR" : "DETECTOR", mesh};
 }
 
 std::map<std::string_view, std::shared_ptr<GltfMesh>> stim_draw_internal::make_gate_primitives() {
@@ -516,6 +516,7 @@ std::map<std::string_view, std::shared_ptr<GltfMesh>> stim_draw_internal::make_g
         make_z_control_mesh(),
         make_xswap_control_mesh(),
         make_zswap_control_mesh(),
-        make_detector_mesh(),
+        make_detector_mesh(false),
+        make_detector_mesh(true),
     };
 }
diff --git a/src/stim/diagram/gltf.cc b/src/stim/diagram/gltf.cc
index 10a2699e..0dc574ec 100644
--- a/src/stim/diagram/gltf.cc
+++ b/src/stim/diagram/gltf.cc
@@ -31,14 +31,14 @@ JsonObj GltfScene::_to_json_local() const {
 JsonObj GltfScene::to_json() {
     // Clear indices.
     visit([&](GltfId &item_id, const char *type, const std::function<JsonObj(void)> &to_json, uintptr_t abs_id) {
-        item_id.index = SIZE_MAX;
+        item_id.index = UINT64_MAX;
     });
 
     // Re-index.
     std::map<std::string, size_t> counts;
     visit([&](GltfId &item_id, const char *type, const std::function<JsonObj(void)> &to_json, uintptr_t abs_id) {
         auto &c = counts[type];
-        if (item_id.index == SIZE_MAX || item_id.index == c) {
+        if (item_id.index == UINT64_MAX || item_id.index == c) {
             item_id.index = c;
             c++;
         } else if (item_id.index > c) {
@@ -97,7 +97,8 @@ void GltfImage::visit(const gltf_visit_callback &callback) {
 
 JsonObj GltfImage::to_json() const {
     return std::map<std::string, JsonObj>{
-        {"name", id.name},
+        // Note: saving space by not including names.
+        //{"name", id.name},
         {"uri", uri},
     };
 }
@@ -116,7 +117,8 @@ void GltfTexture::visit(const gltf_visit_callback &callback) {
 
 JsonObj GltfTexture::to_json() const {
     return std::map<std::string, JsonObj>{
-        {"name", id.name},
+        // Note: saving space by not including names.
+//        {"name", id.name},
         {"sampler", 0},
         {"source", 0},
     };
@@ -137,7 +139,7 @@ void GltfMaterial::visit(const gltf_visit_callback &callback) {
 
 JsonObj GltfMaterial::to_json() const {
     JsonObj result = std::map<std::string, JsonObj>{
-        {"name", id.name},
+//        {"name", id.name},
         {"pbrMetallicRoughness",
          std::map<std::string, JsonObj>{
              {"baseColorFactor",
diff --git a/src/stim/diagram/gltf.h b/src/stim/diagram/gltf.h
index 1abfc97e..5dd0a834 100644
--- a/src/stim/diagram/gltf.h
+++ b/src/stim/diagram/gltf.h
@@ -11,30 +11,23 @@
 
 namespace stim_draw_internal {
 
-constexpr size_t GL_FLOAT = 5126;
-constexpr size_t GL_ARRAY_BUFFER = 34962;
-constexpr size_t GL_UNSIGNED_SHORT = 5123;
-constexpr size_t GL_ELEMENT_ARRAY_BUFFER = 34963;
-constexpr size_t GL_TRIANGLE_STRIP = 5;
-constexpr size_t GL_TRIANGLES = 4;
-constexpr size_t GL_TRIANGLE_FAN = 6;
-
-constexpr size_t GL_LINES = 1;
-constexpr size_t GL_LINE_STRIP = 3;
-constexpr size_t GL_LINE_LOOP = 2;
-
-constexpr size_t GL_REPEAT = 10497;
-constexpr size_t GL_CLAMP = 10496;
-constexpr size_t GL_CLAMP_TO_EDGE = 33071;
-constexpr size_t GL_LINEAR = 9729;
-constexpr size_t GL_LINEAR_MIPMAP_NEAREST = 9987;
-constexpr size_t GL_NEAREST = 9728;
+constexpr uint64_t GL_FLOAT = 5126;
+constexpr uint64_t GL_ARRAY_BUFFER = 34962;
+constexpr uint64_t GL_TRIANGLES = 4;
+constexpr uint64_t GL_TRIANGLE_FAN = 6;
+
+constexpr uint64_t GL_LINES = 1;
+constexpr uint64_t GL_LINE_STRIP = 3;
+constexpr uint64_t GL_LINE_LOOP = 2;
+
+constexpr uint64_t GL_CLAMP_TO_EDGE = 33071;
+constexpr uint64_t GL_NEAREST = 9728;
 
 struct GltfId {
     std::string name;
-    size_t index;
+    uint64_t index;
 
-    GltfId(std::string name) : name(name), index(SIZE_MAX) {
+    GltfId(std::string name) : name(name), index(UINT64_MAX) {
     }
     GltfId() = delete;
 };
@@ -81,7 +74,7 @@ struct GltfBuffer {
         return std::map<std::string, JsonObj>{
             {"name", id.name},
             {"uri", ss.str()},
-            {"byteLength", vertex_data_size},
+            {"byteLength", (uint64_t)vertex_data_size},
         };
     }
 
@@ -90,7 +83,7 @@ struct GltfBuffer {
             {"name", id.name},
             {"buffer", id.index},
             {"byteOffset", 0},
-            {"byteLength", vertices.size() * sizeof(Coord<DIM>)},
+            {"byteLength", (uint64_t)(vertices.size() * sizeof(Coord<DIM>))},
             {"target", GL_ARRAY_BUFFER},
         };
     }
@@ -115,7 +108,7 @@ struct GltfBuffer {
             {"bufferView", id.index},
             {"byteOffset", 0},
             {"componentType", GL_FLOAT},
-            {"count", vertices.size()},
+            {"count", (uint64_t)vertices.size()},
             {"type", "VEC" + std::to_string(DIM)},
             {"min", std::move(min_v)},
             {"max", std::move(max_v)},
@@ -125,10 +118,10 @@ struct GltfBuffer {
 
 struct GltfSampler {
     GltfId id;
-    size_t magFilter;
-    size_t minFilter;
-    size_t wrapS;
-    size_t wrapT;
+    uint64_t magFilter;
+    uint64_t minFilter;
+    uint64_t wrapS;
+    uint64_t wrapT;
 
     void visit(const gltf_visit_callback &callback);
     JsonObj to_json() const;
@@ -165,7 +158,7 @@ struct GltfMaterial {
 
 struct GltfPrimitive {
     GltfId id;
-    size_t element_type;
+    uint64_t element_type;
     std::shared_ptr<GltfBuffer<3>> position_buffer;
     std::shared_ptr<GltfBuffer<2>> tex_coords_buffer;
     std::shared_ptr<GltfMaterial> material;
diff --git a/src/stim/diagram/graph/match_graph_3d_drawer.cc b/src/stim/diagram/graph/match_graph_3d_drawer.cc
index c7558016..bcf5e504 100644
--- a/src/stim/diagram/graph/match_graph_3d_drawer.cc
+++ b/src/stim/diagram/graph/match_graph_3d_drawer.cc
@@ -79,6 +79,7 @@ Basic3dDiagram stim_draw_internal::dem_match_graph_to_basic_3d_diagram(const sti
     auto minmax = Coord<3>::min_max(coords);
     auto center = (minmax.first + minmax.second) * 0.5;
 
+    std::set<uint64_t> boundary_observable_detectors;
     std::vector<Coord<3>> det_coords;
     auto handle_contiguous_targets = [&](SpanRef<const DemTarget> targets) {
         bool has_observables = false;
@@ -102,6 +103,13 @@ Basic3dDiagram stim_draw_internal::dem_match_graph_to_basic_3d_diagram(const sti
             }
             auto a = det_coords[0];
             det_coords.push_back(a + d * 10);
+            if (has_observables) {
+                for (auto t : targets) {
+                    if (t.is_relative_detector_id()) {
+                        boundary_observable_detectors.insert(t.val());
+                    }
+                }
+            }
         }
         if (det_coords.size() == 2) {
             if (has_observables) {
@@ -129,10 +137,6 @@ Basic3dDiagram stim_draw_internal::dem_match_graph_to_basic_3d_diagram(const sti
         }
     };
 
-    for (const auto &c : coords) {
-        out.elements.push_back({"DETECTOR", c});
-    }
-
     dem.iter_flatten_error_instructions([&](const DemInstruction &op) {
         if (op.type != DemInstructionType::DEM_ERROR) {
             return;
@@ -148,5 +152,10 @@ Basic3dDiagram stim_draw_internal::dem_match_graph_to_basic_3d_diagram(const sti
         handle_contiguous_targets({p + start, op.target_data.ptr_end});
     });
 
+    for (size_t k = 0; k < coords.size(); k++) {
+        bool excited = boundary_observable_detectors.find(k) != boundary_observable_detectors.end();
+        out.elements.push_back({excited ? "EXCITED_DETECTOR" : "DETECTOR", coords[k]});
+    }
+
     return out;
 }
diff --git a/src/stim/diagram/json_obj.cc b/src/stim/diagram/json_obj.cc
index 1654c9e9..d59bc578 100644
--- a/src/stim/diagram/json_obj.cc
+++ b/src/stim/diagram/json_obj.cc
@@ -5,41 +5,60 @@
 using namespace stim;
 using namespace stim_draw_internal;
 
-JsonObj::JsonObj(bool boolean) : boolean(boolean), type(4) {
+enum JsonType : uint8_t {
+    JsonTypeEmpty = 0,
+    JsonTypeMap = 1,
+    JsonTypeArray = 2,
+    JsonTypeBool = 3,
+    JsonTypeSingle = 4,
+    JsonTypeDouble = 5,
+    JsonTypeInt64 = 6,
+    JsonTypeUInt64 = 7,
+    JsonTypeString = 8,
+};
+
+JsonObj::JsonObj(float num) : val_float(num), type(JsonTypeSingle) {
+}
+JsonObj::JsonObj(double num) : val_double(num), type(JsonTypeDouble) {
+}
+JsonObj::JsonObj(uint64_t num) : val_uint64_t(num), type(JsonTypeUInt64) {
+}
+JsonObj::JsonObj(uint32_t num) : val_uint64_t(num), type(JsonTypeUInt64) {
 }
-JsonObj::JsonObj(int num) : num(num), type(0) {
+JsonObj::JsonObj(uint16_t num) : val_uint64_t(num), type(JsonTypeUInt64) {
 }
-JsonObj::JsonObj(size_t num) : num(num), type(0) {
+JsonObj::JsonObj(uint8_t num) : val_uint64_t(num), type(JsonTypeUInt64) {
 }
-JsonObj::JsonObj(float num) : num(num), type(0) {
+JsonObj::JsonObj(int8_t num) : val_int64_t(num), type(JsonTypeInt64) {
 }
-JsonObj::JsonObj(double num) : double_num(num), type(11) {
+JsonObj::JsonObj(int16_t num) : val_int64_t(num), type(JsonTypeInt64) {
+}
+JsonObj::JsonObj(int32_t num) : val_int64_t(num), type(JsonTypeInt64) {
+}
+JsonObj::JsonObj(int64_t num) : val_int64_t(num), type(JsonTypeInt64) {
 }
 
-JsonObj::JsonObj(std::string text) : text(text), type(1) {
+JsonObj::JsonObj(std::string text) : text(text), type(JsonTypeString) {
 }
 
-JsonObj::JsonObj(const char *text) : text(text), type(1) {
+JsonObj::JsonObj(const char *text) : text(text), type(JsonTypeString) {
 }
 
-JsonObj::JsonObj(std::map<std::string, JsonObj> map) : map(map), type(2) {
+JsonObj::JsonObj(std::map<std::string, JsonObj> map) : map(map), type(JsonTypeMap) {
 }
 
-JsonObj::JsonObj(std::vector<JsonObj> arr) : arr(arr), type(3) {
+JsonObj::JsonObj(std::vector<JsonObj> arr) : arr(arr), type(JsonTypeArray) {
+
+};
+JsonObj::JsonObj(bool boolean) : val_boolean(boolean), type(JsonTypeBool) {
 }
 
 void JsonObj::clear() {
-    auto old_type = type;
-    if (old_type == 1) {
-        text.clear();
-    } else if (old_type == 2) {
-        map.clear();
-    } else if (old_type == 3) {
-        arr.clear();
-    }
-    type = 0;
-    num = 0;
-    double_num = 0;
+    text.clear();
+    map.clear();
+    arr.clear();
+    type = JsonTypeEmpty;
+    val_uint64_t = 0;
 }
 
 void JsonObj::write_str(std::string_view s, std::ostream &out) {
@@ -70,55 +89,76 @@ void indented_new_line(std::ostream &out, int64_t indent) {
 }
 
 void JsonObj::write(std::ostream &out, int64_t indent) const {
-    if (type == 0) {
-        out << num;
-    } else if (type == 11) {
-        auto p = out.precision();
-        out.precision(std::numeric_limits<double>::digits10);
-        out << double_num;
-        out.precision(p);
-    } else if (type == 1) {
-        write_str(text, out);
-    } else if (type == 2) {
-        out << "{";
-        indented_new_line(out, indent + 2);
-        bool first = true;
-        for (const auto &e : map) {
-            if (first) {
-                first = false;
-            } else {
-                out << ',';
-                indented_new_line(out, indent + 2);
+    switch (type) {
+        case JsonTypeMap: {
+            out << "{";
+            indented_new_line(out, indent + 2);
+            bool first = true;
+            for (const auto &e : map) {
+                if (first) {
+                    first = false;
+                } else {
+                    out << ',';
+                    indented_new_line(out, indent + 2);
+                }
+                write_str(e.first, out);
+                out << ':';
+                e.second.write(out, indent + 2);
             }
-            write_str(e.first, out);
-            out << ':';
-            e.second.write(out, indent + 2);
-        }
-        if (!first) {
-            indented_new_line(out, indent);
+            if (!first) {
+                indented_new_line(out, indent);
+            }
+            out << "}";
+            break;
         }
-        out << "}";
-    } else if (type == 3) {
-        out << "[";
-        indented_new_line(out, indent + 2);
-        bool first = true;
-        for (const auto &e : arr) {
-            if (first) {
-                first = false;
-            } else {
-                out << ',';
-                indented_new_line(out, indent + 2);
+        case JsonTypeArray: {
+            out << "[";
+            indented_new_line(out, indent + 2);
+            bool first = true;
+            for (const auto &e : arr) {
+                if (first) {
+                    first = false;
+                } else {
+                    out << ',';
+                    indented_new_line(out, indent + 2);
+                }
+                e.write(out, indent + 2);
             }
-            e.write(out, indent + 2);
+            if (!first) {
+                indented_new_line(out, indent);
+            }
+            out << "]";
+            break;
+        }
+        case JsonTypeString: {
+            write_str(text, out);
+            break;
+        }
+        case JsonTypeBool: {
+            out << (val_boolean ? "true" : "false");
+            break;
+        }
+        case JsonTypeSingle: {
+            out << val_float;
+            break;
+        }
+        case JsonTypeDouble: {
+            auto p = out.precision();
+            out.precision(std::numeric_limits<double>::digits10);
+            out << val_double;
+            out.precision(p);
+            break;
+        }
+        case JsonTypeInt64: {
+            out << val_int64_t;
+            break;
         }
-        if (!first) {
-            indented_new_line(out, indent);
+        case JsonTypeUInt64: {
+            out << val_uint64_t;
+            break;
         }
-        out << "]";
-    } else if (type == 4) {
-        out << (boolean ? "true" : "false");
-    } else {
-        throw std::invalid_argument("unknown type");
+        default:
+            throw std::invalid_argument("unknown type");
     }
 }
 
diff --git a/src/stim/diagram/json_obj.h b/src/stim/diagram/json_obj.h
index f373d4d8..1cf8c8c7 100644
--- a/src/stim/diagram/json_obj.h
+++ b/src/stim/diagram/json_obj.h
@@ -24,19 +24,29 @@
 namespace stim_draw_internal {
 
 struct JsonObj {
-    float num = 0;
-    double double_num = 0;
+    union {
+        float val_float;
+        double val_double;
+        int64_t val_int64_t;
+        uint64_t val_uint64_t;
+        bool val_boolean;
+    };
     std::string text;
     std::map<std::string, JsonObj> map;
     std::vector<JsonObj> arr;
-    bool boolean = false;
     uint8_t type;
 
     JsonObj(bool boolean);
-    JsonObj(int num);
-    JsonObj(size_t num);
     JsonObj(float num);
     JsonObj(double double_num);
+    JsonObj(uint8_t int_num);
+    JsonObj(uint16_t int_num);
+    JsonObj(uint32_t int_num);
+    JsonObj(uint64_t uint_num);
+    JsonObj(int8_t int_num);
+    JsonObj(int16_t int_num);
+    JsonObj(int32_t int_num);
+    JsonObj(int64_t int_num);
     JsonObj(std::string text);
     JsonObj(const char *text);
     JsonObj(std::map<std::string, JsonObj> map);
diff --git a/testdata/circuit_all_ops_3d.gltf b/testdata/circuit_all_ops_3d.gltf
index a53263e8..780820cd 100644
--- a/testdata/circuit_all_ops_3d.gltf
+++ b/testdata/circuit_all_ops_3d.gltf
@@ -1 +1 @@
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diff --git a/testdata/classical_feedback.gltf b/testdata/classical_feedback.gltf
index 2698ce8c..0ec9ff51 100644
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+++ b/testdata/classical_feedback.gltf
@@ -1 +1 @@
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"}],"materials":[{"doubleSided":false,"pbrMetallicRoughness":{"baseColorFactor":[1,1,1,1],"baseColorTexture":{"index":0,"texCoord":0},"metallicFactor":0.4,"roughnessFactor":0.5}},{"doubleSided":true,"pbrMetallicRoughness":{"baseColorFactor":[0,0,0,1],"metallicFactor":1,"roughnessFactor":1}},{"doubleSided":true,"pbrMetallicRoughness":{"baseColorFactor":[1,0,0,1],"metallicFactor":1,"roughnessFactor":1}}],"meshes":[{"primitives":[{"attributes":{"POSITION":0,"TEXCOORD_0":1},"material":0,"mode":4}]},{"primitives":[{"attributes":{"POSITION":0,"TEXCOORD_0":2},"material":0,"mode":4}]},{"primitives":[{"attributes":{"POSITION":0,"TEXCOORD_0":3},"material":0,"mode":4}]},{"primitives":[{"attributes":{"POSITION":4},"material":1,"mode":1}]},{"primitives":[{"attributes":{"POSITION":5},"material":2,"mode":1}]}],"nodes":[{"mesh":0,"translation":[-0,-0,-0]},{"mesh":1,"translation":[-0,-2,-0]},{"mesh":2,"translation":[-0,-4,-0]},{"mesh":3,"translation":[0,0,0]},{"mesh":4,"translation":[0,0,0]}],"samplers":[{"magFilter":9728,"minFilter":9728,"wrapS":33071,"wrapT":33071}],"scene":0,"scenes":[{"nodes":[0,1,2,3,4]}],"textures":[{"sampler":0,"source":0}]}
\ No newline at end of file
diff --git a/testdata/collapsing.gltf b/testdata/collapsing.gltf
index 9ef1820b..7a3e15ca 100644
--- a/testdata/collapsing.gltf
+++ b/testdata/collapsing.gltf
@@ -1 +1 @@
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diff --git a/testdata/detector_pseudo_targets.gltf b/testdata/detector_pseudo_targets.gltf
index c5e36fca..e2727aaf 100644
--- a/testdata/detector_pseudo_targets.gltf
+++ b/testdata/detector_pseudo_targets.gltf
@@ -1 +1 @@
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QQAOrevbvgtg0MDKhGjRoKyzsTAJWbgwcP0siRI7/rN35oATB+/Hjy8/OrVDc9PT2dDAwMZK69fPmSzMzMBE3H69evqUmTJlxn2Lp1a3r79q2g6rdBgwZ0+/Zt7pqvry85OTlRbm4uRUREkIaGBl2/fv27baWkpFC7du2oefPm9OTJE3J1dSUAZGhoSO/evePiJSUlkY6ODr18+bJC10EzMzMCUGQUSlEPQQBka2sruO2IiAj6/fffebdz8uRJ0tLSKvJRU1MjNTW1YsNOnjzJesgKTs+ePbnRqG/lh94JsFatWoI6AJYHYmJi4OLiInPtxo0baNy4saDpMDY2xk8//YSAgADo6uoiKioKLVu2FGwzpPHjx2Po0KFwdnYGANy9exc7duzAgAEDoK6ujvbt28PAwACzZs36LjuvXr1C06ZNkZ6ejoiICNSuXRsrV66ESCRCamoqJk6cyMUNCQnBypUr5bocsjzy9u1bbg5e0Uj9f1JSUgS33b59e3Ts2JF3O126dMHHjx+LfPz9/bFo0aJiw7p06cImyCswHz9+5FajfA8qP3IhfL4UsKIjdQLMy8sDEck4AObm5kIkEnHX+HICLA4vLy94e3ujffv2ePjwIVasWIH58+fzavPw4cNITk7G1q1buWt//PEHAKBOnTrcNWtra0RFRSE7O/ubndWGDBmC58+fY+PGjdxvNG3aFKNHj8bGjRuxe/dudOrUCbVq1UJSUhJWr15d4evi5yszFI2Ghgb3QGRUfPLz89G2bdtiwy5evCjonjCK5Pjx4/Dw8PjuVU8/tAD475twRWbJkiVYsmQJevbsCR8fH/To0YML69y5M6ZMmVJiw5Cn6tTW1i5y3dbWFhs3bkTXrl152Q74c54/f4558+YhKipKZhmk9A3w8xUhWlpaKCgowNu3b79JANy9exfnz58HAG6kQcqKFSsQFRWFe/fuYdSoUbCwsEBERESlqIsGBgZISUlBTk6OwtPy6dMnAEDVqlVZ71gJKCwsLPEZU1hYWGnK4dChQ5gyZcp3/84PPQVgbW0Na2vrStUArl+/XmS9f0xMjCBTAL/88ssXxZh0/T4fFBQUwMfHB2vWrCmy8Y6uri4AQCwWc9fy8vIA/LuBy7cQFxfH/f348eMib56HDh2ClpYWPnz4gKysLBnbUu7du1fh6qB0iiMjI0PhacnKygIA1K5dm/WOlYAqVarg/33XinwUtSOm0Hz48AF3795FixYtKq8AICKsW7cOGzdurDSV/8WLF1BVVZVZ75yYmIgaNWoI8gaUmpqKyMjIYsOuXbsG4N8pAb4ICgpCs2bNit31qlWrVgCApKQkmbKRbtz0LUi3IAaAgIAA5ObmyoQ/ffoUBgYGUFVVRWJiItzc3GTK5+jRo7h06VKFq4fSrZYTExMVnpbnz58DgOA+MJWdly9fYuzYsVi7dm2levP+nDVr1ijkILBjx46hW7duRfZh+daO9Ifk0qVLnAe6PDy9fwQOHTpEffv2lbl24MAB8vX1FcR+u3btyMzMjM6ePUtExB0GdPfuXTIzM6NBgwZRQUEBL7ajo6OpadOmlJ+fX+Iyvf/973/Upk0bKiwspJiYmBKX6n0Nnp6eXD2zt7engIAAWr58ObVv357q169PDx8+pD/++IN0dHS4eGZmZlSnTh2ytbUVdF24UEgPIurfv7/C0zJ48GACQGfOnKl0XuBLliyhVatWKcT2oEGDuPoeEhJS6cr+zz//5PJ/5coVQW136tSJO4zse/lhBcCbN2/I1taW6tWrJ7MUqyIzZcqUIg1+8uTJtHnzZkHs3759myZNmkSNGjUiR0dHMjU1pWbNmpGHhwevS8LevXtH9vb2FB8fX2q8jIwMGjhwILm7u5OzszOtWbPmu22LxWJat24dOTs7k7a2Nunq6lKLFi1o06ZNMhtwJCYm0sCBA6l69eqkp6dHXl5eRfZqqCiIxWKytrYmKysrhafF1taWzM3Nv2szFMbXs2HDBtLW1iZnZ2fq0KFDpct/Wloa2dnZUd26dSktLU0wu+np6VSnTh2ZzdC+BxHR/2+wzmB8JX5+fhg1apQg58AzyhdhYWEYOHAg7t27xx2QJTQJCQmwtbXF1q1bMXz4cHZTFEB0dDQ2b96Mffv2scL4AVFiRcD4Vjw8PL7ZwY7xY9O/f3+0b98e69atU1ga1q1bh1atWsHX15fdEAWxZ8+eUp2DGeUbNgLAYDC+iXfv3sHV1RW7du3iDkoSigcPHqBbt26IjIwU9Dhwxr/k5OQgODgYJiYmTAAwAcBgMCojSUlJGDlyJNauXQtbW1tBbL5+/Rq+vr6C2mTIEh4eDhcXF1hZWbHCYAKAwWBUVrKzs7F27Vp06NCB9+V4N2/exIkTJzBlyhRu7wcGg8EEAIPBUCCFhYXyWZusYBsMBhMADAaDwWAwKixMSjMYDAaDwQQAg8FgMBgMJgAYDAaDwWAwAcBgMBgMBoMJAAaDwWAwGEwAMBgMBoPBYAKAwWAwGAwGEwAMBoPBYDCYAGAwGAwGg/GjCYD3799j3LhxaNiwIRo1aoQBAwbg1atXgiY8NzcX69evh7m5OTIyMgSzm5OTgxkzZsDS0hJqamowNzfH+PHj8e7dO0HsSyQShISEoF69etDQ0ICFhQXmzJkDsVissEo0bNgwiEQi5ObmCmZz5syZEIlERT7//POPoHlPTk7G3Llz0alTJ4wdOxa7du3i1V7Tpk2Lzbf0Y2FhwXued+/ejZYtW6JDhw5wd3dHy5YtsXv3bsHK/OTJk2jTpg2aNWsGS0tLeHl54dGjR+xpzqgUnDhxAr6+vvD19YW9vT0cHR0RHBwMiUTy9T9GX0lqaio5OjqSt7c3icViIiKaM2cOmZiY0NOnT4lvcnNzafXq1VSnTh0CQADo/fv3JAQFBQXUqlUrUlZWJmtra6pRowaXhjp16lBycjKv9gsLC8nb25vq1atHPj4+5Orqytn39fUlRXDw4EEuDZ8+fRLEZnp6OlWtWpWUlZVlPh07dhQ078uWLSMdHR1avHgx5eXl8W7v1q1bBICUlZWpevXqZGhoKPMRiUQ0btw4XtMwbdo0MjExoUePHnHX7t+/T3p6ejRz5kzey2D58uVkYmJCd+/eJSKiDx8+UIcOHahq1ap07do1YjAqMoGBgeTt7U0FBQVERCQWi2nkyJEEgAYMGPDVv/fVAqBbt26ko6NDGRkZ3LW8vDwyNDQkNzc3Kiws5LUAxGIxvXv3jlJTU0lVVVVQAbBmzRpq3749JSYmctcOHDhAmpqaBIC8vb15tb9//34KDg6WuRYaGsp1wHwLkP+SmJhIderUoapVqwoqAObMmUNLly5VWCOUSCTUp08fUlNToz/++EMwu6NGjaIlS5ZQdnZ2sfcCAP3555+82Y+PjyeRSESrV68uEjZjxgwSiUSUlJTEm/2//vqLlJSUaPv27TLX09LSSFNTkywsLIotG4awbSM8PJxiY2MrTJ5u375NR44c4TpdRZGRkUGqqqq0bNkymes5OTmkp6dHAOjvv//mTwBERUURAPLy8ioS5uvrSwDo+PHjghWImZmZoAJg0KBBlJOTU+T6qlWrCABpaWnxar+km2tra0sA6MGDB4KVvVgsJldXVzp58iSZmpoKJgDevXtHVlZWlJWVpbCGKK3r/+2I+C7vjRs3lhgeHBxMZmZmvArw/fv3E4Bi07Fu3ToCwOtbuKenJwGgx48fFwnz8fEhALR27VrWCyuA9PR0Wrp0KVlaWlL37t3p2bNnFSZvCQkJ9L///Y+srKwoJCRE5uVXSJ4+fUoAqHbt2kXauXQ0ODQ09Kt+86t8AA4ePAgAcHFxKXZuEgDvc6Cfo6amJujci5+fHzQ0NIpc79evHwBALBaDeDxc8aeffir2es2aNeHg4IC6desKVhbz5s1DkyZN0KVLF0HvwerVq5GSkoIePXpg2bJlSE5OFtT+7t27sWPHDrRt2xa+vr6C2VVRUcHo0aNLDD906BD69OkDkUjEWxpMTU0BAJs3b0Z+fr5MWGxsLIyNjdGgQQPe7F+8eBEAYGhoWCSsdevWAIDjx4+zSWIBiYuLw8iRI1G/fn28efMGFy9exLFjxwTxRREKa2trREZG4ujRo4iLi4O1tTXGjRuH+Ph4QdNhaWmJzp07Q0VFBYWFhUX88j5vo7z4ANSuXZsA0L59+4qERUREEAAyNDQUTBFJ/QCEGgEobdhLJBJRw4YNFTIsVLNmTYqJiRHMZmRkJDVu3Jib9xZqBCAjI4OqVavGTXkAoCpVqtD8+fMFGZ77+PEjGRsbEwA6f/58uXlDefLkCQGg69ev82qnsLCQHB0dCQB169aNG26/ffs2VatWjX7//XfebOfm5nL3/MWLF0XCz549SwDI2NhYsBGZVq1akZmZmYw/hFB8+PCBnJycqHbt2vTy5UtBbRcUFNCxY8eobdu2ZGdnRxs2bKCPHz/yZu/kyZOkpaX1VZ+jR4/ylp43b95QUFAQmZiYkIeHB507d06h7T85OZlUVFTIwsKCcnNz+ZkCKCwsJGVlZQJAly5dKnZ4WtpAMzMzK5UAiIuLIwC0atUqQe1mZWVRly5daNu2bYLZTEtLo7p161J8fDx3TSgBkJmZSVeuXKHjx4/TjBkzyNLSkqtzvXr14l0E7Nmzh+tkoqOjydvbm2xsbMja2ppGjRpFqampCql/ixcvJktLS0FsxcfHcyLI2dmZzpw5Qy1btqQbN27wbltLS4sAFPtwP336NAEgbW1twR66IpGIANCuXbsEv+exsbFc3T99+rRgLxshISFUp04d6tSpE/3xxx+8+3yVZ/Ly8mj37t3k4uJC9vb2tHnzZoX4oEybNo2UlZW/6aWkzAIgLS2Nq3DFNfZ79+5x4Xw6ApVHATBnzhyysLAo1j+AD168eEELFy7kfCCUlZVp9uzZgtju1q0b7d69W+aakD4A/30rDAoK4h7EK1eu5NVejx49OAEQFBREsbGxdPv2bW5u2tLSUiEiwNnZmWbMmCGYvcTERHJwcODau1DCt3v37gSAfv755yJhUmfYWrVqCVYOe/bsoaCgIEFWgBRHaGgohYSE8C58k5OTacyYMWRsbEx+fn4y4p/xL5cvX6bevXtTzZo1acaMGZSWliaI3ZSUFNLU1CzVP0guAuDly5dcg79z506pilSoh2B5EACpqamkr69PERERgnZ8iYmJtHfvXnJxceHKfcuWLbzaXbt2LQ0aNKjIdUUJACkrV64kAGRmZsarnbp16xIA2rBhg8z1/Px8sre3JwA0ePBgQfMeHx9PAOjWrVuC2bx+/Tr17t2bAgMDSUlJiQDQ6NGjee+I7ty5w624mTJlCn348IEyMzPpwIED3LOgc+fOrDeSM8+ePSNPT08yMjKigIAASklJYYXyH7KysmjdunVkbm5OP//8syBL4omIJk2aRNOmTfvm75dZAHz8+LHUEYCrV68SABKJRCSRSCqNAOjVqxctXLhQYfYlEgkNGjSIAJC9vT1vdmJjY8nJyalY73tFC4DCwkJq2LAhAaDXr1/zZkdXV7fEIej169cTAKpataqgw6ILFiwgW1tbwexFRESQiYkJt+T0t99+oypVqnAigG+uXbvGiV4lJSWqV68erVu3jqysrAgAbdq0ifVGPPH06VOaPHky1axZk3x8fATzOzp16hTp6up+1Ueo1WiPHz+miRMnkqmpKY0ZM4YePnwo6Iugh4fHd/W3X+UEKH3QF+fsc+rUKcGH4BQtAFasWEEjRoxQeMNMS0sjNTU1UlVV5c2GdOlbWT7STVqEJDAwkADw6hAlnfsurv7fv3+fy/+HDx8Ey7ejoyP5+/sLYuvdu3ekp6dH06dPl7n++++/c3tyCLUZT3p6OjfMeuPGDQJAenp6gpZ9ZX/btbW1pZYtW1J4eLhgL33lhfPnz1PXrl3J2tpaYUsDr169SocOHfqu31D5mhUDrVq1wv79+/HkyZMiYc+ePQMAuLu7V4rlL4cPH8bNmzcRFham8LRUr14dLi4uvG6H2rhxY2RnZ5e4NeWnT5/g5eUFJSUlVKtWTfAyMDExQfXq1WFkZMSbDTs7OyQnJ+PNmzdFwszMzAAA6urq0NbWFiTPjx49wt27d7F//35B7O3fvx/v37+Hq6urzPWOHTsiMDAQs2fPxokTJ7glwXyir6/P/e3v7w8AWLhwIapWrcrW5vGMtrY2/Pz8MHbsWJw5cwZr1qzB1KlT4efnh2HDhimk/QtBTk4O9u7di7Vr18LQ0BDjx49H165doaSkmCN1nj59+v1t7WvUgtTTtrh54OHDhxMAOnXqVIUfATh16hT17NmT8vPzix2SVwT169enfv36KcS2oqcAiIh++eUXmjJlCq821q5dSwBo1KhRRcJevHhRooMan6MeDg4Ogtnz9/cvcQokOTmZAJCfn5+g9126FXXv3r0rtUe6orl37x6NHDmSDAwMaOzYsQpbEcMHb9++penTp5OpqSmNGDGC4uLiykW65FHfv3orYFdXV6pevbrMwz4vL48MDAyoadOmgjZC6b4EQgqAkydPUteuXYtdb/nq1Svy8fHhzXZ+fn6xAuPatWukra1N9+7dq9AC4OnTp3ThwoVi8+/g4MB7PcjJySFzc3PS09Mr4gtx6NAhEolExS6R5Qt7e3sKCgoSzF5kZCQBoKlTpxYJe/jwIQGgEydOCJae69evk6amJnl6eipEfG7fvp3mz5+vkFUA79+/p8mTJ1NQUNBXr/3me2pm6dKl9Ndff1UYAfDnn3/S0qVLKT09vdykKT8/nxYvXvzdS8C/WgA8efKEDA0NuYM/CgoKyM/Pj0xMTOjJkyeCFoL0MJ7nz58LYi8sLIxUVVXJxcWF3NzcuI+rqys5OTmRiooKr0uizM3NSVdXl2bPnk0JCQn07t07OnToENnY2NDZs2cVVhmFEgDS7S5btmxJhw8fpujoaJo/fz41a9ZM5nwGPomJiSEdHR0aMGAAJ8ZevnxJNjY2tGjRIkHfuAAIvgnNiBEjqEqVKhQdHc1dy87Opq5du37TYSTfyq+//ko1atSgRYsWKWSP9mfPnnE+H3v37hXcfkBAAGef7wOgGOWP8PBw7v5/zzMA3/KlxMRE6t27N7m6upKbm5vgQz4bNmyg3r17cwXg6upKS5cu5bUDOnLkCLfevKSPiooKr+UQGBhIpqampKqqSlWrViVnZ2eaOXOmwpflCCUAoqOjycXFhTQ0NEhPT49at25NoaGhxU7F8Mndu3epc+fO5OzsTF5eXtSlSxdBz8CQdgDOzs6C3+vCwkLasmULNWnShDp27EgDBgygLl260ObNm3kf/du3bx9NnTqVunXrRtOmTVPofvO5ubnUtGlTMjMzo4SEBMHtHz9+nHR0dMjFxYVsbGxYj1jJePr0KZmbm1Pjxo2/a/MhERGPm9czGAwGgzeSkpLQr18/XL16lRUG46tRYkXAYDAYPyZ79uzBqFGjWEEwvgkVVgQMBoPxYyGRSLBx40aIxWL4+PiwAmF8E2wKgMFgMH4w/vjjD5iamsLR0ZEVBoMJAAaDwWAwGGWH+QAwGAwGg8EEAIPBYDAYDCYAGAwGg8FgMAHAYDAYDAaDCQAGg8FgMBhMADAYDAaDwWACgMFgMBgMBhMADAaDwWAwmABgMBgMBoPBBACDwWAwGAwh+erDgMRiMU6ePIkzZ85ALBZDXV0dRIScnByoqKjgp59+wuDBg6Gjo8NKl8FgMBiM8gp9Bbt37yY3NzfasGEDZWRkFAkvKCig33//nTw9PemPP/4gvhk7dixdvHiRVxsXLlygmTNnkq6uLgEgAKShoUG2trbk4uJClpaWZGtrSwMGDKCzZ8+SEERFRdHgwYOpTp06ZGxsTA0bNqTmzZvTggUL6Pnz53TlyhVauHDhd9t59OgRLVy4kOrVq8flHQCpqamRsbEx6evrk6WlJbVp04ZmzZpF//zzDy/5PXz4ME2YMIE0NDQIAKmrq5OFhYXMx9TUlNTV1QkA+fv7y9X+gwcPaMGCBWRlZcWVgYmJiYx9fX19LmzBggW83fu3b99SSEgItWjRghwcHMjJyYmaNGlCbdq0oVWrVlFSUhKNGTOG8vLy5Hb/69aty+XN2NiYZsyYQTdu3ODiHTt2jCZMmEBqampcvP/973+0fPlyys7Olmv+79y5Q4GBgWRpacnZMjc3p/nz59OdO3cEaX/S+lC7dm2Z+jBv3jyKiYmRmx1p+depU0em/OfNm8e1tYyMDFq4cCFpa2sTABKJRNSlSxc6cOCA3NJx8OBBGjNmDKmqqnLpcHFxoYCAALp9+7bcy/fhw4c0c+ZMMjIy4uxt3769zN8fOGu29kwAACAASURBVHCgTD0MDg7+6nqYm5tLixYtIjc3N+63+vfvX2L8Z8+e0aJFi8jJyYkAkJOTEy1atIhevHgh17KJiYmhX375hezt7cnW1pYsLCzI3t6eJk6cSE+ePPnq3yuTAMjOzqYePXrQsGHDylSQEomEZsyYQaGhobw1woyMDNLW1qYePXoI0uhXr15NAEhLS6tI2OXLl8nR0ZEAkI+PD4nFYl7SkJmZSX369CFlZWWaPn06JSUlcWE5OTm0a9cuMjU1JWVlZZo0aZJcH7rSRhAeHk4SiYQLi42NpfHjx3MPBx8fH/r48SMv+R89ejQBIDc3txIb7eDBg2nixIm82P/777+5cnjw4EGxD+yGDRvSzJkzebF/6NAh0tHRoRYtWlB0dDQVFhZyYampqTRnzhxSUVEhAHJ98MTGxnL5PnHiRInxpk2bRgCoRo0alJ+fz7sIlqYpMjKSFME///zDpeH06dO82YmJieHsnDx5stg4jo6OZGhoSFFRUbylw9fXlwCQoaGhXATmlzh79iyX73r16snU95J4+fIl9yzS1taWSz2cP38+l47g4OAvihcAlJiYKNeyyM7OJl9fX1JTU6MlS5bQu3fvuLCEhATy9vbmwuQqAHJzc6lFixbf9FY1ZMgQunfvHi+VIyQkhACQsrIyPX/+nPfKeOzYsRIFABFReno6p1hDQkJ4ETz16tUjJSUlOnbsWInxkpKSyMzMjAYPHixX29IG8Pmb3+f8+eefpKenx6luPjqAwMDAUgWA9A15xIgRvNSBly9flioApGJpwoQJcre9YMEC7i2koKCgVJEAgG7duiU322lpaVy+S3vDlbZJR0dH3tvj48ePuTR9y5uPPEhJSeHSEBsbqzA7y5YtI0tLS3r69Cmv+ZV2hE2bNhWkfJOSkkhNTY0bWTp+/PgXvzN16lRuNKROnTpyS4t0BFhJSYl+//33UjtqADIvSd9Lbm4utW7dmgDQoUOHSozn5+dHAGj8+PFl/u0vOgGOGTMGpqamCAoK+urphdWrV8Pf31/u0xaFhYVYv349tLW1UVBQgE2bNvE+VaKsrFxquL6+Pvr27QsA2L17t9ztDx8+HA8ePMDw4cPRvXv3EuPVqlULW7Zswfv37wXLOwC4ubkhLCwMAHDp0iUsXbpU7mWgpPRln9UaNWqga9euCqkDAODo6Fjq/fkWIiIiEBAQACsrK+zcubPUcvDy8sKgQYPw5s0bXvJdmm1pWFnuk1BpqghpKM3Or7/+ig0bNiAyMhJWVlaC5FdFRUWQ8lVVVYWGhgYGDBgAAFi2bFmp8bOysrBt2zYMGzaMl3TWq1cPhYWF6N+/PxISEkptA2V5VpSVCRMmICoqCj169ICXl1eJ8YKDg2FoaIi1a9di7969ZXumlhYYGRmJY8eOYePGjd+UcF1dXdSoUQPPnj2T6404fvw4NDQ0EBgYCADYtm0bcnNzFe5PIb3pqampcv3d33//HeHh4QCAadOmfTG+h4cHzM3NBc9/p06d0LFjRwDAihUrkJWVpZD7wJcAKCtt2rSR22/l5ORg8ODBICJMnz4d6urqX/zO9OnTIZFImINTBWfnzp3w9/fH+fPnYWlpWWHzOXXqVIhEIly5cgVXr14tMV5oaChatWoFOzs7XtKxf/9+mJubIyMjA927dxfk+RYXF4etW7cCAEaPHl1qXE1NTQwcOBAAMGvWLOTl5X2fAAgICMD06dOhr69f7Fv41q1b0bdvX0yaNAmBgYE4deoUWrRogWPHjsl0RufPn5droaxZswbjxo2Dr68vNDU1kZaWhgMHDii0kubk5ODIkSMAgCZNmsj1t0NDQwEANjY2sLa2LtN35s2bp5ByGDRoEAAgMzMTp0+fFtT2/v378c8//ygk32KxGLNmzZL774aFhSE5ORkA0KtXrzJ9x8HBAZ07d2Y9ZAVm3bp18Pf3x4ULF8r8TPhRcXBw4F4sShoFkEgkWLt2LaZOncpbOgwNDXH8+HFoaWnhwYMHGDhwIIiI17yHhYWhsLAQKioqaNGixRfjt27dGgDw8uVLREZGfrsAuH//Pm7cuIGRI0cWCcvLy0OvXr0QHR2NvXv3YtWqVXBwcMCECRNw9epVNG/enItrYWFR4nDJtxAbG4vY2Fj4+PigWrVq8Pb25hqEUBQUFMj8ff36dXTq1AnPnj2Dvr4+Fi9eLFd70hvp4OBQ5u/UqFFDIY21WbNm3N83btwQzO7bt2+xYcMGhYm/xYsX49OnT3L/7ZMnTwIATE1NYWBgwHo+BhYsWIClS5fi4sWLsLW1rRR5lo58njhxAo8ePSoSfvDgQRgbG6Nly5a8psPZ2Rl79uyBSCTCiRMnEBAQwKu9y5cvAwDMzc2hoaHxxfj29vZFvlsaJU6SHD9+HG3atIGenl6Rzq9z5854//49rl69ClVVVQCAra0tnj59ip9++gmGhoZcfC0tLbkOz69ZswbDhg2DlpYWAMDPzw9bt27FrVu38Ndff8mIDz7Izs6GkZERDA0NkZ2djZcvX3LDrV26dMGqVavkqsjT0tLw4cMHhXbqX6uSpaSkpPBi49atWzLDfNnZ2Xj16hXvavxzGjRoAJFIBADIz88HEWHChAlyt/PgwQMAQPXq1UuNt2HDBly5cgXv3r2TSeO4ceNgZmYmt/T06NGjxGkIefqdMIpnxowZOHPmDNzc3OR6X8s7bdq0gYuLC2JiYrB8+XJs27ZNJjwkJARz5swRJC09evTAggULMHfuXCxatAjOzs5lHp37WqSjf9WqVStT/M/jSb/7TSMAN2/eLFZNBQcH4+LFi9ixY4fMg0A6z//focfXr1/D2NhYbm95Bw8ehJ+fH3fNycmJS6cQowBaWlp4+/Yt4uLikJiYiHfv3iE8PBz169fHuXPnMHPmTCQlJcnNXn5+Pvd3WRSgovncSUlNTY0XG40aNcLDhw+5z4sXL/DkyRPUr19fsHzGxsYiNzcXubm5yMnJwdy5c3mxI73/mZmZpcYbO3Ysli9fjqioKJw9exYaGhoIDg6Weydx9OhRmbL//MPHFAijqPBUVlbGlStX0KVLF+Tk5FSavEuH9/fu3SvTuV24cAGZmZno0aOHYGmZM2cO+vfvDyLC4MGDcffuXV7tfT7qXBqfP3PL4ohYogB4/vw56tWrJ3PtyZMnCAwMhKenJxo0aCATdvHixWIFQHx8vNwcVDZv3gx3d/civzd27FgAQHh4OG9vnSWho6ODXr164ebNm3B2dsZvv/2GZs2ayc0LW19fn+tU3759W+4b6ef5NjExEcyulZUVRo0apZA8V6lSBYGBgbzsfikdUXn9+jUKCwtLjWtqaso5fzZs2JD1lhWQAQMGYM+ePVBWVkZkZCS6du3Ky9RTecTLywuWlpbIy8vDmjVruOsrVqzA5MmTBV8NsmPHDjRp0gTZ2dno3r070tPT5W7DyMgIQNlH1z53TDQ1Nf12AfDx48ciww4rV65Efn4+Jk2aVESdHDt2DAYGBnBxcZEJO3PmDDp06PDdBSEWi7Fp0ybExMTAzs5O5uPv7w8VFRWIxWJs2bJFIZVTXV2dm/tPTk7G+vXr5da5SN9s79+/X+4b6d9//8397ebmJqhtd3d3rsEoYuRDulxJnkhHt/Lz8/Hnn39+Mb50So6v0RdG8chz2deX6N+/P/bt2wcVFRVcvHgR3bp1qxQiQFlZmet7Nm/ejKysLNy7dw8xMTEYOnSoQoT/sWPHYGpqisTERPTt27fMb+plRfoMffHixRdHAaUv6VKkDoHfJAA0NTVllhIREQ4dOgRjY+Mi3ohhYWF4/vw5fv75Z25eFPh3/loikXxx/rIsHDp0CDVr1kRSUlKRocf4+HjMmDEDALBlyxaIxWKFVNDPvf/l2Vn369cPAHDnzh0kJiaW6TvZ2dmCzol/Xhekb//t27cX1LaNjY3CBACAIiNm8mDYsGFcm5KWLaP8oaurK6i9Pn36cCLg/Pnz6N69e7lYCi3l4cOHvPzusGHDoK+vjw8fPmDLli1YsWIFxowZo7DpUWNjY5w4cQKampq4cOECpkyZIvcRH2n/WxavfukyyTp16pTJIbJEAWBhYSEznJ6YmIi0tDQZ5yfg3+UG0vlPd3d3md9YvHgxNzz/vaxZs6bUwh07dixUVVWRnJyM3377TSGV4fMhegsLC7n9rnQzJgCYPXt2mUZLZsyYIeM/IASXL1/GiRMnAAALFy5U2FtoXl4epk+fXiE6Fnt7e4wZMwbAv0OOsbGxrLctZ+jo6Mg4vwqFl5cXDhw4ABUVFURERMDT07NciID8/Pwyb0TztWhpaXFTfSEhITh69Kjc+phvpVGjRvj1118hEonkPgLt7OzMvQB+acM7iUSCnTt3AgCWL19epo2QShQAP/30E27duiXzUAWAjIwM7lpKSgqCgoLQtm1bAICrqysXdu7cObx+/Zpbv/k9REdHIykpiSuIkpSYp6cnAGDVqlVyv8llGVUICQn5t1CVlLjdqOT1dnHgwAFoamriwIEDCAoKKvHtPi8vDyNHjsSIESPKtGmMvPIeFxeHvn37orCwECNGjOBlSE46vPalkY358+fzMgf++Ry8vIf6SmPFihXw8PCARCJB//798eLFC0EfcGXNtzRMiLJRxL1IT09H3bp10bVrVxQWFnJ2O3fuzOsUwH+XHX9Or169cPDgQaioqODs2bPo3LkzbxvUSJ8DX3oeBAQEoHHjxt9tTyKRFOv3Mn78eKirqyMlJQX9+/cvsjxWOnL9JZ8ZeaTlczHG15LA0NBQODo64uzZs6WOAs6dOxePHj3C7Nmzy+4QWdIewXFxcWRtbS2zH3GNGjUIAP3yyy80d+5c6tatG71//547fenevXuUl5dHK1asIA8PD+5QmOvXr9Ply5e/aR9ksVhMTZs2JS8vry/G3bJlC7dntjxPwyIiWrFiBQEgTU1N+vDhQ5E94qUH1SgrK/N2CFJUVBRZWFhw++Hv3buX4uPjKSMjgx4/fkxbt26lDh060F9//SVXu7du3eLK9b+nL6akpNDixYtJR0eHtLW1v3hYxvcwbNgwAkCWlpbFnjXw6dMnWrRoEWlpafFyING1a9cEOfylOPLz82nq1KmkpqZGRkZGFBoaSjk5OTJ537VrF+np6ZG6urpc6//nh94cPXq0xHjjxo3jDgPi60AsKZcuXeLSFB0dLcg9uHv3Lmdz7969dP78eapZsybve/DfuHGDs3vq1Kli44wcOZKL4+joyMvZBEOGDCEApKurSwkJCTJnUuTk5ND9+/dp9OjRpKmpKZdTIK9cuUIikajI85aIaPjw4aSkpETx8fElHkqlo6Mjlz35379/X+o5KFIKCwvJy8uL8HWH7JY5DV26dCFVVVUKDg6mzMxMLuzp06fk4+NDGhoatGbNGvkdBtS2bVuZB92ZM2fI1NSUzMzMaP78+ZSbm0tERJGRkWRqakomJibk7u5OYWFhXOUoKCggd3f3Ym/il/j999+pWbNm3BG8o0ePLvEmLFmyRObYUnV1dRoxYkSxFeRruHDhAk2fPp07YAKfHctpZ2dH5ubmpKurSw4ODjRs2DC6e/curw+D7OxsWr9+PbVt25aMjY25o3lbtGhBGzZskKkY38ujR49owYIFZGNjI5N3AwMDcnBwIHt7e7KysqLOnTtTSEgIpaWl8ZLnw4cP09ixY0lJSYlLQ/Xq1alOnTrcx8zMjDsFrG/fvnK1/+DBAwoKCiJra2vOvqGhIQUEBMj1+NeykJiYSIsWLaJWrVpR7dq1ycnJierVq0eWlpbk7u5OK1eupOTkZLne/8/blZGREfn7+xc5DtjPz0/muFghjwO2srKiwMBAQY4DXrBgARkYGJChoSF5e3t/9/OlNO7fv09z5syROYbayMiIZsyYIVPvtmzZQrVq1ZJpo8rKyuTh4UGbN2/+7nQcPHiQRo0aRcrKyjI2Svp07979u+tdUFAQdwyym5sbLV26lN68eSPTJnv16iXzvTNnztCkSZOoSpUqXFo6dOhAy5Yt+6Z6+ObNG1qyZAk1adKEO5Fw4cKF9OjRoxK/k5OTQy4uLrzViYiICPL29iYbGxtycHCgevXqUYMGDWjmzJnfdAKoiEoZT7116xYGDx6MmzdvfvNw8oIFC2BsbIzhw4ezyUIGg8FgMMoJSl9ybvD19cWQIUO+aT4lNDQUycnJrPNnMBgMBuNHEgAAMGnSJDg7O8PT07PMm9t8/PgRfn5+SExMlNt6eAaDwWAwGPKj1CmAz4mOjoa/vz9atmyJQYMGFXsIxePHj7F//35ER0dj9uzZcj0WlcFgMBgMhgIEAPDv8qtz584hPDwcz58/h6qqKpSUlCASiVBQUAArKyt4eXmhVatWMnsFMBgMBoPB+IEFAIPBYDAYjIqBEisCBoPBYDCYAGAwGAwGg8EEAIPBYDAYDCYAGAwGg8FgMAHAYDAYDAaDCQAGg8FgMBhMADAYDAaDwWACgMFgMBgMBhMADAaDwWAwmABgMBgMRjnn3bt3sLa2BttAFrhx4waSkpIqvgCIj4/HwoULYWdnB5FIxH00NDSgp6cHOzs7+Pr6IiYmhvcE379/H+PHj4e9vT3MzMxQvXp1ODk5YebMmXj16pXc7V28eBGzZs2Cnp4el29lZWUYGhqiatWqsLCwgIeHBw4fPixIo7h//z769+8PQ0ND6OrqwsbGBuPGjcOVK1ewcuVKXLp0Se42w8LCZO57WT59+/b9brvnzp3DrFmzUK1aNe53GzZsiCVLluDly5cycRMTE7F48WI4OTlBJBKhVq1amD9/Ph4/fvzN9qOjo9GzZ0+ZfNWrVw+LFy8uNv7p06fRokULLm63bt3w5MmTr7a7bNkymJubc79Tu3ZtrFy5EgAQFxcHX19f7gwOkUiEjh07IiwsTOY3Ll++jJEjR0JJSQmqqqoYMWIEkpOTvyodSUlJmDRpEmrVqiVTBoaGhpgzZw6ys7O5uL/99ht69+7Nxalfvz6CgoK+6/5HRkaiXbt2MrYNDAzg7++PFy9eyMR98uQJRo0axZVL1apVMW3aNLx+/VoubSAqKkqm/Wtraxf5KCsrQyQSQUVFBVevXuXtGZCbmwuRSAQ1NTXUqFGD+/y3TPhAX18ftra2uHbtmkI6rBcvXsjkWU1NDSKRCLm5uYKmQywWY9CgQZg0adKPrWLoK7h58yYBIAB06dIlIiJKT0+nBQsWkLKyMolEIlq1ahXxQUFBAU2fPp1UVVVpypQplJSUREREEomEoqKiyM3NjbS0tGjr1q282F+7di0BoKpVq1J2djYREX369Im2b99O2traBIAGDx5MhYWFxBeXLl0iDQ0NGjBgAL18+ZKIiJKSkmjWrFmkoqJCACgyMlLudjdv3kz29vZ08+ZNysvL4/IurQt//fUXdy8ePXpEnp6e5OHhITf7wcHBnK2UlJRS4yYkJBAAun79utzsz5w5k7N/8eLFUuOKxWJSV1ensWPHfpfNR48ekZKSEgEotk1NmTKFS9OjR49K/J369evTwoULvystubm51K9fP87e1KlTi42XlpZGAGjs2LGUn58vt/KfNm0aZzsgIKDUuM2bNycLCwuKj4+Xaxs4fPgw1a1bl/7+++9i2/i9e/eoSpUqBIBmz55NfCJte+3atSNFsHPnTpowYQKVB37++WcCQJ8+fRLU7rJly8jHx4fq169P58+fpx+VrxIAqampXEO8e/euTNicOXMIAIlEIrk+fImICgsLqU+fPgSANmzYUGycvLw86tixIwGg4OBguRfUsWPHCADp6uoWCdu2bRtXLvv37+flRkkkEjI3N6f69euTRCIpEn7kyBESiUS8CIDly5dznfx/H0KfC4DPw7y8vORmPzw8nACQpqbmF+Pm5eURAHr37p3c7L979460tLQIAK1YsaLUuA8fPiRdXV3KyMj4brvu7u4EgPr06VMk7O3bt6SqqkoA6MiRI8V+Pycnh6pVqyaXshCLxdSqVSsCQDVr1qT3798XiePn50d9+/aVe/0Ti8XUsGFDAkB2dnYkFouLjZeenk56enp07do1uadh48aNdOrUqWLD8vPzufQ1atRIruKnPAqA9+/fk5WVFa8vO+VZALx+/ZrMzMwoJSWFLl68SA4ODiXWyQolAN6+fVuiAHj16hUXNnLkSLkmctGiRQSA/ve//5UaLykpiTQ0NEhJSYnOnTsn1zScPHmyRAEgkUhIXV2dAJCnpycvN+r27dsEgHr37l1inE6dOvEiAPbu3VuksZcmAIiIdu3aJTf7R48eLbHsi+ssAFBWVpZcy2DMmDEEgOzt7UuNN2fOHJo4caLcyh0AaWlp0cePH4uEd+3alQDQwIEDi/3+kSNHqGvXrnIrg5cvX5Kenh4BoCFDhsiE7d+/nxwdHbnRMT7qv3SUa+nSpcXGGTt2LE2ePJkX+0uWLCkxb9IRoipVqtC9e/d4f2grWgAQEXXp0oWio6MrpQAYMGCAzKicl5cXrVmzpnILACLi3pJ+/vlnuSUwJSWFNDU1CQCFh4d/MX7Pnj0JADk5OclVoZYmAIiI6tSpQwCoRYsWvNyoW7ducZ1BSQ+ZHTt28CIASnsIlSQA5El5EADx8fEkEokIAJ09e7bEN0FjY+NSh+S/huzsbG56KSwsrEj4+vXrCQBpa2sX2zn17t2b9u3bJ3cxKL3vZ86cISKiu3fvkrGxsdyH3YsTVwBIQ0ODEhISZMKuXbtGVlZWxQolPrl8+TI3VbN69WpB254iBcCePXvIz8+v0gmA6Ohoql+/vswb//Pnz8nExITevn1beQXAhw8fuLChQ4fKLYHLli3jfrcsQ5nbt2/n4l+9elUQAfDp0ydOpIwaNYqXGyUWi8nU1JRLw6+//lokzqtXr+j58+dMAPAgAKRvPQCoY8eOxYbv37+f2rdvL1ebgwYNIgDF+lT07t2buwf/7egzMzOpRo0avLyR9+jRgwCQmZkZPX/+nGxsbEqchpAnubm5VK9ePW40UCrw8/PzydHRscQher7IzMwkKysrrjMWaki8PAiAzMxMsrS0pIKCgkojACQSCTVo0IAuXLhQJCwoKEjuI98/lADYtGkTF1bSG9L33GBTU9OvelMG8N3OT2UVAAsWLCAApKamxutb0Pnz5zlHIwDUvHlzioqKUkjFqYwC4MKFC5yfy/3794uEt2zZUu4dYUREBAEgFRUVevPmjYzgrlatGicC/isQfv31V/L29ublfqSmplKNGjU4p9hp06YJVu+uXr3KvXFLHX4XLVpUrJ8E3wwdOpQAULVq1ejFixeCtz1FCgAiIk9Pzy86xVYkAbB27doSfZs+ffpE1tbWdOvWrcohAG7cuEFE/3rnHz58mHR0dAiA3OY/pdjZ2REAcnR0LFP8Fy9e8OKLUJwASEpKoilTppBIJKLq1avTiRMneL9h169fp7p163J5BECdO3cutkNiAkD+NGjQoNi6FRcXR7Vq1SrWQfN7KCgo4EZ+1q1bx13fuXMn9ezZk6KioooVCO7u7nT69Gne7snhw4e5+3/y5ElB697EiRO5jjc6OpqMjIwoOTlZ0DRI62RJ0zOVQQDs37+ftxHP8iYA3r59S6ampqWOsB47dozc3NwqhwDo0aMHeXp6UqNGjcjBwYG8vLx4GYIzNzcnAOTi4lKm+Dk5OVwa+/fvL3cBoKKiQu7u7uTg4EAASElJiTZv3sxbh1MceXl5FBwcTLq6ulxeVVVVadGiRUwA8CwAdu7cyc1Dp6WlcddHjx5NCxYs4MWmdBlcs2bNuGsdOnSgo0ePUmFhIVlaWhIAWrt2LRH96zdjaGjIq2fy1atXSVlZmZsK+PDhg2B1Lzs7mxt6V1FRoc2bNwv60ExJSeFGQPhY9fCjCICPHz+ShYWF3EVveR0BqIjI1QmQD1xcXAgA2dralin+u3fvuDSOGTOGtxGAjIwMql27NgGgESNGKOTmpaWl0aRJk7jlYGVZJ/0jCoATJ06UWQDk5uYSAMrJyeFNfBkaGhIATnBlZWVR9erVv7hHwbdy584drqwfP35MKSkpZGBgwO3JMHv2bAJATZo0ISKiNWvW0OjRo3ntAC0tLSk8PJwTocOGDRO07u/Zs4cTYkIvR/Pw8OCmJeW53PRHEwBE//qhREREMAHwg1LutwK2srICALx+/RqFhYVfjP/27Vvu7wYNGvCWLl1dXRw+fBjq6urYunUr9u/fz2s55OTk4P79+zLXqlevjpUrVyI2NhZ169YFACxZsgRpaWkVastNDQ0NrgzoC7stZmdnQ1lZGVWqVOElLWpqahgzZgwAYMOGDRCLxdi9ezfat28PQ0NDXmw6OjpydXnfvn04cOAAevXqBTU1NQCAj48PAODvv//G48ePsW/fPgwYMICXtEgkEvTt2xeTJ09Gr169EBISAgDYvn07zp07J1idqFat2r9bmf7/zn9CsWnTJpw5cwYikQg7d+6Enp5epd4Ot2/fvjh48CDbI7kibwWsSLp06QIA+PjxIx49evTF+LGxsQAAZWVleHh48Jq2Ro0acVu0/vLLL0hISODNVmZmJrZu3VpsWL169XDy5EmoqKhALBbj9u3bFaqS1qxZk9t+MzU19YtbhZqYmPDaKYwePRpVqlTB69evcfDgQWzatAljx47ltQyknXxYWBjCwsIwcOBALszOzg4uLi4AgKCgIKSmpsLNzY2XdEyfPh3GxsYYN24cAGDYsGHo0KEDAGDEiBHIysqqsA/LhIQETJ06FQDg5+fH5bukelgZ6Ny5M86dOweJRMJ6UyYA5E+PHj24DiA8PPyL8Y8dOwYA6NevH8zMzHhP35gxY9C3b19kZWWhT58+vO5JfezYsRJHQWxsbGBnZ8eNTlQkbG1toaWlBQBf3IM8IiICzZo14zU9BgYG8Pb2BgBMmTIFANCyZUtebQ4YMADKysp49OgR0tLSinTwUkGw97mGFQAAIABJREFUZ88e9OvXjxcBdOjQIZw9e7aIEN26dSu0tbWRlJTElUdFQyKRYODAgcjJyYGdnR2Cg4NLjCsWi7Fp06ZK0YFoaGjA1dUV58+fZ73pj8jXzBckJydzc5GxsbGCepsCICMjo1K3WE1ISCB1dXUyNDSUu1ew1BFNR0enSFhmZibZ2NgQAPL19eWlDKRlX5Kj35s3b0hDQ4NsbGwEWZublZXF1YUrV67wbi8gIIDbaKkk57bbt2+Trq4uxcTE8J6euLg4Lv8bN24UpB1Itwb29/cvdl5e6pR3584dudu+ceMGGRgYlLjaRLopEQBBVsNI26OGhoYgZT9v3jzO6VC6AqoktmzZQitXrqwUPgBE/+44+d+dIZkPQAV0Avz777+5Ri70phvSDYF69uxZbAfw/v17cnFxoerVq3+xgX4Lq1ev5taAF+fx/M8//3Br9CdPnix3D+zPxdfAgQM5AVZYWEi3bt2iJk2akL6+viCdHxHRgwcPuPQcPHiQd3u5ubnUq1cvAkCtW7emyMhIyszMpKysLLp16xZNmzaNtLW1aceOHYLVyQ4dOpCOjo5gK0Ckjm8l7TTYsWNHql+/vtztnjx5kvT09Ep19MvLy+PW5+vp6fG+Je66deu4+peens6rrevXr3PbEAcFBZUYLyMjg0JDQ0lLS4vXA2LKmwD49OkTmZubc06pTABUMAHw6NEjCgoKImtra67R1apViwIDAwXrcIj+XWdZq1YtatSoEe3du5fu3LlDN2/epDVr1pCZmRm1a9eOnjx5IlebFy5coBkzZnD7HAAgV1dXCg4OLjLKEBoaysUxNzcnPz8/ev36tdwEwPjx4+nGjRu0YMECcnV1JWNjY6patSpZWFjQqFGjBNmM5NmzZ7Rw4UKqX78+l1cTExOaN28eL8LrcwoKCmjnzp3UoUMHMjQ0JBUVFdLV1SV7e3saM2aM4HshnDlzRq4rTb7Ex48fqW3btiWGh4WF0eLFi+Vm7/Tp09SuXTvuPtesWZPmzp1Lubm5MvGio6NldiXE/29ZPWrUqCJb9n4v0dHRNGvWLO5MAgDUtGlTWrJkCaWmpvJS7p/XdU1NTdLS0iry0dDQkMk/X2kpjwKAiGjgwIGC7wfBBMD3IyIS4BB7OSIWi7Fnzx5MnDiRczhSV1fHhQsXeHN8YjAYjPJCbm4uNDQ00K5du0o/996xY0ecPXsWnz594m3lD3MCLEeoqqrC19cXly5dgqmpKQAgLy8PFy9eZHeTwWAwGIyKKgCkNGrUCHfu3EGfPn0AAIGBgTh16hS7owwGg8FgVGQBAAD6+vo4ePAgoqOj4ebmhl69emHVqlXIz89nd5bBYDAYjFL44XwASuP+/fs4dOgQnj59CltbW7Rv3573NeEMBoMhJFIfADU1NZmdCG/cuIFatWpV6Ly/ePECDRs25P6fmZkJsVhcoX0AYmJi0LRp06/6zpUrV8r0nQolABgMBoPBYJQNJVYEDAaDwWAwAcBgMBgMBoMJAAaDwWAwGEwAMBgMBoPBYAKAwWAwGAwGEwAMBoPBYDCYAGAwGAwGg8EEAIPBYDAYDCYAGAwGg8FgMAHwQxAREYEuXbqgZ8+erDA+IyMjAytWrICFhUWlO55ULBZj6dKl6NChA7S1tdGoUSP89ttvFTKvsbGxGDp0KCwtLctFevr37w9DQ0PExcUpxP7AgQNhZGSEe/fuCWLvxo0bGDJkSLnY7vfly5fw9/eHsbEx/vnnH/YQ/FGhb+DatWukoaFBAOjhw4dU0SksLKQJEyaQubk5AaDu3bsT419u3rxJXl5epKSkRAAoIiKi0uRdIpFQ+/btKTQ0lIiIrl69SlWqVCEAdP78+QqV1/Xr15OzszMBIENDw3KRJktLSwJAR44cUYh96fMgPDycd1s7d+6k9u3bEwCqXr26Qsv9ypUr5OPjQyKRiADQ7du32YPwBwXf+sXDhw+TSCSiZcuWVZrCCg8PZwKgBNzc3CqdANiwYQMBoKysLO7a7t27ycjIiP76668Kl9/Hjx+XKwHw5MkTOn36tMLsx8fH04kTJ6iwsFAQe8nJyeVCAEhxcHBgAuAH55unAHr37o0lS5bg+PHjlWa0pHr16mzIqARq1KhR6fK8f/9+qKqqQltbm7vm4+OD5OTkCnkKZXmr/7Vr14aHh4fC7NvY2KBr164QiUSC2Pv85L/yQHlLD0NgH4AZM2bA0dERb968qRSFpaKiwmoMKxuOhw8fQlVVld1jhiAoKyuz9DDk26a/9wc2bdrESpFRKcnIyIC6ujorCAaDUflGABRBYWEhtm7ditatW6NHjx6oW7cufvrpJ4SFhQmajry8PMycORNGRkbQ0dGBl5cXkpKSBLGdm5uLxYsXw9XVFU2aNEHt2rXxyy+/ICUlRRD758+fh7u7O9q0aQM3Nzf4+vri/fv3gpX9hw8fMHPmTLRo0QJmZmYwNjbG8OHDkZqaKkinb2dnBzs7O0gkEuTk5HD/HzFihCD537lzJ9zd3TFq1Cg4OztDJBLJfAICAgRJx9mzZ/HTTz9BU1MTLVq0wOPHjwWrA4mJiZgzZw6MjY1x8+ZNwZ9DSUlJCAgIgKmpKf7880+FPQ/z8vIwYMAAiEQiGBsbY+bMmYiKiqoQnZNEIsGRI0fw888/cyuvjh49ChcXF2hra6NNmzZcnXv69Ck8PT2ho6MDU1NTbNy4Ue7puXz5Mry9vWFnZwcAuHjxIpo3bw5NTU00b94cCQkJgpTLqVOn0LlzZ3Ts2BE2NjZwc3PD3r17v+3HfjSnhfHjxxMAunfvHhERZWdnk729PQGgP//8k1fbly9fJvwfe+cdFtXxNeB3d+lVEJAqEkSk2nvsGnuLLWJNxN6NLdGfJrbYey8k9hJLjBqj0ajBGnsDCxakWOi9M98fLvsBghplF8t9n4cnZu7unrlz586cOXPOGRAtW7YUX3zxhfj8889FixYtVJ7ftra2IiwsTK11iIuLE9WqVRO+vr4iPT1dCCHE7t27BSCcnJxEfHy8WuWvWrVKGBsbixMnTqjKJk6cKACNOAFGRkaKypUriwMHDgghhMjKyhJLly5V3X90dLTG+qJCoRCGhoYa7f9jx44V2tra4u7du0IIIdLT00XdunUFIHr27CkSExNFZmamWmQnJCSonABXr14t2rZtKxYsWCBatmwpAFGzZk2NtME///wj+vTpo+pzFy5c0OgzOHfunOjfv7/KC97f318jcjMyMgp0Ahw1apRo1KiRRvu+EELUr19frU6A33//vXBzc1M5Xk+ZMkV88803YuHChaJmzZoCEBUqVBAXL14UderUEbNmzRKTJk1SRaj9888/RVaXjRs3iq5duwpAODo6itWrV4tmzZqJ6dOni6ZNmwpAVK5cWe1tPnnyZOHs7CwePXqk6hNDhgwRgPD19dVcFEBxYWxsLLS1tfOUTZo0SQDip59+0ogCUKpUKXH69Ok83sC2trYCED4+PmqtQ48ePYS9vb1ITk5WlaWmpmok/Ozy5ctCS0tLzJs3L095ZmamcHBw0IgC4OPjIyZNmvRSuaenpwDEtGnTPloFICAgQMhkMtGoUaM85QcPHhSAMDMzU6v8HAVAR0dH/Pzzz3mev52dnQBEaGioxtrDy8urWBSAHKpXr17sCsDYsWNF27ZtRWpqqsbvX90KgBD/H3nl6OgoLl++rCpPSkoSJiYmAhDDhw9XLYaEEGLOnDkCEEOHDi3SuoSEhAhA6Ovr5+n/GRkZwsrKSgDi4cOHamuLv/76SwBi27ZtL/WLnPFv/fr1mokCKC5GjRrFuHHj8pQZGxsDkJycrJE61KxZk9q1a6v+38XFhVmzZgHw22+/kZGRoRa5YWFhbN26lS+++AJ9fX1Vua6uLsePH8fPz48GDRqo7b4nT55MZmYmX331VZ5yhUJBlSpV1N7u0dHR7Nixg8OHD9O+ffs8fzo6Ori6umpkG6C4OHPmDEIIrKys8pRXrFgRgJiYGFJTU9VeDzMzM/r06ZPn+bu7u6v6qKYo7qgEc3PzYt0KHTBgAKGhoezevfuj9UUpUaKEqo9XqlRJVW5gYKDqc7169crjjOvl5QVAeHh4kdYlZ56xsrLK0/+1tLSoUKECAE+ePFFbW8ycOROAxo0b5ynX0tJi8ODBAPz000//6Tc/OLfeH3/8EYD09HR27tzJ3r17CQkJUb0UxUXnzp3p3bs3ycnJhIaG4uTkpJYJIDs7u8BMYDVr1lRr6FlSUhKHDx9GV1cXOzu7l65rwiP40qVLZGVlMWrUKLp168anRs6El9/XI2ciMjU1RU9Pr1jqlqOQakIB0WSfex/lCyHo3r07e/fuJSgo6KOOznhVGxsaGqraIzc570BaWprG6mJiYqKal9RBYmIiJ06cKFTxrVevHgBBQUE8ffoUa2vrN/rdDzIVsJ+fHzVq1CAlJYWtW7fSq1evYq+Tnp7eGzf6u6yA1a1lFsajR4/IyMhALi++LpNz/w8ePOBTpFmzZtjZ2XH16lUSEhJU5cHBwQB06dKl2OqWEwtfnEr4p4JMJsPY2FjlAJiZmSk1yntCfmWkqAgNDVX9ds44mBt7e3vVv/+LJfyDUwD69u3LhAkT+P333+nXr997ZfrKzs5GR0cHGxsbtfy+qampyhJQGOrKS56j/aakpBAREVEs7ZuTcGf//v2FfubChQsf7eCir6/PoUOHKFWqFJMmTVL1uRkzZuDu7s7s2bOlEfgTYcmSJVSsWBF/f38mTJggNchHTs7Yn1vhz03OPKilpVWghfajUAD++ecf/Pz8aNWq1XtxIEZuoqKiePbsGc2aNVObGbZq1aoA3Lx5k4MHD750/eTJk2o7GMXJyUll5n3VBKzOFWDOHuD58+fZvn37S9dv3rzJ33///VEPBAYGBhgZGZGcnMzXX3+Nr68v7u7unDt3TsrM9gmhp6fHrl27MDU1Zf78+ezdu1dqlI8YGxsbXFxcAAp81jmLshYtWvynRfEHpQDkOHXcuHFDZQ7Jysri+vXrwP/v+URGRmq8buvXr0dXV5fp06erTUbZsmVp2LChyhJy+vRp1bW//vqLESNG0KZNG7XI1tXVxcfHB3jhDJh/GyIxMRF4EaOvLmxtbWndujUAffr0YdmyZao95/Pnz9O9e3eN+QYIIcjOziYrK0tjfSwlJYWmTZvi4+PD2rVr+fnnn/Hz82PChAkqByV133NRfEaT9fmYyH+/zs7O+Pn5qd6HO3fuFGt9PvZn/CaLG3XWd/z48cCLPCBJSUkvLf7kcvl/twZ9SCGA9+/fF9ra2gIQTZo0EePGjRPVq1cXXbp0EYBwdnYWPXr0UOUIKGpu3rwp9PT0hIGBgVizZo0q9GTXrl3C3Nxc7Nu3TyNtYGNjo4qBdnBwEBYWFkJbW1ucPHlSrbKjoqJEuXLlBCDs7OzEkiVLxO7du0XPnj2Fo6OjAISnp6eYMmWK2g5ICQ0NVckChLa2tjAyMhKAWLt2rUb7Yk4dnj9/rhGZt27dEoBQKBTCyclJuLq6Cjc3N+Hp6Slq1qwpBgwYoMoPoA6Cg4MFIAwNDV96vo0aNdLYyXg5eHt7C0AcOXKkWMajVq1aaTQMMOcwIF1d3Ty5Hvr37y8A4eLiIp4+faqx+885DEidocc5YYD169cvNAzz77//zlP++++/C0DUqVOnSOty584dAYgSJUq81P9zTmpUZ//Pzs4WPXv2VOVFiI2NFUIIcf36dVG6dGkxe/bsjz8PwJYtW4Sjo6MwMDAQbdu2FY8ePRIPHjwQNjY2wsPDQ5w5c0at8h89eiRGjBghnJ2dhbm5uahQoYLo1auXuHPnjsba4PHjx6JHjx7CzMxM6OvriyZNmohz585pRHZkZKQYMGCAsLCwEPr6+qJx48bi33//FZ06dRINGzYUO3fuFBkZGWqtw7Nnz8TAgQOFtbW10NHRERUrVhQ7d+7UWPvPmTNHFYMOiFq1aomFCxeKmzdvql32pEmThK2trbCxsREGBgaqY5hz/szMzMSTJ0+KXO5vv/0m6tWrp5LTqVMncejQIXH9+nUxZswYVVIcNzc34efnp/Z8CMOGDVPVxcvLS/zyyy/FpgDkzgmiLjZt2iQaNmyouueuXbuKP/74Q6SkpIiOHTvmWRDMnDlTNTmog3v37omhQ4eqZHp4eIhly5YVuZzVq1cLFxcXAQi5XC5Gjx4trly5Ii5cuCAGDRqU5/kvWrRICCHEvHnzVIsUuVwuRowYIe7fv//Oddm3b59o0KCBSuZXX30lDh8+LG7cuCHGjh2r6v/ly5cXq1atUqsSsGbNGlG5cmVRsmRJUblyZdG8efO3VoJl4lOzo0lIfKBERETQrVs3du3apYqPziE1NZVHjx4xYMAAfH196dmzp9RgaqZ169YcPHiQa9eu4e3tLTWIxAeHXGoCCYkPAx8fHxo3bvzS5A8vnMLKly9Pw4YNP8mjmYtrT1gul6sl54eEhKQASEhIAHDx4kWOHj2Kv79/ocl2rl27xrlz5/jiiy+kBlMDsbGxeZLLJCYmUrduXY04YEpIqAPpgG8JiQ8ANzc3vL29OXToEE5OTrRu3RpXV1cMDAyIi4vj4sWLREZGsmPHDumcdjWQmprKZ599hra2Ntu2bcPDw4O7d+++MiRWQuJ9R/IBkJD4gCah1atX8+uvv3Lz5k2SkpIwMzOjcuXKqhDIjzktbHHTr18/duzYQXZ2NvXr1+fHH39U5eaQkJAUAAkJCQkJCYkPAskHQEJCQkJCQlIAJCQkJCQkJD4FpA1DCQkJCQmJYkCWc4ymZAGQkJCQkJCQkBQACQkJCQkJCUkBkJCQkJCQkJAUAAkJCQkJCQlJAZCQkJCQkJCQFAAJCQkJCQkJSQGQkJCQ+Ji5f/8+ixcvJikpSWMyfX192bx5s0bvc//+/ezfv5+srCzpoUsKgISEhMSnixCCyZMns3TpUnr37o2hoeFHfb+tW7dGoVDQokULHj16JHWAd0RKBCTxTvj7+1O3bl2pISQkioFZs2Zx+vRpjh079kncr0wmo2XLlqSlpdG0aVOuXLmCkZGR1BEkC4CEprly5Qp+fn5SQ0hIFAPx8fFMmzaNhg0bfnL33qBBA4KCgli1apXUESQFQELTpKamMmDAAKTDJCUkiodLly6RkpJCVFTUJ3fvOffs7+8vdQRJAZDQJGlpafTs2ZMLFy5IjSEhUUzkpJG/evXqJ3fvOfcsl0tTmEYUgOzsbA4ePEj79u1p0aIFQghmzZqFg4MDBgYGNG/enICAAI1U+vLly3Tu3Jnq1atTrlw5atWqxbp166hRowYnTpxQu/wzZ87Qu3dvXFxcEEIwduxYTE1NadOmDdnZ2WqX7+/vT8uWLWnfvj3lypVDJpNRokQJjbS9EII+ffpw8eJFAH7//XcqVqxIxYoVCQ8PV5vcefPm4enpiUwmo2bNmqry06dP07dvX2QyGTKZjNu3b6tF/ooVK7CyslLJ6du3L6Ghoarru3fvxsvLCzMzM9asWVMkMvft24ejo6NK5vTp0wE4dOgQ9evXV5W3bdtWtRLKyspi3LhxyOVyvL29uXHjRpHUZdeuXVStWlUl09vbm1u3bpGWlkanTp2QyWRUrlyZI0eOqKX9p06dir6+PjKZDC0tLSZMmEBcXJzq+qFDh3Bzc0NXV1fVTmoZMOVyzMzM8PLyUvX7ihUrYmJigkwmo3Tp0hqzirm6ugJw8+bNT27iunXrFgDu7u7SLP6OA/obMWPGDFGhQgUBiMaNG4vhw4eLtm3bin79+gkrKysBCHNzcxEcHCzUybp164S1tbU4ceKEqmzz5s1CLpcLQBw/flyt8pcuXSpq1aolAGFnZyd++OEH0a5dO6FQKIRCoRCRkZFqlX/nzh1hbW0twsLChBBCZGdnixkzZghTU1OhSfbu3SsA0bt3b43JPHPmjABEjRo1Xrrm7u4uABEYGKg2+VeuXBEymUwAIjo6+qXrvr6+4ueffy5Smbdu3RJyuVzo6+uLjIwMVXliYqKwsLAQgLh7926e7yQnJ4uSJUuK58+fF2ldUlJSRPXq1QUgvvzyS1X54sWLRc2aNUVSUpJan/+KFSsEIKytrQu83r17dzFx4kS1yc/IyBAeHh4iJSUlT/mNGzeEnp6eUCgU4p9//tHoe2hjYyPMzc1FcdC3b1+xadOmYpH9v//9TwBi9+7d4kPmg1EAhBDir7/+EoCwtLQUW7ZsUZWHhYWJ0qVLC0B89dVXamssf39/oVAoCnzoderU0YgCIIQQwcHBAhB6enpi+fLlqoH61KlTapc9ffp0YW1tLTIzM1Vl2dnZonbt2h+9AhAYGFioApDz/NWpAAghRIsWLQQgNm/e/NKk6+7uLtLT09Um86+//spTPmrUKAGIefPm5SnfvXu3GDhwoFru//79+8LIyEgA4siRIyI0NFS4uLioFFJ1kp2dLby9vQUg/P3981xLTU0VVlZW4vHjx2qTn5ycLKZMmVLgcwfE1KlTNT6BVK5cOY8y9qkoAH///bcAxNmzZyUFQFM+ADnhFl5eXvj4+KjKbW1t+fHHH1Vmy/T0dLVUdvLkyRgZGdG+ffuXrllbW2us0XLM7UZGRgwcOFBliqpTp47aZaenp/P06VP69u1LbGysai9w7NixkjlLAwwbNgyA5cuX5ynfsWMHX375Jdra2kUu85tvvgHgl19+KbDPr127Nk+5n58fvr6+arn/zz77jLlz5wIwZMgQ+vTpw4IFC7C1tdXInveECROAF+Fv+bcoatSogYODg9rk6+vrM3HixDxlI0aMICAggIYNG750Td08ffqUiIiIl9riU6Bhw4Z07dqV48ePa0Te48ePad++PXZ2dtSqVYupU6dy586dAj/r5+fHgwcPPq4tACGEOHv2rGoLID+RkZECEIAICgoqck0pPj5eKBQKUaVKlQKvd+zYUWMWgISEBNUWgKYJCgoSxsbGAhBmZmZi0qRJRW7qlSwAr16Furi4CEBcvHhRVV67dm0REhKiFplpaWnCwsJCGBgYiLi4OCGEEOnp6aJChQqiatWqAhAnT54UQgjx5MmTQt+RoqRp06YCEF988YVG+11mZqZwdnYWgLh69aqqvG7duuLgwYMarcvOnTsFICwsLDRiAcndBv7+/mLQoEEiICCg2FavxWkByNmSGTdunFi6dKmIiopS6ztfv359sWnTJhEYGCj27NkjevbsKYyMjET16tXFkiVLVFvfV69eFY0aNRJZWVkfnwXgVZQsWRJjY2MAMjMzi7yiISEhZGVlqeW3PyScnZ35999/adiwITExMUyfPp2yZcuybt06aXmuAWQyGUOGDAFg6dKlwAunVGtra+zt7dUiU0dHh+7du5OcnMzOnTsB2LJlC+3atWPo0KEAKsfDDRs20KdPH7W3w8iRIwH4+++/VQ6hmkChUKisXTNnzgQgMDCQkJAQmjdvrrF6BAcH079/f2QyGb/88otGLCA5nDp1ioULF9KxY0fc3Nw+2Xcxxxk0JCRErVaQoKAgmjVrRo8ePShfvjwdOnRg48aNPHnyhMGDB7Nv3z6cnZ3R19fnq6++Yv78+R9OdEJRWQCEEMLQ0FDI5XLVKqUouXnzpgCEiYnJJ20ByM2xY8dUTlmadogpDgvA7du3i90CIIQQcXFxwsjISOjp6YmIiAjh6+srjh49qlaZ169fF4D4/PPPRXZ2tqhatap4/vy5SEpKEqampkJPT09ERUWJihUrFuigWNT9v1KlSmLChAkCEB4eHiI1NVVj/SA1NVXY2NgIuVwu7ty5I4YPHy5mzJih0ZVnjiPwqFGjiu39nz17thg9erTIzs7+JC0AFy9eFM2aNRMRERFqlZOSkvKS42d+0tPT36oeH6QFoKBQt4iICJKSkqhWrRomJiZFXlEnJye0tLSIj49n//79n6zWu3r1atLS0gBo1KgRZ8+eZcSIEQBs3Ljxo753HR0dgFceeKKJMEwTExN69epFamoq8+bN48qVKzRu3FitMr28vKhSpQqnTp1i0aJFVKtWDUtLSwwMDOjevTupqakMHjwYDw8PzMzM1FqXIUOGMHz4cH766SeaN2/OrVu3mDJlisb6ga6uLiNHjiQ7O5spU6awfft2+vbtqzH5U6ZM4ezZs1SpUuWllee9e/c0Vo9x48axZ88efv75509uHIyPj6dly5YMHToUCwsLtcrS09NDT0/vlZ/R1tZWez3eGwuAu7v7S9fWrFkjALFr1y61aWLt27cXgHB2dhYPHz5Uld+9e1c4ODho3AJgY2Ojca13/PjxL2ndOfVRZwRGfg4ePCgA0a5dOyHEC29odYeAJiUlCblcLgwMDPKEW27ZskWYmZkV6B2uLgICAgQgZDKZWLx4sUZkLl++XABCW1tb3Lt3T1V+5coVlRXo77//VmsdNm3aJHx8fFT/HxISIoyNjYVCoRBnzpzRWP+Lj48XJUqUEIDo3LmzRq1ucrlcGBsb53kGOfz4448aHQ+8vb2Fp6fnJ2cBWLlypUbfd3XxQSoAgFi3bp2q/N69e8LOzk7069dPrY314MEDVeyzvr6+aNmypWjVqpXw8fER7dq105gCEB4eLgCho6MjEhISNK4AmJqa5ok3PnLkiNDW1tboy5BjjjcwMBCLFy8WnTp1Ek+fPlW73BznM1dXVzF8+HDx+eefi+nTp6u2AKpVqyZmzZqlkTZo0qSJMDQ0FLGxsRqRFxMTI/T09ESnTp1eula1alXh7OysVnPw1atXhY2NjYiJiclTPn36dAGIzz777KVr6mTixIkCEMeOHdOIvIiICGFrayuAPGHQOQQFBYmWLVtqdCtCV1dXGBoafnIKwLhx4wQgVq5cKSkAmlYAatasKQYPHixatWolGjduLKpXry5WrFihkb2ou3fvitatWwsDAwNhb28vZsyYITIzMzXmA7Bz505Rt25dlSJUs2ZNsXXK+OcMAAAgAElEQVTrVo0qADmyK1asKNq3by9atWolzp8/r/HOO3nyZGFkZCS8vLw0kgNBiBc5J5o2bSr09PSEm5ubqu3r168v2rZtK/7880+N7Ynu27dP9O/fX6Nt7uPjU+CzXr16tZg5c6ba5O7atUtYWFgIhUKRJ979xo0befxQPD09xfbt2zXSFhcuXBDlypXTaNvnWGBq1KiR58/Ly0vo6OiIZs2aaaw+OX4hLi4un5wCsGjRIgEIX19fSQF4X5wAixNNOgFKSEgUP99++62YP3/+J3v/hw8f1vgWyPuiAJw8eVIAGrW4fIwKgJYU2CUhIfGhkZiYyPbt27l+/fon2walSpUCPs18+Dnhj5pMAPcx8p8UgByFRbyHR8AK6VhaCYmPmoMHD6Kjo0O9evUYP348Xbt2xdzc/JNtD29vb7y9vUlOTv7k7j0lJQWAnj17Si+GphSAnNO3cp/C9b4QExPz3tZNQkLi3fD396d169bAixP5ypcvz6lTpz7pNpHJZGzatInOnTszYsQI7OzsPpl7nzFjBuPHj6dBgwbSy/EOvFEegNTUVKZMmaKKN7906RL9+vXj5MmTxX4DN27cYMyYMaq6jB8//pPMjS0h8THj6elJtWrVMDU1xcfHh+PHj6s938GHYgU4ePAg06ZNY+7cuaocIR8rJ06cYPDgwdSqVUsa54tCiRSS7VxCQkLigycpKQldXV20tD5e1674+Hi1JJortglYJpNJCoCEhISEhMSntgIvZgVALj0CCQkJCQmJTw8tlM5zxcbRo9JTKE6Up8hJFNMKQOr/xYpo0qR4K9C//3vdPltPncLn88/VJ6C42/8TR7IAfISERUdj3a8fSw4dkhpDQkLircgWgvMaPNxIQlIAJIqA53FxPIuL48bjx1JjSEhIvBWPnj+njJWV1BAfMVImwI+QimXKUNrCgi+rV5ca4wPHWV+fQfb29LCxoZTyOOS07GwWPH7MksePeZqeDsAge3uGOjjgbmhIeFoaP4eHM/3hQ1Lf8Xjk4pYPUM7AgP52dvS3t8dYoQBgTVgYsx894kFKCqZaWgyws2OaszNZwLKQEOYFB/NcWTeJtyMwLAy39zS3wJXr15m5YAEuzs7cCAggMiqKs0eOsPO337h3/z5/nThBjSpVmP3DDwAE3LnDph07SElN5UZAAFvXrqWUpeUn/4xlIjq6eKMApD1QtfDDr7/yv44dUchfY+SRfACK9wV8w/5vpq3N4UqVqGZiwsOUFJxPnyb/izvHxYXqJia0uXqVhKysIq1nccsH8DQy4lTVqphqaVHzwgXO50r6ZaRQEFCrFm2vXeNqQsIb/6bkA1A48/bvp3PNmpwMCGDpn39y8f59tBQKlnz9NYO++AKAPefPM3DtWsyNjJjUsSNtqlRh7bFjLDhwgCcxMZSxtGTNgAE09fYmOS2NVX/9xbcbN9K8YkW+79CBusOGvVXdUlJT6dS7NzGxsaxbsoRLV6/i6uLCsZMn+W7UKGJiY7H38GD7+vU0rFuXph06cPLAAXR0dKjcoAHtWrRgyvjxxf/+m5sXaxSAZAH4iOi5dCmb/f2xMTPDzc6OjvPns//iRUwMDDg8cSLVy5aVGukDJSYjg7ZXr3K1Zk2c9PXpb2fH6rAw1fXKxsZ8YW5Og0uX1DL5Frd8gJuJiQwIDGS7lxezypal4aVLqmvLypdn5N27/2nyz+FucjI/PXrEL+HhzHFxYayj40ufic/MxN7fn5I6OiwqVw4PQ0OWhYay+PFj6pYoQVkDA64nJtLRyooJZcoQk5HBnufPGXD7NmX19albogQBSUl4Ghkxu2xZzLS13/s+9zgyktIWFvSqX58utWvT6McfuXD/Pi0qVVJ9poGHB+729uwbNw5TAwMAxrRpQ8caNag6YQK62to08vQEwEBXl6rOzvSoW5dNbznx56Cvp4e1lRUVvbxwd3XF3dWVIWPHArBo5UoAmjVuTGxcHL8dPIizkxM6SgvW4V270NfXlwYVJB+Aj4ovKlRgRrdu3F28GG9HR3aNHs2RSZP4ukEDSltYSA30gfM0PZ2vb916sTorV44yykHMQlubjZ6edLt5k9jMzI9WPsCOZ8/Y8/w5DczM+MbWFoCvbW2Jz8xkz/Pnb/Wb5QwM+L5MGfTlcpY8fkxGAalR1oWHkykETczNaWdpSVkDA4ba2wMw1dkZP3d3VpYvz8SgIGY+fIi5tja+dnbY6OjQzdqade7uHKpUiT8iI+ly48Z717dCoqJIy8jIU5adnU1OmLqetjbbRozAQEeHIevWvbCeCMGQ9etZ1a+favLPwcnKivWDBnEnPJwFBw4AEJWQwJx9+1gzYEDRrJ5lMnKH0T8ODaV+nTqMHDSIkYMGsWfjRnp27UpwSEieDImWFhYYGRpKA4qkAHxkFoB69fi+QweS09LY/M8/rD56lMZeXizo3RvrEiWkBvoIOBQVxeqwMIwUCvzc3dGWydju5cWPDx4QmJT00csHGHL7NjEZGcxzcaGxuTnf2Noy5h291bVlMrpZW/M0PZ3tT5/muZYlBP/ExOBtbIwiV7lWvhwu1UxM8DQyYluu7+f+jKmWFl9aWXE0OprIfJNtYWxV83kHUQkJjN6wgTazZrH+779fmmBz42hpydyePfnjyhW2njrFrN9+o02VKpQvxE+gfbVqdKtThyk7d3LvyRMGrl3L/F690FeuxIsam1Kl2LVvX56y85cuYWttzYnTp/MoAafPn5cGk9cpAI9DQxk1cSIOnp7IzM1Vf6VcXZk4fTpJuU6h2r1/P51691Z9xrN2babOmfNBNkpWdjZLDh2i4tixGPfqhZWvL42nTmX1X38RGBZGv9Wr32v5N0NCiExI4N+goA+ivZOzspgbHEyzK1eQHT2q+jM6fhzLkycpefIklc6f59u7dwn9yHOdvwnf3r1LUHIyDc3MOFe9OtcSE/n12bNPRv7T9HS+vXcPM21t9lesyNcBAaQXgbOhg54enaysmJ8vemZvRAQd/oM3vPFrUvHKZTIMFYrX/o4mwvC0tbQY07Ytv40bx/wDB0hXWnCexsZiU8BZC/2bNKGxlxdD1q8nJCrqtTkCln7zDcb6+tSaNIkO1avjqrTaFNlYmWu7qVvHjvy6bx/DJ0zgxKlTjP/hBwz09WnTvDlpaWl079+fcxcvMm/ZMuLi41Xfi4mN5bupU5m7dCnVGjcmMSmJ5p060bh9e2Lj4ug5cCAV69UjNDyckLAw6rVqxZNnzzhx6hQLVqygRefObNi2DYC0tDRmLVrEj7Nn07xTJ2JiY1np58fnLVqwZPVqHL296d6/P9lF0F/VrgCUtrdn4YwZBF26xFdffqkq79W1KzMmTcIwl9mnY5s2rF648IWG7uvLlZMnmTxu3Ac5+XeYO5dpu3bxY5cuRPn5EbZ6NWPatGHlkSO4jxrFvSdP3mv5lZycAKis/O/7joFCwVhHRw5VrIiFcm90urMziQ0bElG/PuerVcNSW5sFjx9T4dw5LuV6eT9FkrKy6HXrFllCUNnYmPW59uI/BfkAP4eHE5CUhL5cjms+8/M7KTeOjlxLSOBodLSqbPvTp3QrVeq13z0WHf3CT6GQFfGTtDR2PntGT2tr9OWvN75qIgzPRF8fWzMzylhaUs/NjV9OnABeHQEwp0cPYpOSiHkDi09JY2PGt2tHVEICCcojfIuCS1evcubff9n/559cvnYNgIZ167J87lz27N9P9/798ShfHi93dyxKlmTPpk3cCAigTbduCCFo2bTp/1u1jh6llKUlY4cNY9SgQRgZGvLT5MnExMZSwtSUH8aPJzomBltra3R0dOjfuzemJias37yZ0YMHs3rhQoaMHUt8QgJL1qyhfp06TBk/HlsbGxauXEmzRo24e/8+rb74ghunT+N/9iy7fv/9/VcActDV1WXTqlXUq10bgI07dhBbwLG7P8yeTdcOHVg2Zw7aH4CTS4EDy/Hj7L90ieW+vrSrVg0dLS20FQpaVKrEuZkzqeHi8t7LNzM0pIyl5RsrAOvCwqh38aJq5b3jDVZzyVlZWJw8iezoUT47fZq5wcGEvePqXC6TYa+nB5BnhVTWwIDfK1akrIEB0RkZqsnnUyYyI0PlbLfO3R25hlOKF7f8TlZW3E5KIjU7m5Xly2P0BivqN6GqiQl1S5RgXnAwAP/Gx1PZ2BidV0zYv0VEMP3hQ7Y8fcpvFSrQJ98q90J8PAseP2bS/ft8V6YM69zd36gumg7D+75DB+bs20dmVhaBoaEFyhZCsODAAYa3aMH206fZn8sRsyCiEhI4fecOLSpVYtzmzYRGRb085m3ZQr1WrVTW4zIVKrB5507V9eP+/jRu3x6ZuTl1mjdn74EDVKlYkYBz57h55gyVK1RQfXZw376E3rpFWEAAvb76SlXepH597ly4QMS9e4zN54BYs2pVps2bR99hw2igtGhU8vYmLS2NO0FBXLp2DXMzM06ePs3vhw7RrmVLrt+6RURkJL9s3crf//xD04YNiYqO5tjJk1y7eZNftm6llKUl+np66OjoYGJsjLOTEybGxnRq25YLly9/OAoAgJaWFlvXrsWsRAmeR0QwauLEPNe379nDydOn8Vu27D9X4vSdO3RfsgRZly6Yff01K48cUWmXJ27dou3s2ci6dMF2wADWHTtGYmqq2hrkgPLBeCgdfHKjp63Nkq+/VusDKSr55e3scLGxeaPP+trZcbhyZXSVg9zsR49e+5314eFEKfcxZzg7M9bRETtd3Xe+f0UhE4meXE5v5f0EJCVxMzHxk538jRQKtnl60vbqVYKSk6llasqY0qU/GfllDQwY5uCAz82bzHz4EAc9PWYUYYTLaEdHDkdFcTMxkVWhoQwo4F3MTXtLSyY5OeHn7k7bAmLLq5mYMLp0ada7uzOidOmXfAdepwBsPHmSat99h6xLF7S7dWPlkSOqz+w5fx4rX1/KjxzJZn9/4pKTmbd/P7YDBiDr0gWnIUP46/r1F0p7WhoLDhxA1qULLWbOxD8wMI88FxsbqpUty8Z//iHo6VOcra1fqtOs336jY40aLOjdm8pOTgxau5a4XFvB+ZWFoX5+zOvZk9X9+5MtBIOUDoS5+bp7d04eOIBvz54AuJUrR48uXVTXG9atS4M6dejdrRv+f/xBh9ati7Q/OTo4cOP0aZJTUqhcv75qcevTqRPbd+8m7MkTRgwYwKadO0lITMTYyIjMzEwMDQ3p4+NDHx8f9m7ahK21NZlZWdSpUYM+Pj78NHkyowcPfkmeuZkZJsbGH5YCAGBnY8PS2bMB+GXrVg4pY5hvBgYyeuJEdm/YgMFbhFfUcXVlfq9eAHSuWZNBX3yBmdJLs4GHB3OVHaP755/j27gxRspVojrIORxx3v79BV6vXrYsjmpMIFFU8q1MTSnxHzxd9eVySmpr85m+PlcSEjhcgKaeQ5YQLH78+P+9wNXk1JOfMrme+5s6Ub2O6IwM5gcHIzt6lFZXrxb6ufqXLqF97Bjrw8OJy8xk7/PnlDl1ipInTzIgMJBuN25Q5fx5te+Fy4ANHh4sDw3FPzaWrwMCyBaCqc7OuGvAs7m45evJ5fi5u+MbGEhadjazg4MJTEpiqL09NUxNi0RGWwsLyhoYMObePQwVCkoWkzUzdxie/9Sp1CpXDqDAMLzzM2fSo25dTA0MGNOmDaenTcPcyKjQMLxD339PXTe3l2RO/PJLftq7l5T0dLTzWVWO37pFZEICHapXRyGXs27gQJ7FxTF206YC6z99zx661KqFk5UVDiVL8pOPDwcuXSrQsVEmk7F87lwqeHry57FjHPf3V10LuHOHY//8w5qFC5HLi95vfdfvv2NkaMi2deuo4OnJQ6X1x6dTJ5avX0+VChXo2LYt+/74g3LOzgB4e3hw8vRpft6yhWcREaxYv57klBTq167N4DFjuHf/PjcDA/lV6ZSYmJioGtsD796llTKPwgelAAB079xZpYH1HzmSx6GhfNmrF8vnzsVF2Thv9WIrXzKDAlaRhsoyXQ28iDkv1y8nTtBm9uwCTVaD1Pjwikq+hbGxqk3/y+A+RhkDPesVVoBfnz/H29gYZ6UCoCnj7wPlHqIMimyyMdfW5ltHR5z19TkUGUlAAfualxMSuBAXRxl9ffra2mKqpUUHKysamJnhZmjIajc3tnl50dXamq43bnA6NlZtbTDRyYnn6en8HB4OwKnYWBaHhKArl7PRw+ONV5cfqvylrq6sCg3lnnLVmZ6dzYDAQGQyGevc3F5pqn8VmUKQqRyg5TIZIxwcOBIVxVAHhzyKb2auraecf2e+YjsqM993CuN9CcPzdHDAq3RpIvL52dwJD2fqrl385OOjKqvk5MTApk1Ze+wYf1y5kndSPXeOsOhoOuTKRjq4WTO8HR0Z5udX4Limo6PDz8uWoaWlxYDRo0lNSyMpORnf4cP5edkyVRx/UZOQmEirrl1Zvm4dlStUoKKX14s2dHSkY5s21KtdGxNjY7p26ECzRo0AMDE2ZuPKlfw4Zw4V69allJUVZiVK8O3QodjZ2FClYUO+mzqV9q1aAZCWns68ZctYvm4dtatXz7Nt8UEpAACr5s/HomRJQsPD8apTh/YtWxa5Waa46Ne4sWoSPnDpEuVHjmTyjh3E53JgqalGP4Cikm9pYvJW8nvZ2GCto8OJmBj+LcTZbu6jR4wrIFmKOolIT2eV0tmsj60tNkWw3ZDHClWiBK6GhsxXav+5WRESQldra/LvMuef7HrZ2CCAA5GRammDdpaWtLSwYMTdu3kn5aAg7iQnU8XEhMmffaa2Z1Dc8r93cqK0nh5b84Xp+cfG8ntEBJ5GRsx9i3czKDmZxSEhHIyMVDn/fW1rSw8bG1wNDEjOymLzkycEJCVxLCaGfRERBCUnsyQkBAC/8PCXEhBFZ2SwOiyMJ+npHIyM5EghFrX3MQxvUseOuCm3PZLT0pi6axfVvvuOZ7GxXH74UPW5u0+e8FCZe6HbokXM2bePgNBQ+q9eTdeFC3keF8ejiAjV5/8JCCAlPZ3oxESaTJtWoCWgkrc3Y4YO5d79+0ybO5fBY8YwesgQnNQ43vj27In/H38wxNeXnyZPztPuK+fP//9xYN68PL5tLZs25dG1azy5fZuObdq8WMDq67N9/XriHz9m/7ZtqnwDJc3NGTtsGEN8fRni6/vezHdvpQBYWVqqGiY+IUHlHPgxoJDL+W3sWL5t0waFXE5SWhrTdu/GeehQlhw6RKaaspwVtXxzI6O3kq8rlzNSuZ9bkBXgWHQ0Rlpa1Cwic+vryBCCPyIjqXvxIk/S0uhgZcUSV1e1mLZHli7NlqdPeZYrh3x4WhoKmUyVB/9VxCpXcFZqWKl0LlWKbV5eDAgMfCnkLSU7m2kPHryYjMuUoZUakj4Vp/zG5uYcr1KFGc7OuBoavhSS18nKigrK/j7cwYGtnp5U/Q8KcFkDA5a6unKlRg2amJu/sDoqFGz08HgxqCsU9LCxIalhQx7WqaNKBLTE1RXRpAlbPT2pmG9P11xbmwF2dmQ1bsyVGjX4omTJAmW/j2F4lZ2c6Ne4scoiO7lTJ+I3bCBg4cI8i49yNjYcmDABsXMncRs2MK5dO9zt7VkzYABZO3awZ8wYyuTarmzg4cHdxYsRO3dye9GiQus+Zfx4XMuWZdaiRWgpFHRq2xaJ90gBgBf+AArlHtGgb78l/i1ScL6v6GhpMa9nTy7Nnq3aP4tMSGDEzz9TdcKEPGF4D58/58dff6XkN98g69IFRdeuLDx4kGSlR/ztsDAGrV2LrEsXGk+dyoHXeM3+V/mF8S5+EgPt7THR0uK358+5nc8kPic4WCOr/9VhYTS+fBnzEydodfUqNrq6XKpRgz3e3i95fCdnZbE0JAT3s2dVkQwDAwN5qLSaRGdksPjxY+RHj+J8+jTLQ0IQhVg/jBUKlipXdgArQkMZkssMXBhxmZl8e+8e5Q0NVRnqioIvSpbkSOXK7PTyQl8uZ3Tp0njlU+6alSyJr3IVKJfJ2OPtzcry5V/63IcoP0fpbHjpErKjRylz6hR782X82/X8OU6nT6uevc/Nm1z8QEJF39cwvOLMHKqnq8u0iRPJzs7mZmDgexMz/zZkZ2ez+/ffefrs2XuZfOitzgJ4FhGBT79+7PDzo++wYYSGhzN64kTWLVnyzhU6cu0afZYvzzvAF9OpXhUcHTk2eTKHrlxh3ObN3AwJ4VpwMHUnT+bq3LlYlyiBk5UVUzp3pkvt2tSeNImElBQ616yp8mUob2dH5c8+o3f9+vw8ePBLZr13lV8Yr1sZvApTLS0G2tkxJziYOcHB+CnDlq4nJhKelkbLVwwOG588YWlICBfj49GSyVji6sogpTlxz/PnDLx9G3MtLSY5OdHjFVEKA+zsGFm6NMtCQhh25w4X4+MLDfUyUCgY5uBAH1tbGl+6xIX4eBqYm+Ok9FEw19amuYUFPz95wskqVTAtJFGLvlzOIHt7loeG8n2ZMshkMu4nJ+NtZMTWQuoZmprKmHv3WBsWRm8bG3Z4eRVZSBrAkaioQs3HORyOinql0+aHLP9T4vsOHWgxcybfNGxIYGioSvnPTe4wvCWHDuHz+ee0qVKl0N/MH4bXqnJl7AuxRrwNbWfPxszIiA1DhhTZb6alpbFi/XpaN2vGgcOHWbZ2LcOLKH2wxlfYcjkjBg5kxMCBH4cFIDMzk67ffMPowYPp2KYN86dPB2D95s0cOX783VccFSrwy5Ahef4WKCMENMHF+/dfKmtRqRJX587lR2VoyrO4OGbnSznpZmfHL4MHk5WdzTA/P1X5nfBwfj17ljUDBrzR5P9f5avDAgEvzOG6cjlbnj5VZd+b8+gRYx0dX+n018vGBv+qVaml3CJokWuwaWBmhruhIeerV3/l5J+boQ4OdLKyIjEriy43brzyeFljhYJfvb0poaXFuHv3iFeaU1OysxkUGMheb+9CJ/8chjg4kJSVxc/h4WwID6fXa1bz9np6zHNxob2lJQcjI9/I4UtCoiCKKwzvXcjIyiryFfqoiRMZ+PXXbFixAksLCyZOn87j0FCpg7wPCsC4KVOwKVWKYcpjLPv26EHTBg0A6DdiBAkfeHy23/HjBZrWFHI5kzt1onf9+gAFptltV60afRo04LcLF9h2+jSJqan0X72a9YMGoaOlpRb5ORaIU9OmUcLQEJlMVqgF4uj//kfrV6wWcmOjq0tPGxvSs7NZGBxMSGoqZ+Li6FbAoPSSCU8uZ5uXFwYKBUPu3HkxGPEih/uq8uVfOwnnZ727O2UNDLiWkMBI5e8VhqOeHotcXQlJTWWsMo3qwMBAxjg6qiwCr6KUjg4+1tYsfPyYI9HRNH/D1dLK8uUxVCjoc+sW/0UFMFYo6GVjw/N69Yhr0IBfPDxUf3srVCCjcWN05HJcDAyY5uyMaNKE0Lp12eHlxbHKlblaowaD88Wp1ylRgt3e3ogmTbhaowbbvLz4t3p1TlSpQiPlHndBfKavz8ry5dlboQLr3N1Z4+bG/5yc+PGzz/AwNKSGqSm/eHggmjThZJUqqnpu8vDgdu3azHiHKKAPhdOxsXS8fh3Z0aOYnzzJMaXTYHRGBhOCgjA+fpzZjx6plM//SnGF4b0tA5o25euGDYvs97bv2UN2djZdO3TA3MyM+dOmkZiUxOAxY6TZurgVgJ2//cbhv/9m7eLFecrXLl6MkaEhj0ND+XbSJLVX+klMDF0XLkTWpQt9V64kWql0RCUk8OW8eVT/7jsuKFfS5UaMYPSGDUzavp1J27fT6Mcf0fXx4Waufd7cZAvB3n//LVR2m6pVAV4Ku8lhUZ8+2JcsyTA/P7ovWcL/OnXC4T+Y3N5WflFZIHIz1tERuUzGmrAw/nf/PsMcHNB+w99w1NNjrosLf0RGsvXpU2Y9ekQbS0vKv0X4nomWFju9vNCTy1kdFsb218Ta97axoY2lJWvCwuh96xaOenqv3LYA8lgWRpcuzf2UFJqXLKmydmQVEM6VKYQqI6GBQsFub2+Ox8SoHOLehISsLDY+ecKp2FiepKfT59Yt1V+Ha9cYdfcuRgoF95KT+d/9+2QKwfanT+l64waNL19m+7NnLC9fPo8ScDo2lgXKfPaT7t+n240b1LlwgYSsLA5XqkSFApKQNDY351y1ahyLjqbDtWv4BgTQPzCQQ1FRDHNwwExbm/NxcSxQRkmsDA1V1bPnrVtUOHeOeDU5yMplMvrZ2XGvdm2sNZRzojBylKvu1takZmVRTvkemmtrIwN+q1CB8WXKYKL1dietF2cY3psSER+PRd++KLp2Zckff7Dk0CG0u3XDsm9fnhWQIfZNuX3vHkvXrGHRTz+pynp27UqT+vU5eOQIW3ftei8mzU07dmDt6oqxgwMXle3+7+XLWLq4sHjVKtKLactarQrAxStXGDpuHLs2bHjpKEVHBwdmTZnyQhnYuJH9f/75nyuSs8+fUcAgkqbUpnPiZG3MzNg0bBgeDg7I5XKVx3tJY2NszMw49P33VHN2Jis7m6ldurCgd2+mf/UV37Zpw+3wcCZ36oTnKxy7fti5k6eFxHKfVYZAdatTp8DrpgYGrBs4kKiEBJ7HxdFEGVP6X3hb+e9qgRDKvxzKGRjQ3tKSxKwsfo+MpF8+pySR77/56W9nR2Nzc4bcvk1Iaio+b2A9yJlk8xsVKxkbs0jp/d8vIIBbr3GAWl2+POba2ux89ozRr3BazAnXOhwVxbanT0nJzsbTyAgfa2t6KrcpDkdF8UdkJMGpqfgpEwHtef6c4zEx3EpMZNvTpyRlZeFiYICfuztTHjzg64AAzv6HwTC9kK0Dv/DwPKvJtHzm1qVKh8ZO+XLV5/9chhCsDA1FSyajXb5EUtY6Ouzw8sIvPJxd+RzsLsbHM+j2bdX59YXVMy07m1VvaaZtZ2lJ8Oefc692bRaVK8eicuVY5urKwzp1qGxsRasAACAASURBVGZiQrYQXIiPp6xysv1MX58F5cohmjRhk4eH6ju/entzoGJFjQycq9zcKKWry6DbtwE4GBmJubY2jV9hYXlTijMM700wNTCgT4MGnJ0xg1GtWzPxyy85PmUKfRo0oMRbnssQHBJCm27dmDdtGnr5QnznTp0KwJCxY3nwBllK1U3Prl35bcsW0tLT0VXWNSUlhdk//MCIgQPVlq9AHbyRmnrg8GF6DRrEl61b46bMRpWffr16MXzCBLKzs+k9eDCnDh3C/Q3Dtc7cucMaZVbBfRcuUNnJiS9r1MDM0JB/AgNZd+zYC/PQmTOUt7Oja+3aGOnpsbp/f+pPmUK/xo2pXrYs/oGB1ChblpK5Vjg5K2aAYX5+2JqZMb5du1fWJyQqisrjxzOzWzc61ayJkZ4eSWlprDpyhEUHD9KnQQN61K37yhfEzNCQc/fusf30ab4qRFlQh/xFffpw9MYNhvn5sf306Te2QGQJQWxmJpHp6Xli7MeXKcOe588ZZG//knNbpFJpi3iFxjvHxYUq588T8waZ+7KFIFSZ5jmsgHTPA+zsOBkTw7anT2l++TJ/VKpUqKe5rlyOra4uNxMTmXDvHqsKyHqWs3IbYGf30gEuW3I5YDUrWZJbtWrluf6llRVfFnBQS0crK0STJgDEZmayNCSEsffuMcDOjnnlyhGdkUHH69fpaWOjSm1cGB2trLiUkMCjV3hvm2ppIQOevsE5DCWUSmD+zw6wt6ektjZ+yuQ++dn9/Dmer/DoV8hkDHNwYJHS6lDD1JRRpUsTkZ5OZEYGo0uXpn9gINVMTPi8RAmmPXzIBg8PFjx+zMyHD9kXEUE3a2u0ZDJG5soxsCI0VBVSeT/XPveDlBTWhIUxqnRpZgcH50kLPfg1aXuLCiOFgnVubjS5fJlZjx4RmJTEL8qwwXelspMTFsoxLCcMb3KnTi99LicMLz9rBgwoMNlPThjeu5ITpQTQZvZsbM3MWN2/P5+XL//fF34pKcyYP5+la9aQkJjIxu3bcXRwwFa5WAgJC2PNhg0v3qe4OOq1asXwAQMYN3x4sU6cNatWZUCfPgyfMIF9W7aw58ABFueyXHwUCsAff/3FghUrOHbyJAD7Dx/mfzNnMunbb1WaD4D/2bMsWb1a5QwSExtL9caN6dm1K98OGULZ1yQHqe3qSm1XV34pwJO0npsb9dzc2Dh06MvmOFdXvmnYkIFr13J2+nS2nT7N8r59/39gkstVWQR/v3iRX8+e5dLs2Wi9wkvbWE+PK3PmcO/JE/ZfusTUXbtITksjJT0db0dHNgwZQvdXTP7P4+KYsGULF2fNovakSQzz86ORpydWbxg3/67ycywQzWfMeGMLxOYnT9gXEUFyVhZfBwTQztKSgfb2yIDqJiY0L1mS4bksJn9ERnIwMpLryoH3hwcPiEhPp3OpUtjm6hcCWBAczHAHB5aEhOBjbU2bAtIYJ2dlsTw0lD+jolTnC6xUribrmpnRPtd31ri5cTk+njvJyVQ6f57mJUvStVQp1Wo9R5H4OiCAn93d+S4oiDVhYXQuVapIVmf/hRJaWgxzcMBAoWDR48dkC8HNxETGOToWmDPeRkdHNYmYaWnR0sIClzNnCv39ktrarHZz42l6OtNyrQwLwt3QkGnOzpyLi2NTvjDSFiVLkpiVxd1CnMkyhXgp0c0ge3uaW1ggB2qamnIml7UjITOTCkZGpGVn8+PDh2x79ozozEysdXVx1NfHUU+PJSEheSb1TCFeSqwUkJTEI6UiKAqxFOVngxpP6ixo26SfnR3fBQURWKtWkWbELM4wvP/C5QcPiHyHuhro6zNj0iRmFLJ17GBnx4p581gxb957d+/TJ06kXLVqdOjZk61r1/Ih8koFoGXTpnmOTSyMurVqUTffCklTzO7RA7eRI2k0dSorfX0L3OeOTkxkwJo1rzX9A6pzByqWKUPn/3hPmVlZ9FmxgsVff81npUqxrG9fOi9YwJD16/l19Og3+o13kf+2FogeNjav9Mo/lCv3OEBLCwtaWliw/DUa/6xHj+hoZUVbS0tOxcYy6PZt6pmZveQEmHMc8Ng3yC9gpFBw+zWJpyY/eEArCwuqmpiw1s0Nz3Pn8A0M5EbNmkUaovem9LW15XBUFH0DAqhpaponvWxucnwAcphWiFNdXTMztnl50d7SknVKP4foQiwsfWxtGe3oSL0SJegXGMjmJ0/IyDd52uvpFfr9wlgZGqryxbDS0WF8mTJ5Ju57yckkZmWx9/lzVdy+l6EhDczMWBka+lpHSX25nA5WVi9l/XtdO68PD0dHLmeovT0jSpdm8O3b+Lm7M/7ePSoYG+NqaMjZ2FgGOzhQrQjisi11dLDS0WHKgwfseIvtvg8dz9KlPxhlpagxNTFhqK8vawrYFv9QkH/oD8HM0JBBX3yBQi7Hu5AJZJifH3bm5q81/b8r4zZvpkutWlRQ1qNTzZp0rFGDXefOsfPsWY20R24LRClTU4b5+fH8HRxz3pbjMTFEpqfTwcoKhUzGOnd3nqWnqzzz1cW+iAjC0tLorzTpl9HXZ1bZsjxKSWGcmmW/igXlyrHj2TM8/kNynIOFpBT2j4mh961b3E1Opp6ZGYmvcL77JTycfgEBpGZnU8PU9KXJP8cCY/wOitHz9HQu5Otj2fCSU2A2kJiVVejk72lkxKyyZZnt4oJ/1aqYv8FZFiNLl2ZW2bKscnNjqlJhyhSCf+PjKa2nh5FCwcDbt7mQkMDT9HQqGhlxNDqaSUFBRL+lp37uvmavq8tqNzd2PnvGvlz77Z+MAqB0WvwUiY6JIS09HStLS2bkShksKQAaRldbu9DzyH+7cIFd587xy5AheUz/T2JiirQOyw8f5nFkJH2UIZE5LOvbF22FgoFr1qgcdtRFQRaIyIQEhqxfr9HncSc5makPHvBTriNaKxkbM9DenrVhYfyhplz55+Li+D4oiOX5fE+GODhQwdiYlaGhL2WR0wQC2BAezg4vL/rcukXcG0485+LiCt3/T8/OptetW5Q3MOCH12yx3U9JYYzSD6GglLRn4uIw09ZWne74NmwvglMQbyYmMiEoiPH37tH8ypVX5nzIYdHjx0wICmJgYKAqg2O2EAQrtw72KC0Qt5RJrB6mpnI2Lo714eEkv0PUwoOUFP6MimKQvT3tLS3paGXF4Nu3iX1HpeJDo6y1dZ50v58SPy1cyPgRI1g2Zw4LV6zgXgE5XCQFQANkC0F2ASubqIQEBhZg+o9OTOTojRtFIvufwEBazJzJ0PXrCYuO5nyuVWZ6Zia7zp0jWwhikpJo8MMPLD10qMC6fgwWiOSsLKY+eEC18+d5lp7O5Vz7xneTk1WpebvdvMmc4OB3GoDzD8YDAwOpe/Eiz9PTOZQvxOlARITKxN395k2mPHjAkzdwmisqloWE0MPGhi+trGhlYcHAfOewv46ehWzPXEtI4IcHDxjn6PjasxlWhYZyOCqK9e7uKmfAHBY/fkyWEIwsZGuilI4OTd/Af8JZX5/aRXRGRGRGBif+o5KeO4Ih5+hVkU8RE0Xw7sVmZjLq7t08Bw8tK1+e+MxMhiijAj4VLIyNCw2J/pjx27KFlk2bYmxkRK1q1ejcvj1Dx4374O5D60N/EDdDQvjr+nUCQkM5fO0azXIdszh640aiExNJSElh0vbtqkn5wKVLLC+iE5lynBQLQkdLi6HNmzO0eXO1t0OOBWJB794vWSB+v3iRgWvWUM3ZGacCPNeLCgOFgsmffVbgiXDlDAzUFqL1mb4+q9zcCvX0b2NpWaDzobp5kpbGT48e8Tw9XZUrv4m5OR2vX8dGV5fvnZxUn9WWyQrMsdDU3DxP7LuOXI5eriNv5wQH09bSkm2enlS/cEEVkaGr/Ezuz/YNCOBmrVps8PDgy+vXVTkMriQkMPLuXRaXK0dkRgbzg4NJUa6+yxkY0M3amqnK3AY5ddTOd+yutkzGXBcXvrp5U7Wy0M13PwWVvYqg5GQ8jYzyePm/7vM5R0XHqWklvvnJEyY/eIChQsHDlBRVFMqtxER05XK2Pn2KkULBhDJl3ijx1IeOiYHBO5078qGRnJLCvKVLmb98OX8rs7GmpKZioK/PkePHGfHdd0weO5aSGnY4fltkIjq6eHOXKsP/JN7eAvHT3r38efUqNV1cWNSnDzWUK5P0zEzWHD3KyF9+ISs7m9IWFoxp04YhzZv//5bJmjVSIxbnAHr8OF9ZWzPHxQVTLS12P39OktIyUlJbmy/Mzalw/jxZQtDHxobvnZwISU1lQlAQvz57RoYQuBgYcLVGDZ6lp7MsJITLCQmMKF2a9paW/B0dzU+PHqmOue1ubc1mT0/8Y2NZ/Pgxu3OtmhuamTG2TBncDQ0JTkkhLC2Nf+PjWRISQrYQ1DQ1ZXTp0nQuVYq7ycmqPAfaMhlVTUy4kpDAVzdu0NrCgp+V0QyDb9/m12fPqGhszMry/8feWYdHdXRh/Le72bht3IUYJIQQXIpTqrgVWuzDrUihUKxIW6BI0QLFvVCcAoXiUgoUjRCixN1dNvv9sWFhSQIhBILs+zx5ktw7d2bu3HvnnHnnSE2aGBgwPSSEJeHhSrEKdtWujYZQSPd79xTH9EQivnVwYGZICHoiERlt2mB16RKx+fm4amvzoFkz6vz7Lz5PKAjDra25mJZGZlERkS1aoHn2rKKdrywtGW1jQ9MbNx6zAiUum9WGkoiqb/P8Y6qvX27ioueiuse/ugWwkZGgWttXKQDvOVQKQPVOAKr3n24lKZ41hUJ2xMZSJJOhLhTyqYkJM4KD+T0+nq9tbVnu5sb0kBAOJCQwxsaG0ba2nEhOxj8rC4FAgI2GBp66utS9do0Zjo7MdHRkdmgoCx4+xFgsZpWbGx8ZG/OVn5/CFkSlALwcrgcHY6qvX3lmUaUAqBQAFVQKgEoBUKE6oFIAXg63wsIwNzDAurKUt0oBqFYFQC1VUr0DcLqnahKqVvmvGv9qngFUQ1Cd+PDvapb/b/j4XN51mQ/6Piu1uCMvY/veXvUKViuEqiF495ASncJQi6GcWHFCNRgqqKBCpSArlhF0LUg1ECoFQIW3CekJ6aTHpxPhE6EaDBVUUKFSSHiYgJmDmWog3mGoqYbg3YNDXQdM7Exo1K2RajDeckhaSajxfQ0kbUr26mQQtS6KmA0xZNzMUC43uwaS1hJkRTKi1kQRsTyC3JDct7p9AMMPDHH52QWDpvIYA6nnU3m44CHJJx/He7AaaIXLIhfEJmLidsYRNj+MbL9s1Qv0Eoi+H411Les3sm/3bt9j6U9LcXJxwt/Hn+SkZE5dPcWhvYcICQrh/N/nqd+4PrMXzgbggf8D9mzfQ15uHv4+/qzftR5Tc9P3/hmrGIB3EAKBgDaD2uDVwUs1GG85Ui+kcrPdTeJ2yWPiF6UX8WDMAyXh+6hc8PRgivOLudX+Fg++flAlwre62wdIu5zGzdY3ybwjDywVtzNOSfgDxGyJIe1KGsHTgvH9ylcl/KtIATB3MuePOX/QV6MvvQS9WPO/NcQFP87PEHg1kAnuExhgMICjS44SFxLHqv6r6CXoRS9BL44uPkpO+uOkT5d3XaafTj/G1xzPtQOVz8XgWtOV3JxcLp+/zOyFsxk8ajC3rt8iLCSMb6Z/w/aD21m/aj1/Hf2L7Kxsxg4ey9Q5U/lp2U+kpaaxae0m1QNWMQDvFlb2W8mlHZeQWEqwrmXNku5L+O/of2jrazP95HScGzmrBulthAwCRgZg2MIQTVtNbEbYELk6slQxq/5WhMwMIfVC6rvVPlBcUIz///xpdKMR9t/aE7stluKCx3EENO01ERuJCV8Y/sJ15wTm8HD+Q2K2xODyswv2k0vnFCnKKOKSzSXUjdVxXeaKjocOUavkLIdhC0O0nbXJupeFWXczHKY6UJhaSMKBBAKGB6DlrIVhC0Oy/bPRra2L80JnxBLxG//aJUUkYeFsQc/ve6JtoM3WCVtxbeqKhbPFY0Hc1BVbD1tGbBiBWzN5CO4x28aQm5HLjcM3aNCpAdoGjyMFNu3ZlKNLjjLz75noGulWum+aWpqYWZjhWdcTN3c33NzdmDx6MgBrlq0BoN1H7UhPS+fYoWM4OjmiXhJQa9/JfWi9B0GaVAzAewavDl70+bEPywOXY1/Hnon7JjLj1AxaD2qNiZ2JaoDeYhRlFHF/qDyEsPN8ZzRtlaOv6TfSR7euLhFLI97J9gEyb2cSuSISbRdt7L9VFtKuS1wJnBCIrPjFvZq1XbVxmOaAUEtIxIoIZIWl64jZEIOsSIZReyNMO5ui7ayNzRgbAJzmOuG+yZ2aa2oSPD2YsJ/CEBuJsR5ijbqlOhZ9LHDf4I73CW+Sjifh08vnjXu/kiOTKcxXzghZXFysyK766def4tzImb2z95Kb8ZjZCb0ZirGNsUL4P8KQX4egpa/Ftm+2KR3/a/VfdJ/R/aWE/yMIBAKl7K9REVE0b9WckeNHMnL8SLYd2Ebvfr2JDI8k/4nQ3yamJujo6qgmFZUC8G6hZb+WdJ3WlfycfC7uuMjpdafxbOfJgKUDMLQwVA3QW47kk8nEbI5BpCei1rrHYY8FYgG11tQiYHgAMqnsnW0fIGRWCHmReThOd0TLSb6KM/7EmIKEglLbEi8kTMQCLPpYUBBXQNzvyimIZVIZqRdT0aujB08kTRSoKftw6jfUR7e2LnG748oso2aghlk3M1JOp1CYVLH0y5d3XX6l45mZnMnWiVtZ0HEBZzeeLSVgFX8LBQxfP5yMhAx2TdslH5diGfvn7afXnF6l6pVYSeg7vy83/7zJv/v+BSA1JpXga8E06vpqbJPMLc05vO+w0rGb125iYWXBlfNXlJSAa1euqSaU5ykAl85doku7LhgJjBQ/LqYu/DTzJ6Ijo5XKhoWEMXHERIyFxhgJjLDTt2PW5FnExcRVunOxgbEc+PEA42uOV+wpDbcazs6pOwn577H36Y3DN9gyfotin6qXoBdz2szh6OKj5Oe8eNKXYmkxJ1acYHLdyfTX688QsyHMbTeXv9f9TfT9aNYNXfdKH8rLth/pG0lmUibB14PfipdQmiMlfFE4tz+6zWnBacXPOd1zXDC9wAXjC1zzvkbgN4HkR+W/1x9s4IRA8qPzMf7EGMv+8iRBDlMdSPoricy7me98+9IsKQ/GPkCoKaTmqpoINYXUmFWDkOkvn4lN01YTsx5mRCxRZjESDyZi1rXi1vBqes/eWRUIBYh0np9++XW44amJ1eg0qRPfHvqWP5f8SVGBPIdCWlwaEkvlIDH2dez5fOLnnFpziqBrQZxae4rmfZqjpV82nd5hRAdcm7qy+evN5GbksmvaLvr81Kdq34cnEop179Odw38cZurXU+W2AVNmo6WtxccdPyY/P59hXw7jv3//Y9XiVWSkP1YW01LTmPvdXFYuWkm7hu3Izsqmx8c96NKuC+lp6YzoN4KWdVsSExVDdGQ0n7X8jPjYeC6fv8yvS3+l5yc92b11NwD5+fksW7CMhXMW0uPjHnJ7gzWb+OSDT1i3Yh117Osw7MthFFcg02W1KwAt2rTg0JlDjJ08VnFs8KjBTJs3DWtbZetQRydHlq5dSoMmDbC1t+XczXPMXTQXCyuLSnfO0tWSbtO7MWHvBMWxYeuG8eWCL3Fq4KQ41rBzQwYuG8in4z4FQM9EjxmnZtBxUkc0tDVeWPgu6rqIffP20WtOLzYlb2Jd9Do6TurIqTWnmOA+gdig2Fcq/F+2fUdveZIZx3qOb4VQE2mLsJ9sT90TdRGbyPdGnX5wok1WG1oltqLhtYaITcVELI3gX69/X2ql97ajKL2I+8PlVLzrL65IWkkw72FO2Lyw96J9gMTDiSQeSsT4Y2O8//ImekM0hamFVVK3/Tf2ZN7NJOV0iuJY3O9xmPcxf+61KWdSyPLNwnp42Zbz+bH5xO+Nx6KfBUKt55Ovr8MNT0tfC4mVBFMHU2q1rMX5LeeB8j0Aes7uiZmDGWv+twb/C/40693smYrO8N+Gk5GUwU+f/oSVmxVmjlVzP3du3uH6P9f56+hf3L11VyGvFq1exNEDRxn25TBqetTE3dMdYxNjth/Yjr+PP3069kEmk/Hhpx8q6jp94jSm5qaMnTyWkRNGoqOrw6z5s0hLTcPA0IAps6eQmpKKhZUF6urqDBg2AH0DfXZs3MGoiaP4Zd0vTB49mcyMTH5b8RvNWzVnyvdTsLSyZM0va2j7UVtCAkPo8FkHrvhc4eqlqxzZd+TNVwAeYeZPM6njXQeAg3sPUlROpq3UlFSCAoLYtGcTTi5OVdZJI2ujMv9+Go9obomlBJFYVKm2zm0+x82jNxmyeggNOzdETV0NkViE9yfe/PTvT7g0dnmlD6Qq2teR6GDqYFphBSB6QzT/tfxPsfKO3/P83O7SHCkXTC5wWnCaKzWuEL4onPzol1udC4QCNG3ke8tPrpC0nbWpe6Qu2s7aFKYU4tff75VTzW8yko4lEbs9FrGRmHp/1yNgTADFecXvTfsAAWMDkBXJ0LTRJGZTTJXVq99AH8MWhoQvlhsTZlzPQK+eHkL18qfKxEOJhP0QRtzOOLwOeWE10ErpfMaNDCKWRhAyIwSH7xxw3+Bese/yNbvhdZ3WlcM/H0ZaJCXqflSZbatrqdNrbi+i/KPoMLLDc+u0rW1L6wGtCb4eTKdJnUorRfn5/DjjR6y0rTASGGGpZcniHxaTmpKqxC4P/mIwRgIjatvWZv2q9dStX5d//f/lH99/8KrnpbRA9Yvywz/any/6f6E43qp9K248uEFQYpDSghagQZMGLJ63mLGDx/JBa3nUwzredcjPzyf4QTB3b95FYiThyoUrnDhygk87f4rfPT+SEpPYtWUXF89epM2HbUhJTuHCmQv43vVl15ZdmJqboqmlibq6Onr6ejg6OaKnr0enHp24dePW26MAqKmpsXLTStTU1AgKCGL1ktVllps/az59B/WlfuP6VdtJkVBJSDxLgDyvzPNw60/5g7HxsCl1TqwpZtCKQa/0gVRV+9Y1rbF0saxY2SHW1DtZD6GGfJwfLnz43GtiNsZQmCxfdTn96IT9ZHs0rDVe+v4ForKfnVBTiOUA+f1k+2eT5ZvF+4xHK+78mHzSLqW9d+3nR+VTnF9MYVohVLEuaD/RnuSTyWT5ZhG1Ngqb4TbPLG/axRTHGY64b3LHtFNp33L9hvrYTbTDfaM7duPsStkOPEsBeJ1ueJYuljg3dObitovEBcdh4VQ2e6tnrCdXBjTVK3Qfusa6CIXCMhdlGhoaTP9hOmu2yi33TUxNmDhtIhIjiRK7PH7qeKxtrTl38xxDxwyt0udta2/LFZ8r5Obk0qpeK9LT5Fkue/Ttwf7f9xMbHcvwccPZu30vWZlZ6OrpUlRUhI6ODn0H9qXvwL5sP7gdCysLpEVSGjdvTN+BfZk1fxajJo4q1Z7ESIKevt7bowAAeNb1ZNyUcQAsnLOQ0OBQpfM3r93k7+N/M23utLd6YpWV5Eg/uvhomeedGzljam/6xrdvYGaAjmHFLV2FWkLExmK0amiReTuzlJ+1Uh+lMiKWR6DlIN/7UzdRfy3PRtPhseV5RY2onofClELCl4RzWnCaO5/dKbfczVY3OSM+Q8zGGIrSi0g4mMBlh8tcML7A/eH38enjw7X614j/I/71vKeF1cuAVHf7rxImnUzQdtYmaFIQIh0RYuPqcdl70g3vy4VfApTrhjftxDQ6ftMRCycLxmwbQ8PODeWr2zLc8KxqWvHDPz/QuFvjUm12m96Ng/MPUpBbUGkWtTLo3LMzHbt1JDoyWrGf/iQ2r93M4l8XY2pW9XPvkX1H0NHVYcPuDdT2qk14WLhCAdi4eiNe9b3o1L0Txw8fx8lVzmx71PHgyoUr7Ny8k8T4RDb+upHcnFyatWrGpFGTCAkK4b7vfQ7/ITdKzMrKUsztgfcD6fBZhzfiXX8hL4BJMyfhWsuVvNw8xg8dr7ihwsJCxg0dx8KVC9HW0X6rP37vT7wBOL/lPAs7LiQ5qrQgrAj1Vd3t65noIdZ8wYlLAPaT5O5VDxeUzwIk/JGAXh09hRX260pokxuaq2hPx71q3HjERmLsv7FHy0mLpBNJZPuXDiCTeSuT9BvpaDloYTXYSm7N3dUMSWsJOrV0qLWuFp67PbHobYFPbx/SrqShwlum+BfJkBXJFAyi7Thbkk8lYzvGVknxfVTm0TVP/n5evc/Cm+KGZ1vbFjtPOzISy7ezeWQo+HR/n1W+qLBIIS/Kw4IVC9DV02XOlDlKWwB3b90lKSGJjz7/6JU8+6zMLHp/1psNqzfgVc8Lz7qecibI0Z6O3TvSrGUz9PT16Nq7K20/aiufX/X1WLNtDT/P+ZkWdVtgZm6GocSQMd+MwdLakjb12zD3u7l81uUzAAryC1i1eBUbVm+gUbNGStsWb40CoKGhwcqNKxEKhVw+f5kdG3cAsHLRSlxrub4xWs3LoN3QdgohfPPPm4yvOZ49s/YofXQuTVze+Pb1TfUr1b5lf0vULdRJPZ9KxvWyJ4GHix6W8sN+1ShILCB6rdzzxGqgFRqWGlVav2FzQ3TcdAhfUjqQTOSvkVj0tlByAYPSbmCW/S1BBkl/Jr36ARHwWpWvN619gViAUEuImsHLxzLLCc4hcnkkSceSFMZ/VoOssPzKEm03baQ5UmJ3xJLtn03qmVQSDyfKr1khD4YUsylGEaXwSWYpel00BbEFJB1LIvlU2Yzam+iG131Gd2xqlb3t4XvWl79W/QXAsWXHCLgcUL7yUyzj8q7LXD94HVmxjN9n/P5MA2ZLa0umzZtGUmISc6bOUTCisybN4oelP7yyd6nfkH4cv3ScIaOHMGv+LKVxX7JmieLvxb8uRix+vKj68NMPufvwLgGxAXTs3hEALW0tNv6+kYiMCHYf3a2IN2BkbMTYyWMZMnoICWKRJwAAIABJREFUQ0YPeWPk3Qt/PQ2bNmT418NZs2wNsybPwtnNmd9W/sbF2xdfS4cXdV2EWKPslW126suH/xSKhEw+NJnd03ZzbNkx8rPz2T9vP6fWnKLHzB50GNUBkdqro8aqqv3KBtoQagixG29H8NRgHi54SJ0DdZTOp5xJQU1XDYMmBq9nZVYoI/nvZAInBpIfm49ZVzPcVri9EoFmN96OB+Me4PyTM+rm8m2N/Jh8BCKBwjvhWShMk6+I1M1e/ZbIo/6IjcQIRILXbhRZne0btTXCop8FAqEAbWdtnH5wIuFAApm3KueGqO2sjdtK5XdKpCPCY5uH/G9tEZZfWWL5lbJNjdsKt3LfRbGRGOvh1uV6BCgm4BI3vE+//pS57ebSbkg71NTVnumGd2TxEVr2a0nozdDnuuFd2nGJzV9vxquDV4Xd8BzrOaJnUvYede22tandtnbFPimhgA/6fvCcdMLKGDZ2GH/s+IPtG7bz5aAvCfAL4IM2H2DnYKeiqaqbAXiEGT/OwN7RnvS0dDq37czU2VMxs3g9WaMmH5zMsoBlZf50+a5L1WhF6mr0W9yPhTcXKl72zKRMNo/bzNQGU5W02ODrwcxtN1dhdLN2yFoi/R6HSc3NyGXPzD30EvRilP0ozqw/U6XtlwdNXc1K37/NCBvU9NVIOJRAdoCyUhX+c/hrWf1Hr4vmVrtbnDc6z53P7qBhqUHjm42pc6AOIl1lBSg/Kp/7I+4rvBj+a/kfKX+nKJWJ2RzDeYPznNU+S+jcUApTCstkP0R6IiJXPn5+Ub9GYTva9vk0Z3oRQd8EoVNTB6v/Wb2ycRHpirAdY0vN1TUV/9feURuLvhav5fur7vYBUs6m4D/IX/G8Q2aEVFr4VzfeVDe86oocKhQKWbpuKUKhkHFDx7F943a+/vbrt1bAFhcXc2T/EeLj4t/I4EOVUgC0tLX4bu53Cg12wLAB76R2ZO9lz6wzs/ju+HfY1pYLgfC74cxqMYu0OPk+r3MjZ6afnK5wz2vUtRG2HrZKH/in4z/FyNqI+Tfm025ouyptvzy8iNZdSgExUMN6hDXI5AL/EbLuZZEfk4/Jp2VPDsX5xYTOCeWsxllOC07j/z9/coIfWyCnX03nqvtVzhucJ3xJuFIs96dhPdyaemfq4Txfnr8g47+MUoL/ETRsNKi1thZ2E+WrBIPGBhh9qOwuajXICq0aWnj/5U2NWTUQG5Ve0Qu1hNiMtCFqTRTSHCnFucXkhOSgW6d8NiUvKo+gSUFctruMlpMWjW40qhJaujxIs6RErorkeqPrCgHo08dHkaznVaO623+XUR1ueC+LhZ0WsnrA6iqt06ueF/2G9CPAL4DBowajoaHx1j5ToVDIiHEjiMqKonHzxu+GAgBgYGiguMEn90zedjwZYfARvD/xZtGdRYq9tvT4dA4vfBxyUqQmYtSWUYg1xOycuhNpoVTp+gM/HmDw6sEYmBlUefuvgoEAOR0u1BAStzNOEX3v4c8P5YlSynncQg0hNb6vgfNCudA2aGqAtvNjo1CDpgboeOjgfcIb+2/sn+lbrZjAxthi1sMMaZYUn14+z/Q3d/7RGW03bSJXRz42GCxB4pFEJG0kSFpKnt3eaFuk2VJiNscQszUGq/7PXs1r2mjistgF0y6mJB1LqpDBlwoqlIXqcMN7aYWwUPpKotrVcK4BgKFEFcL8jVQA3lWc23SuTFsCoUhIj1k9aDWglULwKq1Ya1rTfWZ3In0jObTgkOJ42O0wUqJTFG45Vd3+q2IgNCw1sOxnSXFBMeG/hJMXmUf6P+lY9Hk+1Wv7tS36jfQJnR1KUcbjoFEZNzPQtNHEoNmL2Q+4b3RH21mbzLuZPBj/oPyXWVOI+wZ3ivOKuT/s/uNJKlseathp3vODU6mbq2PR14KIXyJIOZWC8cfGFepjzTU1EemI8Bvo90J+6SI9EZb9LWmZ0JLW6a3x2OKh+PE66EW7wnYI1YVou2jjNM+J9rL2tIhqgeceT+qdqUfjO42xGaVssGXY3JA6++vQXtaexnca47nbk0bXG1H/fH2M2pYfSEurhhY119TE66AX7hvcqfVbLRxnOlJjTg10PHQwaGyAxxYP2svaU/9C/cd93e5Bs4BmOP3o9M7PD/F74rlocZHTgtMETQ567I4qg/DFcnfSgFEBlQ5ZXV1ueJXFh8M/pM2gNirBoVIA3g3IimVcP3i93PMNOjYAUPKtfYTOUzpj72XPgR8PEB0QjaxYxo7JOxj4y8BX2n5VMhBPwn6yPQKhgOjfogmZGYLtWFsE4uezPQKhAPf17hQkFBAyLURxX2Hzwqgxp8YLPxM1fTU893oi1BQSvS6a+N/L97U3/MAQm5E2pJxJIWazPEJcyMwQHKY6PDP++pPMgt1EO3JDcuXCv+R2n3YBgxIXrxLjN5G2iDr765B6LpXQeaEVX0FlSondFkva5TQKYgvwG+in+Lnb9S6BEwIR6YrICcohZGYIsiIZcb/H4dPbh1vtbhH/ezw1V9dUUgLSrqQpsvKFzAjBp48PN5rfQJopxfukN3pepQ28jNoZ0fDfhqScSeFu17v4D/Hn/rD7JJ9IxnasLWKJmPRr6YQvlW8JRa2JetzXfn786/Uv0gzpK/kmBUIB1kOtaRbUDHUL9WqdH8x7m+P5uycIQMtJ67FxqAAkLSXYfm1LzV9romFTOdq6Ot3wKoqMxAwGmwymt6g3x1cc58SKE/QR92Gw6WDS49PfafmQl5vHrMmzMBIYMXrgaEXQoOAHwdSxr8P3335PZsbbY49SaQXgUTjg15HU4MkUn8XS8tt7dO5ZZSqCvbP3lrvHHng1EIDmfZqXXs2piRi1aRTSIinrhq7j+PLjNOrWCImV5JW3XyUMhAyl1au2qzamXUyRZklJOpKE9VDr0uWhzBWvbh1d7CbaEbUmivRr6USvjcaijwVq+s/eH1cI2aceoZ63Hm7L5BbX/kP9yfYr3+PDZYELmnaaBE0KIvlEMvmx+Zh8VrbdwiN3reSTycTtjqM4txjd2rpY9LXAsp/c6jv5ZDJJx5PIC88jZlNJIKADCaSeSyXLL4u43XFIs6Vou2jjvsmd0O9D8R/kT/rVik+GsoKyJ+eYTTFKLEpxvvLARK6MBBmY91COVf90OVmhjKg1UQjUBJh2Vg6mom6hjuceT2I2xZCwL0F5sv8vg4CRAYr89eX1szi/mKi1UZX63kw7m/JB+Ac0C2qG6zJXXJe54rbKjeZhzdFvqI+sWEbGjQzFdpJWDS1cl7rSXtYej+0eimvq/FGHun/WfeXzkaS1BOvB1oTMDFFEw0QGEcsjcP7J+aXrry43vIpC20Cb1gNb8+PVH/l8wud0m96N7899T+uBrdE2rJo4MI+S9TyZtOdNgKaWJnMXzaVV+1aI1ESKrXBbB1s+7vgxc36e88ZE+avQ4qqyF8ZExSg0otSUVKXQjVWN5Mhkpb9r1C97FZkULve/TotLQ1okrbS7XnJkMlPqTaHPT31o0qMJmrqa5Gfnc2rtKY4tO0brga1p8VWLMq91rOdIx0kdObzwMLJiGXMvzX1t7Xee0pmrf1zlwI8HaNKzCVauVuyYvIMx28Y8XwBJZRSlFVGQVKDkY+8wxYGEAwnYjLQpZYRXkFQg/51YUGadTrOdSNiXgP///NGtrYvnHs/nKnp5UXny9yo6r9R56+HWpF5IJW53HLc+voX3cW90PUsb6In0RNRaW4vbn97G90tfmvg1KbfN8ty1au987Opk/JExTf2aKp0362aGWbfSFtVm3c1oL2svH5f4Any+8CHxSCKN/m2Ebh1dpJlSfL/0RbeuriLoUnkw625G5s1Mch/mlv8BG6iBAPLjnk85qxnKP/eny9oMt0FsLC43pn7C/gR0a5dvCCkQCbAda0vEMjnrYNDYALsJdhQkFlCYVIjdRDvuD7uPfkN9DD8wJGxeGB5bPYhYGkHYT2EkHk7Eoo8FAjUBgeMDFfVG/RqlcKnMCXlsTJobmkv0b9HYTbAjfGG4Uljop7dDXhVcFrmQ+GciQZOCcN/sTsymGMx7mlcoy9/zUJ1ueBUSGiVeSgALOy5EYiVh2Lph1Pyg5kvXnZqSyoHfD7Bz804AVi9ZTX5ePl8M+AI1NTXeFCxYvoDW9VozYtwI3D3d2bRmE6O/Gf3WMRovPKJXL13l9InTbF67WXGs16e9+KzLZ3z5vy+rNFRjbGAs/+z9h0s7LimOrR+5ntBboTTo1ECREfDG4Rv4nPbh73V/A3KXuR8+/IF6n9Wjw6gOL5QRUFNPk59v/0xsUCw3j95k39x95OfkU5BbgH0de0ZvHU2LL1s8s47WA1tzeOFharao+cJ5CV6m/UcMxHeNvmPd0HU07ta4QgxE7I5YEg8nIs2R4j/IH9POptiMsAEB6DfSx/hjY2y/fmxXkHQ8iaRjSWTdk0+8obNDKUgswLynORpWj8daqCXEaa4Tvl/5KtzGyqTBc6RErY4i+a9kxYoqao18NSlpIcG0y+N3qtZvtci4lUHOgxyueV/D+GNjzHubK1brCqH9iTHq5upoOWhVedCgikLdXB2PLR5cq3+N7AfZcm8CkTw2vOPM0oma1C3l5QHUJGqYfGrCPy7/lK+8GIupta4WBXEFz83Gp+Oug9M8J9L/TSd2e2ypsZJmSckJzCmXlXk60I3NSBtMPjYBIRg0MSD9n8dsR1FmEbpeuhTnFxM2J4z43fEUpRShYaGBlr0WmvaaRK6IVBLqsiJZqcBK2f7Z5D3MK5NlKs/YMnZr7OuZOA3VcFvuhk9vH0w6mpB2OQ33ze5VVn91ueG9KEJvhWKSVHV9lRhJGDxqMINHDX6j79vN3Y0BwwYwfeJ01u9aT1ZmFvaO9rxteGEFoGmLpjRt0ZSZP8185Z2zdLWk+4zudJ/R/ZnlGnZuSMPODfnfyv+9dJv9Fsk1W4e6DjTt2fS1P5CXbb8yDERZQU6ehPcJb+XJ6VMTTD41eaZQf1JIgdxArzw8SgdsP/n5H5BIV0SzgGZvzQcm1BTisdWDu53uImklIXZLrJIypcSolNgAKBiUcowWJS0keO72xLSLKdEbovEb4FdmXAOQR020n2iPYUtD7g+9T+yO2FJx/DVtNMu9vjxErYlS2GKom6njMMVBSXDnBOUgzZKScDCBhIPybQUdTx0krSVy5e4529FCLSFmXc1eyL3QarAVMRtjEKoLsRljg904OwJGBeC+yZ2gKUHoeemh46ZD2tU0bEfZcq3hy/llm/cyJ3ZbLL59fWka0JT3EXa17d4aZaWqMXXOVBq6NmTYl8PYvHfzW3kPKiPAdxCtB7YGqBQDoULVQ7+BPtZDrbnz2R10PXUrHCcg6VjZIYVTL6XiN8CPnMAcJC0lSLPKN76L2RKD/1B/ivOKMWhsUGYSH2mOFJFe5anrgoQC0m88Ze9QTGmjwGJ5HIHyhL9ubV2cFzjjstCFBpcalBmroZQAGm+H8wJnaq2thdNcJwU7kHE9A007TUS6IgJGBJB5I5OCuAJ06+qScjqF4BnBFKUUvfyKtbUEka5IkRjrfcMjo8X3EYYSQ/oM7IO1jbXCFuCdZwBUeD7ys/OVfr/PeBTs52mjtNcBaY4Uabb0jRgHmzE2hC8Jx/gT4wpfk/5v+jPH1a+/Hw2vN6TG7BoETwsut2xuSC5Bk4KouaYmCQcSSsWlT/8nHcsBlmg5aD3T3uBZeJZnRkWR5ZtF8FT5fYhNxJh1eX7UuohlEQobAIeHchZCViwjL1y+dZBwIEGh9OjV0yMvLI/0q+kvZKCpQvmwcLbAyNrovb1/DQ2Nt3qRpWIAqhh3T95l/7z9AFw7cI3zW85XSY6CtxEpZ1OIXBWpmKjTLr+eLHmZdzIJnhqMNFNKtn824YvDyQnKqdaxeJlgWU/bNyju824mobNDsf/W/rm5GaLWRpF8Mhn3je4KY0CFEF0egUwqw3Z82VsT6ubqpSIrlgUtJ60XjvFQHgqTCkk9n/pC1zzpwaBweXuSbZBRZa5wiiormO3vXYWeiV6ZLtHvzQKnuFjJS03FALzn8PrIC6+PqjfV45vCQBi1NXpm4JlXNinV1UOvrh7OC5zfiHdCVigj8ajcyDLxaCKmHUsbygrEgjJjLBh9aKTk+y5UFyrZU4T/HI5pJ1Nq767NjUY3FB4ZQg15mSfL+g/2p6lvUzy2enCv2z1FDIPM25kEjg/EdbkrhUmF8jDNuXLGRttVG4s+FoTODVX0E0AoFpbqv8siF3y/8FUsLQQaglLLjVLHnoGc4Bx0a+sqWfk/r/yjVNFF6UWv/Lmmnk8l4WACRelFRK6MxPwLc9RN1d+r+U5bX/ul8o68zQi8H8jl85fJzMjE546PIo2wSgFQoVoZiFNrTikYiBr1a9Cwc0N0JDqqwamu1b9YgNUgK6wGlQ4rLNITYfGFBUZtjVAzUKPOH3UU2xZiYzFGHYy45nUNbRdtLAdaIhALMO1kSvo/6cT/EY+sUIZffz8a32lMw2sNiVwVSeatTOzGyfdlbUbaUJRWRMrpFPKj8wkYE0DtHbWpf64+EcsjSNgvXzVHrookyy8Lh8kOWA+xJjc8l/zofDKuZ8g9DGRya/9H+RYcZzhi1M5IcX/6DfTJvJ1JcUExJp+bYNhUHsLVvKc58X/Eo1dXD/Oe5mg5aOE4zVGuZDy5LSSkVIhpkZ4I897mcgVA8BST8kj/eOoa62HWpF18zDQJRAKlFbpAVHV0raS1hEbXGr3X77a6lvoLeVm9S3Ct5crJf06+3XNTiiylWvmL05xWSYhqxG/8phqE6nz/Bar336ybPMWzUFMo91IokiFUF2LyqQnBM4KJ/z0e269tcVvuRsj0EHlcijE22I62JflEMln+WQgEAjRsNND11OVa3Ws4znDEcaYjobNDebjgIWJjMW6r3DD+yBi/r/wUngmPYjZUF4Yx7K1+dsHXg9E31a9wlsGn0Z727/W7byQwqlYDApUCoFIAVIOgUgDeW6gUgJdD2K0wDMwNKm0IqFIAqlcBUCNVUr0jcLqnahaqVg1ANf7VC5WbZrXiw7+rt/23QP5furQTZ+eGWFq6ljrnCBDyMhqY6hWsTqi8AN5BpKREM3SoBSdOrFANhgoqqPBSCAz8ByMja9VAqBQAFd4GpKcnkJ4eT0SEj2owVFBBhZdCXl42GhoqI+J3ESovgHcQDg51MTGxo1GjbqrBeIfg7X0CY+OPKSxMpqhIOaaCUKiJhoZ8lRYQMJKoqLXvXPt6evVo3PgmeXmRFBTEKvn0a2u7IBYbERe3G1/fvqqX5R3Gn38eZP36lbRp04HLl88THh7G5cv3iImJYs+ebaSlpXLjxlXmz19Oo0bysOFbtqwjJSWZe/duIxKJWLlyI9raKqVGpQC8gxAIBLRpMwgvrw6qwXiXPlY1fe7e7URi4tFS5zw8tmJp2Z+kpD9fifB9E9pXVzchIuIXAgMnKh3X1LSnaVNfCgriefBgrOpFqUJkZ6eiqys38Ltx4zC//NILN7dmaGs/DviUnp5AYOBVLC1dWbToDurqWnz7rTe5uRnY2LgjFD4OM+3vf5Hs7FRGjtxImzaVy93Stm0HZs2axKVL51i8+FcuXz6HQCBgxoyJbN26HzU1NX75ZT79+nXj3r2H7Nu3i9DQYObOXURubg41ahjTqlU7+vcfqppTVK/4u4OVK/tx6dIOJBJLrK1rsWRJd/777yja2vpMn34SZ+dGqkF6i5GW9k+ZwtfcvBeWlv0pKEjA33/wO9u+WGzMw4c/P63u4u6+CZFIFz+/ARQWJr9wvTk5gTx8OJ+YmC24uPyMvf3kUmWKijK4dMkGdXVjXF2XoaPjQVTUKiIilmNo2AJtbWeysu5hZtYdB4epFBamkpBwgICA4WhpOWNo2ILsbH90dWvj7LwQsVjyVrxzUVH3sbGpBUBmZhLffLOf+vU/Vyozf/6nCARCRo3ajLq6PCeClZUbY8ZsQ03tcWCkwMCr/PffUerW/bjSwl/O9uhgZGRC+/Yf4+johKOjE2fPniQ+Po7161cBkJWViadnXRIS4lm7djk//vgLAFpa2ty8GYSxsalqQlEpAO8WvLw6YGNTi08++Zo9e2by1VeL8Pe/wK1bxzAxsVMN0FuOhIR9pY5patpSq9a6ktXVIAoKEt7Z9lNSzlJQoJxzwMZmBEZGbYmP30NCwoFKChRXHBymERe3h4iIFdjZjUcgUE5EFBOzAZmsCCOj9piadi5pewwREctxcpqLRNKajIwbXL/eGJmsGEfH6VhbDyE0dDYWFn2oUWM2RUXpXL3qQW5uGPXq/f1WvHPR0fextpYrACKRWinhf/bsRm7fPkHHjt/g5vY4S6e39ydKwr+gIJfVqweipaXH8OHrX7pf8oBQjz1oIiPDMTExZeTI8aXKhoeHUVhYoPjfyspGNZmUQGUE+A6hZct+dO06jfz8HC5e3MHp0+vw9GzHgAFLMTS0UA3QW4709GtPTYJCPDy2o6ZmSGTkapKSjr/T7T8t/LW0HHBx+ZmCggQCAsa8pEARY2HRh4KCOOLiflc6J5NJSU29iJ5eHUD0xDXK6yd9/Ybo6tYmLm53mWXU1AwwM+tGSsppCguTKty3S5d2Ehsb+ErH9u7dU0yf3hRf37PlKgCtWg1QOpecHMXWrROxtHSld+95SueeLrt793RiYwMZMOAXjI2rXgBbWFhx+fJ54uNjFcdCQ4OJj4/FysqGkyf/VCp/8eJZ1YTyIgzAlSsX6NixtVxrEArR1Cyd/jIvL5fi4mJEIhHHjl1UGGC8S4iK8mf//nn4+p6loCAPAwMz6tb9mObNvyAo6BqOjvXw8GhdJW0VF0s5eXI1Z89uIj4+BHV1LezsPGnatBfu7i3588+lZWrTkZG+ZGYmERx8nY8+Gv3C7ebkPCAmZgsxMVsoKJDnY3d334iVVcVoOz+/fsTG7gBAImmNickn2NiMQSR68aQhKSlnSUn5m6iotQrDM4FAiFhsglSai1gsQUfHAyurQZib9+B98qu3t5+CRNKK7Oz7BAVNfs/aF1Cr1sYS6n/gCwnU8qCpaYuZWQ8iIpZgadlPcTwx8SBmZl2JilpTsUlVTe85yoYQkajiBmiBgf/QqFGXVyj8TxIZ6UfbtoPZt28utWu3VZzLyEhCT6/sDJZr1w4hLy+L0aO3KKj/svDgwRWOH19eQv0PqrJ+FxdLn1j8tMXIyJguXdozbdpctLS0OXbsEL/8so6+fQcyb9407O0dadasJQcO7KFnzy8V16alpbJixc9IJEYcOrSXI0fOMWBAD4qKCtm6dT9TpozF39+H33//E5lMxrBhX7Jp0x6Cgh5w794tzp37m27dvqBPnwHk5+ezZs0v5Ofnc+PGVTZs2M2BA7/zxx876dKlF6tXL6FJkw9Yu3Y7QmH1r78r3IPk5EScnd04c+Y6iYlFREVlKf2cOXMdsVhO+YwbN+WdFP7+/heYOrUBAoGQhQtvsXVrOt9/fxYtLT1mz27Ntm3fVOnLvWhRV/btm0evXnPYtCmZdeui6dhxEqdOrWHCBHdiY4PKvNbR0bvkd71Kta2t7Yaz83w8PLY+QaMtptxE7k8gPz+auLg9JZShLvXqncLe/ttKCX8AI6O2ODvPx8lpbsnkqk/r1pm0bBlPq1YJ1KjxPWlpl/Dx6YWf36AK9fFdgL5+A5yc5lBcXICv75cUF+e+V+3b2Iwsof73kpCwvwqVmm/IzLxLSsrjCI1xcb9jbt6nAsrqGbKyfLG2Hl7OtxFLfPxeLCz6IRRqVbhPr9oNz8vrIz7/fCJt2/6P1NRY7t+/+NxrzpzZwN27J/n88wm4ujZ9BmuTy6+/Dqoy6h/g3LlTBAc/4ODBvdy7d7uEDdJm797jSCRGjBo1kNWrlzJp0gwARo2ayOjR37B8+UIGD/6CRo2aUaeOt6K+06dPYGpqztixkxk5cgI6OrrMmjWftLRUDAwMmTJlNqmpKVhYWKGurs6AAcPQ1zdgx46NjBo1kV9+WcfkyaPJzMzgt99W0Lx5K6ZM+R5LSyvWrPmFtm0/IiQkkA4dPuPKFR+uXr3EkSP73i4GICkpkR9+WIK3d8NS5woLCxk+/Cvy8/Pw8qrHlCmz37kJt7hYyqpV/TE3d2LMmG0Ky1ZjY1v69PkJJ6eGLFnSvcraO3duMzdvHmXChD00bNhZcdzb+xNq127D7Nnlsww6OhJMTR0qrQA8rqcWQqFcqcvOvk9i4lFMTTs985qIiGUIhRpIpYWoq5uX2kut/OrMTrHye6RMCIWaJayEDH//IcTGbsXE5GPMzb+ocL1FRWnExm4jPHwxeXmR6OnVpXHj28+8Jjc3lH/+cUUmk2Ju3hNz8y/Q1fUkKelPwsJ+oLAwBXV1c7S1XZFKsygsTEZPrx4ODlMwMGjy0mMhEulQu/ZOBAIxwcHfkpl5+7V+C9XdvpaWIy4uCykoSCQgYHSV1q2v3wBDwxaEhy/GyKg9GRnX0dOrp/gOykJi4iHS0i6TmxuKl9ehUt9IRsYNIiKWkpXlh4PDd9jajn4j5ziBQEjXrt+xb988Zs78m4KCXDQ0tMuQBRFs2/YNVlZufPHFD8+sc9eu74iNDWLkyE3PpP6/+WYkW7f+hpOTK/r6Bk/NKQ9JTIzHycmVCxdu0aZNB8LCSqeKrlnTg+PHL5XByKgxe/ZCZs9eWGbbDRo0oV27hvj7+zB9unwro04db/Lz8wkOfoCv710kEiOuXLlAWFgw3bp9gZ/fPZKSEtm1awsAbdp8SEpKMhcunEFXV4+goAeYmpqjqamFuro6enr6ODo6AdCpUw9u3bpBly693h4FICMjnRYt2pR5bv78Wdy7dxsNDU3Wrt2OWFzxST8uLphLl3ayb99cZLJiunadRuvWA7G0dCE6OoCzZzdy9OhiRCI1evacTYsWX2Jq6vDaByoiwoekpAiaNOmh5Na8x+OPAAAgAElEQVTyCI0adaVu3Y+rrL1bt/4sWel4lDonFmsyaNAKduz4ttzrra1rYmnp8nL0kFCMUKiFmVlXYmK2EB7+8zMVAKk0k+joDVhbDyYiYnmpPdKXm5xE5Z6ztBxIQMBoiovziYvb80IKgJqaIba2X6OmZoCf30AyM++QnHwCY+NPyr0mPHwxMpmcfnxkgQ5gZzeB3NwwIiNX4uKyGEvLr0q+nevcvv0x//13DG/v4xgZvVz8U1fXZWhru5Kaep6IiCWv/Vuo3vYFuLvLqX9///9VCfVfmgWYyN27XcnK8iUqai0uLoueWd7UtAsSSetnKBUNsbObWKm+vG43vBYtvuKPP+YQGHgVdXUtrKzcSpV5RP2PGrUZsbj8VMABAZc5cWIl3t6fPJf6z8hI58iRczRr1lLpeEJCHM2beyIWi1m/ftcr8d23tbXnyhUfZsz4hlat6nH9egAGBob06NGX/ft/R19fn+HDx7F373Zq1aqNrq4eRUVF6Ojo0LfvQAD69h1Ifn4+UmkRjRs3x93ds4T1ySc5OVGpPYnESCmGRXWiwlsA48dPRUurtDb477+XWbFC7prz/fcLcHNzf6EOWFg407Pn91haumBp6UKfPj8qBJe1dU369VuEoaEFDg516dZterUIf0DxwG7fPkFUlH+ZZZo06Vnl7R09urjM887OjTA1tS/3egMDM3R0DKtoQpwECEhLu0J6+j/llouK+g2JpCXa2jVf88pFhIaGTQkbVTmBIBYbo6/fAICwsPnPoDQTSEk5g7q6GQKBSCH8H9djVIYAaISDw3fIZIWEhHz/UvdqatoFa+shFBWl4efXH5ms+LWOdXW3b2s7ComkDfHxfxAf/0cZTJF9pbebHsHEpBPa2s4EBU1CJNJBLDautgm6LDe8778/x+TJhxQ/OjqGZbrh/fLLfaZMOaoo17nzFHJy0p/phicSqdGlyxT27ZurZAD4mC7/jXv3/ubzzyeWSf3n5maUCL6cZ1L/j8o9gpubeynhL5PJGDGiP8nJSUybNo+6deu/kjE+cmQfOjq6bNiwm9q1vQgPDwOgR4++bNy4Gi+v+nTq1J3jxw/j5CTPh+DhUYcrVy6wc+dmEhPj2bjxV3Jzc2jWrBWTJo0iJCSI+/d9OXxY/o5mZWUp5vTAwPt06PDZ26UAlIWsrExGjuxPcXExrVq1Y/jwrytdl1isibp62R+uhoYOamrVm3Pazs4TIyNr8vOzmTGjGRcubC1Vpm7djzA3r1El7Xl7y1eg589vYeHCjiQnR5Uq06HDyHKv19MzeaZ2/iLQ0fHA2FjObpT2w370sRYRGbmiRFl4vSguzqOgQG79q6tbu9L1GBo2x9CwOWlpl0hLu1JmmcjIFdjYjHrhrQ0tLecSBSKu0v3T0LDC3X0DAPfvjyAvL7LMcmZmryYCZHW3r6XliLOznPp/8KBsGt3KagDFxXmVULiLkMmKShRKIba240hOPoWt7ZgnykgVZR5d8+Tv59VbGVTUDe/zzydUmRte69aDiIjw4cKFbQrlA+TU//btk0qo/3llfBu+PHx4B5BT/3FxwQwY8EuZeQQuXtyh9H+/fqXjR/z661LOn/+bDz5ozdixr87INCsrk969P2PDhtV4edXD07NuycLHkY4du9OsWUv09PTp2rU3bdt+VDK/6rNmzTZ+/nkOLVrUxczMHENDCWPGfIOlpTVt2tRn7tzv+OyzLiXjn8+qVYvZsGE1jRo1w8urHm8CXkoB+O67cYSHh2FgYMjq1VtKfDPfTYhEaowevRWxWJOcnHRWrx7IjBnNlAxmJBKrKvO3b9duqEIJuHnzT8aPr8mePbOUNGcXlybPoB2rNtCFg4P8A0xMPEJOzoNS5+Pj96ChYYmhYYvX/mzCwxcjleYgFKpXmmp9fJ9TSxSd0iyAVJpFfPwerK2HvHC9qannSt6RNpXlOfDw2IJYbExs7Dbi4/eU/UELtTAx+fRV8CzV3r58u0WHBw/GUFCQWAbr1RSJpN0LsxI5OcFERi4nKemYwvjPymoQlpZfoa3thlSaQ2zsDrKz/UlNPUNi4uGSa+TJtmJiNpGZeUepzsLCFKKj11FQEEtS0jGSk089sw9vkhueWKxBx46TCAi4jJGRjWI1vmbNYPLyssuk/ouKCti16ztsbWtz//5F/vqrfOr//v1LxMUFKx0zN7dU+v/evdvMmzcNQ0PJK7eY79dvCMePX2LIkNHMmjVfSY4tWfLY82Px4l+Vtrc//PBT7t59SEBALB07di9RUrXZuPF3IiIy2L37KDo6cobQyMiYsWMnM2TIaIYMeXNsQCq9SXvs2CF27twMwKJFq9+L4Aqenu2YM+cCq1b1JybmAYGBV/n++1bUq/cZ/fotKkWXvZRmJhQxefIhdu+exrFjy8jPz2b//nmcOrWGHj1m0qHDKESi8h/fo33DqoJE0gZ9/fpkZNzk4cNFipXgYyG8BEfH6a/1eeTlRRIZuZzw8KWIxca4u29GW/vl7B5MTD4rMeg7RlbWPXR16zwxGa/HwuLLF3LhkkpziIxcSWTkKiSSlri4LKxUv+ztJ2Jk9CG5uWHlhrtVVzfDzW0VeXlhVT7W1d2+ldVAJJLWyGRS7OwmllL0xGIjtLWdiY3d/sJ1a2s74+a28imFXwcPj20lf2tjafmVwqbjEdzcVuDmtqIcIWqEtfXwcj0ClIX/m+eG1779MHx8TiuE4cWL2/DxOY2OjoTDhxeWEv4PH94FZOjoGPLrr/9DJpORnZ3GokXK7osZGYkEBv7L0KHlu1Tm5uYwdGhfCgoKWLduhypwz5umACQmxjN+vDyOcteuvenR4/1JvuHs3IjFi+9x7NgyDh78iZycdG7dOsbdu6fo1Ws2XbtOq7qHo6ZOv36LadmyH1u3TsTX9yyZmUls3jyOs2c3MXHiH+Ua+mlq6r4CITAJH58+xMXtwMlpHhoacq09JeUMRUUZmJp2feXjL5Vmc/v2R+TlRZOd7YdAIKRWrTUlgrkq7lmAvf23+Pn14+HDBdSuvatkBVRIVNQ6Gja8UqFaYmO3kJCwl+Tkk4jFJtSrdxqJpDUCwYuvZLS0HHFy+lEhWJo0uVeGwqiBuro5IMDX96sqHfPqbl++yt5MTMzmd3JO8fL6CC+vj5DJijlyZBH371+kVq2Wz7zmkRtex47fvBI3PA0NbQYNWq7EKDzNKgAsXdqTvLxs1q2LVhxbuTL4pcbju+/GExQUwJdfDqJz555v9bMtLi7myJH9xMfHce3aFRo3bv72KwBjxvyP5OQkLC2tlSiSl0VSUgSrVw8sdTwjI+GNimSnpqZO587f0rbtYA4c+JG//lqFVFrI7t3TKSzMp1evOVUseL2YNesMt2+fYMeOb4mM9CU8/C6zZrVg0aI7ZY7NBx9UvVJmZtYTLa3vyM19SGTkcpydF5Ss/hdjbz+xUsLtRSES6eDtfZKionSuXatHbm4oGRk3K7TSqigsLL4gNHQm8fF7cXKah5aWE3FxuzA2/qjCBmGWlgOxtPySW7c+JCXlDIWFiZUen9zcMM6e1ay29726239fUJ1ueGXB3NzpuWWiovzJykqpsjH488+DbNu2nho1nFmwYMVb/0yFQiEjRoxjxIhxb2b/XvSCTZvW8PffxxEIBKxevRlDw6pLamFiYsfo0VtK/ejrm1X7QOXn55Sy/tfTM2bAgKUsXnxX4S5z8OB8MjNf3jUpJOS/Use8vT9h0aI7CgUjPT2+FB33aicoEXZ2E0o+/LVIpZlkZ/uRmXkTK6tBr/V5qKkZUKfOHwiFGkRHr1cKv/ry96mGnd03yGTSEqNHGRERy7C3f9FATwI8PLYjFptw//5w8vLCVVJOhWeiRYuviIsLJjDwKjExD16bG15l0aBBJ6U4JS+D2Nhoxo0bgpqaGr/9tlOxf67CG8IAhIQEMXOm3Mp76NAxtG79Ybllo6Mjsba2fWcGKjc3gzNn1jNgwC+lzllb12LKlKNMnOiOVFpIWNht6tT58KXaO3duExYWTujoSJ7SKEX06DGL+PhQLlzYSnDw9dc6DlZWgwkNnUNhYQpRUevIzvbDxmbUC0U2qyro6dXD1XUpAQGjuX9/OPr6DV7aBuDxMx1MWNhcYmO3oq9fH11dzyeCEVUcGhqWeHhs5s6djvj6fkn9+heUYhqIRHqYmXXFxWUxQqEGiYkHlZQcE5PPOXdOB01Neywt++PoOIP8/GjS0q4gFpsgFhsTHf0bUVG/Kq4zNGyOnd1EzMy6kZl5l5yc+2hpOSGV5hAWNpeUlLLjoGtp1cDefjIaGhYUFiYjkxWTlxeJQKBGfPxe1NR0sbEZiaXlAFJTLz6x1y/CwKAh8fH7CQmZ/s5PmlJpDhcvmmJi0gk1tUf++MXExGxGR6cWjRr998zAQc9muB674bVq1b9cN7yOHSeV64anpaVfITc8LS39lx6Lxo27kZmZ/NL1FBcXM2JEP1JTU5g+/Qfq1WtUZpng4Ae4utZChdfMABQVFTF8+Ffk5ubg4lKz3KhKII8MuGnTmlfW6atX9zJunBu9egk4fly+T5Wdncr69SP49ltvfH3PEhZ2m8mT69Krl4C//lqlsAyOiXnAuHGurF49gKSkiBdq9/r1Q+VaGFtaumBlJfd/fzJIR2UhkxVz/frBZ2jeHausrReboHSwsRkByA3/EhIOYmNTfVatNjajMDfvjVSaiY9Pr0q5gJVN3Wlha/s1xcX5BASMxsFhyos+wSeYrc+xtR1DWtoVQkO/f0qYZBIbu420tMsUFMTi5zdQ8XP3blcCAycgEumSkxNESMhMZLIi4uJ+x8enN7dutSM+/ndq1lyNjc0oRZ1paVeIiFhaorTPwMenDzduNEcqzcTb+yR6el6lemtk1I6GDf8lJeUMd+92xd9/CPfvDyM5+QS2tmMRiyWkp18jPHxpCQO05om+9uPff72QSjNeEfMkxNp6KM2aBaGuXv1bgYWFidSqtR5Pz93UqrWWWrXWoq3tXML4bKu08H+E6nDDexEEBV2jXz9devcWsXPnVE6dWsNXX2nTp486Fy9ur1SdK1cu4tKlczRt2oIJE74rs8zp0ydISUnmTUCPHh/z3Xfj+PHHGfz44wyaNatNs2a1KSwsfDcVgCVLfuDWreuoqamxdu32MpMBPcKuXZsxMXkxN7SCghyk0sJylI98ioryFf83bdqLWbPkFqlFRQUlglAe9Ob7789Su3ZbHB29mTRpP+rqWujoSBT7r6amDri6NmPUqC0v7LKXmPiQQ4cWlHkuIyOR+PgQLC1dcHJqUCUPZ+/e2aSlle03Hhh4FYDmzfu8spdD7sNcWuGxtf0aoVCDgoI4LCz6oK5uWuq6R6uiquzLI8Xoabi7r0db24XMzDuVDg1bVJROUVH6U8rFaEQiPYyNP0ZHx0NJuEulmchkUqTSrKfqSSsREsr7oi4ui9DV9SQs7KcyLdVlsoIy+xUTs4mioownVkH5T036KwFZSSIkyi0nN2Jcg0Cgpkhn+wjq6hZ4eu4hJmZTqZS/GRn/ERAwUpG/vrx+FhfnExW1tlJjb2ramQ8+CKdZsyBcXZfh6roMN7dVNG8ehr5+Q2SyYjIybpQIWTlT4eq6lPbtZXh4bFdcU6fOH9St++ernzSFmkqujtnZfoSEzKJGjVno6dV96fqrww3vRWBsbEPbtv9jwYIbdOs2nfbth7Fo0R06dBiJnZ3nC9d3+/Z//PTTTPT1Dcp1+Xv4MJTp0yfi4VHnjRCc/fsPYf785Uyf/gN9+w7i4cNQli5d+0JRcN8EVGgL4Nat6yxZIrcCnjx5Ft7eDcoRgukcOrSX6dMnsnPn4Qp1IC4umGvX9hMXF4xQKOLgwfk0adJDEQr4+vWDJCdHkZYWz6FDC2je/AtMTR0wNrblf/9byW+/Dadp0578889e2rYdrESZm5s7/Z+98w6Pouza+G93s5tOKimkk0ogdDD0IggKgnTpSBMUlI4UBQEpgr6gKCJNikqRJioKRkVKAggGKQkhJCE9Ib0nm939/phkk00jgYSEj72vKxfszDNzzzw7O+c8pzJs2HIOHFhE+/avoq/fiF9+2cLQoUsfu2bB998vIzo6iEGDFuLk1BKVSkVERCA7dsxAJtNnzpyDtRYMl5wcxeLFbRk9ei2+vsPR0zMiPz+bM2e+4uefN9Oz5yS6dRtXZw9HXl4kCkUWhYUZ6Og0KiUwrLG1HU9s7O4K8+7z8gTLSn5+PCqVosoyvtVFbu6DohVz+euRSIzx8TnC1au+xMbuRkfHFHf3DdUqRVxYmE5CwiGioraSlxeFkZEPFhavYGjohVRqhr39dI3shuTkX0lMPKEWykFB07GyGl6UOnhKLdyjooSeCFZWryGT2SAW6+Hjc5DLl9tz+/ZEEhOPYWs7tsprs7IaRmbmNXJzIyr/AeuYACLy8x9dYEhHx1T9vWgqOm8ilVoQG7u7wuMSE49WWWBJJJLg4DCbyMjNAJiYvICj41wKCh4ilyfh6DiPoKDpNGrUAVPTroSHr6Z5871ERn5KePhaHj48iY3NaEQiHUJCSvq5R0d/iUwmxP/k5Nwv9SyEERPzNY6Oc3nwYANZWbc0LEJ1DSHboUSxunVrAkZGLXF2XlJrHPWZhvcomJvb8cYbQoDeZ5+NJSsrhaVLT2tkDdQE06ePQS6XY2IiY/LkURXKlfDwUJo0scfYuBENAcUFgQAWLHiLYcNex9e36zPnAhClpDy6KHGXLj4EBd0q0r4NKhSeSqWSvLySjmB37ybQuPGjg/d+//3JbmD9+oEkJ0fTu/cUXn65fH6yQiFn0aI2tGjRm4ED53H+/LcMHVpzP2VaWjzHj6+je/fxBAb+SmDgaRITw8nLy8bIyIzWrV9m6NBltdbrev/+hXTrNpa4uHtcu3aK4OAL5OfnUFCQi5NTS/r2nUG3bmOfmOfrr8tvy8kJISHhELGxe8nNvY+paRcsLV+lSZPJ6tV+dnYw9+8vp2XLH0oJx9MkJ58lOnqb2hRvbt4XC4u+Ravpx20HfIaoqC9RKDKLBExnrKwGY2s7QcMkHBOzg6Cg6YDQPKhx40E4Oy9Vpys2RPz+u/Bb8vE5iIXFy+oYAB0dMywtX+HSJXcNBaBXryyio7/i3r0FSKUWNG/+DY0atefatd5kZwepxzVq1J6OHa8SGPgqSUk/YWjoTevWpygoSOTatd4a3fs6dAjAyKg5f/5p/MjrNTT0olOnoFIxAGJMTHxJT7/E7duTisZ407LlUZTKfMLDP8TCoh+JiUextZ2IufmLhIWtRiazJDv7rrqgUIsWBxCL9fjvP01LhkRigEKRg0RiRK9emfz9ty0FBfEYGLjRufM9AgJ8NBQAicQQhSK7BoL2yWqy37//AQ8ebOSFF/7F0LDmJbCnT698X0LC/WpF4tcnZs50ID8/h927H88036cPzzSOHTvIokWzuHw5GAsLy8dQpuq3el61LAAXL95ssF/AmDHrWLCgZaW+cIlEyvTp21m5sicpKbHMnv14PipTUxu1huvq2p5hw5bX6X2NHy80IHF2bk2nTk83F9bAwAMXl/dxcXm/SkFQWvgLpsGXsbB4GQ+PT2vtWszNexe1BF7/yLF2dtOws5v2zL5MimMAiuHqurrCcWZm3fDx+Z7GjV8jJmYnt29PLOdyKEaTJpNwcpqHqWl3goKmERd3AJVK09Wmp2df6fGVITp6GwkJB4tWxFYaMRLZ2XfIybmHQpFFYuJxEhOPFz0zPpiZ9SQ6ehuPattc3IQqPv67al9TkyZTiI3dhVgsw95+Fo6O7xIc/Bbe3ru5d28xxsatMDT0JC3NHweHt7h8ucMTfV8ZGf8QEbEOd/ePH0v4PwoNXfgDODq2RC7P43lEZmYGy5bNY+XKDY8l/BsCdJ71L8HPbycjRqxg3775tG37CsbG5b8IT88ueHl1xd7eu8qKWVpo0ZCQlPRzhdtTU89z//5SOna8iplZ93JxCKURG/sN2dlB+PrewMTkhQqL6SgUOUilj/8CKyhIJD39almbYAVBgcqia61Y+BsZtcDNbT0ikQgzsxeJi/umGgJoDgUFSUilplhavkps7C5UqkIyMq6gp+eIRGJEcPAMcnJC0NW1xtp6FGFhq8nPj6Ww8PHz15XKPG7fnoCpaWccHN59bp9RZ+dWyOX5z+W9r169FGfnpowdO/mZvYdnWgH45ZctdO06Gje3jly//jPffDO30hW+jo5undaT1kKL2kZ6ekAVAqiA27cn0KHDFZo2XUloaOUVKHNz73Pv3gK8vLaRmHisXF369PRL2NpORF/fucp4g6pQbA14EmRl3SI0VOjFIJVaYmX12iOPiYzcrHYBODsL1y6kLwoxI4mJx9QWD2PjtuTlhZOe7k96uv8TXWto6DLy8qJo3fpnjZifwsIM8vNj68Qi0BDRpIkneXnZz91v899//2H//p34+V1Vu8QVCgWpqSk1DoCvTzyzEvHOnXMolQrc3X0RicRMn/41Fy9+zz///FjheJVKiVKpRAstnjXY2o6vcHtm5g3Cwlbi5LQIExPfKs8RHf0Vycm/4e29Sx0MWCJEt6BSKXBwmFPhsTKZNebmj65roa/violJ51q5Z7k8idTUv2p0TOkMhpJ+66WtDapa6cOelnaeqKjNeHhsQl/fRWNfVNTWUrUB/v+jUaPGtd53pKFDoVAwf/4Mpk9/B2/vkqyH338/TUHBs2UNeeYUgPz8HH78cSPr1w/UaJJRWJiPrq4BX3wxsVwu6s2bfjx48B+3bgn/aqFFQ4NIJK2wxbC5eV+NQEexWIZYXJIC9uDBx2RkXKVFi+810jHFYt2if/VKKc1TkEiMaN58r0ZmRmbmv4SEzMHBYTYuLss1ijoZGHhgbz9D3SWv+BrFYmm563d330hm5j/qV4tIpFvudVN+W+XIyQmtUXvnnJxQQFQmZbN2oVLJuX17EiKRlPT0q9y5M1X9d/36S0RGftqgg05rGwYGpnXSd6QhY+/erwkMvEZhoVxdB2DJkndZtGjWM9e46JlzAejqGjBo0EIGDdLsD+3u7svevRUXIvHxeZEdO+LRQouGBonEGBub1zE3760ub1wcxS6VWmBu/hKXL7fCwMAdW1tB8DRuPIj09EskJBwpEkgTeOGFQDp0uExU1FYyM6/j6Cj4pe3tZ1JYmEZKyu/k58cQHDyLFi0O0K7dn0RGbiEx8ah65ZqVdRtn54XY2U0lN/cB+fkxZGRcITx8NaDCxMRXnfbp4rIcc/MX1cK/UaP2ZGb+i1JZgKXlQExNhSp11tYjSEg4grFxa6ytR6Cv74yLy1IePPikTK0CMSAqNzfW1qOKTPyiIi5RmbWL5jF2dtNJS/u7lGIiKVWXgidOSRWJpHTpcl/74BZBT8/wuQsCnDx5JpMnzyy3fd26Lc/cvVQrDbAu8aRpgFo8GSpKA9TiaT7/oud+DqyshuLp+RlisV5RlkIhYrEMS8tXCA1dTkLCQRwc3sHTcwv37y8jMfEY9vazcHB4m+Tk02Rl3UEkEqGra4+RkQ+XL7fGxWU5Li7vExa2koiI9UilFnh6bsXCoh+3b49TZyY8aRrgk6KqNMBnAbGxd8nKSqmyI2FVeNbTAJ8U9Z0GqFUAtAqAFloF4LmFVgF4MiQkhJGdnUrTpu20CsCzqACoVPWrAMCRemav337TI0XPtwCo98fvOYfoeX/+6lkC9T1bzxNQzxfQp8+GeuV/7733nuvnX5sXp4UWWmihhRZaBUALLeoHu3btwtTUlCtXrjyX/FpooYUWTxvPZCGgq1fvs337WUJD4zE21kdHR4y+vozBgzuQmZmLqakhw4f71hn//atXObt9O/GhoegbGyPW0UGmr0+HwYPJzczE0NQU3+HDtU9XDaCvr4+pqSm6upppYvfu3WPp0qXExMSQk5PDnTt31C03b968SYsWLf5f8GuhhRYlyM/Px9/fn8uXL5OamopEImHRokWYmFRdYyEqKoovvvgCgKZNm9K8eXM6d+7cIF1dOjqwZg00agRz5sDw4TB1KkyYADt3wi+/QHQ0NG8On30Gx48L2zw94dAhzfi5NWvg/fdBpRLOd/QoHDsG27eDUgkmJsLxfn7w8CF07AjvvfeMWQAKCxUsXLif3r0/pFev5vj5fcCpU4s5fnwh27ZN49q1MKZN205KSlad8CsKC9m/cCEf9u5N8169+MDPj8WnTrHw+HGmbdtG2LVrbJ82jayUmpUY7dKlC0ePHi3qLBjBjz/+yOHDh7l69SqHDx+mW7du6rHGxsZMmDCBxMRE0tPT+eabb9R/x48fRy6XI5PJ8PDwYM2aNahUKmJiYjh58iTXrl3jzJkzdOnSpUHxA4wZM4aIiAhatSrpVX/79m3atWtH3759uXTpEoGBgURFRTFkyJBa/27rm///I1xdXdm+fTvHjx9Xb5s7dy6HDx9+Lvi1eHzo6urSs2dPhhctpBQKBefPn3/kcefOnVP/f/To0XTp0qXBxrkUFsKtW3D9OhQUwJUrEBYmCP3QUPjjD0GI/+9/kJ4OISFw4oTwef78kvOYmkLLltCjh/A5IwPu3oXz5wXhDyXHHz0qBH7/8INwnmdKAXjvve/YtOkUBw7MZuzYbkgkJZdvYmLAxx+PY968gXWmAHz33nuc2rSJ2QcO0G3sWMSSkpxiAxMTxn38MQPnzauxAnDx4kX+97//ATBr1iwGDRrEyJEj6datG5GRkZw7d465c+cCkJmZyb59+7hw4QJxcXFMmjRJ/TdkyBDmzp2LkZERISEhvP/++yiVSvbv38/gwYPx9fVFLpfj5+ensXKtb/7KsGnTJqysrJheKlTa2tqaQ4cOaQjqukJ98z8tdOrUibNnz6JSqTh48CAHDx7E39+fwYMHP9F54+LiUCgU6OuXFBY6ffo0O3bsaFD8WjRcmJiYYGpqilgs5sqVK+Tk5FQ6NgGCJ+8AACAASURBVDk5mdjYWLXANzAweKbvvUsXeOONEsFeDCcniIws+dymjWA5GD26+uc+exZ6936GFICAgHt88skpOnf2ZPDgyrt4rVw5gsJCRa3z3wsI4NQnn+DZuTMdqngxjVi5EkVhYY3Pn5eXV+G2BQsW8P3337Nx40batm2r3ldQUFDheXbv3k1GhlAQSaVSqc3VAHK5nM2bN6Orq8vYsWMbFH9FSEhIICYmhpCQEI3tUqmUGTNm1PkzV9/8Twv+/v4cOiS05X399dd5/fXXOXr0KMeOHdOw/tQUOTk5REdHa2wLDg7m7NmzDYpfi4YLkUiEiYkJLVu2pKCggEuXLlU69u+//9ZY8T8rGS6tW8Nrr5VPibx4EfbsgYSEkm2DBsHs2ZoWAE9P6NxZUAyMqlmUUaWCvLxnSAHYuvVXAF57reoWnsbG+syY8VKt8/+6dSsAHV6rukGJvrExL9WycFi1ahUSiYTZs2dXOW7YsGFYWVlRWIUCUpx2l5mZ2WD4o6OjWb16Nc7OzgQElDTA6d+/P3l5eXTp0oWDBzWbzQwYMABra2sAPvnkE3R1dRGJRGzevBmAvXv3YmNjg0gkYty4cdy7d08tbLy8vOjUqZNaONQ3f8MwRxaWU+TEYjGjRo16ovNWt/9GffNr0bDRo2gZfOnSJY1FRTGys7MJDg6mQ4cOz9y9BQYKpv3KauLcuiX49QF+/BHi4wWTP4CPj3DsiRNC3MDIkeWPNzAACwvNbb17C1aAGisAN2/eZNWqVbi4uCASiRCJRDg5OfHhhx9y8+bNOpukc+fuANCsmd0jx1paGtc6/50i35Jds2aPHGtsWbu9oe/evUtSUhK+vpqBjba2tmr/+8mTJ8sJqbLQ09Nj4cKFpKenc+DAgQbDf/v2bc6fP8+DBw80xs+aNYsxY8aQlJTE6NGj8fX1xc/PDwAHBwcaNxZq38+fP585c4RGNn37Ck1rJk6cyOrVqwEYNWoU7u7ugGButrGx4ccff8Te3r5B8DdkFCtqS5cuZePGjXz99decOHECfX19mjZtysmTJwkMDATAy8uLv/76i59++qnCczVr1ozdu3dr+OQbOr8WDQO2trZ4eHiQk5NTYabOxYsXad++PTKZ7Jm5Jx0dYfXv7Q0ymWDKd3EBOztwdYVXXoGhQ2HdOpBIwMsL2rYVLAAffggDB8KCBVBs6EhPF4IJvbwEq8Do0UJA4ebNQiyAlxe8+iqMGAEvvQSLFj2GAuDj48MHH3zA3r171dv27t3LihUr8PHxqZOJUipVxMYKfnULC+On/kWplEpSYmMF4V5WlXpKSE5OxsrKSmNbaR/84MGDWb9+fYXHduzYkWXLlrFz505CQkJo27YtkaWdSPXM369fP1555ZVyx4nFYr799lu+/fZb7O3tuXz5Mn369KFfv37lzPIzZsxAJBLx7bffqrcNHz4ckUjEnj171NuCg4Nxd3dXC++GwN8QMWfOHPLy8ti7dy9eXl68//77LFy4kDfffJNOnTrRp08fwsLCNIRpcHAwv/32W6XnDA0NJSMjQ8Mn31D5tWh46NmzJwDnz5/XsOwUFBRw7do1Onfu/EzdT2GhIMDnzROCAI8cgRdfhJgYePll+PhjIQhw7lxITYWePeHwYcjOhr594aefYOJEiIsTznf2rGAZCA4W9i9bBvv2CdUmi4/fuFHgWbRICBZ8bBeAnV3JStzR0bFOJ0osFiGT6RStCHKf+hclEovRKdIsc2tgOq9NmJmZkZSUVOWYn3/+ucLtV65c4aOPPmLcuHHMnj2bsLCwBsdfNv2uNMaMGUNISAjr16/H1NSUM2fO0L59ew1zvYuLC71792bfvn0oFEIMyIkTJ3Bzc+PUqVPEFf1Kdu3apRHU11D4GwpmzZrFunXrsLKyomPHjgQHBxMaGkq/fv0Qi8W8/PLLqFQqGhXbJMsqy1VUdpTL5SSUdmg2QH4tGi6aNm2Kg4MDaWlpaqsPwNWrV/H29sbQ0FA7STWVrY97oKRUBLxYXPehBG3aCH23796NrZeJcmnTBoDYu3efOrebmxtWVlb8/fffVY4LCAggIiLimeSvKGDnv/9KWjfr6+uzePFiQkNDGTx4MJmZmcycqdmRa9q0acTExHDmzBlUKhVHjhzh0KFDFBYWsmvXLuRyOTdv3qzQT1jf/A0FW7duZcmSJcyYMUPt0issLMTMzIz169cTERFBUlLSYwdYPar089PmD87O5u3gYES//06f69crPS42Px+Znx+W586xJzaWbEXtBBpnB2cT/HYwv4t+53qfyvnzY/Pxk/lxzvIcsXtiUWTXDn9Ozl3u3VvA77+L+OsvEwoLMyodm5V1i99/F/Hnn0ZER2+joODpK1PFsQDnzp1DpVKhVCq5dOnSYwWLisVK1q+HL78UTPBjxgipd/b28Ouv8M47ggn+/feFPPo//hBW7Dt2lA/YW7OmxBTfqJGwGp85E4pFY/Hxy5YJK/KdO6Gsp1gsFszzCoWwSt+9W7gON7eaz9PIkUIqYcniDCp67TwzQYATJwpf/OHD/o8cGxISV/sP3sSJAPhXI4c4rox5+EmxdOlSCgoK1Kl6j8L48eP/X/Dv2LGjXMCPhYUFhw8fxtnZmcDAQI3gsSFDhmBhYaH28w4YMIA2bdrg6+vLjh07OHnyZI1Sy+qbv6GgS5cubNiwgcWLF3Pnzh2NfQqFQmMx8KzxexkastnTEx2RCL+UFG5UYuH7IjoaJdDb3Jw3mjTBsJbu2dDLEM/Nnoh0RKT4pZB5o2L+6C+iQQnmvc1p8kYTJIa1w29g4Im7+yZ0de0pLMwgNnZnpWMjI4UAV1PT7tjbz0Qms37qz2Lz5s2xtLQkISGB4OBgbt68iZ2dHebm5jU+l1Iprvc8fM3rEQR/cjJ88glMngxRUfDddzWfp1OnBEWmGO+8IwQbPrMKwJQpvfH1defChWB27vSrdNyFC8Hcvh1V6/y9p0zB3deX4AsX8NtZ+Y8k+MIFom7frvH5KzJB6+josGLFCsaNG8fUqVM1Xn5SqRSpVFrumL59+2JjY6Ne1Uql0mr5POubvyJkZmZq+NSLIZPJcHBwwNnZGR0dHY3t48eP58cff+SLL75g8uTJAEyfPp3IyEiWLFlSrfTDhsL/NFF8H6Xvpxjt27fHwMAAY2Nj2rZti6WlJQYGBri4uBAfH4+Liwt2dnY0a9aMHj160LhxY/WzURwoXNrSUtHqvT75pSIRvczMMJdK+aSC2JhcpZIr6ek46+khq4PUMpFUhFkvM6TmUiI/Kc+vzFWSfiUdPWc9RLK6SW0zMHDFyKgFkZGfoVKVty7I5Unk5oYhkRgikdRffr1IJFJbAf766y/+/vtv9efaVzzrPg+/YsWktDwTggRritwynvL796GC5IlnRwHQ0ZHw889L6NjRjenTv2bevL1ERpb4pDMyctm69VeuXw9nyJCOtc4v0dFhyc8/49axI19Pn87eefNIKvUU5GZk8OvWrYRfv07HGlaK69q1K4sWLQJg7dq17N27l507d3L27FkcHBxo27Yt+/fvB4RKfNOmTaN37964uLhw5MgRdST+qVOn+Omnnzh16hQeHh6sXbsWsVjMa6+9xpgxYyoU2A2BH0rqEJStLzBr1iwNvzrA999/T0BAAJ9++mm580ydOpWCggJ69+6tVjxGjRqFiYkJ3bt3r9R3/DT4u3TpQufOnenVq1e54x4+fMi7777Lxo0b6dixI+PGjUMul7N8+XJsbGyIiooiICAAExMTPvnkE/UxAwYMwN/fv+g3kKHORihGREQE3bp1Y+DAgaxatYoXX3yR22UU1E6dOjG66O21cOFCHBwcNPYfPXqUrKwsbt26Rfv27fnjjz+YPHky2dnZ+Pn54efnx3///cekSZM4d+4c6enp9OjRAycnJ/r160erVq3o1q0bLi4u9O3bFx8fH42MkvrmBzCQSHjTzo6D8fHE5udr7NsfF8d4W9s6fb9JDCTYvWlH/MF48mM1+eP2x2E73rbO37GOjnPIy3tAYuKx8haI6O3Y27/51N/7FbmM2rRpg7GxMQ8ePEBfX18jHq30MdXtNFqfefiPXHj2LkkPXLdOiPI/dQq6dhVcDdu3Q3FC1fz5QspgWbRrB5cvC8eUk6vPkinS3NyIS5fWsGfPn3z//UXat38PmUwHFxcrXFysmDnzJTp18qgzfiNzc9ZcusSfe/Zw8fvvea99e3RkMqxcXLByceGlmTPx6NSpxue9cOECFy5cqPaqdMeOHdWqZrZkyRKWLFnS4Pl/+eUXdhZZVTZu3IihoSHt2gn9xbOzs5k4cSLz5s3Dzc2NnJwcrKysOHPmjDoquKyJ8KWXXuLtt98utboxYPz48YwbN65e+YcOHcr+/fuJjIxEpVJprES3bt1K165dGTFiBLNmzWLTpk1IpVKWLVvGtm3bkMlk+Pr6Mn78eLUy0rhxY3r27Emnomfu22+/5bvvvmP58uVYFjkYnZ2dadeuHc7OzsyZM4cVK1awYMECTp8+reb29/fnxRdfrPT7iY6OxrvUMuTrr7/W2F/WrVE6G6TsHPWuYNlT3/xqZc/BgU0PHvB5VBTrihyvKuBQQgKnW7dm1WMEz9YEDrMceLDpAVGfR+G2rsjxq4KEQwm0Pt2asFV1y29jM5bQ0CVERn6KtfWIUsJKTlLSKdq3v8CdO1Oe6js/JyeH7Ozsctairl27cvr06XKr/9zcXLXgz87OrlThL43iPHw3N2jfvvz+snn4LVoIJv9Ll0ry8OPjhbS+kSMF372mdQXKGkGL8/ArQ//+0KuXYGnYtAkMDYUMgY4dISVFsExMmSKco7g0zcmTwvayuHatioU1zxgkEjFTp77I1Kkv1gu/WCLhxalTeXHqVLSoHbzyyisVpuEVWxZqiopSwT7//PMGwd+vXz+cnZ3LmaFbt27Nu+++i4GBAQMGDGDChAmAEHw4ZMgQDh48qN5/4MABFi1aRGBgIG2KglOVSiWpqamMGTOG7du3s2zZsgqvLSsriyZNmmgfugrQRFeXUTY2bI+JYbmLC4YSCb8lJ9PLzAzZUwh01m2ii80oG2K2x+Cy3AWJoYTk35Ix62WGWFb3/GKxHnZ2bxIevob0dH9MTATFMiHhCI0bD0EkenriIj8/n2vXrnHt2jWSkpI4efIkHh4eNCuqw+Lr68vdu3fV9TUALl++rGHdOnLkCM2bN+eFF16o0O0kFitp3VoIvqssD9/DA7p1g1WrNPPwT5yALVuEoL333hPOl54OH3wgKAbFefh37wor78WLS/LwfXyEgLwio2uF+PVXKJVkBAjXMWqUoARUkbRULZfAM6sAaFHaImLOvHnz8PDwYGRFJaDqcrXi4MC2bdvo3r07cXFxrF69ukbFhZ4njB49muTkZN555x21O2Dfvn3s2LFD3eBkyJAhKBQK3nrrLZo2bcr27dvVx0+YMIH58+czc+ZMbGxsUCgUBAYG8ueff/Luu+8WrUx+ZNCgQRgYGNCrVy8WL16s4U+/ffs2u3fvplGjRqxYsUL7pVSCuY6OHIiLY09sLLMcHNgeHc2Ox3HCPiYc5zoSdyCO2D2xOMxyIHp7NN47vJ/i7/ptHjz4mAcPPqVlyyMAxMTspGXLo0/1e9DV1aVz586V5vbr6uqWS6d94YUXeOGFF6rNoVSKNYTwkSPCHwh5+MU4dqzYmlSyrajeF6VrThXn4ZfeD0Iuftnji3mqC0ND+P57YdWvUJSs+qvp5ahc6dP+5J9N6Ojo8OKLLzJ48OBqmblqEyKRiF27dnHhwgVmzpzJw4cP2b9/P/37968zzuTkZKZNm8abb77J2LFj8fLy0ljVX716lWnTptGmTRvS09MZPXo0xsbGNGvW7JEVCit+OShZu3Yto0aNYsaMGXh7e/PWW2+RX+QfjoyM5IMPPsDe3p6oqCg+/PBDGjdujL29PcuXL9fIDggNDeXmzZv88MMPXL16lTt37nD27FnulkopjYqKYvjw4dy9e5euXbvSt29fdbGTbt26kZyczObNmxkwYADjx49XF+IqTsE9e/Ys//zzD3///Tfm5uYcPar5wm7evDmTJ09mxYoVT/15eZbQ1tiY7mZmbImK4lZWFlYyGZZVxK7UNozbGmPW3YyoLVFk3cpCZiVDavn0+GUyG6ytR/Hw4XFycyNIT/fH0NATqdTs/+13bmcnmMnnzxcE6/z50KmTsEpPT4dx42DXLpg0qfyxX34pVOkrWZQJVfomTgR/f8GU37o1pKUJ537tNfjqq0dZYjTPCUJAYuPGQivfJk2EMUZGkJlZEu3fqlV5V8Mj5Yj2J/9sorCwkCNHjtCnTx+cnJyeKnerVq1Yt24df/75JyAUvAkJCWHkyJH8+uuvdcI5fvx4cnNz1ZyLFy/mnXfeoU+fPlhbW/PgwQOOHj2KqakpCxcu5KWXXqJHjx4sX76c0aNHo6enx2uP6ONQGps2bWLVqlWkp6ejq6vL6dOneeWVV2jZsiUzZszgxo0bnD17lpiYGD7++GMcHBz4/PPP2bBhAx999BFZWVnqvgBXr15Vn/ejjz7C2dmZpUuXavD98MMPvPHGG5iamrJ69Wr2799PXl4eBgYG6n4Cp06dYtGiRYwbNw53d3d1Y5R///2XAQMGqN0YNjY2rFq16onq6Ds7OzNlyhSWL1/OyZMnCS1KKtbX12fMmDE4OjrWqJ8ECMGmy5cvZ9u2bZw8efKp8vfo0YPPP/8cFxcXLl26xIwZMwgPD69w7DxHR167cYMRN29ytHhJ9xThOM+RG6/d4OaIm7Q8Wg/8jnOJi9tPVNRn5OfH0bRp3VqMbt68ye+//05CQgIuLi7o6uqSkpKCt7c3ffr0KZcZcv36daRSaYWVZ0ufy9bWFktLSxQKBenp6djb29OzZ0/MzDSVmZgYCA+Hc+fgn3+KFDFjQbimpgpBdn/9BTduQGmPYLNmYGsLQ4YIaX0guA0KCmDvXiFYr3VrIcYgLU1wGwD4+VUu+IcNE+r2Dx8upA0+fFh8z8L2334TrA6tWoGDA/z9txAcGBAAW7cKpv4uXYTURF1d6NdPuDc3N+H/ly5pZhnUuwKQkZHL/v1/s3r1DyQkpDN8uC+bNo3Hyakxt25F8dZbO0lMTGfFihF0796M998/xJ49f2JjY8qOHW8ycKAQrBUXl8q7737Db78F8tFHo5k27UXWrDnGyZNXeeEFd3Xr4LCwBM6e/Y8lS4awdq0QeXzTz4/Px42jVb9+yPT0ACjIzeXcvn14dunC+2fP8suWLRz64AMUcjlTvviCXm+8gUxfn8KCAk5u2MDhFSsYMHcufd98k//OnuWH1atJT0jAd/hwxm/aRGMnJ6Ju3WLnW2+RnpjIiBUr6FKTvJFKUFFjjLrGnTt3NKLls7OzuXz5snp1XBdIS0ujS5cuJSu1os6E4eHhNGvWjOHDh/Ppp59y9+5d1qxZoy5b3KRJEwYPHszatWtrpACkpaXh4+OjTo8s5iuuYvjqq6/i7+9PQEAAr732mjqIrWfPnri6urJt2zZWrFih8bKJjo5m3bp1NGrUiOHDh2vULU9OTsbX15cxY8aQn5/PqlWrNNqZTpgwQZ1eaW9vz/Tp02nTpg1xcXHMmTOHNWvWqMdaWlri7+/Pxo0bGTp0KP/++y9RUVG8/vrr6nM8ChEREWzZsoXly5fzzTffcKL47YWQfmVsbFwjAWxmZoZEIqF79+589aglUC3zm5mZ8fbbbzNt2jQMDAz46quvOH78OK1btwZAoVKRUyrL41VLS1z19XHS08O7VHW5ApWK/KK3Z6pczvaYGN6/f582xsYcbdkSBz09voyOZkNEBF94eWGqo8OCe/e4nJ7ONi8vZtjb8yAvjxH//UdHExOWu7gAMlQKFYqcEn7LVy3Rd9VHz0kPQ+8SflWBCmW+wJ8fnc+dqXdI/i0Zj80eOL4rVGNNPp3MfyP+w3OLJyJdEUHTgjD0NqTl4Zbou+ojT5Fza5wQKu611YuKFozGxm0wM+tOTMwOzM17YWjoVencZmX9x61bY7G0HKCOEYiP/x4Q4ev7H7m5oVXuB6G8fEJCAgkJCUyZMgUdHR3Cw8P5+uuvSUtL4/XXX9fgvHTpErq6uhUqAD4+Pjx8+JAzZ84wc+ZM9W8sMzOTQ4cO8b///Y/Jkyfj7Oxc7thOnQRrQKdOJX79Yri5QVE/L41tU6YIefrFCsDp00L9gGbNhHiAP/4QtkskgjXA0lJYuVf0EyiuA1CReyApSYhHKEbpkKaieGWgJCNAsNSW/L+ytiOPrQCUrsWseIKqWI0a6fP22/3o27clHToIs+7kJNRJd3W1xtTUgOPHF6h7AOzePZOHDzO4eDGYbt1KGvPY2prh7m7DtGnz6dtX0JpNTAy4enUdurrSImGpoFOnZbRu7czKlSVRrlnJyaw6fx6bUiWX9rzzDnpGRszatw+Zvj6vvfceEh0d9i9ciKOPD7IiW4uOTIZbx44MXbaMUUXNX2w9PGjZty/vFZVealy0Qrd2dcXA1JQFx4/XW0+B2kBFrYBtbW2rDLR7Uly8eBGRSERqair79+9XWxpKX4tYLMbMzEyjZ8GgQYNwdHTk2rVrNSoas3btWj766CPkcjnHjx/n96JcnLJ8AJ6enuptNjY2DB8+nH379hEYGKiR8mdhYUFBQQEymaxc05I1a9ZoCPGycHNzw63U87llyxb1vJ8ralRVjPbt22ukQJXdXxMrU0U4efJktVOsipGamsq5c+eIj49/6vweHh5MnTpV3ab6rbfe4vfff8fS0pK7OTnsjokhID2dXbGxDLOywlRHh3cdHXEvUsCi8/M5GB9PVF4eOQoF38TGMsLamvecnZGIRHweFUXjou8zS6HglzZtaF6kOPzapg0t/P1RFF2voURCN1NTPil6m+fczSFmdwzpAenE7orFapgVOqY6OL7riIG7gVrYxx+MJy8qD0WOgthvYrEeYU2L71vg7+mP1KLERSCzleG23o0mU4RAz7wHecTti0PPWVjYSM2lGLgZ4LbeDYmBpNRvWrPMt4PDHP77byj29iXZLCqVEqUyD4Uip9QCJJk2bX5FV9euSHG+SETEBtq29UMiMXjkfrUgKrPKd3FxwdHRkRs3bjBs2DB1CnF0dDT6+vrcu3ePhw8fVthTo6JaEsbGxkycOJEtW7bw7bffsmjRonJpyf7+ggUgNbVkm1QqVO4bOVJovlMMKysh7U8iEVb8HTsKhYSSk4VMgpkzhUJAU6cKSoFCIQT2AXTvXvXz6uQE69cLqYXFyVaNGwvVBCtyQ1QEV1fhHDt3ClaDSt0Nj/tCjooqKbYTG/vk5Xk9PGzZtm0aP/wQwMmTgsl07ty9rFnzerkGQF99NQ2lUsWSJSUlksLDE8nOzlcLf4CBA9uqhT/AypWHuXUrigMHZqt7CwA4tmypIfxvnDnDr1u3MmnzZqybNi0537x5uHXsyO5Zs1AUrbwVhYX89c03DHv/fU2B6OHBtG3bCPjhB64WmTv3zp3L62vWPNPCvyJ4eXkRFxenNs/XBfLy8li0aBHz58+nT58+Naqn7+bmhlKprLG1ZNeuXQwbNgxzc3M2bNhQIz6gnEVEX1+f5s2bP5MtS4vRokULOnbsiFwup23btnz//fd8/fXXbNu2jZiYGKZOncq5c+d45513uHjxYrny0U/anvdx+C9fvqwW/sVWnPT0dNLT0/E0MGCDuzsZvXoxpUkTTIuEx2wHB/oX/U7tdXVZ4OSEqk8fknr0YFKpSoDzHB2xkErZEBHB9cxMDMRitfAHMNXRYYunJyvCwkiRy1kTHs4Hpd4pBp4GuG9wp1dGL5pMaYKOqcDvMNsBi/4Cv669Lk4LnOij6kOPpB40mSRUApSaSXFd48r9ZfdR5gnzGrc/DvsZJcs9p/lOqOQqYrbHAJB+OR2TTiZq4Z+Tc4/w8LVkZ9/m/v0PyM6+UyRwBmNpOQALi5fUK/3Q0EWoVArS0s4TE/M1BQUJGBu3VQt3pTKXO3fewMHhbczMuhcJ3qr3Pwo6OjoaSntgYCDjxo3D0tJSoxdHdSCVSunWrRuZmZkaZb7L4u+/BUuAoOAIQvjhQ80gvi5dBJP7iRNCqeC5c4Xt/foJ/372mZAFUFFsdunzV4QHDwSlISpKKDG8Zg28+y788ktNlHdwdNS0AtSKBeDmzZscP35co8PZxIkTmTRpEkOGDHmijoBjxnTl+PErzJy5k1u3oujSxZOWLcv7t+3szPn443HMmLGD0aO70K1bM1auPMLmzZPKCCa7UivIu2zYcJKPPx5H8+aahUbsvEpMXFkpKXz5xhu0HTiQ3lM0c15FYjEzd+9mcdu2nNiwgWHLl3Nq0yb6vf22ulmQhs9zzBiuHD/Ozpkzibp1C88uXXCqB59iXUIkEjF//nymTZtWZxxyuZxevXrh7e3N7qIk25AalFsWiUTY2NigV+TeqQ7mzZvHr7/+yvXr19HT0yMtLa1GfMWrmIpWo6VTl54FjB8/Hl9fX2QyGSNGjFAX7bl16xYikYhu3boxbNgw/v33X44dO8aKFSvo2bMnb7zxhrqeQkPi79q1K1u3bq0V95lEJOILLy/6Xr9OVF4eX1fQLnyYlRVfx8TQ+/p1Nrq7Y6JTe57XJlObEL0tmgebHtCoYyPMe5sj0il564v1xLhvcifozSCsR1uT8H0CHv8rsSUbGLjj4rIUF5elZZ5hMa1bl4S4Gxm1xN19E+7umyq9ltDQpahUSlxd15YS4CZV7q/8XKFERkbSpUsXtaUtOzsbQ0NDdHV16dq1K7/99hv9+/evssBYWRSb/iMjI9XPhq0tODsLJnp7e0FwJiYKvnNLSyFtb9IkwadvZQU3bwo++jNnhM582dlCcN+rrwqr9F9+gW+/FXz0n30mZAZYWAgpg4WFQlrgl18+ysJeftuPP1b/uXjwQKhN8EgFjlUe1AAAIABJREFUq6YPnI+Pj7olcF1g27ZptGgxj2PHLnPtWuWrrmnTXuTgwYtMnfoVCxcOon//1piZVdwNKjMzlwkTttK9ezPmzh1QJf+OGTNQFhYyo5Jyvw7NmzNkyRKOrVmDc6tWpMXH41VRiaXi69y2jXktWnD52DE2VFWR4RnFvHnz2LJlC8nJyXXGcfnyZS5fvqzOja9qRVnWPaFUKgkODmb48OHV5lOpVGzdupXXXnutnNJQ0Qq2LOft27dp0aKFhmug9Auorrtn1jb279+v9sGXLiBUUFBAQkICV65c4c6dO+pS0SkpKZw6dYqQkJAaKWpPg18qlfLqq6/yRkUVUx4TnU1M8DUxIaWwEHElS67lLi70vHat1jMKRGIRnp958u8r/2I73havL8v7662GWhH9RTT/9v0X90/doQ6qCaelXSAqams503519xfj/PnzZGdnk5GRwbBhwzQUuMDAQDp2FKq8tmvXjrNnzxIYGFgji1pxXE3p4kJxcRUXABIUn5L/v/RS6essraxoRt9XlA1tXMqIXaqDdbUxbJjQR0BfX2go5OkppACqVEJsQvHnwkKhqZHwHqsDBaCuoaMjplkze/766zbHj1+ptKyvSCRix44Z+PjM59ChS5w9+36l53z33W9ITs7kzz9XVNlF7Ny+ffgfOcKikycxKeVHLouhy5YR8MMPfDl5Mp+VjQwpA7GODvbNmnH7r7+4cvx4jcsEN2RMmjSJgIAAbpWqP2loaFiucteTojhw7X//+x9OTk6EhYXxQ1HUzYULF4iLi1NX3ouJiSEwMFAd4LVr1y7EYnGNct9FIhHW1tacOnWKvXv3oqury49F6vetW7f4/PPPmVTKGffzzz8ze/ZsQAiQPHXqlIagKg1ra+vHalzSUHD+/HmNGAuVSlXOH1/RtobCv3jxYpYuXVqrz+jFtDRetrDgw7AwziQn81IFLr6TDx8y3c6OWcHBXOjQoVZlsGk3Uwy9DDHxNal0jOM8R+7OvotZ99pP51Mocqo07T9qf2l069atQh++UqkkKChIw91sZGREQEBAjRSA3KKKOKUDbBsqmjQRSv9KpTBtmqAA5OYKsQbDhsELL4CNjVBgqPTnGsnbhnTDKpWK+fP3ceDAbGbN2sXMmTvo0cMbc/OKCyy7ulrj6GhZzqRfGidOXGXPnj/Zt28Wjo6WlY57+OABu2fPptfkybQfNKhqs59UilfXroQEBGBoalrl/eybP5/ZBw6wa9YsdsyciXePHhjVogCQSCRPpR1zWcycOZOmTZsSHx9P//79kclkDB06lBUrVtS6AuDm5sbatWvZuHEjc+bMYe7cuRw4cID27dtz9epV5s2bpyFgv/zyS/T19UlOTkYul3PhwgV1adzqYseOHcyYMYPFixczatQoduzYQXh4OBEREfj4+GBcSqUPCgpi5syZFBQUEBcXx+nTpyttT2pqavpM5+Hn5eURGRmJj4+Pul3vs8I/efJkfvvtN3VKYW0gU6HgxMOHbHR3p1Cl4p27d7nZqRPSUguNr2NiGGNjg6u+Ph6XLrE/Lo4JtdxbQKwrrjKi61H7nwT37y9FpVKVM+0XFMQjk9lUub+6CAoKol+/fhp9IuLi4tiyZQtRUVHl+kdUhgcPHgAVu+caGmJjoSiTmNJVqHNyhOY+GRnCn6Oj5udnVgH46KNjjB3bDTs7c7Ztm4a391xmz97Nt9++81jnS0hIZ9q0rxg+3Jfx4zU1T3//EHXfAJVSyRcTJ2JsYcGk4hkvNl3Fx1OQm4vVYzwwxz76iG5jx2JuZ8e0bduY6+3N7tmzeaeCDnOPg5EjR9KnTx/MzMyYOHEiBw4ceKKMjOpixowZfFnkxFqwYEHJSujiRfUPrLZRUV+BhISECk18ZWvFPw769+9PREREmWem4lbUS5cuxb6yPJtypkBjDA0NnwlhX6xYlrWaubi40K9fP7UAriizorJsi8q6AdY1//Tp0ykoKODhw4c4Oztja2sr9Bd4wud1xf37LCnyK89zdGRnTAwbIyJYWvS+uJmVRVphIW2LFMZ1bm4sunePVy0tMatFd4BKqQLl4+9/XKSlnS8y7f+hYdpPSfFDJrMiJ+delfuri5CQEIaUsZ7a2tpiZWVFQEBAtRSAwsJCzp8/j4WFBS2fsVis4rpetW24aDAKwKFDQlGT3r1bAGBjY8onn0xg8uRtDBzYjtGju1TypSooLFRUovF/iVSqw1dfTStjklLy3XcX1ArAj5s2EXT+PB+eO4e+sWbGwalPPmFEBeZjRWEhykrSlAAuHToEQIui5iOmNjZM+OQTtk2eTLuBA2ulBsDhw4c5fPjwU/+uvvrqq2rlcmtRHvr6+o/dHvlpwtnZmRkzZgBCh77iGgxGRkaMGDGCvn370qJFC3r06IG1tTW9e/fmjz/+oH///ri6ujJu3DguXbpEUFAQIJRuHTp0KE2aNGHo0KHcuXNHoxJiXfK/9dZbfPHFF+U4OnfuLNRYfQxE5uXxXmgod7OzmVeU5vtQLsdKJmNlWBjmUilGEgnz791jZamof4CEggJG3LzJF15ewJO/0VP+SCEnOIek00mY9TRDz1EzbqUgoYDEY4nkx+aT9FMSlgMta+UZUank3LkzGT09B1JSzpCScqbonZxOQsIRunS5z+XLrSvd37VrJPCLWjiDEPBb1gUQHR1daaCfp6cn/v7+9O/fX22Vqyh9NDc3l8OHD5OTk8OUKVOqnQ5cX6hIRx44EP79t1g5LqssV+8c5cao6spZV01ERHzJunXH2bnTj4ULB/HBB8MxMNAlL0/Ohg0nWLnyCPr6MlavHsWbb/bFyEivSIPM4vjxK8yYsQMXFys2bRrPoEElkRy7d//JlCnbaNvWhTZtSlbvcrmCK1dC6dTJg927Z7I52IeFrVphbGFBm1INYVQqFfGhoSRHRbG1TBewy8eOsW/+fFJjY5m+fTsdhwzBwETwvz2MiOD4unX47dzJoIULGf7BB+gaGCDPy+PEhg0cWbkSmb4+o1avpu+bbzKhjMLxvKE2H78OHTqQkJBAZAU93esCCxcuZNOmTdy/f5+mZV7yleHMmTPY29trdLer3xeN6Pl+/sr2gH3K6Hu2niegni+gT58N/Pfff5w9e5aHDx/SuXNnOnbsqI77CQ8P59ixY4jFYl599VWNWhixsbEcO3aM6OhonJyceOWVV8jIyODMmTM8fPgQV1dXzMzM1I2ynJ2d6dq1q4YF7r2yFX8aAJychA6AvXrBp58KGQEWFkKzoldeEfL7X38dBgyA27c1PxcrCC1bCgWFfvpJSCMsXdugQSkAcKSe2UfUK//I5/0FXAuPX3R0NDt27GD9+vXI5XKWLVvGpEmTcHV1rZNrlsvlfPHFF3z66adERUUxcuRIZs+eTdcqskGK8ccff+Dk5FRn16ZVALQKwLOmANQnGqIC8FR//1oFQKsA/H+xAGihVQC0CoBWAdAqADVRAPr0UWl/APWHs/TVvoDqEb///pxLQC200OK5hbYdsBZaaKGFFlpoFQAttNBCCy200OJ5gDrfIkeh4MOwMM6lpSFXKrmTnU1eUdnTzF69MJJI+DY+nh0xMZxLTUVfLMbL0JBcpRJdsZihjRuz0NkZfbGY4OxsjiUmsio8nHylEic9PaxkMhIKCmhjbMx7zs74mmhWrVLkKAj7MIy0c2ko5Uqy72SrG1z0yuyFxEhC/LfxxOyIIfVcKmJ9MYZehihzlYh1xTQe2hjnhc6I9cVkB2eTeCyR8FXhKPOV6DnpIbOSUZBQgHEbY5zfcy5XNUuhyCEs7EPS0s6hVMrJzr6DUpkn8PfKRCIxIj7+W2JidpCaeg6xWB9DQy+UylzEYl0aNx6Ks/NCxGJ9srODSUw8Rnj4KpTKfPT0nJDJrIqaZ7TB2fk9TEx8Nfi181+/86+FFlpo8bxBHQPQ5/p1rGQy9nh7oysWkyyX82ZQEEcTE9UCCOBCWhrd/vmHtx0c2OrpiQrYHBnJvJAQepmZ4deunbrMZZ/r1/FLSeFhjx5YSqVE5+fz0vXrhObk8HObNvQ1N1e7gK/3uY7MSob3Hm/EumLkyXKC3gwi8WiiWgABpF1I459u/+DwtgOeWz1BBZGbIwmZF4JZLzPa+bVT17q+3uc6KX4p9HjYA6mllPzofK6/dJ2c0Bza/NwG877mah/09et9kMms8Pbeg1isi1yeTFDQmyQmHlULIBBqWv/zTzccHN7G03MroCIycjMhIfMwM+tFu3Z+FF/A9et9SEnxo0ePh0illuTnR3P9+kvk5ITSps3PmJv3VccAaOe/fuZfGwOghRZaPK8QA1zNyMAvJYVlLi7oFlUUsJBK+a5FCzzKlB4yLVOkQQTMdXSklbExf6am8ktSUqVj7XV1WevmhlylYmmpcpwZVzNI8UvBZZmLULISkFpIafFdCww8NPmL22WWvgDHuY4YtzIm9c9Ukn5JqnSsrr0ubmvdUMlVhC4txZ9xlZQUP1xcliEW6wr8UgtatPgOAwMPTX6dsqV/RTg6zsXYuBWpqX+SlPRLpWN1de1xc1uLSiUnNLSk+5Z2/ut3/rXQQgstnlsFILGom9n5MtUCZGIx46pZs7pFUXGF8KJmCzUZV5Ao8Kee1+QXy8TYjqsev2EL4by54bk1HldQkCjwp57X5BfLsLUdVz1+Q6GCYW5ueI3Haee/fudfCy200OK5VQB8TUwwkEiYffcua8PDKSyVmz3Cykq9Kq0K93JyAGhuZFT1uCLBU3qcia8JEgMJd2ffJXxtOKrCEn6rEVbqVWlVyLkn8Bs1r5o/915uuXEmJr5IJAbcvTub8PC1qFQlpSStrEaoV6VV8ucIXQGNjJpXzZ9bfpx2/ut3/rXQQgstnlsFwEIqZY+3NyJg2f37tAwI4KciU7KXoaFGZ6uK8FV0NFcyMhhgaUkvs8rbTcYXFLAkNBSpSMS6UiUdpRZSvPd4gwjuL7tPQMsAkn4S+A29DBFJq+aP/iqajCsZWA6wxKxX5fwF8QWELglFJBXhtq4Uv9QCb+89gIj795cRENCSpKSfilaMXohEVTftiI7+ioyMK1haDsDMrFfl/AXxhIYuQSSS4ua2Tr1dO//1O/9aaKGFFs8j1E7akdbWeBgYMC0oiH8yMng1MJC+5uZsb9YMlwqal/inpbHw3j0i8/IoVKn40suL6XZ2FZKsCgtDCYTl5NDJxIRDPj54lvFtW4+0xsDDgKBpQWT8k0Hgq4GY9zWn2fZm6LuU50/zT+PewnvkReahKlTh9aUXdtMr5g9bFQZKyAnLwaSTCT6HfDDwLMNvPRIDAw+CgqaRkfEPgYGvYm7el2bNtqOvX74TYFqaP/fuLSQvLxKVqhAvry+xs5teMX/YKkBJTk4YJiad8PE5hIGBp8YY7fzX7/xroYUWWjy3CgBAa2NjLnfowO7YWJbfv8/ZlBQ6XrmCf4cOuJURGJ1MTdno7l4tkg+aNsWyGq0vjVsb0+FyB2J3x3J/+X1SzqZwpeMVOvh3wMCtTDBcJ1PcN1aPv+kHTZFaVoPfuDUdOlwmNnY39+8vJyXlLFeudKRDB38MDNw0+U074e6+sXr8TT9AKn10By7t/Nfv/GuhhRZaPE8Qg2AaTpbLhQ0iEVPt7Aju3JnBjRuTJJez/P79Or2IgvgC5MkCv0gswm6qHZ2DO9N4cGPkSXLuL69j/oJ45PJkgV8kxs5uKp07B9O48WDk8iTu319ep/za+a/f+ddCCy20eG4VgIjcXPxSUjRXWDo6HPTxwVRHh8DMzDq9iNyIXFL8NPl1THXwOeiDjqkOmYF1zJ8bQUqKnya/jik+PgfR0TElMzOwTvm181+/86+FFlpo8dwqAAB74+LK7dQTi3HQ06NpBT7omnRxq87YuL3l+cV6YvQc9NBv+hT44/aW5xfroafngL5+0zrn185//c6/FlpoocVzqwD8kpTEnJAQshQK9c7v4uO5l5PDylK9y9MLhRSt1MLCR568JmOTfkkiZE4IiqwS/vjv4sm5l4PryhL+wnThXIWpjz5nTcYmJf1CSMgcFIqsEv7478jJuYer68qScxamF/2b+mj+GozVzn/9zr8WWmihxfMGkapPH1VAejqdrl4FwFAiwdvQkAKVisZSKWtcXXmhqG780tBQdsXGqgvXSEQi5jo6ssrVVV2DfktUFF9FR2Oio0OWQoFCpWKgpSVvNGnCUCurchfQ9yykB6RztZPALzGUYOhtiKpAhbSxFNc1rpi8IPAHvRlE3N44lPlCjXqTF0xouqopFi9ZAEI9+4h1EYR/FI5IIgIVqBQqLAda0uSNJlgNLc9P37Okpwdw9WongV9iiKGhNypVAVJpY1xd12Bi8kKRQPqWmJivSU39GwBdXTtkssbqPHWFIpesrP8QiaTY288gJmY7SmUBlpYDadLkDayshpajP0tfKpt/cx0dPA0N+S05WV24x1RHh7TCQowlEla6uqJSqfg8KooHeXmMsbFhvpMTBmIxR4t6ARQolUhFImbY2/OZp2eN5l9iJEHSSELyacE/L2ssQ8dUh5x7OUiMJbiudEVmKyPyk0gyrmVg0smEpu83Rc9Fj8SjRb0ACpSIpCLsZ9jj+ZnnY82/WCzl8uV2SKUWqFQFFBZmIhJJ0NW1o7Awg8LCNGxsRtOixXcARb0AjhIW9gEqlRJLy1do0mRKhfOvLQWshRZaPNcKQE0P2h8Xx4TbtzGTSont1g29UoVqzqelMSs4mHPt25crRVsRatoOPjc8l4CWASiyFHS+17lcdDoq+Nv6b1oebYlpN1Nq/QKAGzcG4+HxKfr6rhrb796dTVTUVlxdV+PiUr3AteJeAFUho7CQnteu8W9mJuvd3Fjs7Kyx/8Xr1xlrY8PkJk3KHXvi4UOG3LjBAienCrMGqnP7wW8HE/1lNDZjbWhxoEW5/ZGbI0k5m0Krk60Q6WjK04cnHnJjyA2cFjhVnDVQjQtITj5DcvKveHh8qrE9L+8B/v4tkEgM6dTpNlKphcb+y5dbk5V1ix49UtDRaVThubUKgBZaaPG84rHaAY+3tWWGvT2pcjnrIyLU23MUCt67d48TrVpVS/g/DvRd9HFbK6SEha8uX8414VACthNtqyf8HxNmZj3KCf/U1HNERX2BsXFbnJ3fq1W+Rjo6HGnZEkOJhI0PHpBUlDEA8E1sLC0MDSsU/gB9zM0xkkgYZW392PzuG9zRc9Qj4XACOSE5mvqWQkXCoQSa7WhWTvgDmPcxR2IkwXrU4/PL5ck4Oy+irKZ3585kFIosvLy+LCf8ARo3HoKFRf9Khb8WWmihhVYBeAxsdHfHUU+PdRERBGVnA/BWcDDznZwqLFxTm7B/255G7RsRtz+OtPNp6u2KbAWRWyJpuqJpnfLb2IzV+KxQZHPnzmREIh2aN9+DSFT7yo+rvj5r3dxIlsuZFxICwO3sbPbExZVb2StVKgbduMHrN29yNzubt+ztkYpEzA8JocOVK9zKyqoRt8RIgufnnqjkKoLfCtbYF7U1CuuR1ug2KSnXq1KquDHoBjdfv0n23Wzs37JHJBURMj+EKx2ukHWrZvzm5r2RyWw0tkVHf0VKyh9YW4/SMO0nJf3ElSsdiYraiqXly1hYvERCwkGuX3+RkJC52l+8FlpoocWTKgBGEgnbmzWjQKlkelAQO2NiMNLRqdDPX9sQiUU0294MkVhE0MwgVHLBixH2Ydj/tXfn0VHV9//4nzczk5kMSSYJJEP2PcQQEYQPi8ivHql6WrFgIaAU27LosQX9Bi3iVywBMRCKCFgsigsVi9Til+rx49HWICiLmiAQlsEMZJJM9oVsZLbMzL2/PzKELJMhGwwtz8c5niOZe+c19/1+vd/33vdd3ojJjOmYuvZ68fXtejZ78eJKWCwGJCT8Ef7+Y65b3GVRUbhLo8H7VVX4uK4Oi3U6vJuWBt9ucwVIAIrMZvxw+TL21tSgzm7Hlw0N+La5GT+aTGjoNILQV6G/CEXorFA0HGhA9QfVANrfH1C7rxbRT0V3PzmHuciMyz9cRs3eGtjr7Gj4sgHN3zbD9KMJ9gb7oMrbYinBhQvPwdc3DKmp23uMFpjNejQ0/Bv19V/AYilGQ8MBtLaehclUyBZPRHRlXzqQewA6+825c9hdVYV0f38cnzixTxPXdDaAS/Ad9Mv1MG41ImlDEkJnhkL/jB7jPh+HG/YDADQ2HsIPP9yLgICxmDgxr99n/325B6CzH00mjP3+e9glCX9NS8NjfZgtcPqJE/jkjjvgL5MNavNt5TYcSzsGmVqGuwrvQuGyQkQ8HoHg/y/Y43onpp/AHZ/c4f7ArN/lL+HEiZ+ioeErjBnzEcLCZve6ZH39Z2hp+QEJCat7XYb3ABARRwAGKCcpCTJBQJnV2vE2uxslcV0iVNEqFK8rxvnHzyPl1ZQbGr/z0H9a2vUZ+u8uddgwLI6IgChJON2Hofx9NTX4qqEBWUPwNkFllBKJ6xLRVtOGgpkFgIBr7vxr9tWg4asGFGUNzdsEy8t3uIb+53rc+QPAhQvPoaRkQ8d0w0RENIQHANklJZin1aLZ4cCywhs7xCrzlyH5T8lwmp1Qj1Jj2G3Dbmj8Cxeeg8VSjPj4VQgIuOOGxCyyWHDOZMJtw4Zhi9GIE9d4S6DMdYIb0oe5APoielk0hqUNQ+PXjUjMTrzm8oKsPb4iZPDxLZZiXLiwEr6+oUhNff3asQUZAAEKRQhbOhHRUB4A/KOmBk5Jwp70dEwPCcE/a2vxz9obe7bll9R+w6EiWHFD4zY2HkR5+Q4EBIxFfPwLNySmTRSxSKfDW7fdhjdvuw2iJOFxnQ5OD2+6S/f3BwCMCwgYkt8gyISO2QH7Uub+6e3xA8YNNr4EnW4xnM5WjBr1ep8m9/H3T4e/f/oNGZkhIrplDgD0ZjP+XFaGLSntw+47UlOh8vHBssLCjjfQ/bdyOluh0y12Df3/1e189SaTbsjj/p/CQjwZGYlktRrTgoKwMCICJy5fxhajsdd1kvz8oPLxQWK32QRv5AGaj8oH6sTBxS8r+wsaGw9Cq82AVpvR43OrtRROZ9dHFIcNS+/xuCYREQ3iAMDsdGLhuXN4Jy2t4yVAyWo1VsXHo9Jmw4oLF/6rC+3q0P8Lbof+7fYGNDTkDmnMPdXVEAE8OvLq43CbkpMR5uuL1UVFuGg2u69gQUCCnx+ilEqvlJXgI8AvwQ/KqIHHt1iKcfFi+9D/qFHuh/4rK9+Dj4+qy9/U6mSoVFFs5UREQ3EAYBFFPHr2LGaEhiKl21nlyrg4hPr64q2KCuy/QZcCOt4333xjRh0aGr5CefkbCAi4A/Hxq3p8LooWFBY+7XYCm4HKbWjAygsXOkZbrghRKPB/4+JgEUX86uxZ2ETR7fpRKhWGyWReK3NVlAqyYQONf+WFPyaMGrUdvr6hPZZobv4WjY0HIAhd09nXN5TX/4mIetGvi6NvVVQgp6QEBosFrU4n7gsJwYTA9resOSQJW43GjuH/X509i6eio7E8Jgbh1+nss+r9KlS8WQEAqP1nLYalD4N2jhbKyOt1tivh/PnFACTY7U04fnxat52/HRZLERyOZiQmrht0tItmM7JLSvBeZSVkgoC/lJfjD7GxuPLcWl5LCz6tr+/4/5/88AOei43t8S6GoTr7N503ofajWjR/3z7Jjn65HuG/DseIGZ6vxw/m7L+y8q9obDwEQZDBaHwVRuOrPUZbzOaLCA9/rGdyyzWQyQLYyomI3Bj0ewAGa5CP4f/H/4D+vgdgIFYVFSE7MdFrm1+0qqj3Jwau4w8wmy+iqekwIiIW9j66wvcAENEtyodF8N8vRO7du+DlId6JL5OpIJOpmQBERDwAuDUFevsAINA78QXBt8eNgURExAOAW4a/TObV+Nd7bobeDwDkPAAgIurt5IxF8N+v86OD3X153434BSOBd735A17p/aP7JC/fA3OfV3PD67fgeL11fHlLF8AtfguW18vf2/cgcQSAiIjoFsQDACIiIh4AEBEREQ8AiIiI6L9Sj5sAW51ObDUacaypCSOVSsgEAYEyGcYHBqLF4cBcrRb7a2uxXK+HBGBaUBAsoohWpxNLo6KwMCICerMZu6uqkF1cjAilEhMCA1FutWK4QoGshARMDQrq9Qc5W50wbjWi6VgTlCOVEGQCZIEyBI4PhKPFAe1cLWr310K/XA9IQNC0IIgWEc5WJ6KWRiFiYQTMejOqdlehOLsYygglAicEwlpuhWK4AglZCQia6iG+sxVG41Y0NR2DUjkSgiCDTBaIwMDxcDhaoNXORW3tfuj1ywFICAqaBlG0wOlsRVTUUkRELITZrEdV1W4UF2dDqYxAYOAEWK3lUCiGIyEhC0FBU3uN7+3y93r8Vie2bjXi2LEmjByphEwmIDBQhvHjA9HS4sDcuVrs31+L5cv1kCRg2rQgWCwiWludWLo0CgsXRkCvN2P37ipkZxcjIkKJCRMCUV5uxfDhCmRlJWDqVE/b34qtxq041nQMI5UjIRNkCJQFYnzgeLQ4WjBXOxf7a/djuX45JEiYFjQNFtGCVmcrlkYtxcKIhdCb9dhdtRvZxdmIUEZgQuAElFvLMVwxHFkJWZjqof69nf/eLn+vt//WVhi3bkXTsWNQjhwJQSaDLDAQgePHw9HSAu3cuajdvx/65csBSULQtGkQLRY4W1sRtXQpIhYuhFmvR9Xu3SjOzoYyIgKBEybAWl4OxfDhSMjKQtDUqTdx/+Pt/L+1y9+rBwBlVivuP3kSD40YgU/Hju2YS76mrQ0zTp3CzNBQhCgUWBIZiT3V1bCIIj4fNw4AsL64GIt0OjgkCY9HRmJdYiI2lJTgsfBw5CQlwS5JmFVQgOknTuD4xIkd09R2Zi2z4uT9JzHioREY++nYjrnk22racGrGKYTODIUiRIHIJZGo3lMN0SJi3Oft8YvXF0O3SAfJISFYnou9AAAgAElEQVTy8UgkrktEyYYShD8WjqScJEh2CQWzCnBi+glMPD6xY5raLvGtZTh58n6MGPEQxo791DWfPNDWVoNTp2YgNHQmFIoQREYuQXX1HoiiBePGfd4ev3g9dLpFkCQHIiMfR2LiOpSUbEB4+GNISsqBJNlRUDALJ05Mx8SJx+Hvn94jvrfL3+vxy6y4//6TeOihEfj007GQueq/pqYNM2acwsyZoQgJUWDJkkjs2VMNi0XE5676X7++GIsW6eBwSHj88UisW5eIDRtK8Nhj4cjJSYLdLmHWrAJMn34Cx49PRHq6u+0vw/0n78dDIx7Cp2M/hcxV/zVtNZhxagZmhs5EiCIESyKXYE/1HlhECz531f/64vVYpFsEh+TA45GPY13iOmwo2YDHwh9DTlIO7JIdswpmYfqJ6Tg+8TjS3dS/t/Pf2+Xv9fZfVoaT99+PEQ89hLGffgrB9fhsW00NTs2YgdCZM6EICUHkkiWo3rMHosWCcZ+72v/69dAtWgTJ4UDk448jcd06lGzYgPDHHkNSTg4kux0Fs2bhxPTpmHj8OPzT02/C/sfb+X9rl79XLwFIAOafPYsguRwbk5M7On8A0Pr6Yv+YMWh1Ojv+pvTpevXgmdhYyAQB71ZWAgAEAIpO36EQBGTGxMAmithTXd3zl0jA2flnIQ+SI3ljckfjBwBfrS/G7B8DZ+vV+D7KrvFjn4mFIBNQ+W57fAiAoLj6HYJCQExmDESbiOo9buJDwtmz8yGXByE5eWNH5QOAr68WY8bsh9PZejW+T9f328fGPgNBkKGy8srzbkKXaYIFQYGYmEyIog3V1Xvcbb5Xy9/r8SVg/vyzCAqSY+PG5I6dDwBotb7Yv38MWjvVv7Jb/T/zTCxkMgHvuupfEABFp/pXKARkZsbAZhOxZ4+77Zcw/+x8BMmDsDF5Y0fn1779Wuwfsx+tnepf2a3+n4l9BjJBhndd9S9AgKJT/SsEBTJjMmETbdjjpv69nf/eLn+vt39Jwtn58yEPCkLyxo0dO5/2+FqM2b8fztZO7b/b/BqxzzwDQSZD5bvvXmnwEBSd2r9CgZjMTIg2G6r37LkJ+x9v5/+tXf5ePwD4prERR5qasDAiAu4eTIxWqTCn2yQznV1ZJ8DDS2c8LdP4TSOajjQhYmEE3P0AVbQKYXN6j39lHVmAbEDLNDZ+g6amI673xvf8ASpVNMLC5uBaX+558pnel/F2+Xs9/jeNOHKkCQsXRsDdk7HR0SrM8VD/V9YJ8FD/npb5pvEbHGk6goURCyG4KYFoVTTmeKj/K+sEeKh/T8t4O/+9Xf5eb//ffIOmI0cQsXAh3BWAKjoaYXM8tH/XOrKAgAEt4/3+x9v5f2uXv9cPAD6/dAkAMN5DAV6Z+c+d9SUlcEoSlkZHu/3cKorYVFoKjVyOBeHhPT6/9Hl7/IDxvccPnNB7/JL1JZCcEqKXuo8vWkWUbiqFXCNH+AI38S997uqcxvceP3BC7/FL1kOSnIiOXuo+vmhFaekmyOUahIcv6PG5t8vf6/Fd9T/eQ/1P8FD/69eXwOmUsLSX+rdaRWzaVAqNRo4FC9xt/+eu7R/vYfsneNj+9XBKTiztpf6tohWbSjdBI9dggZv693b+e7v8vd7+XUPJAeM9tP8JHtr/+vWQnE5EL+2l/VutKN20CXKNBuELFtyE/Y+38//WLn9v6bgHoNxqBdA+x3xfVdlsHdMD20QRB8ePxz3BwV2WyWtuRnZxMc6bTEhRq7EjNRUxqp6vZ7WWt8dXhPQ9vq3KhpKcElgMFog2EeMPjkfwPV3jN+c1ozi7GKbzJqhT1EjdkQpVjJv41nLXUGXf54+32apQUpIDi8UAUbRh/PiDCA6+p2v85jwUF2fDZDoPtToFqak7oFLF9Pgub5e/1+O76j+kH/VfVWVDTk4JDAYLbDYRBw+Oxz3d6j8vrxnZ2cU4f96ElBQ1duxIRUyMu+0vd21/SD+2vwo5JTkwWAywiTYcHH8Q93Sr/7zmPGQXZ+O86TxS1CnYkboDMW7q39v57+3y93r7L3e1/5B+tP+qKpTk5MBiMEC02TD+4EEE39Ot/efloTg7G6bz56FOSUHqjh1QxcTchP2Pt/P/1i5/rx8AqF3Dspc7Xee9lnClEs/HxXlcZqJGg1Xx8df8Lpm6Pb7zct/jK8OViHvec3zNRA3iV/UhvmvWOKfzct/jK8MRF/e85/iaiYiPX3XN7/J2+Xs9vqv+L/ej/sPDlXj+GvU/caIGq1b1ZfvVru2/3I/tD8fz16j/iZqJWNWH+vd2/nu7/L3e/tWu9n+5H+0/PBxxz1+j/U+ciPhVq/4D+h9v5/+tXf7e0nEJ4C6NBgBwoqXFKz9Ec1d7/JYTXoqvuas9fssJr8T3dvl7Pb6r/k+c8Nb23+Xa/hO3ZP57u/y93v7vcrX/E16qf6/3P97O/1u7/L1+ADBXq0WUUont5eVwupkfRZQk7HV39/4Q0c7VQhmlRPn2ckjOnvElUUL13usYXzsXSmUUysu3Q5J6noVIkojq6r3XLb63y9/r8edqERWlxPbt5XC6qX9RlLB37/Xc/rmIUkZhe/l2ON3UvyiJ2Hsd69/b+e/t8vd6+587F8qoKJRv3w7JzSiYJIqo3rv3v7j/8Xb+39rl7/UDALVMhn1jxqDQZMK8M2dQ09bWsVCTw4E1BgOmd7o+YxNFWDwMF0sA7JLkcZmuQ0AyjNk3BqZCE87MO4O2mqvxHU0OGNYYEDL9anzRJsJp8fDdEiDZJc/LdBsCGjNmH0ymQpw5Mw9tbTVX4zuaYDCsQUjI9E4dog1OpwWefoAk2a+xzFXeLn+vx1fLsG/fGBQWmjBv3hnUdKr/piYH1qwxYHqn+rfZRFg81K0kAXa75HGZrtuvxr4x+1BoKsS8M/NQ06n+mxxNWGNYg+md6t8m2mDxULcSJNglu8dlbqb893b5e739q9UYs28fTIWFODNvHtpqOrX/piYY1qxByPRO7d9mg9PioW4lCZLd7nmZm6r/8Xb+39rl7y1dXgQ0WaNBweTJeMlgwKS8PIT5+iJWpUKSWo0VsbEIUSjQYLdjX20t8ltaYBNFbC8rw+ywMIR3ei5TbzZjV2UlREnCx3V1mKTRIEOr7fJcuNthmMkaTC6YDMNLBuRNyoNvmC9UsSqok9SIXRELRYgC9gY7avfVoiW/BaJNRNn2MoTNDoMy/Gp8s96Myl2VkEQJdR/XQTNJA22Gtstzwe6HgSZj8uQCGAwvIS9vEnx9w6BSxUKtTkJs7AooFCGw2xtQW7sPLS35EEUbysq2IyxsNpTKq3cWm816VFbugiSJqKv7GBrNJGi1GV2eC3XH2+Xv9fiTNSgomIyXXjJg0qQ8hIX5IjZWhaQkNVasiEVIiAINDXbs21eL/PwW2Gwitm8vw+zZYQjvVP96vRm7dlVCFCV8/HEdJk3SICND2+W5dPfbPxkFkwvwkuElTMqbhDDfMMSqYpGkTsKK2BUIUYSgwd6AfbX7kN+SD5tow/ay7ZgdNhvhnepfb9ZjV+UuiJKIj+s+xiTNJGRoM7o8F30z5r+3y9/r7X/yZEwuKIDhpZeQN2kSfMPCoIqNhTopCbErVkAREgJ7QwNq9+1DS34+RJsNZdu3I2z2bCg7Pdli1utRuWsXJFFE3ccfQzNpErQZGV2eS785+x9v5/+tXf7eIEg//amX50P3cgl4+Qd8eRPMiO7lAril6/++L++7tYv/Vk/A+9j8buXyz829xlnRjboEQERERLcOtwcAFTYb/lRaCvmBAwg+dAjvVFai2eHosdyhxkY8cuYMhNxcCLm5yNTrUW+3AwC+b27G1Px8BB86hA0lJTD34/EyW4UNpX8qxQH5ARwKPoTKdyrhaO4Zv/qDahyOPIxcIRf5U/PRdLip4zNLiQUFDxfggOwACp8uhK3S1q+CsdkqUFr6Jxw4IMehQ8GorHwHDkez22VPn87A0aNJKCh4GKdPz8Hp03Nw6tSDyM0V8O23oyGK/YutN5ux8sIFKL/6CkJuLh44eRLnTCYAQInFgiU6HeQHDmBZYSEMbq5x9bX+hjLmoNfXm7Fy5QUolV9BEHLxwAMnce6ca/0SC5Ys0UEuP4BlywphMPRc/7vvmjF5cj4EIRcjR36DDz64esOY2ezEqlVFEIRc/PznJ3H8uPs7zQ81HsIjZx6BkCtAyBWQqc9Evb3elc/fY2r+VAQfCsaGkg0wO81uvyPjdAaSjibh4YKHMef0HMw5PQcPnnoQQq6A0d+Ohq2PuTDQ3BZtIip3VXasa3jJAEtR365DfvBBNSIjD0MQcjF1aj4Od4pZUmLBww8XQCY7gKefLkRlp5jNzQ688kopIiLa142PP4ovv2zoKPtXXzVCEHLxs5+d7PKdQ7XNVqMV5359DrlCLnKFXJS+Utqlv6j+oBoHhx3EsdRjqN1f22t80WaDYe1afKVUIlcQoFu0COaLF69u57ff4tu0NBzSaFC6eTPETvfJAIDp3Dl8pVTixH334fScOR3/HY2PR64goOr99/vVD5SVvY6vvx6OU6ce6uhXTp+eg4MH/fHVV2qYzfqe2yDaUFm5C4cPRyI3V4DB8BIslqI+xxxI/urNeqy8sBLKr5QQcgU8cPIBnDOdc7X9EizRLYH8gBzLCpfBYDF4jH86IwNHk5JQ8PDDHeV36sEHkSsI+Hb0aIi2nvGbv/sO+ZMnI1cQ8M3Ikaj+4IOOz5xmM4pWrUKuIODkz3+OluPHr1kGA+3PB1Jf3iZ398dIpRLPxcbi7YoKjFKrsTgiwu3K9wQH457gYEQoldhiNGJCQABGuK6zTNJoMMLXF4dSU3FHQP9efaiMVCL2uVhUvF0B9Sg1Iha7jz9y/kiok9XIn5IPvzg/BE27OsuXX5wfQu4NgWayBnEr4/pdMEplJGJjn0NFxdtQq0chImJxr8uqVNFIT38fPj6qTju0TAiCHKNHv9fjvdHXkqJWY2NyMqYEBeGXBQWIVioxetgwAECcnx9iVCq8kZqKJZGRGEz9DWXMQa+fosbGjcmYMiUIv/xlAaKjlRg92rV+nB9iYlR4441ULFnifv3JkzX497/HIT39O/j4tN/VfoVaLcMjj2hx8mQLPvtsHHobdLsn+B7cE3wPIpQR2GLcggkBEzBCMcKVz5MwwncEDqUewh0Bd/RajtGqaLyf/j5UnXIhU58JuSDHe6Pf6/EO9d4MNLd9lD6IWBiB5m+bUb23GgmrE/qcd/Pnj0RyshpTpuQjLs4P0zrFjIvzw733hmDyZA1Wdoup0cjxhz/EYvbsMEyYkAelUsC99wZ3lP2ECQFYsCAc778/+rpssypGhdG7R8PR4kDdJ3UI/UUo5JqrXZs2Q4vSzaW488s7Pb5oyEepREJWFuQaDfTLl0MzZQrUSUlXt3PKFAwbPRppb7/d8dhaZ2319Ri9eze08+Z1Ojgx4rvbb0fozJkIf+yxfvUDomjCxIk/wM/v6vbW1X2C2tr/h5SULVCrU3pug48SEREL0dz8Laqr9yIhYXW/Yg4kf1PUKdiYvBFTgqbglwW/RLQyGqOHjXa1/TjEqGLwRuobWBK55JrxVdHRSH//ffh0elmYPjMTglyO0e+912MOAKD93oFx//43vktPB3x8oJ07t+MzmVoN7SOPoOXkSYz77DOgDyPuA+3PB1JfN+UIwBW+gtBj0hd3NiYnY0JgIFZevNhxprmptBRztdp+7/w7E3yFHpN+dBf4P4GIWR6Dmr/XoCXv6pmds9WJhi8bEPuH2EEVkCD4XnMHPmLEjC7J0tj4NYzG1xAXt9Lj6yOvZVZoKJ6OicGuqip819w++nCsuRnVbW297kgHUn9DGXPQ688KxdNPx2DXrip8951r/WPNqK5u63Xn35ELgXLs2JGK0lIrtm0zdvksJ6cEb755W1/aPzYmb8SEwAlYeXElml2jPptKN2Gudq7HnT8AzBgxo0vn+XXj13jN+BpWxq30+CrVoc5tH1+fa7Ydd/7nfwKxfHkM/v73GuR1itna6sSXXzbgDx5ixsf74Z130lBYaMarr7aX/6VLdvzpT6XYufO2677NqX9JhTxQDv2zXc+0yl4vQ/yL8X1+y2D0008jcOJEGNasgaPTezFafvgBqqgotzt/ABDkcoTOmnX1D5IE3aJFEBQK3Pbmm/2ui4CACV12JnZ7Pc6ffwJBQdMQHf20547dx7ffJx6Dzd9ZobPwdMzT2FW1C981f+dq+8dQ3Vbdp50/AIyYMaPLzr/x669hfO01xK1c6fFVwPLAQKTu2AFraSmM27Z1+awkJ6e9/Pt4uX2g/flg6uumPABwZ8HZs5h35kyXvykEAbvS0lBvt2PFhQs43NSEEosFvxo5csh/8NkFZ3FmXtf4CWsToIpR4fwT5yE52u9pNKw1IP7F+C6zig0FSbLj6NFElJf/peNvISH3Xu2onK3Q6RbC3/92xMevHnS89YmJiFepsESnQ4XNhuziYmxO6XkkubOiAnFHjsAmijcsprtc6M/6vcZfn4j4eBWWLNGhosKG7OxibN7sJv6Cs5jXLRcefHAEMjK0yMoyoLS0/fWyn35ah3HjAhAdrepTfIWgwK60Xai312PFhRU43HQYJZYS/Grkr9yUwQLMO3P1jO/eTrnQ6mzFQt1C3O5/O1YPMBf6ktutp1vxdcjXuHzy8pDk+Nq1CYiJUeGJJ87D4Yq5dq0BL74Y3zFL4M6dFYiLOwKbTexxAPfooyORlVWECxfMePLJ89i8OQV+fj5Dus0VOytwJO4IxE7xlRFKJG1IQv3/1qP2o/ahflulDc3fNyPs4bC+H/T7+CDtrbfQVluLohdeaG/3oojideuQsHbt1UttO3fiSFxcx7B00NSpXc5Qy15/HQ0HDiD19dfhq9X2ux469ysAcP787+B0mjB69C4IwtXyrKjYiSNH4vp9qdGdvubvzoqdiDsS1+OSwPrE9YhXxWOJbgkqbBXILs7G5pTNfd/mezv1pa2t0C1cCP/bb0f86q7xu5c9AIx48EFoMzJgyMqCtbS0/Qz8008RMG4cVL3MUXKtcvfUn589uwBnOrX9vtbXf/QBQLhSiQg3wzDp/v54MT4eb1dUYHVRUb86/H4NzYcroYzoGl+mliH1L6m4XHAZxi1GtJ5uhWgTETgx8Dr8AhlUqphe3xmt1z8Lq7XcNVTkO+hoapkM76SlQWcyYWp+PrakpMDPzVl9sFyOWD8/yIfgptK+xuwtF/q6fq/x1TK8804adDoTpk7Nx5Yt7ncg4eFKRET0jP/aa6OgUAj4/e9/hNnsxFtvVSIzs3/v3073T8eL8S/i7Yq3sbpoda+dWLgyHBFK95dYntU/i3JrOd4b/R58B5gLfcltH7UPVLEqyIbJhiTD1WoZ/vKXVBQUXMaWLUacPt0Km03ExE4xg4PliI31g1zeM9/+/OdRCAiQY8qUfDz8cBhGjVIP+TbLg+Xwi/WD0C1+5JOR0EzRoPDpQjhaHLj4wkUkrU/qdxn4jxmDmGeeQfmOHWj+/ntUvPEGRj76KOSBnX9DMPxiYyHIe15JtRQV4eLKlQibM6fLJYGBqq7ei9raj5Cc/Cf4+SV2PfuVB8PPLxaCIB/Sns5T/gbLgxHrFwt5t5hqmRrvpL0DnUmHqflTsSVlC/x8/AYUX//ss7CWl7cP/ft2jd9b2Y967TUICgV+/P3v4TSbUfnWW4jJzBxwGXjqz5XKcCh7afue6utm0u+M2ZSc3Otnz8fFYZvRCKPVClG6Pk8XJm9yH3/4z4ZD+4gWhjUGNBxowO1/v/26xBcEH4wff9DtZ5cu/QsVFTuRkLAWAQFjhyzmT4KDMSM0FF/U1/d6hp+h1SJjAGcZg4npKRf6sr7H+D8JxowZofjii/oeZ5kd8XvJhZEjfZGTk4wnnzyPBx44iZycJLc7qmt5Pu55bDNug9FqhCj1Vgab3P79X5f+hZ0VO7E2YS3GDjIXrpXb6iQ1Jp2cNKR5/rOfDccjj2ixZo0BBw404O/dYmZkaJGR4T7fhg9XYOXKODz7rL5fcwv0Z5u1GVpo3cQXfATctvM2fH/n9zj181MY8eAI+MUPbAeUuGYNaj/6CLpFi+Cfno7bP/yw22/IgDYjo+cooSji3G9+A5m/P27bsWPQdWGzVaGwcBlCQu5FVNTvenyu1WZAq80Y0vq/Vv5maDOQ0UvMnwT/BDNCZ+CL+i/6fNNrj770X/9Cxc6dSFi7FgFje8bvrex9R45Eck4Ozj/5JE4+8ACScnLcHqD16Tdcoz9P7qXtX6u+/qNHADzZajTiyagolFiteLGo6IZvTMorKXCandBM1kAeJL+hsR2OJuh0ixEQcCfi418Y0u8+1tyMSKUSob6+WKzTuX1V71AbbMxBr3+sGZGRSoSG+mLxYp3b19N68sQTkUhJUUMmEzB1atAA83krnox6EiXWErxY9GKf12tyNGGxbjHuDLgTLwxRLngjt195JQVmsxOTJ2sQ1I+Yly7ZcfRoE372s+F47rkLKC+33dBt9k/3R8RvItCc1zyoe4B8/PyQ+NJLMOl0iPpd3zty4+bNaDp6FKk7dkAxYsSg6+H8+cchinakpb0Ld3PVD7XB5u+x5mOIVEYi1DcUi3WL3b5a2GNf2tQE3eLFCLjzTsS/0P/4kU88AXVKCgSZDEFTp97w/vxG19dNcQBwuKkJNW1teDkxEcuiorCtrAx5N3hiGcXw9pt8fFQ3/npLYeFTsNvrMHr0e0M6FFfX1oZNJSXYlpKC11NTkd/Sgi1G43XdlsHGHPT6dW3YtKkE27al4PXXU5Gf34ItW/q3zYIABAcroBpgLhxuOoyathq8nPgylkUtw7aybchryevTuk8VPoU6ex3eG/1ejyHS/6TcHu6K2Z8ylCRg2bIf8coryXjzzdsgihJ+97vzN3ybFcMVEHyEa77979rfM9z1G/p2/4hJp0PRH/+IkfPnI+yXvxx0HVRWvoP6+s+QkrIZKlXsDan3weRvXVsdNpVswraUbXg99XXkt+Rji3FL//rSp56Cva4Oo997b2Bn74IARXBwn+tsKPtzb9SX1w8Aatra8EppKdYntl/ryE5KQrRSiUXnzqFtCG5Ku9nV1X2Cqqq/ISFhLfz907t8ZrUa0dx8bEDfK0oSlhYW4t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diff --git a/testdata/empty_match_graph.gltf b/testdata/empty_match_graph.gltf
index ce9f02b7..daccda52 100644
--- a/testdata/empty_match_graph.gltf
+++ b/testdata/empty_match_graph.gltf
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/testdata/lattice_surgery_cnot.gltf b/testdata/lattice_surgery_cnot.gltf
index d8240a95..3c6c61b5 100644
--- a/testdata/lattice_surgery_cnot.gltf
+++ b/testdata/lattice_surgery_cnot.gltf
@@ -1 +1 @@
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vXqZGhoKPMRiUQ0btw4XtMwbdo0MjExoUePHnHX7t+/T3p6ejRz5kzey2D58uVkYmJCd+/eJSKiDx8+UIcOHahq1ap07do1YjAqMoGBgeTt7U0FBQVERCQWi2nkyJEEgAYMGPDVv/fVAqBbt26ko6NDGRkZ3LW8vDwyNDQkNzc3Kiws5LUAxGIxvXv3jlJTU0lVVVVQAbBmzRpq3749JSYmctcOHDhAmpqaBIC8vb15tb9//34KDg6WuRYaGsp1wHwLkP+SmJhIderUoapVqwoqAObMmUNLly5VWCOUSCTUp08fUlNToz/++EMwu6NGjaIlS5ZQdnZ2sfcCAP3555+82Y+PjyeRSESrV68uEjZjxgwSiUSUlJTEm/2//vqLlJSUaPv27TLX09LSSFNTkywsLIotG4awbSM8PJxiY2MrTJ5u375NR44c4TpdRZGRkUGqqqq0bNkymes5OTmkp6dHAOjvv//mTwBERUURAPLy8ioS5uvrSwDo+PHjghWImZmZoAJg0KBBlJOTU+T6qlWrCABpaWnxar+km2tra0sA6MGDB4KVvVgsJldXVzp58iSZmpoKJgDevXtHVlZWlJWVpbCGKK3r/+2I+C7vjRs3lhgeHBxMZmZmvArw/fv3E4Bi07Fu3ToCwOtbuKenJwGgx48fFwnz8fEhALR27VrWCyuA9PR0Wrp0KVlaWlL37t3p2bNnFSZvCQkJ9L///Y+srKwoJCRE5uVXSJ4+fUoAqHbt2kXauXQ0ODQ09Kt+86t8AA4ePAgAcHFxKXZuEgDvc6Cfo6amJujci5+fHzQ0NIpc79evHwBALBaDeDxc8aeffir2es2aNeHg4IC6desKVhbz5s1DkyZN0KVLF0HvwerVq5GSkoIePXpg2bJlSE5OFtT+7t27sWPHDrRt2xa+vr6C2VVRUcHo0aNLDD906BD69OkDkUjEWxpMTU0BAJs3b0Z+fr5MWGxsLIyNjdGgQQPe7F+8eBEAYGhoWCSsdevWAIDjx4+zSWIBiYuLw8iRI1G/fn28efMGFy9exLFjxwTxRREKa2trREZG4ujRo4iLi4O1tTXGjRuH+Ph4QdNhaWmJzp07Q0VFBYWFhUX88j5vo7z4ANSuXZsA0L59+4qERUREEAAyNDQUTBFJ/QCEGgEobdhLJBJRw4YNFTIsVLNmTYqJiRHMZmRkJDVu3Jib9xZqBCAjI4OqVavGTXkAoCpVqtD8+fMFGZ77+PEjGRsbEwA6f/58uXlDefLkCQGg69ev82qnsLCQHB0dCQB169aNG26/ffs2VatWjX7//XfebOfm5nL3/MWLF0XCz549SwDI2NhYsBGZVq1akZmZmYw/hFB8+PCBnJycqHbt2vTy5UtBbRcUFNCxY8eobdu2ZGdnRxs2bKCPHz/yZu/kyZOkpaX1VZ+jR4/ylp43b95QUFAQmZiYkIeHB507d06h7T85OZlUVFTIwsKCcnNz+ZkCKCwsJGVlZQJAly5dKnZ4WtpAMzMzK5UAiIuLIwC0atUqQe1mZWVRly5daNu2bYLZTEtLo7p161J8fDx3TSgBkJmZSVeuXKHjx4/TjBkzyNLSkqtzvXr14l0E7Nmzh+tkoqOjydvbm2xsbMja2ppGjRpFqampCql/ixcvJktLS0FsxcfHcyLI2dmZzpw5Qy1btqQbN27wbltLS4sAFPtwP336NAEgbW1twR66IpGIANCuXbsEv+exsbFc3T99+rRgLxshISFUp04d6tSpE/3xxx+8+3yVZ/Ly8mj37t3k4uJC9vb2tHnzZoX4oEybNo2UlZW/6aWkzAIgLS2Nq3DFNfZ79+5x4Xw6ApVHATBnzhyysLAo1j+AD168eEELFy7kfCCUlZVp9uzZgtju1q0b7d69W+aakD4A/30rDAoK4h7EK1eu5NVejx49OAEQFBREsbGxdPv2bW5u2tLSUiEiwNnZmWbMmCGYvcTERHJwcODau1DCt3v37gSAfv755yJhUmfYWrVqCVYOe/bsoaCgIEFWgBRHaGgohYSE8C58k5OTacyYMWRsbEx+fn4y4p/xL5cvX6bevXtTzZo1acaMGZSWliaI3ZSUFNLU1CzVP0guAuDly5dcg79z506pilSoh2B5EACpqamkr69PERERgnZ8iYmJtHfvXnJxceHKfcuWLbzaXbt2LQ0aNKjIdUUJACkrV64kAGRmZsarnbp16xIA2rBhg8z1/Px8sre3JwA0ePBgQfMeHx9PAOjWrVuC2bx+/Tr17t2bAgMDSUlJiQDQ6NGjee+I7ty5w624mTJlCn348IEyMzPpwIED3LOgc+fOrDeSM8+ePSNPT08yMjKigIAASklJYYXyH7KysmjdunVkbm5OP//8syBL4omIJk2aRNOmTfvm75dZAHz8+LHUEYCrV68SABKJRCSRSCqNAOjVqxctXLhQYfYlEgkNGjSIAJC9vT1vdmJjY8nJyalY73tFC4DCwkJq2LAhAaDXr1/zZkdXV7fEIej169cTAKpataqgw6ILFiwgW1tbwexFRESQiYkJt+T0t99+oypVqnAigG+uXbvGiV4lJSWqV68erVu3jqysrAgAbdq0ifVGPPH06VOaPHky1axZk3x8fATzOzp16hTp6up+1Ueo1WiPHz+miRMnkqmpKY0ZM4YePnwo6Iugh4fHd/W3X+UEKH3QF+fsc+rUKcGH4BQtAFasWEEjRoxQeMNMS0sjNTU1UlVV5c2GdOlbWT7STVqEJDAwkADw6hAlnfsurv7fv3+fy/+HDx8Ey7ejoyP5+/sLYuvdu3ekp6dH06dPl7n++++/c3tyCLUZT3p6OjfMeuPGDQJAenp6gpZ9ZX/btbW1pZYtW1J4eLhgL33lhfPnz1PXrl3J2tpaYUsDr169SocOHfqu31D5mhUDrVq1wv79+/HkyZMiYc+ePQMAuLu7V4rlL4cPH8bNmzcRFham8LRUr14dLi4uvG6H2rhxY2RnZ5e4NeWnT5/g5eUFJSUlVKtWTfAyMDExQfXq1WFkZMSbDTs7OyQnJ+PNmzdFwszMzAAA6urq0NbWFiTPjx49wt27d7F//35B7O3fvx/v37+Hq6urzPWOHTsiMDAQs2fPxokTJ7glwXyir6/P/e3v7w8AWLhwIapWrcrW5vGMtrY2/Pz8MHbsWJw5cwZr1qzB1KlT4efnh2HDhimk/QtBTk4O9u7di7Vr18LQ0BDjx49H165doaSkmCN1nj59+v1t7WvUgtTTtrh54OHDhxMAOnXqVIUfATh16hT17NmT8vPzix2SVwT169enfv36KcS2oqcAiIh++eUXmjJlCq821q5dSwBo1KhRRcJevHhRooMan6MeDg4Ogtnz9/cvcQokOTmZAJCfn5+g9126FXXv3r0rtUe6orl37x6NHDmSDAwMaOzYsQpbEcMHb9++penTp5OpqSmNGDGC4uLiykW65FHfv3orYFdXV6pevbrMwz4vL48MDAyoadOmgjZC6b4EQgqAkydPUteuXYtdb/nq1Svy8fHhzXZ+fn6xAuPatWukra1N9+7dq9AC4OnTp3ThwoVi8+/g4MB7PcjJySFzc3PS09Mr4gtx6NAhEolExS6R5Qt7e3sKCgoSzF5kZCQBoKlTpxYJe/jwIQGgEydOCJae69evk6amJnl6eipEfG7fvp3mz5+vkFUA79+/p8mTJ1NQUNBXr/3me2pm6dKl9Ndff1UYAfDnn3/S0qVLKT09vdykKT8/nxYvXvzdS8C/WgA8efKEDA0NuYM/CgoKyM/Pj0xMTOjJkyeCFoL0MJ7nz58LYi8sLIxUVVXJxcWF3NzcuI+rqys5OTmRiooKr0uizM3NSVdXl2bPnk0JCQn07t07OnToENnY2NDZs2cVVhmFEgDS7S5btmxJhw8fpujoaJo/fz41a9ZM5nwGPomJiSEdHR0aMGAAJ8ZevnxJNjY2tGjRIkHfuAAIvgnNiBEjqEqVKhQdHc1dy87Opq5du37TYSTfyq+//ko1atSgRYsWKWSP9mfPnnE+H3v37hXcfkBAAGef7wOgGOWP8PBw7v5/zzMA3/KlxMRE6t27N7m6upKbm5vgQz4bNmyg3r17cwXg6upKS5cu5bUDOnLkCLfevKSPiooKr+UQGBhIpqampKqqSlWrViVnZ2eaOXOmwpflCCUAoqOjycXFhTQ0NEhPT49at25NoaGhxU7F8Mndu3epc+fO5OzsTF5eXtSlSxdBz8CQdgDOzs6C3+vCwkLasmULNWnShDp27EgDBgygLl260ObNm3kf/du3bx9NnTqVunXrRtOmTVPofvO5ubnUtGlTMjMzo4SEBMHtHz9+nHR0dMjFxYVsbGxYj1jJePr0KZmbm1Pjxo2/a/MhERGPm9czGAwGgzeSkpLQr18/XL16lRUG46tRYkXAYDAYPyZ79uzBqFGjWEEwvgkVVgQMBoPxYyGRSLBx40aIxWL4+PiwAmF8E2wKgMFgMH4w/vjjD5iamsLR0ZEVBoMJAAaDwWAwGGWH+QAwGAwGg8EEAIPBYDAYDCYAGAwGg8FgMAHAYDAYDAaDCQAGg8FgMBhMADAYDAaDwWACgMFgMBgMBhMADAaDwWAwmABgMBgMBoPBBACDwWAwGAwh+erDgMRiMU6ePIkzZ85ALBZDXV0dRIScnByoqKjgp59+wuDBg6Gjo8NKl8FgMBiM8gp9Bbt37yY3NzfasGEDZWRkFAkvKCig33//nTw9PemPP/4gvhk7dixdvHiRVxsXLlygmTNnkq6uLgEgAKShoUG2trbk4uJClpaWZGtrSwMGDKCzZ8+SEERFRdHgwYOpTp06ZGxsTA0bNqTmzZvTggUL6Pnz53TlyhVauHDhd9t59OgRLVy4kOrVq8flHQCpqamRsbEx6evrk6WlJbVp04ZmzZpF//zzDy/5PXz4ME2YMIE0NDQIAKmrq5OFhYXMx9TUlNTV1QkA+fv7y9X+gwcPaMGCBWRlZcWVgYmJiYx9fX19LmzBggW83fu3b99SSEgItWjRghwcHMjJyYmaNGlCbdq0oVWrVlFSUhKNGTOG8vLy5Hb/69aty+XN2NiYZsyYQTdu3ODiHTt2jCZMmEBqampcvP/973+0fPlyys7Olmv+79y5Q4GBgWRpacnZMjc3p/nz59OdO3cEaX/S+lC7dm2Z+jBv3jyKiYmRmx1p+depU0em/OfNm8e1tYyMDFq4cCFpa2sTABKJRNSlSxc6cOCA3NJx8OBBGjNmDKmqqnLpcHFxoYCAALp9+7bcy/fhw4c0c+ZMMjIy4uxt3769zN8fOGu29kwAACAASURBVHCgTD0MDg7+6nqYm5tLixYtIjc3N+63+vfvX2L8Z8+e0aJFi8jJyYkAkJOTEy1atIhevHgh17KJiYmhX375hezt7cnW1pYsLCzI3t6eJk6cSE+ePPnq3yuTAMjOzqYePXrQsGHDylSQEomEZsyYQaGhobw1woyMDNLW1qYePXoI0uhXr15NAEhLS6tI2OXLl8nR0ZEAkI+PD4nFYl7SkJmZSX369CFlZWWaPn06JSUlcWE5OTm0a9cuMjU1JWVlZZo0aZJcH7rSRhAeHk4SiYQLi42NpfHjx3MPBx8fH/r48SMv+R89ejQBIDc3txIb7eDBg2nixIm82P/777+5cnjw4EGxD+yGDRvSzJkzebF/6NAh0tHRoRYtWlB0dDQVFhZyYampqTRnzhxSUVEhAHJ98MTGxnL5PnHiRInxpk2bRgCoRo0alJ+fz7sIlqYpMjKSFME///zDpeH06dO82YmJieHsnDx5stg4jo6OZGhoSFFRUbylw9fXlwCQoaGhXATmlzh79iyX73r16snU95J4+fIl9yzS1taWSz2cP38+l47g4OAvihcAlJiYKNeyyM7OJl9fX1JTU6MlS5bQu3fvuLCEhATy9vbmwuQqAHJzc6lFixbf9FY1ZMgQunfvHi+VIyQkhACQsrIyPX/+nPfKeOzYsRIFABFReno6p1hDQkJ4ETz16tUjJSUlOnbsWInxkpKSyMzMjAYPHixX29IG8Pmb3+f8+eefpKenx6luPjqAwMDAUgWA9A15xIgRvNSBly9flioApGJpwoQJcre9YMEC7i2koKCgVJEAgG7duiU322lpaVy+S3vDlbZJR0dH3tvj48ePuTR9y5uPPEhJSeHSEBsbqzA7y5YtI0tLS3r69Cmv+ZV2hE2bNhWkfJOSkkhNTY0bWTp+/PgXvzN16lRuNKROnTpyS4t0BFhJSYl+//33UjtqADIvSd9Lbm4utW7dmgDQoUOHSozn5+dHAGj8+PFl/u0vOgGOGTMGpqamCAoK+urphdWrV8Pf31/u0xaFhYVYv349tLW1UVBQgE2bNvE+VaKsrFxquL6+Pvr27QsA2L17t9ztDx8+HA8ePMDw4cPRvXv3EuPVqlULW7Zswfv37wXLOwC4ubkhLCwMAHDp0iUsXbpU7mWgpPRln9UaNWqga9euCqkDAODo6Fjq/fkWIiIiEBAQACsrK+zcubPUcvDy8sKgQYPw5s0bXvJdmm1pWFnuk1BpqghpKM3Or7/+ig0bNiAyMhJWVlaC5FdFRUWQ8lVVVYWGhgYGDBgAAFi2bFmp8bOysrBt2zYMGzaMl3TWq1cPhYWF6N+/PxISEkptA2V5VpSVCRMmICoqCj169ICXl1eJ8YKDg2FoaIi1a9di7969ZXumlhYYGRmJY8eOYePGjd+UcF1dXdSoUQPPnj2T6404fvw4NDQ0EBgYCADYtm0bcnNzFe5PIb3pqampcv3d33//HeHh4QCAadOmfTG+h4cHzM3NBc9/p06d0LFjRwDAihUrkJWVpZD7wJcAKCtt2rSR22/l5ORg8ODBICJMnz4d6urqX/zO9OnTIZFImINTBWfnzp3w9/fH+fPnYWlpWWHzOXXqVIhEIly5cgVXr14tMV5oaChatWoFOzs7XtKxf/9+mJubIyMjA927dxfk+RYXF4etW7cCAEaPHl1qXE1NTQwcOBAAMGvWLOTl5X2fAAgICMD06dOhr69f7Fv41q1b0bdvX0yaNAmBgYE4deoUWrRogWPHjsl0RufPn5droaxZswbjxo2Dr68vNDU1kZaWhgMHDii0kubk5ODIkSMAgCZNmsj1t0NDQwEANjY2sLa2LtN35s2bp5ByGDRoEAAgMzMTp0+fFtT2/v378c8//ygk32KxGLNmzZL774aFhSE5ORkA0KtXrzJ9x8HBAZ07d2Y9ZAVm3bp18Pf3x4ULF8r8TPhRcXBw4F4sShoFkEgkWLt2LaZOncpbOgwNDXH8+HFoaWnhwYMHGDhwIIiI17yHhYWhsLAQKioqaNGixRfjt27dGgDw8uVLREZGfrsAuH//Pm7cuIGRI0cWCcvLy0OvXr0QHR2NvXv3YtWqVXBwcMCECRNw9epVNG/enItrYWFR4nDJtxAbG4vY2Fj4+PigWrVq8Pb25hqEUBQUFMj8ff36dXTq1AnPnj2Dvr4+Fi9eLFd70hvp4OBQ5u/UqFFDIY21WbNm3N83btwQzO7bt2+xYcMGhYm/xYsX49OnT3L/7ZMnTwIATE1NYWBgwHo+BhYsWIClS5fi4sWLsLW1rRR5lo58njhxAo8ePSoSfvDgQRgbG6Nly5a8psPZ2Rl79uyBSCTCiRMnEBAQwKu9y5cvAwDMzc2hoaHxxfj29vZFvlsaJU6SHD9+HG3atIGenl6Rzq9z5854//49rl69ClVVVQCAra0tnj59ip9++gmGhoZcfC0tLbkOz69ZswbDhg2DlpYWAMDPzw9bt27FrVu38Ndff8mIDz7Izs6GkZERDA0NkZ2djZcvX3LDrV26dMGqVavkqsjT0tLw4cMHhXbqX6uSpaSkpPBi49atWzLDfNnZ2Xj16hXvavxzGjRoAJFIBADIz88HEWHChAlyt/PgwQMAQPXq1UuNt2HDBly5cgXv3r2TSeO4ceNgZmYmt/T06NGjxGkIefqdMIpnxowZOHPmDNzc3OR6X8s7bdq0gYuLC2JiYrB8+XJs27ZNJjwkJARz5swRJC09evTAggULMHfuXCxatAjOzs5lHp37WqSjf9WqVStT/M/jSb/7TSMAN2/eLFZNBQcH4+LFi9ixY4fMg0A6z//focfXr1/D2NhYbm95Bw8ehJ+fH3fNycmJS6cQowBaWlp4+/Yt4uLikJiYiHfv3iE8PBz169fHuXPnMHPmTCQlJcnNXn5+Pvd3WRSgovncSUlNTY0XG40aNcLDhw+5z4sXL/DkyRPUr19fsHzGxsYiNzcXubm5yMnJwdy5c3mxI73/mZmZpcYbO3Ysli9fjqioKJw9exYaGhoIDg6Weydx9OhRmbL//MPHFAijqPBUVlbGlStX0KVLF+Tk5FSavEuH9/fu3SvTuV24cAGZmZno0aOHYGmZM2cO+vfvDyLC4MGDcffuXV7tfT7qXBqfP3PL4ohYogB4/vw56tWrJ3PtyZMnCAwMhKenJxo0aCATdvHixWIFQHx8vNwcVDZv3gx3d/civzd27FgAQHh4OG9vnSWho6ODXr164ebNm3B2dsZvv/2GZs2ayc0LW19fn+tU3759W+4b6ef5NjExEcyulZUVRo0apZA8V6lSBYGBgbzsfikdUXn9+jUKCwtLjWtqaso5fzZs2JD1lhWQAQMGYM+ePVBWVkZkZCS6du3Ky9RTecTLywuWlpbIy8vDmjVruOsrVqzA5MmTBV8NsmPHDjRp0gTZ2dno3r070tPT5W7DyMgIQNlH1z53TDQ1Nf12AfDx48ciww4rV65Efn4+Jk2aVESdHDt2DAYGBnBxcZEJO3PmDDp06PDdBSEWi7Fp0ybExMTAzs5O5uPv7w8VFRWIxWJs2bJFIZVTXV2dm/tPTk7G+vXr5da5SN9s79+/X+4b6d9//8397ebmJqhtd3d3rsEoYuRDulxJnkhHt/Lz8/Hnn39+Mb50So6v0RdG8chz2deX6N+/P/bt2wcVFRVcvHgR3bp1qxQiQFlZmet7Nm/ejKysLNy7dw8xMTEYOnSoQoT/sWPHYGpqisTERPTt27fMb+plRfoMffHixRdHAaUv6VKkDoHfJAA0NTVllhIREQ4dOgRjY+Mi3ohhYWF4/vw5fv75Z25eFPh3/loikXxx/rIsHDp0CDVr1kRSUlKRocf4+HjMmDEDALBlyxaIxWKFVNDPvf/l2Vn369cPAHDnzh0kJiaW6TvZ2dmCzol/Xhekb//t27cX1LaNjY3CBACAIiNm8mDYsGFcm5KWLaP8oaurK6i9Pn36cCLg/Pnz6N69e7lYCi3l4cOHvPzusGHDoK+vjw8fPmDLli1YsWIFxowZo7DpUWNjY5w4cQKampq4cOECpkyZIvcRH2n/WxavfukyyTp16pTJIbJEAWBhYSEznJ6YmIi0tDQZ5yfg3+UG0vlPd3d3md9YvHgxNzz/vaxZs6bUwh07dixUVVWRnJyM3377TSGV4fMhegsLC7n9rnQzJgCYPXt2mUZLZsyYIeM/IASXL1/GiRMnAAALFy5U2FtoXl4epk+fXiE6Fnt7e4wZMwbAv0OOsbGxrLctZ+jo6Mg4vwqFl5cXDhw4ABUVFURERMDT07NciID8/Pwyb0TztWhpaXFTfSEhITh69Kjc+phvpVGjRvj1118hEonkPgLt7OzMvQB+acM7iUSCnTt3AgCWL19epo2QShQAP/30E27duiXzUAWAjIwM7lpKSgqCgoLQtm1bAICrqysXdu7cObx+/Zpbv/k9REdHIykpiSuIkpSYp6cnAGDVqlVyv8llGVUICQn5t1CVlLjdqOT1dnHgwAFoamriwIEDCAoKKvHtPi8vDyNHjsSIESPKtGmMvPIeFxeHvn37orCwECNGjOBlSE46vPalkY358+fzMgf++Ry8vIf6SmPFihXw8PCARCJB//798eLFC0EfcGXNtzRMiLJRxL1IT09H3bp10bVrVxQWFnJ2O3fuzOsUwH+XHX9Or169cPDgQaioqODs2bPo3LkzbxvUSJ8DX3oeBAQEoHHjxt9tTyKRFOv3Mn78eKirqyMlJQX9+/cvsjxWOnL9JZ8ZeaTlczHG15LA0NBQODo64uzZs6WOAs6dOxePHj3C7Nmzy+4QWdIewXFxcWRtbS2zH3GNGjUIAP3yyy80d+5c6tatG71//547fenevXuUl5dHK1asIA8PD+5QmOvXr9Ply5e/aR9ksVhMTZs2JS8vry/G3bJlC7dntjxPwyIiWrFiBQEgTU1N+vDhQ5E94qUH1SgrK/N2CFJUVBRZWFhw++Hv3buX4uPjKSMjgx4/fkxbt26lDh060F9//SVXu7du3eLK9b+nL6akpNDixYtJR0eHtLW1v3hYxvcwbNgwAkCWlpbFnjXw6dMnWrRoEWlpafFyING1a9cEOfylOPLz82nq1KmkpqZGRkZGFBoaSjk5OTJ537VrF+np6ZG6urpc6//nh94cPXq0xHjjxo3jDgPi60AsKZcuXeLSFB0dLcg9uHv3Lmdz7969dP78eapZsybve/DfuHGDs3vq1Kli44wcOZKL4+joyMvZBEOGDCEApKurSwkJCTJnUuTk5ND9+/dp9OjRpKmpKZdTIK9cuUIikajI85aIaPjw4aSkpETx8fElHkqlo6Mjlz35379/X+o5KFIKCwvJy8uL8HWH7JY5DV26dCFVVVUKDg6mzMxMLuzp06fk4+NDGhoatGbNGvkdBtS2bVuZB92ZM2fI1NSUzMzMaP78+ZSbm0tERJGRkWRqakomJibk7u5OYWFhXOUoKCggd3f3Ym/il/j999+pWbNm3BG8o0ePLvEmLFmyRObYUnV1dRoxYkSxFeRruHDhAk2fPp07YAKfHctpZ2dH5ubmpKurSw4ODjRs2DC6e/curw+D7OxsWr9+PbVt25aMjY25o3lbtGhBGzZskKkY38ujR49owYIFZGNjI5N3AwMDcnBwIHt7e7KysqLOnTtTSEgIpaWl8ZLnw4cP09ixY0lJSYlLQ/Xq1alOnTrcx8zMjDsFrG/fvnK1/+DBAwoKCiJra2vOvqGhIQUEBMj1+NeykJiYSIsWLaJWrVpR7dq1ycnJierVq0eWlpbk7u5OK1eupOTkZLne/8/blZGREfn7+xc5DtjPz0/muFghjwO2srKiwMBAQY4DXrBgARkYGJChoSF5e3t/9/OlNO7fv09z5syROYbayMiIZsyYIVPvtmzZQrVq1ZJpo8rKyuTh4UGbN2/+7nQcPHiQRo0aRcrKyjI2Svp07979u+tdUFAQdwyym5sbLV26lN68eSPTJnv16iXzvTNnztCkSZOoSpUqXFo6dOhAy5Yt+6Z6+ObNG1qyZAk1adKEO5Fw4cKF9OjRoxK/k5OTQy4uLrzViYiICPL29iYbGxtycHCgevXqUYMGDWjmzJnfdAKoiEoZT7116xYGDx6MmzdvfvNw8oIFC2BsbIzhw4ezyUIGg8FgMMoJSl9ybvD19cWQIUO+aT4lNDQUycnJrPNnMBgMBuNHEgAAMGnSJDg7O8PT07PMm9t8/PgRfn5+SExMlNt6eAaDwWAwGPKj1CmAz4mOjoa/vz9atmyJQYMGFXsIxePHj7F//35ER0dj9uzZcj0WlcFgMBgMhgIEAPDv8qtz584hPDwcz58/h6qqKpSUlCASiVBQUAArKyt4eXmhVatWMnsFMBgMBoPB+IEFAIPBYDAYjIqBEisCBoPBYDCYAGAwGAwGg8EEAIPBYDAYDCYAGAwGg8FgMAHAYDAYDAaDCQAGg8FgMBhMADAYDAaDwWACgMFgMBgMBhMADAaDwWAwmABgMBgMRjnn3bt3sLa2BttAFrhx4waSkpIqvgCIj4/HwoULYWdnB5FIxH00NDSgp6cHOzs7+Pr6IiYmhvcE379/H+PHj4e9vT3MzMxQvXp1ODk5YebMmXj16pXc7V28eBGzZs2Cnp4el29lZWUYGhqiatWqsLCwgIeHBw4fPixIo7h//z769+8PQ0ND6OrqwsbGBuPGjcOVK1ewcuVKXLp0Se42w8LCZO57WT59+/b9brvnzp3DrFmzUK1aNe53GzZsiCVLluDly5cycRMTE7F48WI4OTlBJBKhVq1amD9/Ph4/fvzN9qOjo9GzZ0+ZfNWrVw+LFy8uNv7p06fRokULLm63bt3w5MmTr7a7bNkymJubc79Tu3ZtrFy5EgAQFxcHX19f7gwOkUiEjh07IiwsTOY3Ll++jJEjR0JJSQmqqqoYMWIEkpOTvyodSUlJmDRpEmrVqiVTBoaGhpgzZw6ys7O5uL/99ht69+7Nxalfvz6CgoK+6/5HRkaiXbt2MrYNDAzg7++PFy9eyMR98uQJRo0axZVL1apVMW3aNLx+/VoubSAqKkqm/Wtraxf5KCsrQyQSQUVFBVevXuXtGZCbmwuRSAQ1NTXUqFGD+/y3TPhAX18ftra2uHbtmkI6rBcvXsjkWU1NDSKRCLm5uYKmQywWY9CgQZg0adKPrWLoK7h58yYBIAB06dIlIiJKT0+nBQsWkLKyMolEIlq1ahXxQUFBAU2fPp1UVVVpypQplJSUREREEomEoqKiyM3NjbS0tGjr1q282F+7di0BoKpVq1J2djYREX369Im2b99O2traBIAGDx5MhYWFxBeXLl0iDQ0NGjBgAL18+ZKIiJKSkmjWrFmkoqJCACgyMlLudjdv3kz29vZ08+ZNysvL4/IurQt//fUXdy8ePXpEnp6e5OHhITf7wcHBnK2UlJRS4yYkJBAAun79utzsz5w5k7N/8eLFUuOKxWJSV1ensWPHfpfNR48ekZKSEgEotk1NmTKFS9OjR49K/J369evTwoULvystubm51K9fP87e1KlTi42XlpZGAGjs2LGUn58vt/KfNm0aZzsgIKDUuM2bNycLCwuKj4+Xaxs4fPgw1a1bl/7+++9i2/i9e/eoSpUqBIBmz55NfCJte+3atSNFsHPnTpowYQKVB37++WcCQJ8+fRLU7rJly8jHx4fq169P58+fpx+VrxIAqampXEO8e/euTNicOXMIAIlEIrk+fImICgsLqU+fPgSANmzYUGycvLw86tixIwGg4OBguRfUsWPHCADp6uoWCdu2bRtXLvv37+flRkkkEjI3N6f69euTRCIpEn7kyBESiUS8CIDly5dznfx/H0KfC4DPw7y8vORmPzw8nACQpqbmF+Pm5eURAHr37p3c7L979460tLQIAK1YsaLUuA8fPiRdXV3KyMj4brvu7u4EgPr06VMk7O3bt6SqqkoA6MiRI8V+Pycnh6pVqyaXshCLxdSqVSsCQDVr1qT3798XiePn50d9+/aVe/0Ti8XUsGFDAkB2dnYkFouLjZeenk56enp07do1uadh48aNdOrUqWLD8vPzufQ1atRIruKnPAqA9+/fk5WVFa8vO+VZALx+/ZrMzMwoJSWFLl68SA4ODiXWyQolAN6+fVuiAHj16hUXNnLkSLkmctGiRQSA/ve//5UaLykpiTQ0NEhJSYnOnTsn1zScPHmyRAEgkUhIXV2dAJCnpycvN+r27dsEgHr37l1inE6dOvEiAPbu3VuksZcmAIiIdu3aJTf7R48eLbHsi+ssAFBWVpZcy2DMmDEEgOzt7UuNN2fOHJo4caLcyh0AaWlp0cePH4uEd+3alQDQwIEDi/3+kSNHqGvXrnIrg5cvX5Kenh4BoCFDhsiE7d+/nxwdHbnRMT7qv3SUa+nSpcXGGTt2LE2ePJkX+0uWLCkxb9IRoipVqtC9e/d4f2grWgAQEXXp0oWio6MrpQAYMGCAzKicl5cXrVmzpnILACLi3pJ+/vlnuSUwJSWFNDU1CQCFh4d/MX7Pnj0JADk5OclVoZYmAIiI6tSpQwCoRYsWvNyoW7ducZ1BSQ+ZHTt28CIASnsIlSQA5El5EADx8fEkEokIAJ09e7bEN0FjY+NSh+S/huzsbG56KSwsrEj4+vXrCQBpa2sX2zn17t2b9u3bJ3cxKL3vZ86cISKiu3fvkrGxsdyH3YsTVwBIQ0ODEhISZMKuXbtGVlZWxQolPrl8+TI3VbN69WpB254iBcCePXvIz8+v0gmA6Ohoql+/vswb//Pnz8nExITevn1beQXAhw8fuLChQ4fKLYHLli3jfrcsQ5nbt2/n4l+9elUQAfDp0ydOpIwaNYqXGyUWi8nU1JRLw6+//lokzqtXr+j58+dMAPAgAKRvPQCoY8eOxYbv37+f2rdvL1ebgwYNIgDF+lT07t2buwf/7egzMzOpRo0avLyR9+jRgwCQmZkZPX/+nGxsbEqchpAnubm5VK9ePW40UCrw8/PzydHRscQher7IzMwkKysrrjMWaki8PAiAzMxMsrS0pIKCgkojACQSCTVo0IAuXLhQJCwoKEjuI98/lADYtGkTF1bSG9L33GBTU9OvelMG8N3OT2UVAAsWLCAApKamxutb0Pnz5zlHIwDUvHlzioqKUkjFqYwC4MKFC5yfy/3794uEt2zZUu4dYUREBAEgFRUVevPmjYzgrlatGicC/isQfv31V/L29ublfqSmplKNGjU4p9hp06YJVu+uXr3KvXFLHX4XLVpUrJ8E3wwdOpQAULVq1ejFixeCtz1FCgAiIk9Pzy86xVYkAbB27doSfZs+ffpE1tbWdOvWrcohAG7cuEFE/3rnHz58mHR0dAiA3OY/pdjZ2REAcnR0LFP8Fy9e8OKLUJwASEpKoilTppBIJKLq1avTiRMneL9h169fp7p163J5BECdO3cutkNiAkD+NGjQoNi6FRcXR7Vq1SrWQfN7KCgo4EZ+1q1bx13fuXMn9ezZk6KioooVCO7u7nT69Gne7snhw4e5+3/y5ElB697EiRO5jjc6OpqMjIwoOTlZ0DRI62RJ0zOVQQDs37+ftxHP8iYA3r59S6ampqWOsB47dozc3NwqhwDo0aMHeXp6UqNGjcjBwYG8vLx4GYIzNzcnAOTi4lKm+Dk5OVwa+/fvL3cBoKKiQu7u7uTg4EAASElJiTZv3sxbh1MceXl5FBwcTLq6ulxeVVVVadGiRUwA8CwAdu7cyc1Dp6WlcddHjx5NCxYs4MWmdBlcs2bNuGsdOnSgo0ePUmFhIVlaWhIAWrt2LRH96zdjaGjIq2fy1atXSVlZmZsK+PDhg2B1Lzs7mxt6V1FRoc2bNwv60ExJSeFGQPhY9fCjCICPHz+ShYWF3EVveR0BqIjI1QmQD1xcXAgA2dralin+u3fvuDSOGTOGtxGAjIwMql27NgGgESNGKOTmpaWl0aRJk7jlYGVZJ/0jCoATJ06UWQDk5uYSAMrJyeFNfBkaGhIATnBlZWVR9erVv7hHwbdy584drqwfP35MKSkpZGBgwO3JMHv2bAJATZo0ISKiNWvW0OjRo3ntAC0tLSk8PJwTocOGDRO07u/Zs4cTYkIvR/Pw8OCmJeW53PRHEwBE//qhREREMAHwg1LutwK2srICALx+/RqFhYVfjP/27Vvu7wYNGvCWLl1dXRw+fBjq6urYunUr9u/fz2s55OTk4P79+zLXqlevjpUrVyI2NhZ169YFACxZsgRpaWkVastNDQ0NrgzoC7stZmdnQ1lZGVWqVOElLWpqahgzZgwAYMOGDRCLxdi9ezfat28PQ0NDXmw6OjpydXnfvn04cOAAevXqBTU1NQCAj48PAODvv//G48ePsW/fPgwYMICXtEgkEvTt2xeTJ09Gr169EBISAgDYvn07zp07J1idqFat2r9bmf7/zn9CsWnTJpw5cwYikQg7d+6Enp5epd4Ot2/fvjh48CDbI7kibwWsSLp06QIA+PjxIx49evTF+LGxsQAAZWVleHh48Jq2Ro0acVu0/vLLL0hISODNVmZmJrZu3VpsWL169XDy5EmoqKhALBbj9u3bFaqS1qxZk9t+MzU19YtbhZqYmPDaKYwePRpVqlTB69evcfDgQWzatAljx47ltQyknXxYWBjCwsIwcOBALszOzg4uLi4AgKCgIKSmpsLNzY2XdEyfPh3GxsYYN24cAGDYsGHo0KEDAGDEiBHIysqqsA/LhIQETJ06FQDg5+fH5bukelgZ6Ny5M86dOweJRMJ6UyYA5E+PHj24DiA8PPyL8Y8dOwYA6NevH8zMzHhP35gxY9C3b19kZWWhT58+vO5JfezYsRJHQWxsbGBnZ8eNTlQkbG1toaWlBQBf3IM8IiICzZo14zU9BgYG8Pb2BgBMmTIFANCyZUtebQ4YMADKysp49OgR0tLSinTwUkGw97mGFQAAIABJREFUZ88e9OvXjxcBdOjQIZw9e7aIEN26dSu0tbWRlJTElUdFQyKRYODAgcjJyYGdnR2Cg4NLjCsWi7Fp06ZK0YFoaGjA1dUV58+fZ73pj8jXzBckJydzc5GxsbGCepsCICMjo1K3WE1ISCB1dXUyNDSUu1ew1BFNR0enSFhmZibZ2NgQAPL19eWlDKRlX5Kj35s3b0hDQ4NsbGwEWZublZXF1YUrV67wbi8gIIDbaKkk57bbt2+Trq4uxcTE8J6euLg4Lv8bN24UpB1Itwb29/cvdl5e6pR3584dudu+ceMGGRgYlLjaRLopEQBBVsNI26OGhoYgZT9v3jzO6VC6AqoktmzZQitXrqwUPgBE/+44+d+dIZkPQAV0Avz777+5Ri70phvSDYF69uxZbAfw/v17cnFxoerVq3+xgX4Lq1ev5taAF+fx/M8//3Br9CdPnix3D+zPxdfAgQM5AVZYWEi3bt2iJk2akL6+viCdHxHRgwcPuPQcPHiQd3u5ubnUq1cvAkCtW7emyMhIyszMpKysLLp16xZNmzaNtLW1aceOHYLVyQ4dOpCOjo5gK0Ckjm8l7TTYsWNHql+/vtztnjx5kvT09Ep19MvLy+PW5+vp6fG+Je66deu4+peens6rrevXr3PbEAcFBZUYLyMjg0JDQ0lLS4vXA2LKmwD49OkTmZubc06pTABUMAHw6NEjCgoKImtra67R1apViwIDAwXrcIj+XWdZq1YtatSoEe3du5fu3LlDN2/epDVr1pCZmRm1a9eOnjx5IlebFy5coBkzZnD7HAAgV1dXCg4OLjLKEBoaysUxNzcnPz8/ev36tdwEwPjx4+nGjRu0YMECcnV1JWNjY6patSpZWFjQqFGjBNmM5NmzZ7Rw4UKqX78+l1cTExOaN28eL8LrcwoKCmjnzp3UoUMHMjQ0JBUVFdLV1SV7e3saM2aM4HshnDlzRq4rTb7Ex48fqW3btiWGh4WF0eLFi+Vm7/Tp09SuXTvuPtesWZPmzp1Lubm5MvGio6NldiXE/29ZPWrUqCJb9n4v0dHRNGvWLO5MAgDUtGlTWrJkCaWmpvJS7p/XdU1NTdLS0iry0dDQkMk/X2kpjwKAiGjgwIGC7wfBBMD3IyIS4BB7OSIWi7Fnzx5MnDiRczhSV1fHhQsXeHN8YjAYjPJCbm4uNDQ00K5du0o/996xY0ecPXsWnz594m3lD3MCLEeoqqrC19cXly5dgqmpKQAgLy8PFy9eZHeTwWAwGIyKKgCkNGrUCHfu3EGfPn0AAIGBgTh16hS7owwGg8FgVGQBAAD6+vo4ePAgoqOj4ebmhl69emHVqlXIz89nd5bBYDAYjFL44XwASuP+/fs4dOgQnj59CltbW7Rv3573NeEMBoMhJFIfADU1NZmdCG/cuIFatWpV6Ly/ePECDRs25P6fmZkJsVhcoX0AYmJi0LRp06/6zpUrV8r0nQolABgMBoPBYJQNJVYEDAaDwWAwAcBgMBgMBoMJAAaDwWAwGEwAMBgMBoPBYAKAwWAwGAwGEwAMBoPBYDCYAGAwGAwGg8EEAIPBYDAYDCYAGAwGg8FgMAHwQxAREYEuXbqgZ8+erDA+IyMjAytWrICFhUWlO55ULBZj6dKl6NChA7S1tdGoUSP89ttvFTKvsbGxGDp0KCwtLctFevr37w9DQ0PExcUpxP7AgQNhZGSEe/fuCWLvxo0bGDJkSLnY7vfly5fw9/eHsbEx/vnnH/YQ/FGhb+DatWukoaFBAOjhw4dU0SksLKQJEyaQubk5AaDu3bsT419u3rxJXl5epKSkRAAoIiKi0uRdIpFQ+/btKTQ0lIiIrl69SlWqVCEAdP78+QqV1/Xr15OzszMBIENDw3KRJktLSwJAR44cUYh96fMgPDycd1s7d+6k9u3bEwCqXr26Qsv9ypUr5OPjQyKRiADQ7du32YPwBwXf+sXDhw+TSCSiZcuWVZrCCg8PZwKgBNzc3CqdANiwYQMBoKysLO7a7t27ycjIiP76668Kl9/Hjx+XKwHw5MkTOn36tMLsx8fH04kTJ6iwsFAQe8nJyeVCAEhxcHBgAuAH55unAHr37o0lS5bg+PHjlWa0pHr16mzIqARq1KhR6fK8f/9+qKqqQltbm7vm4+OD5OTkCnkKZXmr/7Vr14aHh4fC7NvY2KBr164QiUSC2Pv85L/yQHlLD0NgH4AZM2bA0dERb968qRSFpaKiwmoMKxuOhw8fQlVVld1jhiAoKyuz9DDk26a/9wc2bdrESpFRKcnIyIC6ujorCAaDUflGABRBYWEhtm7ditatW6NHjx6oW7cufvrpJ4SFhQmajry8PMycORNGRkbQ0dGBl5cXkpKSBLGdm5uLxYsXw9XVFU2aNEHt2rXxyy+/ICUlRRD758+fh7u7O9q0aQM3Nzf4+vri/fv3gpX9hw8fMHPmTLRo0QJmZmYwNjbG8OHDkZqaKkinb2dnBzs7O0gkEuTk5HD/HzFihCD537lzJ9zd3TFq1Cg4OztDJBLJfAICAgRJx9mzZ/HTTz9BU1MTLVq0wOPHjwWrA4mJiZgzZw6MjY1x8+ZNwZ9DSUlJCAgIgKmpKf7880+FPQ/z8vIwYMAAiEQiGBsbY+bMmYiKiqoQnZNEIsGRI0fw888/cyuvjh49ChcXF2hra6NNmzZcnXv69Ck8PT2ho6MDU1NTbNy4Ue7puXz5Mry9vWFnZwcAuHjxIpo3bw5NTU00b94cCQkJgpTLqVOn0LlzZ3Ts2BE2NjZwc3PD3r17v+3HfjSnhfHjxxMAunfvHhERZWdnk729PQGgP//8k1fbly9fJvwfe+cdFtXxNeB3d+lVEJAqEkSk2nvsGnuLLWJNxN6NLdGfJrbYey8k9hJLjBqj0ajBGnsDCxakWOi9M98fLvsBghplF8t9n4cnZu7unrlz586cOXPOGRAtW7YUX3zxhfj8889FixYtVJ7ftra2IiwsTK11iIuLE9WqVRO+vr4iPT1dCCHE7t27BSCcnJxEfHy8WuWvWrVKGBsbixMnTqjKJk6cKACNOAFGRkaKypUriwMHDgghhMjKyhJLly5V3X90dLTG+qJCoRCGhoYa7f9jx44V2tra4u7du0IIIdLT00XdunUFIHr27CkSExNFZmamWmQnJCSonABXr14t2rZtKxYsWCBatmwpAFGzZk2NtME///wj+vTpo+pzFy5c0OgzOHfunOjfv7/KC97f318jcjMyMgp0Ahw1apRo1KiRRvu+EELUr19frU6A33//vXBzc1M5Xk+ZMkV88803YuHChaJmzZoCEBUqVBAXL14UderUEbNmzRKTJk1SRaj9888/RVaXjRs3iq5duwpAODo6itWrV4tmzZqJ6dOni6ZNmwpAVK5cWe1tPnnyZOHs7CwePXqk6hNDhgwRgPD19dVcFEBxYWxsLLS1tfOUTZo0SQDip59+0ogCUKpUKXH69Ok83sC2trYCED4+PmqtQ48ePYS9vb1ITk5WlaWmpmok/Ozy5ctCS0tLzJs3L095ZmamcHBw0IgC4OPjIyZNmvRSuaenpwDEtGnTPloFICAgQMhkMtGoUaM85QcPHhSAMDMzU6v8HAVAR0dH/Pzzz3mev52dnQBEaGioxtrDy8urWBSAHKpXr17sCsDYsWNF27ZtRWpqqsbvX90KgBD/H3nl6OgoLl++rCpPSkoSJiYmAhDDhw9XLYaEEGLOnDkCEEOHDi3SuoSEhAhA6Ovr5+n/GRkZwsrKSgDi4cOHamuLv/76SwBi27ZtL/WLnPFv/fr1mokCKC5GjRrFuHHj8pQZGxsDkJycrJE61KxZk9q1a6v+38XFhVmzZgHw22+/kZGRoRa5YWFhbN26lS+++AJ9fX1Vua6uLsePH8fPz48GDRqo7b4nT55MZmYmX331VZ5yhUJBlSpV1N7u0dHR7Nixg8OHD9O+ffs8fzo6Ori6umpkG6C4OHPmDEIIrKys8pRXrFgRgJiYGFJTU9VeDzMzM/r06ZPn+bu7u6v6qKYo7qgEc3PzYt0KHTBgAKGhoezevfuj9UUpUaKEqo9XqlRJVW5gYKDqc7169crjjOvl5QVAeHh4kdYlZ56xsrLK0/+1tLSoUKECAE+ePFFbW8ycOROAxo0b5ynX0tJi8ODBAPz000//6Tc/OLfeH3/8EYD09HR27tzJ3r17CQkJUb0UxUXnzp3p3bs3ycnJhIaG4uTkpJYJIDs7u8BMYDVr1lRr6FlSUhKHDx9GV1cXOzu7l65rwiP40qVLZGVlMWrUKLp168anRs6El9/XI2ciMjU1RU9Pr1jqlqOQakIB0WSfex/lCyHo3r07e/fuJSgo6KOOznhVGxsaGqraIzc570BaWprG6mJiYqKal9RBYmIiJ06cKFTxrVevHgBBQUE8ffoUa2vrN/rdDzIVsJ+fHzVq1CAlJYWtW7fSq1evYq+Tnp7eGzf6u6yA1a1lFsajR4/IyMhALi++LpNz/w8ePOBTpFmzZtjZ2XH16lUSEhJU5cHBwQB06dKl2OqWEwtfnEr4p4JMJsPY2FjlAJiZmSk1yntCfmWkqAgNDVX9ds44mBt7e3vVv/+LJfyDUwD69u3LhAkT+P333+nXr997ZfrKzs5GR0cHGxsbtfy+qampyhJQGOrKS56j/aakpBAREVEs7ZuTcGf//v2FfubChQsf7eCir6/PoUOHKFWqFJMmTVL1uRkzZuDu7s7s2bOlEfgTYcmSJVSsWBF/f38mTJggNchHTs7Yn1vhz03OPKilpVWghfajUAD++ecf/Pz8aNWq1XtxIEZuoqKiePbsGc2aNVObGbZq1aoA3Lx5k4MHD750/eTJk2o7GMXJyUll5n3VBKzOFWDOHuD58+fZvn37S9dv3rzJ33///VEPBAYGBhgZGZGcnMzXX3+Nr68v7u7unDt3TsrM9gmhp6fHrl27MDU1Zf78+ezdu1dqlI8YGxsbXFxcAAp81jmLshYtWvynRfEHpQDkOHXcuHFDZQ7Jysri+vXrwP/v+URGRmq8buvXr0dXV5fp06erTUbZsmVp2LChyhJy+vRp1bW//vqLESNG0KZNG7XI1tXVxcfHB3jhDJh/GyIxMRF4EaOvLmxtbWndujUAffr0YdmyZao95/Pnz9O9e3eN+QYIIcjOziYrK0tjfSwlJYWmTZvi4+PD2rVr+fnnn/Hz82PChAkqByV133NRfEaT9fmYyH+/zs7O+Pn5qd6HO3fuFGt9PvZn/CaLG3XWd/z48cCLPCBJSUkvLf7kcvl/twZ9SCGA9+/fF9ra2gIQTZo0EePGjRPVq1cXXbp0EYBwdnYWPXr0UOUIKGpu3rwp9PT0hIGBgVizZo0q9GTXrl3C3Nxc7Nu3TyNtYGNjo4qBdnBwEBYWFkJbW1ucPHlSrbKjoqJEuXLlBCDs7OzEkiVLxO7du0XPnj2Fo6OjAISnp6eYMmWK2g5ICQ0NVckChLa2tjAyMhKAWLt2rUb7Yk4dnj9/rhGZt27dEoBQKBTCyclJuLq6Cjc3N+Hp6Slq1qwpBgwYoMoPoA6Cg4MFIAwNDV96vo0aNdLYyXg5eHt7C0AcOXKkWMajVq1aaTQMMOcwIF1d3Ty5Hvr37y8A4eLiIp4+faqx+885DEidocc5YYD169cvNAzz77//zlP++++/C0DUqVOnSOty584dAYgSJUq81P9zTmpUZ//Pzs4WPXv2VOVFiI2NFUIIcf36dVG6dGkxe/bsjz8PwJYtW4Sjo6MwMDAQbdu2FY8ePRIPHjwQNjY2wsPDQ5w5c0at8h89eiRGjBghnJ2dhbm5uahQoYLo1auXuHPnjsba4PHjx6JHjx7CzMxM6OvriyZNmohz585pRHZkZKQYMGCAsLCwEPr6+qJx48bi33//FZ06dRINGzYUO3fuFBkZGWqtw7Nnz8TAgQOFtbW10NHRERUrVhQ7d+7UWPvPmTNHFYMOiFq1aomFCxeKmzdvql32pEmThK2trbCxsREGBgaqY5hz/szMzMSTJ0+KXO5vv/0m6tWrp5LTqVMncejQIXH9+nUxZswYVVIcNzc34efnp/Z8CMOGDVPVxcvLS/zyyy/FpgDkzgmiLjZt2iQaNmyouueuXbuKP/74Q6SkpIiOHTvmWRDMnDlTNTmog3v37omhQ4eqZHp4eIhly5YVuZzVq1cLFxcXAQi5XC5Gjx4trly5Ii5cuCAGDRqU5/kvWrRICCHEvHnzVIsUuVwuRowYIe7fv//Oddm3b59o0KCBSuZXX30lDh8+LG7cuCHGjh2r6v/ly5cXq1atUqsSsGbNGlG5cmVRsmRJUblyZdG8efO3VoJl4lOzo0lIfKBERETQrVs3du3apYqPziE1NZVHjx4xYMAAfH196dmzp9RgaqZ169YcPHiQa9eu4e3tLTWIxAeHXGoCCYkPAx8fHxo3bvzS5A8vnMLKly9Pw4YNP8mjmYtrT1gul6sl54eEhKQASEhIAHDx4kWOHj2Kv79/ocl2rl27xrlz5/jiiy+kBlMDsbGxeZLLJCYmUrduXY04YEpIqAPpgG8JiQ8ANzc3vL29OXToEE5OTrRu3RpXV1cMDAyIi4vj4sWLREZGsmPHDumcdjWQmprKZ599hra2Ntu2bcPDw4O7d+++MiRWQuJ9R/IBkJD4gCah1atX8+uvv3Lz5k2SkpIwMzOjcuXKqhDIjzktbHHTr18/duzYQXZ2NvXr1+fHH39U5eaQkJAUAAkJCQkJCYkPAskHQEJCQkJCQlIAJCQkJCQkJD4FpA1DCQkJCQmJYkCWc4ymZAGQkJCQkJCQkBQACQkJCQkJCUkBkJCQkJCQkJAUAAkJCQkJCQlJAZCQkJCQkJCQFAAJCQkJCQkJSQGQkJCQ+Ji5f/8+ixcvJikpSWMyfX192bx5s0bvc//+/ezfv5+srCzpoUsKgISEhMSnixCCyZMns3TpUnr37o2hoeFHfb+tW7dGoVDQokULHj16JHWAd0RKBCTxTvj7+1O3bl2pISQkioFZs2Zx+vRpjh079kncr0wmo2XLlqSlpdG0aVOuXLmCkZGR1BEkC4CEprly5Qp+fn5SQ0hIFAPx8fFMmzaNhg0bfnL33qBBA4KCgli1apXUESQFQELTpKamMmDAAKTDJCUkiodLly6RkpJCVFTUJ3fvOffs7+8vdQRJAZDQJGlpafTs2ZMLFy5IjSEhUUzkpJG/evXqJ3fvOfcsl0tTmEYUgOzsbA4ePEj79u1p0aIFQghmzZqFg4MDBgYGNG/enICAAI1U+vLly3Tu3Jnq1atTrlw5atWqxbp166hRowYnTpxQu/wzZ87Qu3dvXFxcEEIwduxYTE1NadOmDdnZ2WqX7+/vT8uWLWnfvj3lypVDJpNRokQJjbS9EII+ffpw8eJFAH7//XcqVqxIxYoVCQ8PV5vcefPm4enpiUwmo2bNmqry06dP07dvX2QyGTKZjNu3b6tF/ooVK7CyslLJ6du3L6Ghoarru3fvxsvLCzMzM9asWVMkMvft24ejo6NK5vTp0wE4dOgQ9evXV5W3bdtWtRLKyspi3LhxyOVyvL29uXHjRpHUZdeuXVStWlUl09vbm1u3bpGWlkanTp2QyWRUrlyZI0eOqKX9p06dir6+PjKZDC0tLSZMmEBcXJzq+qFDh3Bzc0NXV1fVTmoZMOVyzMzM8PLyUvX7ihUrYmJigkwmo3Tp0hqzirm6ugJw8+bNT27iunXrFgDu7u7SLP6OA/obMWPGDFGhQgUBiMaNG4vhw4eLtm3bin79+gkrKysBCHNzcxEcHCzUybp164S1tbU4ceKEqmzz5s1CLpcLQBw/flyt8pcuXSpq1aolAGFnZyd++OEH0a5dO6FQKIRCoRCRkZFqlX/nzh1hbW0twsLChBBCZGdnixkzZghTU1OhSfbu3SsA0bt3b43JPHPmjABEjRo1Xrrm7u4uABEYGKg2+VeuXBEymUwAIjo6+qXrvr6+4ueffy5Smbdu3RJyuVzo6+uLjIwMVXliYqKwsLAQgLh7926e7yQnJ4uSJUuK58+fF2ldUlJSRPXq1QUgvvzyS1X54sWLRc2aNUVSUpJan/+KFSsEIKytrQu83r17dzFx4kS1yc/IyBAeHh4iJSUlT/mNGzeEnp6eUCgU4p9//tHoe2hjYyPMzc1FcdC3b1+xadOmYpH9v//9TwBi9+7d4kPmg1EAhBDir7/+EoCwtLQUW7ZsUZWHhYWJ0qVLC0B89dVXamssf39/oVAoCnzoderU0YgCIIQQwcHBAhB6enpi+fLlqoH61KlTapc9ffp0YW1tLTIzM1Vl2dnZonbt2h+9AhAYGFioApDz/NWpAAghRIsWLQQgNm/e/NKk6+7uLtLT09Um86+//spTPmrUKAGIefPm5SnfvXu3GDhwoFru//79+8LIyEgA4siRIyI0NFS4uLioFFJ1kp2dLby9vQUg/P3981xLTU0VVlZW4vHjx2qTn5ycLKZMmVLgcwfE1KlTNT6BVK5cOY8y9qkoAH///bcAxNmzZyUFQFM+ADnhFl5eXvj4+KjKbW1t+fHHH1Vmy/T0dLVUdvLkyRgZGdG+ffuXrllbW2us0XLM7UZGRgwcOFBliqpTp47aZaenp/P06VP69u1LbGysai9w7NixkjlLAwwbNgyA5cuX5ynfsWMHX375Jdra2kUu85tvvgHgl19+KbDPr127Nk+5n58fvr6+arn/zz77jLlz5wIwZMgQ+vTpw4IFC7C1tdXInveECROAF+Fv+bcoatSogYODg9rk6+vrM3HixDxlI0aMICAggIYNG750Td08ffqUiIiIl9riU6Bhw4Z07dqV48ePa0Te48ePad++PXZ2dtSqVYupU6dy586dAj/r5+fHgwcPPq4tACGEOHv2rGoLID+RkZECEIAICgoqck0pPj5eKBQKUaVKlQKvd+zYUWMWgISEBNUWgKYJCgoSxsbGAhBmZmZi0qRJRW7qlSwAr16Furi4CEBcvHhRVV67dm0REhKiFplpaWnCwsJCGBgYiLi4OCGEEOnp6aJChQqiatWqAhAnT54UQgjx5MmTQt+RoqRp06YCEF988YVG+11mZqZwdnYWgLh69aqqvG7duuLgwYMarcvOnTsFICwsLDRiAcndBv7+/mLQoEEiICCg2FavxWkByNmSGTdunFi6dKmIiopS6ztfv359sWnTJhEYGCj27NkjevbsKYyMjET16tXFkiVLVFvfV69eFY0aNRJZWVkfnwXgVZQsWRJjY2MAMjMzi7yiISEhZGVlqeW3PyScnZ35999/adiwITExMUyfPp2yZcuybt06aXmuAWQyGUOGDAFg6dKlwAunVGtra+zt7dUiU0dHh+7du5OcnMzOnTsB2LJlC+3atWPo0KEAKsfDDRs20KdPH7W3w8iRIwH4+++/VQ6hmkChUKisXTNnzgQgMDCQkJAQmjdvrrF6BAcH079/f2QyGb/88otGLCA5nDp1ioULF9KxY0fc3Nw+2Xcxxxk0JCRErVaQoKAgmjVrRo8ePShfvjwdOnRg48aNPHnyhMGDB7Nv3z6cnZ3R19fnq6++Yv78+R9OdEJRWQCEEMLQ0FDI5XLVKqUouXnzpgCEiYnJJ20ByM2xY8dUTlmadogpDgvA7du3i90CIIQQcXFxwsjISOjp6YmIiAjh6+srjh49qlaZ169fF4D4/PPPRXZ2tqhatap4/vy5SEpKEqampkJPT09ERUWJihUrFuigWNT9v1KlSmLChAkCEB4eHiI1NVVj/SA1NVXY2NgIuVwu7ty5I4YPHy5mzJih0ZVnjiPwqFGjiu39nz17thg9erTIzs7+JC0AFy9eFM2aNRMRERFqlZOSkvKS42d+0tPT36oeH6QFoKBQt4iICJKSkqhWrRomJiZFXlEnJye0tLSIj49n//79n6zWu3r1atLS0gBo1KgRZ8+eZcSIEQBs3Ljxo753HR0dgFceeKKJMEwTExN69epFamoq8+bN48qVKzRu3FitMr28vKhSpQqnTp1i0aJFVKtWDUtLSwwMDOjevTupqakMHjwYDw8PzMzM1FqXIUOGMHz4cH766SeaN2/OrVu3mDJlisb6ga6uLiNHjiQ7O5spU6awfft2+vbtqzH5U6ZM4ezZs1SpUuWllee9e/c0Vo9x48axZ88efv75509uHIyPj6dly5YMHToUCwsLtcrS09NDT0/vlZ/R1tZWez3eGwuAu7v7S9fWrFkjALFr1y61aWLt27cXgHB2dhYPHz5Uld+9e1c4ODho3AJgY2Ojca13/PjxL2ndOfVRZwRGfg4ePCgA0a5dOyHEC29odYeAJiUlCblcLgwMDPKEW27ZskWYmZkV6B2uLgICAgQgZDKZWLx4sUZkLl++XABCW1tb3Lt3T1V+5coVlRXo77//VmsdNm3aJHx8fFT/HxISIoyNjYVCoRBnzpzRWP+Lj48XJUqUEIDo3LmzRq1ucrlcGBsb53kGOfz4448aHQ+8vb2Fp6fnJ2cBWLlypUbfd3XxQSoAgFi3bp2q/N69e8LOzk7069dPrY314MEDVeyzvr6+aNmypWjVqpXw8fER7dq105gCEB4eLgCho6MjEhISNK4AmJqa5ok3PnLkiNDW1tboy5BjjjcwMBCLFy8WnTp1Ek+fPlW73BznM1dXVzF8+HDx+eefi+nTp6u2AKpVqyZmzZqlkTZo0qSJMDQ0FLGxsRqRFxMTI/T09ESnTp1eula1alXh7OysVnPw1atXhY2NjYiJiclTPn36dAGIzz777KVr6mTixIkCEMeOHdOIvIiICGFrayuAPGHQOQQFBYmWLVtqdCtCV1dXGBoafnIKwLhx4wQgVq5cKSkAmlYAatasKQYPHixatWolGjduLKpXry5WrFihkb2ou3fvitatWwsDAwNhb28vZsyYITIzMzXmA7Bz505Rt25dlSJUs2ZNsXXK+OcMAAAgAElEQVTrVo0qADmyK1asKNq3by9atWolzp8/r/HOO3nyZGFkZCS8vLw0kgNBiBc5J5o2bSr09PSEm5ubqu3r168v2rZtK/7880+N7Ynu27dP9O/fX6Nt7uPjU+CzXr16tZg5c6ba5O7atUtYWFgIhUKRJ979xo0befxQPD09xfbt2zXSFhcuXBDlypXTaNvnWGBq1KiR58/Ly0vo6OiIZs2aaaw+OX4hLi4un5wCsGjRIgEIX19fSQF4X5wAixNNOgFKSEgUP99++62YP3/+J3v/hw8f1vgWyPuiAJw8eVIAGrW4fIwKgJYU2CUhIfGhkZiYyPbt27l+/fon2walSpUCPs18+Dnhj5pMAPcx8p8UgByFRbyHR8AK6VhaCYmPmoMHD6Kjo0O9evUYP348Xbt2xdzc/JNtD29vb7y9vUlOTv7k7j0lJQWAnj17Si+GphSAnNO3cp/C9b4QExPz3tZNQkLi3fD396d169bAixP5ypcvz6lTpz7pNpHJZGzatInOnTszYsQI7OzsPpl7nzFjBuPHj6dBgwbSy/EOvFEegNTUVKZMmaKKN7906RL9+vXj5MmTxX4DN27cYMyYMaq6jB8//pPMjS0h8THj6elJtWrVMDU1xcfHh+PHj6s938GHYgU4ePAg06ZNY+7cuaocIR8rJ06cYPDgwdSqVUsa54tCiRSS7VxCQkLigycpKQldXV20tD5e1674+Hi1JJortglYJpNJCoCEhISEhMSntgIvZgVALj0CCQkJCQmJTw8tlM5zxcbRo9JTKE6Up8hJFNMKQOr/xYpo0qR4K9C//3vdPltPncLn88/VJ6C42/8TR7IAfISERUdj3a8fSw4dkhpDQkLircgWgvMaPNxIQlIAJIqA53FxPIuL48bjx1JjSEhIvBWPnj+njJWV1BAfMVImwI+QimXKUNrCgi+rV5ca4wPHWV+fQfb29LCxoZTyOOS07GwWPH7MksePeZqeDsAge3uGOjjgbmhIeFoaP4eHM/3hQ1Lf8Xjk4pYPUM7AgP52dvS3t8dYoQBgTVgYsx894kFKCqZaWgyws2OaszNZwLKQEOYFB/NcWTeJtyMwLAy39zS3wJXr15m5YAEuzs7cCAggMiqKs0eOsPO337h3/z5/nThBjSpVmP3DDwAE3LnDph07SElN5UZAAFvXrqWUpeUn/4xlIjq6eKMApD1QtfDDr7/yv44dUchfY+SRfACK9wV8w/5vpq3N4UqVqGZiwsOUFJxPnyb/izvHxYXqJia0uXqVhKysIq1nccsH8DQy4lTVqphqaVHzwgXO50r6ZaRQEFCrFm2vXeNqQsIb/6bkA1A48/bvp3PNmpwMCGDpn39y8f59tBQKlnz9NYO++AKAPefPM3DtWsyNjJjUsSNtqlRh7bFjLDhwgCcxMZSxtGTNgAE09fYmOS2NVX/9xbcbN9K8YkW+79CBusOGvVXdUlJT6dS7NzGxsaxbsoRLV6/i6uLCsZMn+W7UKGJiY7H38GD7+vU0rFuXph06cPLAAXR0dKjcoAHtWrRgyvjxxf/+m5sXaxSAZAH4iOi5dCmb/f2xMTPDzc6OjvPns//iRUwMDDg8cSLVy5aVGukDJSYjg7ZXr3K1Zk2c9PXpb2fH6rAw1fXKxsZ8YW5Og0uX1DL5Frd8gJuJiQwIDGS7lxezypal4aVLqmvLypdn5N27/2nyz+FucjI/PXrEL+HhzHFxYayj40ufic/MxN7fn5I6OiwqVw4PQ0OWhYay+PFj6pYoQVkDA64nJtLRyooJZcoQk5HBnufPGXD7NmX19albogQBSUl4Ghkxu2xZzLS13/s+9zgyktIWFvSqX58utWvT6McfuXD/Pi0qVVJ9poGHB+729uwbNw5TAwMAxrRpQ8caNag6YQK62to08vQEwEBXl6rOzvSoW5dNbznx56Cvp4e1lRUVvbxwd3XF3dWVIWPHArBo5UoAmjVuTGxcHL8dPIizkxM6SgvW4V270NfXlwYVJB+Aj4ovKlRgRrdu3F28GG9HR3aNHs2RSZP4ukEDSltYSA30gfM0PZ2vb916sTorV44yykHMQlubjZ6edLt5k9jMzI9WPsCOZ8/Y8/w5DczM+MbWFoCvbW2Jz8xkz/Pnb/Wb5QwM+L5MGfTlcpY8fkxGAalR1oWHkykETczNaWdpSVkDA4ba2wMw1dkZP3d3VpYvz8SgIGY+fIi5tja+dnbY6OjQzdqade7uHKpUiT8iI+ly48Z717dCoqJIy8jIU5adnU1OmLqetjbbRozAQEeHIevWvbCeCMGQ9etZ1a+favLPwcnKivWDBnEnPJwFBw4AEJWQwJx9+1gzYEDRrJ5lMnKH0T8ODaV+nTqMHDSIkYMGsWfjRnp27UpwSEieDImWFhYYGRpKA4qkAHxkFoB69fi+QweS09LY/M8/rD56lMZeXizo3RvrEiWkBvoIOBQVxeqwMIwUCvzc3dGWydju5cWPDx4QmJT00csHGHL7NjEZGcxzcaGxuTnf2Noy5h291bVlMrpZW/M0PZ3tT5/muZYlBP/ExOBtbIwiV7lWvhwu1UxM8DQyYluu7+f+jKmWFl9aWXE0OprIfJNtYWxV83kHUQkJjN6wgTazZrH+779fmmBz42hpydyePfnjyhW2njrFrN9+o02VKpQvxE+gfbVqdKtThyk7d3LvyRMGrl3L/F690FeuxIsam1Kl2LVvX56y85cuYWttzYnTp/MoAafPn5cGk9cpAI9DQxk1cSIOnp7IzM1Vf6VcXZk4fTpJuU6h2r1/P51691Z9xrN2babOmfNBNkpWdjZLDh2i4tixGPfqhZWvL42nTmX1X38RGBZGv9Wr32v5N0NCiExI4N+goA+ivZOzspgbHEyzK1eQHT2q+jM6fhzLkycpefIklc6f59u7dwn9yHOdvwnf3r1LUHIyDc3MOFe9OtcSE/n12bNPRv7T9HS+vXcPM21t9lesyNcBAaQXgbOhg54enaysmJ8vemZvRAQd/oM3vPFrUvHKZTIMFYrX/o4mwvC0tbQY07Ytv40bx/wDB0hXWnCexsZiU8BZC/2bNKGxlxdD1q8nJCrqtTkCln7zDcb6+tSaNIkO1avjqrTaFNlYmWu7qVvHjvy6bx/DJ0zgxKlTjP/hBwz09WnTvDlpaWl079+fcxcvMm/ZMuLi41Xfi4mN5bupU5m7dCnVGjcmMSmJ5p060bh9e2Lj4ug5cCAV69UjNDyckLAw6rVqxZNnzzhx6hQLVqygRefObNi2DYC0tDRmLVrEj7Nn07xTJ2JiY1np58fnLVqwZPVqHL296d6/P9lF0F/VrgCUtrdn4YwZBF26xFdffqkq79W1KzMmTcIwl9mnY5s2rF648IWG7uvLlZMnmTxu3Ac5+XeYO5dpu3bxY5cuRPn5EbZ6NWPatGHlkSO4jxrFvSdP3mv5lZycAKis/O/7joFCwVhHRw5VrIiFcm90urMziQ0bElG/PuerVcNSW5sFjx9T4dw5LuV6eT9FkrKy6HXrFllCUNnYmPW59uI/BfkAP4eHE5CUhL5cjms+8/M7KTeOjlxLSOBodLSqbPvTp3QrVeq13z0WHf3CT6GQFfGTtDR2PntGT2tr9OWvN75qIgzPRF8fWzMzylhaUs/NjV9OnABeHQEwp0cPYpOSiHkDi09JY2PGt2tHVEICCcojfIuCS1evcubff9n/559cvnYNgIZ167J87lz27N9P9/798ShfHi93dyxKlmTPpk3cCAigTbduCCFo2bTp/1u1jh6llKUlY4cNY9SgQRgZGvLT5MnExMZSwtSUH8aPJzomBltra3R0dOjfuzemJias37yZ0YMHs3rhQoaMHUt8QgJL1qyhfp06TBk/HlsbGxauXEmzRo24e/8+rb74ghunT+N/9iy7fv/9/VcActDV1WXTqlXUq10bgI07dhBbwLG7P8yeTdcOHVg2Zw7aH4CTS4EDy/Hj7L90ieW+vrSrVg0dLS20FQpaVKrEuZkzqeHi8t7LNzM0pIyl5RsrAOvCwqh38aJq5b3jDVZzyVlZWJw8iezoUT47fZq5wcGEvePqXC6TYa+nB5BnhVTWwIDfK1akrIEB0RkZqsnnUyYyI0PlbLfO3R25hlOKF7f8TlZW3E5KIjU7m5Xly2P0BivqN6GqiQl1S5RgXnAwAP/Gx1PZ2BidV0zYv0VEMP3hQ7Y8fcpvFSrQJ98q90J8PAseP2bS/ft8V6YM69zd36gumg7D+75DB+bs20dmVhaBoaEFyhZCsODAAYa3aMH206fZn8sRsyCiEhI4fecOLSpVYtzmzYRGRb085m3ZQr1WrVTW4zIVKrB5507V9eP+/jRu3x6ZuTl1mjdn74EDVKlYkYBz57h55gyVK1RQfXZw376E3rpFWEAAvb76SlXepH597ly4QMS9e4zN54BYs2pVps2bR99hw2igtGhU8vYmLS2NO0FBXLp2DXMzM06ePs3vhw7RrmVLrt+6RURkJL9s3crf//xD04YNiYqO5tjJk1y7eZNftm6llKUl+np66OjoYGJsjLOTEybGxnRq25YLly9/OAoAgJaWFlvXrsWsRAmeR0QwauLEPNe379nDydOn8Vu27D9X4vSdO3RfsgRZly6Yff01K48cUWmXJ27dou3s2ci6dMF2wADWHTtGYmqq2hrkgPLBeCgdfHKjp63Nkq+/VusDKSr55e3scLGxeaPP+trZcbhyZXSVg9zsR49e+5314eFEKfcxZzg7M9bRETtd3Xe+f0UhE4meXE5v5f0EJCVxMzHxk538jRQKtnl60vbqVYKSk6llasqY0qU/GfllDQwY5uCAz82bzHz4EAc9PWYUYYTLaEdHDkdFcTMxkVWhoQwo4F3MTXtLSyY5OeHn7k7bAmLLq5mYMLp0ada7uzOidOmXfAdepwBsPHmSat99h6xLF7S7dWPlkSOqz+w5fx4rX1/KjxzJZn9/4pKTmbd/P7YDBiDr0gWnIUP46/r1F0p7WhoLDhxA1qULLWbOxD8wMI88FxsbqpUty8Z//iHo6VOcra1fqtOs336jY40aLOjdm8pOTgxau5a4XFvB+ZWFoX5+zOvZk9X9+5MtBIOUDoS5+bp7d04eOIBvz54AuJUrR48uXVTXG9atS4M6dejdrRv+f/xBh9ati7Q/OTo4cOP0aZJTUqhcv75qcevTqRPbd+8m7MkTRgwYwKadO0lITMTYyIjMzEwMDQ3p4+NDHx8f9m7ahK21NZlZWdSpUYM+Pj78NHkyowcPfkmeuZkZJsbGH5YCAGBnY8PS2bMB+GXrVg4pY5hvBgYyeuJEdm/YgMFbhFfUcXVlfq9eAHSuWZNBX3yBmdJLs4GHB3OVHaP755/j27gxRspVojrIORxx3v79BV6vXrYsjmpMIFFU8q1MTSnxHzxd9eVySmpr85m+PlcSEjhcgKaeQ5YQLH78+P+9wNXk1JOfMrme+5s6Ub2O6IwM5gcHIzt6lFZXrxb6ufqXLqF97Bjrw8OJy8xk7/PnlDl1ipInTzIgMJBuN25Q5fx5te+Fy4ANHh4sDw3FPzaWrwMCyBaCqc7OuGvAs7m45evJ5fi5u+MbGEhadjazg4MJTEpiqL09NUxNi0RGWwsLyhoYMObePQwVCkoWkzUzdxie/9Sp1CpXDqDAMLzzM2fSo25dTA0MGNOmDaenTcPcyKjQMLxD339PXTe3l2RO/PJLftq7l5T0dLTzWVWO37pFZEICHapXRyGXs27gQJ7FxTF206YC6z99zx661KqFk5UVDiVL8pOPDwcuXSrQsVEmk7F87lwqeHry57FjHPf3V10LuHOHY//8w5qFC5HLi95vfdfvv2NkaMi2deuo4OnJQ6X1x6dTJ5avX0+VChXo2LYt+/74g3LOzgB4e3hw8vRpft6yhWcREaxYv57klBTq167N4DFjuHf/PjcDA/lV6ZSYmJioGtsD796llTKPwgelAAB079xZpYH1HzmSx6GhfNmrF8vnzsVF2Thv9WIrXzKDAlaRhsoyXQ28iDkv1y8nTtBm9uwCTVaD1Pjwikq+hbGxqk3/y+A+RhkDPesVVoBfnz/H29gYZ6UCoCnj7wPlHqIMimyyMdfW5ltHR5z19TkUGUlAAfualxMSuBAXRxl9ffra2mKqpUUHKysamJnhZmjIajc3tnl50dXamq43bnA6NlZtbTDRyYnn6en8HB4OwKnYWBaHhKArl7PRw+ONV5cfqvylrq6sCg3lnnLVmZ6dzYDAQGQyGevc3F5pqn8VmUKQqRyg5TIZIxwcOBIVxVAHhzyKb2auraecf2e+YjsqM993CuN9CcPzdHDAq3RpIvL52dwJD2fqrl385OOjKqvk5MTApk1Ze+wYf1y5kndSPXeOsOhoOuTKRjq4WTO8HR0Z5udX4Limo6PDz8uWoaWlxYDRo0lNSyMpORnf4cP5edkyVRx/UZOQmEirrl1Zvm4dlStUoKKX14s2dHSkY5s21KtdGxNjY7p26ECzRo0AMDE2ZuPKlfw4Zw4V69allJUVZiVK8O3QodjZ2FClYUO+mzqV9q1aAZCWns68ZctYvm4dtatXz7Nt8UEpAACr5s/HomRJQsPD8apTh/YtWxa5Waa46Ne4sWoSPnDpEuVHjmTyjh3E53JgqalGP4Cikm9pYvJW8nvZ2GCto8OJmBj+LcTZbu6jR4wrIFmKOolIT2eV0tmsj60tNkWw3ZDHClWiBK6GhsxXav+5WRESQldra/LvMuef7HrZ2CCAA5GRammDdpaWtLSwYMTdu3kn5aAg7iQnU8XEhMmffaa2Z1Dc8r93cqK0nh5b84Xp+cfG8ntEBJ5GRsx9i3czKDmZxSEhHIyMVDn/fW1rSw8bG1wNDEjOymLzkycEJCVxLCaGfRERBCUnsyQkBAC/8PCXEhBFZ2SwOiyMJ+npHIyM5EghFrX3MQxvUseOuCm3PZLT0pi6axfVvvuOZ7GxXH74UPW5u0+e8FCZe6HbokXM2bePgNBQ+q9eTdeFC3keF8ejiAjV5/8JCCAlPZ3oxESaTJtWoCWgkrc3Y4YO5d79+0ybO5fBY8YwesgQnNQ43vj27In/H38wxNeXnyZPztPuK+fP//9xYN68PL5tLZs25dG1azy5fZuObdq8WMDq67N9/XriHz9m/7ZtqnwDJc3NGTtsGEN8fRni6/vezHdvpQBYWVqqGiY+IUHlHPgxoJDL+W3sWL5t0waFXE5SWhrTdu/GeehQlhw6RKaaspwVtXxzI6O3kq8rlzNSuZ9bkBXgWHQ0Rlpa1Cwic+vryBCCPyIjqXvxIk/S0uhgZcUSV1e1mLZHli7NlqdPeZYrh3x4WhoKmUyVB/9VxCpXcFZqWKl0LlWKbV5eDAgMfCnkLSU7m2kPHryYjMuUoZUakj4Vp/zG5uYcr1KFGc7OuBoavhSS18nKigrK/j7cwYGtnp5U/Q8KcFkDA5a6unKlRg2amJu/sDoqFGz08HgxqCsU9LCxIalhQx7WqaNKBLTE1RXRpAlbPT2pmG9P11xbmwF2dmQ1bsyVGjX4omTJAmW/j2F4lZ2c6Ne4scoiO7lTJ+I3bCBg4cI8i49yNjYcmDABsXMncRs2MK5dO9zt7VkzYABZO3awZ8wYyuTarmzg4cHdxYsRO3dye9GiQus+Zfx4XMuWZdaiRWgpFHRq2xaJ90gBgBf+AArlHtGgb78l/i1ScL6v6GhpMa9nTy7Nnq3aP4tMSGDEzz9TdcKEPGF4D58/58dff6XkN98g69IFRdeuLDx4kGSlR/ztsDAGrV2LrEsXGk+dyoHXeM3+V/mF8S5+EgPt7THR0uK358+5nc8kPic4WCOr/9VhYTS+fBnzEydodfUqNrq6XKpRgz3e3i95fCdnZbE0JAT3s2dVkQwDAwN5qLSaRGdksPjxY+RHj+J8+jTLQ0IQhVg/jBUKlipXdgArQkMZkssMXBhxmZl8e+8e5Q0NVRnqioIvSpbkSOXK7PTyQl8uZ3Tp0njlU+6alSyJr3IVKJfJ2OPtzcry5V/63IcoP0fpbHjpErKjRylz6hR782X82/X8OU6nT6uevc/Nm1z8QEJF39cwvOLMHKqnq8u0iRPJzs7mZmDgexMz/zZkZ2ez+/ffefrs2XuZfOitzgJ4FhGBT79+7PDzo++wYYSGhzN64kTWLVnyzhU6cu0afZYvzzvAF9OpXhUcHTk2eTKHrlxh3ObN3AwJ4VpwMHUnT+bq3LlYlyiBk5UVUzp3pkvt2tSeNImElBQ616yp8mUob2dH5c8+o3f9+vw8ePBLZr13lV8Yr1sZvApTLS0G2tkxJziYOcHB+CnDlq4nJhKelkbLVwwOG588YWlICBfj49GSyVji6sogpTlxz/PnDLx9G3MtLSY5OdHjFVEKA+zsGFm6NMtCQhh25w4X4+MLDfUyUCgY5uBAH1tbGl+6xIX4eBqYm+Ok9FEw19amuYUFPz95wskqVTAtJFGLvlzOIHt7loeG8n2ZMshkMu4nJ+NtZMTWQuoZmprKmHv3WBsWRm8bG3Z4eRVZSBrAkaioQs3HORyOinql0+aHLP9T4vsOHWgxcybfNGxIYGioSvnPTe4wvCWHDuHz+ee0qVKl0N/MH4bXqnJl7AuxRrwNbWfPxszIiA1DhhTZb6alpbFi/XpaN2vGgcOHWbZ2LcOLKH2wxlfYcjkjBg5kxMCBH4cFIDMzk67ffMPowYPp2KYN86dPB2D95s0cOX783VccFSrwy5Ahef4WKCMENMHF+/dfKmtRqRJX587lR2VoyrO4OGbnSznpZmfHL4MHk5WdzTA/P1X5nfBwfj17ljUDBrzR5P9f5avDAgEvzOG6cjlbnj5VZd+b8+gRYx0dX+n018vGBv+qVaml3CJokWuwaWBmhruhIeerV3/l5J+boQ4OdLKyIjEriy43brzyeFljhYJfvb0poaXFuHv3iFeaU1OysxkUGMheb+9CJ/8chjg4kJSVxc/h4WwID6fXa1bz9np6zHNxob2lJQcjI9/I4UtCoiCKKwzvXcjIyiryFfqoiRMZ+PXXbFixAksLCyZOn87j0FCpg7wPCsC4KVOwKVWKYcpjLPv26EHTBg0A6DdiBAkfeHy23/HjBZrWFHI5kzt1onf9+gAFptltV60afRo04LcLF9h2+jSJqan0X72a9YMGoaOlpRb5ORaIU9OmUcLQEJlMVqgF4uj//kfrV6wWcmOjq0tPGxvSs7NZGBxMSGoqZ+Li6FbAoPSSCU8uZ5uXFwYKBUPu3HkxGPEih/uq8uVfOwnnZ727O2UNDLiWkMBI5e8VhqOeHotcXQlJTWWsMo3qwMBAxjg6qiwCr6KUjg4+1tYsfPyYI9HRNH/D1dLK8uUxVCjoc+sW/0UFMFYo6GVjw/N69Yhr0IBfPDxUf3srVCCjcWN05HJcDAyY5uyMaNKE0Lp12eHlxbHKlblaowaD88Wp1ylRgt3e3ogmTbhaowbbvLz4t3p1TlSpQiPlHndBfKavz8ry5dlboQLr3N1Z4+bG/5yc+PGzz/AwNKSGqSm/eHggmjThZJUqqnpu8vDgdu3azHiHKKAPhdOxsXS8fh3Z0aOYnzzJMaXTYHRGBhOCgjA+fpzZjx6plM//SnGF4b0tA5o25euGDYvs97bv2UN2djZdO3TA3MyM+dOmkZiUxOAxY6TZurgVgJ2//cbhv/9m7eLFecrXLl6MkaEhj0ND+XbSJLVX+klMDF0XLkTWpQt9V64kWql0RCUk8OW8eVT/7jsuKFfS5UaMYPSGDUzavp1J27fT6Mcf0fXx4Waufd7cZAvB3n//LVR2m6pVAV4Ku8lhUZ8+2JcsyTA/P7ovWcL/OnXC4T+Y3N5WflFZIHIz1tERuUzGmrAw/nf/PsMcHNB+w99w1NNjrosLf0RGsvXpU2Y9ekQbS0vKv0X4nomWFju9vNCTy1kdFsb218Ta97axoY2lJWvCwuh96xaOenqv3LYA8lgWRpcuzf2UFJqXLKmydmQVEM6VKYQqI6GBQsFub2+Ox8SoHOLehISsLDY+ecKp2FiepKfT59Yt1V+Ha9cYdfcuRgoF95KT+d/9+2QKwfanT+l64waNL19m+7NnLC9fPo8ScDo2lgXKfPaT7t+n240b1LlwgYSsLA5XqkSFApKQNDY351y1ahyLjqbDtWv4BgTQPzCQQ1FRDHNwwExbm/NxcSxQRkmsDA1V1bPnrVtUOHeOeDU5yMplMvrZ2XGvdm2sNZRzojBylKvu1takZmVRTvkemmtrIwN+q1CB8WXKYKL1dietF2cY3psSER+PRd++KLp2Zckff7Dk0CG0u3XDsm9fnhWQIfZNuX3vHkvXrGHRTz+pynp27UqT+vU5eOQIW3ftei8mzU07dmDt6oqxgwMXle3+7+XLWLq4sHjVKtKLactarQrAxStXGDpuHLs2bHjpKEVHBwdmTZnyQhnYuJH9f/75nyuSs8+fUcAgkqbUpnPiZG3MzNg0bBgeDg7I5XKVx3tJY2NszMw49P33VHN2Jis7m6ldurCgd2+mf/UV37Zpw+3wcCZ36oTnKxy7fti5k6eFxHKfVYZAdatTp8DrpgYGrBs4kKiEBJ7HxdFEGVP6X3hb+e9qgRDKvxzKGRjQ3tKSxKwsfo+MpF8+pySR77/56W9nR2Nzc4bcvk1Iaio+b2A9yJlk8xsVKxkbs0jp/d8vIIBbr3GAWl2+POba2ux89ozRr3BazAnXOhwVxbanT0nJzsbTyAgfa2t6KrcpDkdF8UdkJMGpqfgpEwHtef6c4zEx3EpMZNvTpyRlZeFiYICfuztTHjzg64AAzv6HwTC9kK0Dv/DwPKvJtHzm1qVKh8ZO+XLV5/9chhCsDA1FSyajXb5EUtY6Ouzw8sIvPJxd+RzsLsbHM+j2bdX59YXVMy07m1VvaaZtZ2lJ8Oefc692bRaVK8eicuVY5urKwzp1qGxsRasAACAASURBVGZiQrYQXIiPp6xysv1MX58F5cohmjRhk4eH6ju/entzoGJFjQycq9zcKKWry6DbtwE4GBmJubY2jV9hYXlTijMM700wNTCgT4MGnJ0xg1GtWzPxyy85PmUKfRo0oMRbnssQHBJCm27dmDdtGnr5QnznTp0KwJCxY3nwBllK1U3Prl35bcsW0tLT0VXWNSUlhdk//MCIgQPVlq9AHbyRmnrg8GF6DRrEl61b46bMRpWffr16MXzCBLKzs+k9eDCnDh3C/Q3Dtc7cucMaZVbBfRcuUNnJiS9r1MDM0JB/AgNZd+zYC/PQmTOUt7Oja+3aGOnpsbp/f+pPmUK/xo2pXrYs/oGB1ChblpK5Vjg5K2aAYX5+2JqZMb5du1fWJyQqisrjxzOzWzc61ayJkZ4eSWlprDpyhEUHD9KnQQN61K37yhfEzNCQc/fusf30ab4qRFlQh/xFffpw9MYNhvn5sf306Te2QGQJQWxmJpHp6Xli7MeXKcOe588ZZG//knNbpFJpi3iFxjvHxYUq588T8waZ+7KFIFSZ5jmsgHTPA+zsOBkTw7anT2l++TJ/VKpUqKe5rlyOra4uNxMTmXDvHqsKyHqWs3IbYGf30gEuW3I5YDUrWZJbtWrluf6llRVfFnBQS0crK0STJgDEZmayNCSEsffuMcDOjnnlyhGdkUHH69fpaWOjSm1cGB2trLiUkMCjV3hvm2ppIQOevsE5DCWUSmD+zw6wt6ektjZ+yuQ++dn9/Dmer/DoV8hkDHNwYJHS6lDD1JRRpUsTkZ5OZEYGo0uXpn9gINVMTPi8RAmmPXzIBg8PFjx+zMyHD9kXEUE3a2u0ZDJG5soxsCI0VBVSeT/XPveDlBTWhIUxqnRpZgcH50kLPfg1aXuLCiOFgnVubjS5fJlZjx4RmJTEL8qwwXelspMTFsoxLCcMb3KnTi99LicMLz9rBgwoMNlPThjeu5ITpQTQZvZsbM3MWN2/P5+XL//fF34pKcyYP5+la9aQkJjIxu3bcXRwwFa5WAgJC2PNhg0v3qe4OOq1asXwAQMYN3x4sU6cNatWZUCfPgyfMIF9W7aw58ABFueyXHwUCsAff/3FghUrOHbyJAD7Dx/mfzNnMunbb1WaD4D/2bMsWb1a5QwSExtL9caN6dm1K98OGULZ1yQHqe3qSm1XV34pwJO0npsb9dzc2Dh06MvmOFdXvmnYkIFr13J2+nS2nT7N8r59/39gkstVWQR/v3iRX8+e5dLs2Wi9wkvbWE+PK3PmcO/JE/ZfusTUXbtITksjJT0db0dHNgwZQvdXTP7P4+KYsGULF2fNovakSQzz86ORpydWbxg3/67ycywQzWfMeGMLxOYnT9gXEUFyVhZfBwTQztKSgfb2yIDqJiY0L1mS4bksJn9ERnIwMpLryoH3hwcPiEhPp3OpUtjm6hcCWBAczHAHB5aEhOBjbU2bAtIYJ2dlsTw0lD+jolTnC6xUribrmpnRPtd31ri5cTk+njvJyVQ6f57mJUvStVQp1Wo9R5H4OiCAn93d+S4oiDVhYXQuVapIVmf/hRJaWgxzcMBAoWDR48dkC8HNxETGOToWmDPeRkdHNYmYaWnR0sIClzNnCv39ktrarHZz42l6OtNyrQwLwt3QkGnOzpyLi2NTvjDSFiVLkpiVxd1CnMkyhXgp0c0ge3uaW1ggB2qamnIml7UjITOTCkZGpGVn8+PDh2x79ozozEysdXVx1NfHUU+PJSEheSb1TCFeSqwUkJTEI6UiKAqxFOVngxpP6ixo26SfnR3fBQURWKtWkWbELM4wvP/C5QcPiHyHuhro6zNj0iRmFLJ17GBnx4p581gxb957d+/TJ06kXLVqdOjZk61r1/Ih8koFoGXTpnmOTSyMurVqUTffCklTzO7RA7eRI2k0dSorfX0L3OeOTkxkwJo1rzX9A6pzByqWKUPn/3hPmVlZ9FmxgsVff81npUqxrG9fOi9YwJD16/l19Og3+o13kf+2FogeNjav9Mo/lCv3OEBLCwtaWliw/DUa/6xHj+hoZUVbS0tOxcYy6PZt6pmZveQEmHMc8Ng3yC9gpFBw+zWJpyY/eEArCwuqmpiw1s0Nz3Pn8A0M5EbNmkUaovem9LW15XBUFH0DAqhpaponvWxucnwAcphWiFNdXTMztnl50d7SknVKP4foQiwsfWxtGe3oSL0SJegXGMjmJ0/IyDd52uvpFfr9wlgZGqryxbDS0WF8mTJ5Ju57yckkZmWx9/lzVdy+l6EhDczMWBka+lpHSX25nA5WVi9l/XtdO68PD0dHLmeovT0jSpdm8O3b+Lm7M/7ePSoYG+NqaMjZ2FgGOzhQrQjisi11dLDS0WHKgwfseIvtvg8dz9KlPxhlpagxNTFhqK8vawrYFv9QkH/oD8HM0JBBX3yBQi7Hu5AJZJifH3bm5q81/b8r4zZvpkutWlRQ1qNTzZp0rFGDXefOsfPsWY20R24LRClTU4b5+fH8HRxz3pbjMTFEpqfTwcoKhUzGOnd3nqWnqzzz1cW+iAjC0tLorzTpl9HXZ1bZsjxKSWGcmmW/igXlyrHj2TM8/kNynIOFpBT2j4mh961b3E1Opp6ZGYmvcL77JTycfgEBpGZnU8PU9KXJP8cCY/wOitHz9HQu5Otj2fCSU2A2kJiVVejk72lkxKyyZZnt4oJ/1aqYv8FZFiNLl2ZW2bKscnNjqlJhyhSCf+PjKa2nh5FCwcDbt7mQkMDT9HQqGhlxNDqaSUFBRL+lp37uvmavq8tqNzd2PnvGvlz77Z+MAqB0WvwUiY6JIS09HStLS2bkShksKQAaRldbu9DzyH+7cIFd587xy5AheUz/T2JiirQOyw8f5nFkJH2UIZE5LOvbF22FgoFr1qgcdtRFQRaIyIQEhqxfr9HncSc5makPHvBTriNaKxkbM9DenrVhYfyhplz55+Li+D4oiOX5fE+GODhQwdiYlaGhL2WR0wQC2BAezg4vL/rcukXcG0485+LiCt3/T8/OptetW5Q3MOCH12yx3U9JYYzSD6GglLRn4uIw09ZWne74NmwvglMQbyYmMiEoiPH37tH8ypVX5nzIYdHjx0wICmJgYKAqg2O2EAQrtw72KC0Qt5RJrB6mpnI2Lo714eEkv0PUwoOUFP6MimKQvT3tLS3paGXF4Nu3iX1HpeJDo6y1dZ50v58SPy1cyPgRI1g2Zw4LV6zgXgE5XCQFQANkC0F2ASubqIQEBhZg+o9OTOTojRtFIvufwEBazJzJ0PXrCYuO5nyuVWZ6Zia7zp0jWwhikpJo8MMPLD10qMC6fgwWiOSsLKY+eEC18+d5lp7O5Vz7xneTk1WpebvdvMmc4OB3GoDzD8YDAwOpe/Eiz9PTOZQvxOlARITKxN395k2mPHjAkzdwmisqloWE0MPGhi+trGhlYcHAfOewv46ehWzPXEtI4IcHDxjn6PjasxlWhYZyOCqK9e7uKmfAHBY/fkyWEIwsZGuilI4OTd/Af8JZX5/aRXRGRGRGBif+o5KeO4Ih5+hVkU8RE0Xw7sVmZjLq7t08Bw8tK1+e+MxMhiijAj4VLIyNCw2J/pjx27KFlk2bYmxkRK1q1ejcvj1Dx4374O5D60N/EDdDQvjr+nUCQkM5fO0azXIdszh640aiExNJSElh0vbtqkn5wKVLLC+iE5lynBQLQkdLi6HNmzO0eXO1t0OOBWJB794vWSB+v3iRgWvWUM3ZGacCPNeLCgOFgsmffVbgiXDlDAzUFqL1mb4+q9zcCvX0b2NpWaDzobp5kpbGT48e8Tw9XZUrv4m5OR2vX8dGV5fvnZxUn9WWyQrMsdDU3DxP7LuOXI5eriNv5wQH09bSkm2enlS/cEEVkaGr/Ezuz/YNCOBmrVps8PDgy+vXVTkMriQkMPLuXRaXK0dkRgbzg4NJUa6+yxkY0M3amqnK3AY5ddTOd+yutkzGXBcXvrp5U7Wy0M13PwWVvYqg5GQ8jYzyePm/7vM5R0XHqWklvvnJEyY/eIChQsHDlBRVFMqtxER05XK2Pn2KkULBhDJl3ijx1IeOiYHBO5078qGRnJLCvKVLmb98OX8rs7GmpKZioK/PkePHGfHdd0weO5aSGnY4fltkIjq6eHOXKsP/JN7eAvHT3r38efUqNV1cWNSnDzWUK5P0zEzWHD3KyF9+ISs7m9IWFoxp04YhzZv//5bJmjVSIxbnAHr8OF9ZWzPHxQVTLS12P39OktIyUlJbmy/Mzalw/jxZQtDHxobvnZwISU1lQlAQvz57RoYQuBgYcLVGDZ6lp7MsJITLCQmMKF2a9paW/B0dzU+PHqmOue1ubc1mT0/8Y2NZ/Pgxu3OtmhuamTG2TBncDQ0JTkkhLC2Nf+PjWRISQrYQ1DQ1ZXTp0nQuVYq7ycmqPAfaMhlVTUy4kpDAVzdu0NrCgp+V0QyDb9/m12fPqGhszMry/8feWYdHdXRh/Le72bht3IUYJIQQXIpTqrgVWuzDrUihUKxIW6BI0QLFvVCcAoXiUgoUjRCixN1dNvv9sWFhSQIhBILs+zx5ktw7d2bu3HvnnHnnSE2aGBgwPSSEJeHhSrEKdtWujYZQSPd79xTH9EQivnVwYGZICHoiERlt2mB16RKx+fm4amvzoFkz6vz7Lz5PKAjDra25mJZGZlERkS1aoHn2rKKdrywtGW1jQ9MbNx6zAiUum9WGkoiqb/P8Y6qvX27ioueiuse/ugWwkZGgWttXKQDvOVQKQPVOAKr3n24lKZ41hUJ2xMZSJJOhLhTyqYkJM4KD+T0+nq9tbVnu5sb0kBAOJCQwxsaG0ba2nEhOxj8rC4FAgI2GBp66utS9do0Zjo7MdHRkdmgoCx4+xFgsZpWbGx8ZG/OVn5/CFkSlALwcrgcHY6qvX3lmUaUAqBQAFVQKgEoBUKE6oFIAXg63wsIwNzDAurKUt0oBqFYFQC1VUr0DcLqnahKqVvmvGv9qngFUQ1Cd+PDvapb/b/j4XN51mQ/6Piu1uCMvY/veXvUKViuEqiF495ASncJQi6GcWHFCNRgqqKBCpSArlhF0LUg1ECoFQIW3CekJ6aTHpxPhE6EaDBVUUKFSSHiYgJmDmWog3mGoqYbg3YNDXQdM7Exo1K2RajDeckhaSajxfQ0kbUr26mQQtS6KmA0xZNzMUC43uwaS1hJkRTKi1kQRsTyC3JDct7p9AMMPDHH52QWDpvIYA6nnU3m44CHJJx/He7AaaIXLIhfEJmLidsYRNj+MbL9s1Qv0Eoi+H411Les3sm/3bt9j6U9LcXJxwt/Hn+SkZE5dPcWhvYcICQrh/N/nqd+4PrMXzgbggf8D9mzfQ15uHv4+/qzftR5Tc9P3/hmrGIB3EAKBgDaD2uDVwUs1GG85Ui+kcrPdTeJ2yWPiF6UX8WDMAyXh+6hc8PRgivOLudX+Fg++flAlwre62wdIu5zGzdY3ybwjDywVtzNOSfgDxGyJIe1KGsHTgvH9ylcl/KtIATB3MuePOX/QV6MvvQS9WPO/NcQFP87PEHg1kAnuExhgMICjS44SFxLHqv6r6CXoRS9BL44uPkpO+uOkT5d3XaafTj/G1xzPtQOVz8XgWtOV3JxcLp+/zOyFsxk8ajC3rt8iLCSMb6Z/w/aD21m/aj1/Hf2L7Kxsxg4ey9Q5U/lp2U+kpaaxae0m1QNWMQDvFlb2W8mlHZeQWEqwrmXNku5L+O/of2jrazP95HScGzmrBulthAwCRgZg2MIQTVtNbEbYELk6slQxq/5WhMwMIfVC6rvVPlBcUIz///xpdKMR9t/aE7stluKCx3EENO01ERuJCV8Y/sJ15wTm8HD+Q2K2xODyswv2k0vnFCnKKOKSzSXUjdVxXeaKjocOUavkLIdhC0O0nbXJupeFWXczHKY6UJhaSMKBBAKGB6DlrIVhC0Oy/bPRra2L80JnxBLxG//aJUUkYeFsQc/ve6JtoM3WCVtxbeqKhbPFY0Hc1BVbD1tGbBiBWzN5CO4x28aQm5HLjcM3aNCpAdoGjyMFNu3ZlKNLjjLz75noGulWum+aWpqYWZjhWdcTN3c33NzdmDx6MgBrlq0BoN1H7UhPS+fYoWM4OjmiXhJQa9/JfWi9B0GaVAzAewavDl70+bEPywOXY1/Hnon7JjLj1AxaD2qNiZ2JaoDeYhRlFHF/qDyEsPN8ZzRtlaOv6TfSR7euLhFLI97J9gEyb2cSuSISbRdt7L9VFtKuS1wJnBCIrPjFvZq1XbVxmOaAUEtIxIoIZIWl64jZEIOsSIZReyNMO5ui7ayNzRgbAJzmOuG+yZ2aa2oSPD2YsJ/CEBuJsR5ijbqlOhZ9LHDf4I73CW+Sjifh08vnjXu/kiOTKcxXzghZXFysyK766def4tzImb2z95Kb8ZjZCb0ZirGNsUL4P8KQX4egpa/Ftm+2KR3/a/VfdJ/R/aWE/yMIBAKl7K9REVE0b9WckeNHMnL8SLYd2Ebvfr2JDI8k/4nQ3yamJujo6qgmFZUC8G6hZb+WdJ3WlfycfC7uuMjpdafxbOfJgKUDMLQwVA3QW47kk8nEbI5BpCei1rrHYY8FYgG11tQiYHgAMqnsnW0fIGRWCHmReThOd0TLSb6KM/7EmIKEglLbEi8kTMQCLPpYUBBXQNzvyimIZVIZqRdT0aujB08kTRSoKftw6jfUR7e2LnG748oso2aghlk3M1JOp1CYVLH0y5d3XX6l45mZnMnWiVtZ0HEBZzeeLSVgFX8LBQxfP5yMhAx2TdslH5diGfvn7afXnF6l6pVYSeg7vy83/7zJv/v+BSA1JpXga8E06vpqbJPMLc05vO+w0rGb125iYWXBlfNXlJSAa1euqSaU5ykAl85doku7LhgJjBQ/LqYu/DTzJ6Ijo5XKhoWEMXHERIyFxhgJjLDTt2PW5FnExcRVunOxgbEc+PEA42uOV+wpDbcazs6pOwn577H36Y3DN9gyfotin6qXoBdz2szh6OKj5Oe8eNKXYmkxJ1acYHLdyfTX688QsyHMbTeXv9f9TfT9aNYNXfdKH8rLth/pG0lmUibB14PfipdQmiMlfFE4tz+6zWnBacXPOd1zXDC9wAXjC1zzvkbgN4HkR+W/1x9s4IRA8qPzMf7EGMv+8iRBDlMdSPoricy7me98+9IsKQ/GPkCoKaTmqpoINYXUmFWDkOkvn4lN01YTsx5mRCxRZjESDyZi1rXi1vBqes/eWRUIBYh0np9++XW44amJ1eg0qRPfHvqWP5f8SVGBPIdCWlwaEkvlIDH2dez5fOLnnFpziqBrQZxae4rmfZqjpV82nd5hRAdcm7qy+evN5GbksmvaLvr81Kdq34cnEop179Odw38cZurXU+W2AVNmo6WtxccdPyY/P59hXw7jv3//Y9XiVWSkP1YW01LTmPvdXFYuWkm7hu3Izsqmx8c96NKuC+lp6YzoN4KWdVsSExVDdGQ0n7X8jPjYeC6fv8yvS3+l5yc92b11NwD5+fksW7CMhXMW0uPjHnJ7gzWb+OSDT1i3Yh117Osw7MthFFcg02W1KwAt2rTg0JlDjJ08VnFs8KjBTJs3DWtbZetQRydHlq5dSoMmDbC1t+XczXPMXTQXCyuLSnfO0tWSbtO7MWHvBMWxYeuG8eWCL3Fq4KQ41rBzQwYuG8in4z4FQM9EjxmnZtBxUkc0tDVeWPgu6rqIffP20WtOLzYlb2Jd9Do6TurIqTWnmOA+gdig2Fcq/F+2fUdveZIZx3qOb4VQE2mLsJ9sT90TdRGbyPdGnX5wok1WG1oltqLhtYaITcVELI3gX69/X2ql97ajKL2I+8PlVLzrL65IWkkw72FO2Lyw96J9gMTDiSQeSsT4Y2O8//ImekM0hamFVVK3/Tf2ZN7NJOV0iuJY3O9xmPcxf+61KWdSyPLNwnp42Zbz+bH5xO+Nx6KfBUKt55Ovr8MNT0tfC4mVBFMHU2q1rMX5LeeB8j0Aes7uiZmDGWv+twb/C/40693smYrO8N+Gk5GUwU+f/oSVmxVmjlVzP3du3uH6P9f56+hf3L11VyGvFq1exNEDRxn25TBqetTE3dMdYxNjth/Yjr+PP3069kEmk/Hhpx8q6jp94jSm5qaMnTyWkRNGoqOrw6z5s0hLTcPA0IAps6eQmpKKhZUF6urqDBg2AH0DfXZs3MGoiaP4Zd0vTB49mcyMTH5b8RvNWzVnyvdTsLSyZM0va2j7UVtCAkPo8FkHrvhc4eqlqxzZd+TNVwAeYeZPM6njXQeAg3sPUlROpq3UlFSCAoLYtGcTTi5OVdZJI2ujMv9+Go9obomlBJFYVKm2zm0+x82jNxmyeggNOzdETV0NkViE9yfe/PTvT7g0dnmlD6Qq2teR6GDqYFphBSB6QzT/tfxPsfKO3/P83O7SHCkXTC5wWnCaKzWuEL4onPzol1udC4QCNG3ke8tPrpC0nbWpe6Qu2s7aFKYU4tff75VTzW8yko4lEbs9FrGRmHp/1yNgTADFecXvTfsAAWMDkBXJ0LTRJGZTTJXVq99AH8MWhoQvlhsTZlzPQK+eHkL18qfKxEOJhP0QRtzOOLwOeWE10ErpfMaNDCKWRhAyIwSH7xxw3+Bese/yNbvhdZ3WlcM/H0ZaJCXqflSZbatrqdNrbi+i/KPoMLLDc+u0rW1L6wGtCb4eTKdJnUorRfn5/DjjR6y0rTASGGGpZcniHxaTmpKqxC4P/mIwRgIjatvWZv2q9dStX5d//f/lH99/8KrnpbRA9Yvywz/any/6f6E43qp9K248uEFQYpDSghagQZMGLJ63mLGDx/JBa3nUwzredcjPzyf4QTB3b95FYiThyoUrnDhygk87f4rfPT+SEpPYtWUXF89epM2HbUhJTuHCmQv43vVl15ZdmJqboqmlibq6Onr6ejg6OaKnr0enHp24dePW26MAqKmpsXLTStTU1AgKCGL1ktVllps/az59B/WlfuP6VdtJkVBJSDxLgDyvzPNw60/5g7HxsCl1TqwpZtCKQa/0gVRV+9Y1rbF0saxY2SHW1DtZD6GGfJwfLnz43GtiNsZQmCxfdTn96IT9ZHs0rDVe+v4ForKfnVBTiOUA+f1k+2eT5ZvF+4xHK+78mHzSLqW9d+3nR+VTnF9MYVohVLEuaD/RnuSTyWT5ZhG1Ngqb4TbPLG/axRTHGY64b3LHtFNp33L9hvrYTbTDfaM7duPsStkOPEsBeJ1ueJYuljg3dObitovEBcdh4VQ2e6tnrCdXBjTVK3Qfusa6CIXCMhdlGhoaTP9hOmu2yi33TUxNmDhtIhIjiRK7PH7qeKxtrTl38xxDxwyt0udta2/LFZ8r5Obk0qpeK9LT5Fkue/Ttwf7f9xMbHcvwccPZu30vWZlZ6OrpUlRUhI6ODn0H9qXvwL5sP7gdCysLpEVSGjdvTN+BfZk1fxajJo4q1Z7ESIKevt7bowAAeNb1ZNyUcQAsnLOQ0OBQpfM3r93k7+N/M23utLd6YpWV5Eg/uvhomeedGzljam/6xrdvYGaAjmHFLV2FWkLExmK0amiReTuzlJ+1Uh+lMiKWR6DlIN/7UzdRfy3PRtPhseV5RY2onofClELCl4RzWnCaO5/dKbfczVY3OSM+Q8zGGIrSi0g4mMBlh8tcML7A/eH38enjw7X614j/I/71vKeF1cuAVHf7rxImnUzQdtYmaFIQIh0RYuPqcdl70g3vy4VfApTrhjftxDQ6ftMRCycLxmwbQ8PODeWr2zLc8KxqWvHDPz/QuFvjUm12m96Ng/MPUpBbUGkWtTLo3LMzHbt1JDoyWrGf/iQ2r93M4l8XY2pW9XPvkX1H0NHVYcPuDdT2qk14WLhCAdi4eiNe9b3o1L0Txw8fx8lVzmx71PHgyoUr7Ny8k8T4RDb+upHcnFyatWrGpFGTCAkK4b7vfQ7/ITdKzMrKUsztgfcD6fBZhzfiXX8hL4BJMyfhWsuVvNw8xg8dr7ihwsJCxg0dx8KVC9HW0X6rP37vT7wBOL/lPAs7LiQ5qrQgrAj1Vd3t65noIdZ8wYlLAPaT5O5VDxeUzwIk/JGAXh09hRX260pokxuaq2hPx71q3HjERmLsv7FHy0mLpBNJZPuXDiCTeSuT9BvpaDloYTXYSm7N3dUMSWsJOrV0qLWuFp67PbHobYFPbx/SrqShwlum+BfJkBXJFAyi7Thbkk8lYzvGVknxfVTm0TVP/n5evc/Cm+KGZ1vbFjtPOzISy7ezeWQo+HR/n1W+qLBIIS/Kw4IVC9DV02XOlDlKWwB3b90lKSGJjz7/6JU8+6zMLHp/1psNqzfgVc8Lz7qecibI0Z6O3TvSrGUz9PT16Nq7K20/aiufX/X1WLNtDT/P+ZkWdVtgZm6GocSQMd+MwdLakjb12zD3u7l81uUzAAryC1i1eBUbVm+gUbNGStsWb40CoKGhwcqNKxEKhVw+f5kdG3cAsHLRSlxrub4xWs3LoN3QdgohfPPPm4yvOZ49s/YofXQuTVze+Pb1TfUr1b5lf0vULdRJPZ9KxvWyJ4GHix6W8sN+1ShILCB6rdzzxGqgFRqWGlVav2FzQ3TcdAhfUjqQTOSvkVj0tlByAYPSbmCW/S1BBkl/Jr36ARHwWpWvN619gViAUEuImsHLxzLLCc4hcnkkSceSFMZ/VoOssPzKEm03baQ5UmJ3xJLtn03qmVQSDyfKr1khD4YUsylGEaXwSWYpel00BbEFJB1LIvlU2Yzam+iG131Gd2xqlb3t4XvWl79W/QXAsWXHCLgcUL7yUyzj8q7LXD94HVmxjN9n/P5MA2ZLa0umzZtGUmISc6bOUTCisybN4oelP7yyd6nfkH4cv3ScIaOHMGv+LKVxX7JmieLvxb8uRix+vKj68NMPufvwLgGxAXTs3hEALW0tNv6+kYiMCHYf3a2IN2BkbMTYyWMZMnoICWKRJwAAIABJREFUQ0YPeWPk3Qt/PQ2bNmT418NZs2wNsybPwtnNmd9W/sbF2xdfS4cXdV2EWKPslW126suH/xSKhEw+NJnd03ZzbNkx8rPz2T9vP6fWnKLHzB50GNUBkdqro8aqqv3KBtoQagixG29H8NRgHi54SJ0DdZTOp5xJQU1XDYMmBq9nZVYoI/nvZAInBpIfm49ZVzPcVri9EoFmN96OB+Me4PyTM+rm8m2N/Jh8BCKBwjvhWShMk6+I1M1e/ZbIo/6IjcQIRILXbhRZne0btTXCop8FAqEAbWdtnH5wIuFAApm3KueGqO2sjdtK5XdKpCPCY5uH/G9tEZZfWWL5lbJNjdsKt3LfRbGRGOvh1uV6BCgm4BI3vE+//pS57ebSbkg71NTVnumGd2TxEVr2a0nozdDnuuFd2nGJzV9vxquDV4Xd8BzrOaJnUvYede22tandtnbFPimhgA/6fvCcdMLKGDZ2GH/s+IPtG7bz5aAvCfAL4IM2H2DnYKeiqaqbAXiEGT/OwN7RnvS0dDq37czU2VMxs3g9WaMmH5zMsoBlZf50+a5L1WhF6mr0W9yPhTcXKl72zKRMNo/bzNQGU5W02ODrwcxtN1dhdLN2yFoi/R6HSc3NyGXPzD30EvRilP0ozqw/U6XtlwdNXc1K37/NCBvU9NVIOJRAdoCyUhX+c/hrWf1Hr4vmVrtbnDc6z53P7qBhqUHjm42pc6AOIl1lBSg/Kp/7I+4rvBj+a/kfKX+nKJWJ2RzDeYPznNU+S+jcUApTCstkP0R6IiJXPn5+Ub9GYTva9vk0Z3oRQd8EoVNTB6v/Wb2ycRHpirAdY0vN1TUV/9feURuLvhav5fur7vYBUs6m4D/IX/G8Q2aEVFr4VzfeVDe86oocKhQKWbpuKUKhkHFDx7F943a+/vbrt1bAFhcXc2T/EeLj4t/I4EOVUgC0tLX4bu53Cg12wLAB76R2ZO9lz6wzs/ju+HfY1pYLgfC74cxqMYu0OPk+r3MjZ6afnK5wz2vUtRG2HrZKH/in4z/FyNqI+Tfm025ouyptvzy8iNZdSgExUMN6hDXI5AL/EbLuZZEfk4/Jp2VPDsX5xYTOCeWsxllOC07j/z9/coIfWyCnX03nqvtVzhucJ3xJuFIs96dhPdyaemfq4Txfnr8g47+MUoL/ETRsNKi1thZ2E+WrBIPGBhh9qOwuajXICq0aWnj/5U2NWTUQG5Ve0Qu1hNiMtCFqTRTSHCnFucXkhOSgW6d8NiUvKo+gSUFctruMlpMWjW40qhJaujxIs6RErorkeqPrCgHo08dHkaznVaO623+XUR1ueC+LhZ0WsnrA6iqt06ueF/2G9CPAL4DBowajoaHx1j5ToVDIiHEjiMqKonHzxu+GAgBgYGiguMEn90zedjwZYfARvD/xZtGdRYq9tvT4dA4vfBxyUqQmYtSWUYg1xOycuhNpoVTp+gM/HmDw6sEYmBlUefuvgoEAOR0u1BAStzNOEX3v4c8P5YlSynncQg0hNb6vgfNCudA2aGqAtvNjo1CDpgboeOjgfcIb+2/sn+lbrZjAxthi1sMMaZYUn14+z/Q3d/7RGW03bSJXRz42GCxB4pFEJG0kSFpKnt3eaFuk2VJiNscQszUGq/7PXs1r2mjistgF0y6mJB1LqpDBlwoqlIXqcMN7aYWwUPpKotrVcK4BgKFEFcL8jVQA3lWc23SuTFsCoUhIj1k9aDWglULwKq1Ya1rTfWZ3In0jObTgkOJ42O0wUqJTFG45Vd3+q2IgNCw1sOxnSXFBMeG/hJMXmUf6P+lY9Hk+1Wv7tS36jfQJnR1KUcbjoFEZNzPQtNHEoNmL2Q+4b3RH21mbzLuZPBj/oPyXWVOI+wZ3ivOKuT/s/uNJKlseathp3vODU6mbq2PR14KIXyJIOZWC8cfGFepjzTU1EemI8Bvo90J+6SI9EZb9LWmZ0JLW6a3x2OKh+PE66EW7wnYI1YVou2jjNM+J9rL2tIhqgeceT+qdqUfjO42xGaVssGXY3JA6++vQXtaexnca47nbk0bXG1H/fH2M2pYfSEurhhY119TE66AX7hvcqfVbLRxnOlJjTg10PHQwaGyAxxYP2svaU/9C/cd93e5Bs4BmOP3o9M7PD/F74rlocZHTgtMETQ567I4qg/DFcnfSgFEBlQ5ZXV1ueJXFh8M/pM2gNirBoVIA3g3IimVcP3i93PMNOjYAUPKtfYTOUzpj72XPgR8PEB0QjaxYxo7JOxj4y8BX2n5VMhBPwn6yPQKhgOjfogmZGYLtWFsE4uezPQKhAPf17hQkFBAyLURxX2Hzwqgxp8YLPxM1fTU893oi1BQSvS6a+N/L97U3/MAQm5E2pJxJIWazPEJcyMwQHKY6PDP++pPMgt1EO3JDcuXCv+R2n3YBgxIXrxLjN5G2iDr765B6LpXQeaEVX0FlSondFkva5TQKYgvwG+in+Lnb9S6BEwIR6YrICcohZGYIsiIZcb/H4dPbh1vtbhH/ezw1V9dUUgLSrqQpsvKFzAjBp48PN5rfQJopxfukN3pepQ28jNoZ0fDfhqScSeFu17v4D/Hn/rD7JJ9IxnasLWKJmPRr6YQvlW8JRa2JetzXfn786/Uv0gzpK/kmBUIB1kOtaRbUDHUL9WqdH8x7m+P5uycIQMtJ67FxqAAkLSXYfm1LzV9romFTOdq6Ot3wKoqMxAwGmwymt6g3x1cc58SKE/QR92Gw6WDS49PfafmQl5vHrMmzMBIYMXrgaEXQoOAHwdSxr8P3335PZsbbY49SaQXgUTjg15HU4MkUn8XS8tt7dO5ZZSqCvbP3lrvHHng1EIDmfZqXXs2piRi1aRTSIinrhq7j+PLjNOrWCImV5JW3XyUMhAyl1au2qzamXUyRZklJOpKE9VDr0uWhzBWvbh1d7CbaEbUmivRr6USvjcaijwVq+s/eH1cI2aceoZ63Hm7L5BbX/kP9yfYr3+PDZYELmnaaBE0KIvlEMvmx+Zh8VrbdwiN3reSTycTtjqM4txjd2rpY9LXAsp/c6jv5ZDJJx5PIC88jZlNJIKADCaSeSyXLL4u43XFIs6Vou2jjvsmd0O9D8R/kT/rVik+GsoKyJ+eYTTFKLEpxvvLARK6MBBmY91COVf90OVmhjKg1UQjUBJh2Vg6mom6hjuceT2I2xZCwL0F5sv8vg4CRAYr89eX1szi/mKi1UZX63kw7m/JB+Ac0C2qG6zJXXJe54rbKjeZhzdFvqI+sWEbGjQzFdpJWDS1cl7rSXtYej+0eimvq/FGHun/WfeXzkaS1BOvB1oTMDFFEw0QGEcsjcP7J+aXrry43vIpC20Cb1gNb8+PVH/l8wud0m96N7899T+uBrdE2rJo4MI+S9TyZtOdNgKaWJnMXzaVV+1aI1ESKrXBbB1s+7vgxc36e88ZE+avQ4qqyF8ZExSg0otSUVKXQjVWN5Mhkpb9r1C97FZkULve/TotLQ1okrbS7XnJkMlPqTaHPT31o0qMJmrqa5Gfnc2rtKY4tO0brga1p8VWLMq91rOdIx0kdObzwMLJiGXMvzX1t7Xee0pmrf1zlwI8HaNKzCVauVuyYvIMx28Y8XwBJZRSlFVGQVKDkY+8wxYGEAwnYjLQpZYRXkFQg/51YUGadTrOdSNiXgP///NGtrYvnHs/nKnp5UXny9yo6r9R56+HWpF5IJW53HLc+voX3cW90PUsb6In0RNRaW4vbn97G90tfmvg1KbfN8ty1au987Opk/JExTf2aKp0362aGWbfSFtVm3c1oL2svH5f4Any+8CHxSCKN/m2Ebh1dpJlSfL/0RbeuriLoUnkw625G5s1Mch/mlv8BG6iBAPLjnk85qxnKP/eny9oMt0FsLC43pn7C/gR0a5dvCCkQCbAda0vEMjnrYNDYALsJdhQkFlCYVIjdRDvuD7uPfkN9DD8wJGxeGB5bPYhYGkHYT2EkHk7Eoo8FAjUBgeMDFfVG/RqlcKnMCXlsTJobmkv0b9HYTbAjfGG4Uljop7dDXhVcFrmQ+GciQZOCcN/sTsymGMx7mlcoy9/zUJ1ueBUSGiVeSgALOy5EYiVh2Lph1Pyg5kvXnZqSyoHfD7Bz804AVi9ZTX5ePl8M+AI1NTXeFCxYvoDW9VozYtwI3D3d2bRmE6O/Gf3WMRovPKJXL13l9InTbF67WXGs16e9+KzLZ3z5vy+rNFRjbGAs/+z9h0s7LimOrR+5ntBboTTo1ECREfDG4Rv4nPbh73V/A3KXuR8+/IF6n9Wjw6gOL5QRUFNPk59v/0xsUCw3j95k39x95OfkU5BbgH0de0ZvHU2LL1s8s47WA1tzeOFharao+cJ5CV6m/UcMxHeNvmPd0HU07ta4QgxE7I5YEg8nIs2R4j/IH9POptiMsAEB6DfSx/hjY2y/fmxXkHQ8iaRjSWTdk0+8obNDKUgswLynORpWj8daqCXEaa4Tvl/5KtzGyqTBc6RErY4i+a9kxYoqao18NSlpIcG0y+N3qtZvtci4lUHOgxyueV/D+GNjzHubK1brCqH9iTHq5upoOWhVedCgikLdXB2PLR5cq3+N7AfZcm8CkTw2vOPM0oma1C3l5QHUJGqYfGrCPy7/lK+8GIupta4WBXEFz83Gp+Oug9M8J9L/TSd2e2ypsZJmSckJzCmXlXk60I3NSBtMPjYBIRg0MSD9n8dsR1FmEbpeuhTnFxM2J4z43fEUpRShYaGBlr0WmvaaRK6IVBLqsiJZqcBK2f7Z5D3MK5NlKs/YMnZr7OuZOA3VcFvuhk9vH0w6mpB2OQ33ze5VVn91ueG9KEJvhWKSVHV9lRhJGDxqMINHDX6j79vN3Y0BwwYwfeJ01u9aT1ZmFvaO9rxteGEFoGmLpjRt0ZSZP8185Z2zdLWk+4zudJ/R/ZnlGnZuSMPODfnfyv+9dJv9Fsk1W4e6DjTt2fS1P5CXbb8yDERZQU6ehPcJb+XJ6VMTTD41eaZQf1JIgdxArzw8SgdsP/n5H5BIV0SzgGZvzQcm1BTisdWDu53uImklIXZLrJIypcSolNgAKBiUcowWJS0keO72xLSLKdEbovEb4FdmXAOQR020n2iPYUtD7g+9T+yO2FJx/DVtNMu9vjxErYlS2GKom6njMMVBSXDnBOUgzZKScDCBhIPybQUdTx0krSVy5e4529FCLSFmXc1eyL3QarAVMRtjEKoLsRljg904OwJGBeC+yZ2gKUHoeemh46ZD2tU0bEfZcq3hy/llm/cyJ3ZbLL59fWka0JT3EXa17d4aZaWqMXXOVBq6NmTYl8PYvHfzW3kPKiPAdxCtB7YGqBQDoULVQ7+BPtZDrbnz2R10PXUrHCcg6VjZIYVTL6XiN8CPnMAcJC0lSLPKN76L2RKD/1B/ivOKMWhsUGYSH2mOFJFe5anrgoQC0m88Ze9QTGmjwGJ5HIHyhL9ubV2cFzjjstCFBpcalBmroZQAGm+H8wJnaq2thdNcJwU7kHE9A007TUS6IgJGBJB5I5OCuAJ06+qScjqF4BnBFKUUvfyKtbUEka5IkRjrfcMjo8X3EYYSQ/oM7IO1jbXCFuCdZwBUeD7ys/OVfr/PeBTs52mjtNcBaY4Uabb0jRgHmzE2hC8Jx/gT4wpfk/5v+jPH1a+/Hw2vN6TG7BoETwsut2xuSC5Bk4KouaYmCQcSSsWlT/8nHcsBlmg5aD3T3uBZeJZnRkWR5ZtF8FT5fYhNxJh1eX7UuohlEQobAIeHchZCViwjL1y+dZBwIEGh9OjV0yMvLI/0q+kvZKCpQvmwcLbAyNrovb1/DQ2Nt3qRpWIAqhh3T95l/7z9AFw7cI3zW85XSY6CtxEpZ1OIXBWpmKjTLr+eLHmZdzIJnhqMNFNKtn824YvDyQnKqdaxeJlgWU/bNyju824mobNDsf/W/rm5GaLWRpF8Mhn3je4KY0CFEF0egUwqw3Z82VsT6ubqpSIrlgUtJ60XjvFQHgqTCkk9n/pC1zzpwaBweXuSbZBRZa5wiiormO3vXYWeiV6ZLtHvzQKnuFjJS03FALzn8PrIC6+PqjfV45vCQBi1NXpm4JlXNinV1UOvrh7OC5zfiHdCVigj8ajcyDLxaCKmHUsbygrEgjJjLBh9aKTk+y5UFyrZU4T/HI5pJ1Nq767NjUY3FB4ZQg15mSfL+g/2p6lvUzy2enCv2z1FDIPM25kEjg/EdbkrhUmF8jDNuXLGRttVG4s+FoTODVX0E0AoFpbqv8siF3y/8FUsLQQaglLLjVLHnoGc4Bx0a+sqWfk/r/yjVNFF6UWv/Lmmnk8l4WACRelFRK6MxPwLc9RN1d+r+U5bX/ul8o68zQi8H8jl85fJzMjE546PIo2wSgFQoVoZiFNrTikYiBr1a9Cwc0N0JDqqwamu1b9YgNUgK6wGlQ4rLNITYfGFBUZtjVAzUKPOH3UU2xZiYzFGHYy45nUNbRdtLAdaIhALMO1kSvo/6cT/EY+sUIZffz8a32lMw2sNiVwVSeatTOzGyfdlbUbaUJRWRMrpFPKj8wkYE0DtHbWpf64+EcsjSNgvXzVHrookyy8Lh8kOWA+xJjc8l/zofDKuZ8g9DGRya/9H+RYcZzhi1M5IcX/6DfTJvJ1JcUExJp+bYNhUHsLVvKc58X/Eo1dXD/Oe5mg5aOE4zVGuZDy5LSSkVIhpkZ4I897mcgVA8BST8kj/eOoa62HWpF18zDQJRAKlFbpAVHV0raS1hEbXGr3X77a6lvoLeVm9S3Ct5crJf06+3XNTiiylWvmL05xWSYhqxG/8phqE6nz/Bar336ybPMWzUFMo91IokiFUF2LyqQnBM4KJ/z0e269tcVvuRsj0EHlcijE22I62JflEMln+WQgEAjRsNND11OVa3Ws4znDEcaYjobNDebjgIWJjMW6r3DD+yBi/r/wUngmPYjZUF4Yx7K1+dsHXg9E31a9wlsGn0Z727/W7byQwqlYDApUCoFIAVIOgUgDeW6gUgJdD2K0wDMwNKm0IqFIAqlcBUCNVUr0jcLqnahaqVg1ANf7VC5WbZrXiw7+rt/23QP5furQTZ+eGWFq6ljrnCBDyMhqY6hWsTqi8AN5BpKREM3SoBSdOrFANhgoqqPBSCAz8ByMja9VAqBQAFd4GpKcnkJ4eT0SEj2owVFBBhZdCXl42GhoqI+J3ESovgHcQDg51MTGxo1GjbqrBeIfg7X0CY+OPKSxMpqhIOaaCUKiJhoZ8lRYQMJKoqLXvXPt6evVo3PgmeXmRFBTEKvn0a2u7IBYbERe3G1/fvqqX5R3Gn38eZP36lbRp04HLl88THh7G5cv3iImJYs+ebaSlpXLjxlXmz19Oo0bysOFbtqwjJSWZe/duIxKJWLlyI9raKqVGpQC8gxAIBLRpMwgvrw6qwXiXPlY1fe7e7URi4tFS5zw8tmJp2Z+kpD9fifB9E9pXVzchIuIXAgMnKh3X1LSnaVNfCgriefBgrOpFqUJkZ6eiqys38Ltx4zC//NILN7dmaGs/DviUnp5AYOBVLC1dWbToDurqWnz7rTe5uRnY2LgjFD4OM+3vf5Hs7FRGjtxImzaVy93Stm0HZs2axKVL51i8+FcuXz6HQCBgxoyJbN26HzU1NX75ZT79+nXj3r2H7Nu3i9DQYObOXURubg41ahjTqlU7+vcfqppTVK/4u4OVK/tx6dIOJBJLrK1rsWRJd/777yja2vpMn34SZ+dGqkF6i5GW9k+ZwtfcvBeWlv0pKEjA33/wO9u+WGzMw4c/P63u4u6+CZFIFz+/ARQWJr9wvTk5gTx8OJ+YmC24uPyMvf3kUmWKijK4dMkGdXVjXF2XoaPjQVTUKiIilmNo2AJtbWeysu5hZtYdB4epFBamkpBwgICA4WhpOWNo2ILsbH90dWvj7LwQsVjyVrxzUVH3sbGpBUBmZhLffLOf+vU/Vyozf/6nCARCRo3ajLq6PCeClZUbY8ZsQ03tcWCkwMCr/PffUerW/bjSwl/O9uhgZGRC+/Yf4+johKOjE2fPniQ+Po7161cBkJWViadnXRIS4lm7djk//vgLAFpa2ty8GYSxsalqQlEpAO8WvLw6YGNTi08++Zo9e2by1VeL8Pe/wK1bxzAxsVMN0FuOhIR9pY5patpSq9a6ktXVIAoKEt7Z9lNSzlJQoJxzwMZmBEZGbYmP30NCwoFKChRXHBymERe3h4iIFdjZjUcgUE5EFBOzAZmsCCOj9piadi5pewwREctxcpqLRNKajIwbXL/eGJmsGEfH6VhbDyE0dDYWFn2oUWM2RUXpXL3qQW5uGPXq/f1WvHPR0fextpYrACKRWinhf/bsRm7fPkHHjt/g5vY4S6e39ydKwr+gIJfVqweipaXH8OHrX7pf8oBQjz1oIiPDMTExZeTI8aXKhoeHUVhYoPjfyspGNZmUQGUE+A6hZct+dO06jfz8HC5e3MHp0+vw9GzHgAFLMTS0UA3QW4709GtPTYJCPDy2o6ZmSGTkapKSjr/T7T8t/LW0HHBx+ZmCggQCAsa8pEARY2HRh4KCOOLiflc6J5NJSU29iJ5eHUD0xDXK6yd9/Ybo6tYmLm53mWXU1AwwM+tGSsppCguTKty3S5d2Ehsb+ErH9u7dU0yf3hRf37PlKgCtWg1QOpecHMXWrROxtHSld+95SueeLrt793RiYwMZMOAXjI2rXgBbWFhx+fJ54uNjFcdCQ4OJj4/FysqGkyf/VCp/8eJZ1YTyIgzAlSsX6NixtVxrEArR1Cyd/jIvL5fi4mJEIhHHjl1UGGC8S4iK8mf//nn4+p6loCAPAwMz6tb9mObNvyAo6BqOjvXw8GhdJW0VF0s5eXI1Z89uIj4+BHV1LezsPGnatBfu7i3588+lZWrTkZG+ZGYmERx8nY8+Gv3C7ebkPCAmZgsxMVsoKJDnY3d334iVVcVoOz+/fsTG7gBAImmNickn2NiMQSR68aQhKSlnSUn5m6iotQrDM4FAiFhsglSai1gsQUfHAyurQZib9+B98qu3t5+CRNKK7Oz7BAVNfs/aF1Cr1sYS6n/gCwnU8qCpaYuZWQ8iIpZgadlPcTwx8SBmZl2JilpTsUlVTe85yoYQkajiBmiBgf/QqFGXVyj8TxIZ6UfbtoPZt28utWu3VZzLyEhCT6/sDJZr1w4hLy+L0aO3KKj/svDgwRWOH19eQv0PqrJ+FxdLn1j8tMXIyJguXdozbdpctLS0OXbsEL/8so6+fQcyb9407O0dadasJQcO7KFnzy8V16alpbJixc9IJEYcOrSXI0fOMWBAD4qKCtm6dT9TpozF39+H33//E5lMxrBhX7Jp0x6Cgh5w794tzp37m27dvqBPnwHk5+ezZs0v5Ofnc+PGVTZs2M2BA7/zxx876dKlF6tXL6FJkw9Yu3Y7QmH1r78r3IPk5EScnd04c+Y6iYlFREVlKf2cOXMdsVhO+YwbN+WdFP7+/heYOrUBAoGQhQtvsXVrOt9/fxYtLT1mz27Ntm3fVOnLvWhRV/btm0evXnPYtCmZdeui6dhxEqdOrWHCBHdiY4PKvNbR0bvkd71Kta2t7Yaz83w8PLY+QaMtptxE7k8gPz+auLg9JZShLvXqncLe/ttKCX8AI6O2ODvPx8lpbsnkqk/r1pm0bBlPq1YJ1KjxPWlpl/Dx6YWf36AK9fFdgL5+A5yc5lBcXICv75cUF+e+V+3b2Iwsof73kpCwvwqVmm/IzLxLSsrjCI1xcb9jbt6nAsrqGbKyfLG2Hl7OtxFLfPxeLCz6IRRqVbhPr9oNz8vrIz7/fCJt2/6P1NRY7t+/+NxrzpzZwN27J/n88wm4ujZ9BmuTy6+/Dqoy6h/g3LlTBAc/4ODBvdy7d7uEDdJm797jSCRGjBo1kNWrlzJp0gwARo2ayOjR37B8+UIGD/6CRo2aUaeOt6K+06dPYGpqztixkxk5cgI6OrrMmjWftLRUDAwMmTJlNqmpKVhYWKGurs6AAcPQ1zdgx46NjBo1kV9+WcfkyaPJzMzgt99W0Lx5K6ZM+R5LSyvWrPmFtm0/IiQkkA4dPuPKFR+uXr3EkSP73i4GICkpkR9+WIK3d8NS5woLCxk+/Cvy8/Pw8qrHlCmz37kJt7hYyqpV/TE3d2LMmG0Ky1ZjY1v69PkJJ6eGLFnSvcraO3duMzdvHmXChD00bNhZcdzb+xNq127D7Nnlsww6OhJMTR0qrQA8rqcWQqFcqcvOvk9i4lFMTTs985qIiGUIhRpIpYWoq5uX2kut/OrMTrHye6RMCIWaJayEDH//IcTGbsXE5GPMzb+ocL1FRWnExm4jPHwxeXmR6OnVpXHj28+8Jjc3lH/+cUUmk2Ju3hNz8y/Q1fUkKelPwsJ+oLAwBXV1c7S1XZFKsygsTEZPrx4ODlMwMGjy0mMhEulQu/ZOBAIxwcHfkpl5+7V+C9XdvpaWIy4uCykoSCQgYHSV1q2v3wBDwxaEhy/GyKg9GRnX0dOrp/gOykJi4iHS0i6TmxuKl9ehUt9IRsYNIiKWkpXlh4PDd9jajn4j5ziBQEjXrt+xb988Zs78m4KCXDQ0tMuQBRFs2/YNVlZufPHFD8+sc9eu74iNDWLkyE3PpP6/+WYkW7f+hpOTK/r6Bk/NKQ9JTIzHycmVCxdu0aZNB8LCSqeKrlnTg+PHL5XByKgxe/ZCZs9eWGbbDRo0oV27hvj7+zB9unwro04db/Lz8wkOfoCv710kEiOuXLlAWFgw3bp9gZ/fPZKSEtm1awsAbdp8SEpKMhcunEFXV4+goAeYmpqjqamFuro6enr6ODo6AdCpUw9u3bpBly693h4FICMjnRYt2pR5bv78Wdy7dxsNDU3Wrt2OWFzxST8uLphLl3ayb99cZLJiunadRuvWA7G0dCE6OoCzZzdy9OhiRCI1evacTYsWX2Jq6vDaByoiwoekpAiaNOmh5Na8x+OPAAAgAElEQVTyCI0adaVu3Y+rrL1bt/4sWel4lDonFmsyaNAKduz4ttzrra1rYmnp8nL0kFCMUKiFmVlXYmK2EB7+8zMVAKk0k+joDVhbDyYiYnmpPdKXm5xE5Z6ztBxIQMBoiovziYvb80IKgJqaIba2X6OmZoCf30AyM++QnHwCY+NPyr0mPHwxMpmcfnxkgQ5gZzeB3NwwIiNX4uKyGEvLr0q+nevcvv0x//13DG/v4xgZvVz8U1fXZWhru5Kaep6IiCWv/Vuo3vYFuLvLqX9///9VCfVfmgWYyN27XcnK8iUqai0uLoueWd7UtAsSSetnKBUNsbObWKm+vG43vBYtvuKPP+YQGHgVdXUtrKzcSpV5RP2PGrUZsbj8VMABAZc5cWIl3t6fPJf6z8hI58iRczRr1lLpeEJCHM2beyIWi1m/ftcr8d23tbXnyhUfZsz4hlat6nH9egAGBob06NGX/ft/R19fn+HDx7F373Zq1aqNrq4eRUVF6Ojo0LfvQAD69h1Ifn4+UmkRjRs3x93ds4T1ySc5OVGpPYnESCmGRXWiwlsA48dPRUurtDb477+XWbFC7prz/fcLcHNzf6EOWFg407Pn91haumBp6UKfPj8qBJe1dU369VuEoaEFDg516dZterUIf0DxwG7fPkFUlH+ZZZo06Vnl7R09urjM887OjTA1tS/3egMDM3R0DKtoQpwECEhLu0J6+j/llouK+g2JpCXa2jVf88pFhIaGTQkbVTmBIBYbo6/fAICwsPnPoDQTSEk5g7q6GQKBSCH8H9djVIYAaISDw3fIZIWEhHz/UvdqatoFa+shFBWl4efXH5ms+LWOdXW3b2s7ComkDfHxfxAf/0cZTJF9pbebHsHEpBPa2s4EBU1CJNJBLDautgm6LDe8778/x+TJhxQ/OjqGZbrh/fLLfaZMOaoo17nzFHJy0p/phicSqdGlyxT27ZurZAD4mC7/jXv3/ubzzyeWSf3n5maUCL6cZ1L/j8o9gpubeynhL5PJGDGiP8nJSUybNo+6deu/kjE+cmQfOjq6bNiwm9q1vQgPDwOgR4++bNy4Gi+v+nTq1J3jxw/j5CTPh+DhUYcrVy6wc+dmEhPj2bjxV3Jzc2jWrBWTJo0iJCSI+/d9OXxY/o5mZWUp5vTAwPt06PDZ26UAlIWsrExGjuxPcXExrVq1Y/jwrytdl1isibp62R+uhoYOamrVm3Pazs4TIyNr8vOzmTGjGRcubC1Vpm7djzA3r1El7Xl7y1eg589vYeHCjiQnR5Uq06HDyHKv19MzeaZ2/iLQ0fHA2FjObpT2w370sRYRGbmiRFl4vSguzqOgQG79q6tbu9L1GBo2x9CwOWlpl0hLu1JmmcjIFdjYjHrhrQ0tLecSBSKu0v3T0LDC3X0DAPfvjyAvL7LMcmZmryYCZHW3r6XliLOznPp/8KBsGt3KagDFxXmVULiLkMmKShRKIba240hOPoWt7ZgnykgVZR5d8+Tv59VbGVTUDe/zzydUmRte69aDiIjw4cKFbQrlA+TU//btk0qo/3llfBu+PHx4B5BT/3FxwQwY8EuZeQQuXtyh9H+/fqXjR/z661LOn/+bDz5ozdixr87INCsrk969P2PDhtV4edXD07NuycLHkY4du9OsWUv09PTp2rU3bdt+VDK/6rNmzTZ+/nkOLVrUxczMHENDCWPGfIOlpTVt2tRn7tzv+OyzLiXjn8+qVYvZsGE1jRo1w8urHm8CXkoB+O67cYSHh2FgYMjq1VtKfDPfTYhEaowevRWxWJOcnHRWrx7IjBnNlAxmJBKrKvO3b9duqEIJuHnzT8aPr8mePbOUNGcXlybPoB2rNtCFg4P8A0xMPEJOzoNS5+Pj96ChYYmhYYvX/mzCwxcjleYgFKpXmmp9fJ9TSxSd0iyAVJpFfPwerK2HvHC9qannSt6RNpXlOfDw2IJYbExs7Dbi4/eU/UELtTAx+fRV8CzV3r58u0WHBw/GUFCQWAbr1RSJpN0LsxI5OcFERi4nKemYwvjPymoQlpZfoa3thlSaQ2zsDrKz/UlNPUNi4uGSa+TJtmJiNpGZeUepzsLCFKKj11FQEEtS0jGSk089sw9vkhueWKxBx46TCAi4jJGRjWI1vmbNYPLyssuk/ouKCti16ztsbWtz//5F/vqrfOr//v1LxMUFKx0zN7dU+v/evdvMmzcNQ0PJK7eY79dvCMePX2LIkNHMmjVfSY4tWfLY82Px4l+Vtrc//PBT7t59SEBALB07di9RUrXZuPF3IiIy2L37KDo6cobQyMiYsWMnM2TIaIYMeXNsQCq9SXvs2CF27twMwKJFq9+L4Aqenu2YM+cCq1b1JybmAYGBV/n++1bUq/cZ/fotKkWXvZRmJhQxefIhdu+exrFjy8jPz2b//nmcOrWGHj1m0qHDKESi8h/fo33DqoJE0gZ9/fpkZNzk4cNFipXgYyG8BEfH6a/1eeTlRRIZuZzw8KWIxca4u29GW/vl7B5MTD4rMeg7RlbWPXR16zwxGa/HwuLLF3LhkkpziIxcSWTkKiSSlri4LKxUv+ztJ2Jk9CG5uWHlhrtVVzfDzW0VeXlhVT7W1d2+ldVAJJLWyGRS7OwmllL0xGIjtLWdiY3d/sJ1a2s74+a28imFXwcPj20lf2tjafmVwqbjEdzcVuDmtqIcIWqEtfXwcj0ClIX/m+eG1779MHx8TiuE4cWL2/DxOY2OjoTDhxeWEv4PH94FZOjoGPLrr/9DJpORnZ3GokXK7osZGYkEBv7L0KHlu1Tm5uYwdGhfCgoKWLduhypwz5umACQmxjN+vDyOcteuvenR4/1JvuHs3IjFi+9x7NgyDh78iZycdG7dOsbdu6fo1Ws2XbtOq7qHo6ZOv36LadmyH1u3TsTX9yyZmUls3jyOs2c3MXHiH+Ua+mlq6r4CITAJH58+xMXtwMlpHhoacq09JeUMRUUZmJp2feXjL5Vmc/v2R+TlRZOd7YdAIKRWrTUlgrkq7lmAvf23+Pn14+HDBdSuvatkBVRIVNQ6Gja8UqFaYmO3kJCwl+Tkk4jFJtSrdxqJpDUCwYuvZLS0HHFy+lEhWJo0uVeGwqiBuro5IMDX96sqHfPqbl++yt5MTMzmd3JO8fL6CC+vj5DJijlyZBH371+kVq2Wz7zmkRtex47fvBI3PA0NbQYNWq7EKDzNKgAsXdqTvLxs1q2LVhxbuTL4pcbju+/GExQUwJdfDqJz555v9bMtLi7myJH9xMfHce3aFRo3bv72KwBjxvyP5OQkLC2tlSiSl0VSUgSrVw8sdTwjI+GNimSnpqZO587f0rbtYA4c+JG//lqFVFrI7t3TKSzMp1evOVUseL2YNesMt2+fYMeOb4mM9CU8/C6zZrVg0aI7ZY7NBx9UvVJmZtYTLa3vyM19SGTkcpydF5Ss/hdjbz+xUsLtRSES6eDtfZKionSuXatHbm4oGRk3K7TSqigsLL4gNHQm8fF7cXKah5aWE3FxuzA2/qjCBmGWlgOxtPySW7c+JCXlDIWFiZUen9zcMM6e1ay29726239fUJ1ueGXB3NzpuWWiovzJykqpsjH488+DbNu2nho1nFmwYMVb/0yFQiEjRoxjxIhxb2b/XvSCTZvW8PffxxEIBKxevRlDw6pLamFiYsfo0VtK/ejrm1X7QOXn55Sy/tfTM2bAgKUsXnxX4S5z8OB8MjNf3jUpJOS/Use8vT9h0aI7CgUjPT2+FB33aicoEXZ2E0o+/LVIpZlkZ/uRmXkTK6tBr/V5qKkZUKfOHwiFGkRHr1cKv/ry96mGnd03yGTSEqNHGRERy7C3f9FATwI8PLYjFptw//5w8vLCVVJOhWeiRYuviIsLJjDwKjExD16bG15l0aBBJ6U4JS+D2Nhoxo0bgpqaGr/9tlOxf67CG8IAhIQEMXOm3Mp76NAxtG79Ybllo6Mjsba2fWcGKjc3gzNn1jNgwC+lzllb12LKlKNMnOiOVFpIWNht6tT58KXaO3duExYWTujoSJ7SKEX06DGL+PhQLlzYSnDw9dc6DlZWgwkNnUNhYQpRUevIzvbDxmbUC0U2qyro6dXD1XUpAQGjuX9/OPr6DV7aBuDxMx1MWNhcYmO3oq9fH11dzyeCEVUcGhqWeHhs5s6djvj6fkn9+heUYhqIRHqYmXXFxWUxQqEGiYkHlZQcE5PPOXdOB01Neywt++PoOIP8/GjS0q4gFpsgFhsTHf0bUVG/Kq4zNGyOnd1EzMy6kZl5l5yc+2hpOSGV5hAWNpeUlLLjoGtp1cDefjIaGhYUFiYjkxWTlxeJQKBGfPxe1NR0sbEZiaXlAFJTLz6x1y/CwKAh8fH7CQmZ/s5PmlJpDhcvmmJi0gk1tUf++MXExGxGR6cWjRr998zAQc9muB674bVq1b9cN7yOHSeV64anpaVfITc8LS39lx6Lxo27kZmZ/NL1FBcXM2JEP1JTU5g+/Qfq1WtUZpng4Ae4utZChdfMABQVFTF8+Ffk5ubg4lKz3KhKII8MuGnTmlfW6atX9zJunBu9egk4fly+T5Wdncr69SP49ltvfH3PEhZ2m8mT69Krl4C//lqlsAyOiXnAuHGurF49gKSkiBdq9/r1Q+VaGFtaumBlJfd/fzJIR2UhkxVz/frBZ2jeHausrReboHSwsRkByA3/EhIOYmNTfVatNjajMDfvjVSaiY9Pr0q5gJVN3Wlha/s1xcX5BASMxsFhyos+wSeYrc+xtR1DWtoVQkO/f0qYZBIbu420tMsUFMTi5zdQ8XP3blcCAycgEumSkxNESMhMZLIi4uJ+x8enN7dutSM+/ndq1lyNjc0oRZ1paVeIiFhaorTPwMenDzduNEcqzcTb+yR6el6lemtk1I6GDf8lJeUMd+92xd9/CPfvDyM5+QS2tmMRiyWkp18jPHxpCQO05om+9uPff72QSjNeEfMkxNp6KM2aBaGuXv1bgYWFidSqtR5Pz93UqrWWWrXWoq3tXML4bKu08H+E6nDDexEEBV2jXz9devcWsXPnVE6dWsNXX2nTp486Fy9ur1SdK1cu4tKlczRt2oIJE74rs8zp0ydISUnmTUCPHh/z3Xfj+PHHGfz44wyaNatNs2a1KSwsfDcVgCVLfuDWreuoqamxdu32MpMBPcKuXZsxMXkxN7SCghyk0sJylI98ioryFf83bdqLWbPkFqlFRQUlglAe9Ob7789Su3ZbHB29mTRpP+rqWujoSBT7r6amDri6NmPUqC0v7LKXmPiQQ4cWlHkuIyOR+PgQLC1dcHJqUCUPZ+/e2aSlle03Hhh4FYDmzfu8spdD7sNcWuGxtf0aoVCDgoI4LCz6oK5uWuq6R6uiquzLI8Xoabi7r0db24XMzDuVDg1bVJROUVH6U8rFaEQiPYyNP0ZHx0NJuEulmchkUqTSrKfqSSsREsr7oi4ui9DV9SQs7KcyLdVlsoIy+xUTs4mioownVkH5T036KwFZSSIkyi0nN2Jcg0Cgpkhn+wjq6hZ4eu4hJmZTqZS/GRn/ERAwUpG/vrx+FhfnExW1tlJjb2ramQ8+CKdZsyBcXZfh6roMN7dVNG8ehr5+Q2SyYjIybpQIWTlT4eq6lPbtZXh4bFdcU6fOH9St++ernzSFmkqujtnZfoSEzKJGjVno6dV96fqrww3vRWBsbEPbtv9jwYIbdOs2nfbth7Fo0R06dBiJnZ3nC9d3+/Z//PTTTPT1Dcp1+Xv4MJTp0yfi4VHnjRCc/fsPYf785Uyf/gN9+w7i4cNQli5d+0JRcN8EVGgL4Nat6yxZIrcCnjx5Ft7eDcoRgukcOrSX6dMnsnPn4Qp1IC4umGvX9hMXF4xQKOLgwfk0adJDEQr4+vWDJCdHkZYWz6FDC2je/AtMTR0wNrblf/9byW+/Dadp0578889e2rYdrESZm5s7/Z+98w6Pouza+G93s5tOKimkk0ogdDD0IggKgnTpSBMUlI4UBQEpgr6gKCJNikqRJioKRkVKAggGKQkhJCE9Ib0nm939/phkk00jgYSEj72vKxfszDNzzzw7O+c8pzJs2HIOHFhE+/avoq/fiF9+2cLQoUsfu2bB998vIzo6iEGDFuLk1BKVSkVERCA7dsxAJtNnzpyDtRYMl5wcxeLFbRk9ei2+vsPR0zMiPz+bM2e+4uefN9Oz5yS6dRtXZw9HXl4kCkUWhYUZ6Og0KiUwrLG1HU9s7O4K8+7z8gTLSn5+PCqVosoyvtVFbu6DohVz+euRSIzx8TnC1au+xMbuRkfHFHf3DdUqRVxYmE5CwiGioraSlxeFkZEPFhavYGjohVRqhr39dI3shuTkX0lMPKEWykFB07GyGl6UOnhKLdyjooSeCFZWryGT2SAW6+Hjc5DLl9tz+/ZEEhOPYWs7tsprs7IaRmbmNXJzIyr/AeuYACLy8x9dYEhHx1T9vWgqOm8ilVoQG7u7wuMSE49WWWBJJJLg4DCbyMjNAJiYvICj41wKCh4ilyfh6DiPoKDpNGrUAVPTroSHr6Z5871ERn5KePhaHj48iY3NaEQiHUJCSvq5R0d/iUwmxP/k5Nwv9SyEERPzNY6Oc3nwYANZWbc0LEJ1DSHboUSxunVrAkZGLXF2XlJrHPWZhvcomJvb8cYbQoDeZ5+NJSsrhaVLT2tkDdQE06ePQS6XY2IiY/LkURXKlfDwUJo0scfYuBENAcUFgQAWLHiLYcNex9e36zPnAhClpDy6KHGXLj4EBd0q0r4NKhSeSqWSvLySjmB37ybQuPGjg/d+//3JbmD9+oEkJ0fTu/cUXn65fH6yQiFn0aI2tGjRm4ED53H+/LcMHVpzP2VaWjzHj6+je/fxBAb+SmDgaRITw8nLy8bIyIzWrV9m6NBltdbrev/+hXTrNpa4uHtcu3aK4OAL5OfnUFCQi5NTS/r2nUG3bmOfmOfrr8tvy8kJISHhELGxe8nNvY+paRcsLV+lSZPJ6tV+dnYw9+8vp2XLH0oJx9MkJ58lOnqb2hRvbt4XC4u+Ravpx20HfIaoqC9RKDKLBExnrKwGY2s7QcMkHBOzg6Cg6YDQPKhx40E4Oy9Vpys2RPz+u/Bb8vE5iIXFy+oYAB0dMywtX+HSJXcNBaBXryyio7/i3r0FSKUWNG/+DY0atefatd5kZwepxzVq1J6OHa8SGPgqSUk/YWjoTevWpygoSOTatd4a3fs6dAjAyKg5f/5p/MjrNTT0olOnoFIxAGJMTHxJT7/E7duTisZ407LlUZTKfMLDP8TCoh+JiUextZ2IufmLhIWtRiazJDv7rrqgUIsWBxCL9fjvP01LhkRigEKRg0RiRK9emfz9ty0FBfEYGLjRufM9AgJ8NBQAicQQhSK7BoL2yWqy37//AQ8ebOSFF/7F0LDmJbCnT698X0LC/WpF4tcnZs50ID8/h927H88036cPzzSOHTvIokWzuHw5GAsLy8dQpuq3el61LAAXL95ssF/AmDHrWLCgZaW+cIlEyvTp21m5sicpKbHMnv14PipTUxu1huvq2p5hw5bX6X2NHy80IHF2bk2nTk83F9bAwAMXl/dxcXm/SkFQWvgLpsGXsbB4GQ+PT2vtWszNexe1BF7/yLF2dtOws5v2zL5MimMAiuHqurrCcWZm3fDx+Z7GjV8jJmYnt29PLOdyKEaTJpNwcpqHqWl3goKmERd3AJVK09Wmp2df6fGVITp6GwkJB4tWxFYaMRLZ2XfIybmHQpFFYuJxEhOPFz0zPpiZ9SQ6ehuPattc3IQqPv67al9TkyZTiI3dhVgsw95+Fo6O7xIc/Bbe3ru5d28xxsatMDT0JC3NHweHt7h8ucMTfV8ZGf8QEbEOd/ePH0v4PwoNXfgDODq2RC7P43lEZmYGy5bNY+XKDY8l/BsCdJ71L8HPbycjRqxg3775tG37CsbG5b8IT88ueHl1xd7eu8qKWVpo0ZCQlPRzhdtTU89z//5SOna8iplZ93JxCKURG/sN2dlB+PrewMTkhQqL6SgUOUilj/8CKyhIJD39almbYAVBgcqia61Y+BsZtcDNbT0ikQgzsxeJi/umGgJoDgUFSUilplhavkps7C5UqkIyMq6gp+eIRGJEcPAMcnJC0NW1xtp6FGFhq8nPj6Ww8PHz15XKPG7fnoCpaWccHN59bp9RZ+dWyOX5z+W9r169FGfnpowdO/mZvYdnWgH45ZctdO06Gje3jly//jPffDO30hW+jo5undaT1kKL2kZ6ekAVAqiA27cn0KHDFZo2XUloaOUVKHNz73Pv3gK8vLaRmHisXF369PRL2NpORF/fucp4g6pQbA14EmRl3SI0VOjFIJVaYmX12iOPiYzcrHYBODsL1y6kLwoxI4mJx9QWD2PjtuTlhZOe7k96uv8TXWto6DLy8qJo3fpnjZifwsIM8vNj68Qi0BDRpIkneXnZz91v899//2H//p34+V1Vu8QVCgWpqSk1DoCvTzyzEvHOnXMolQrc3X0RicRMn/41Fy9+zz///FjheJVKiVKpRAstnjXY2o6vcHtm5g3Cwlbi5LQIExPfKs8RHf0Vycm/4e29Sx0MWCJEt6BSKXBwmFPhsTKZNebmj65roa/violJ51q5Z7k8idTUv2p0TOkMhpJ+66WtDapa6cOelnaeqKjNeHhsQl/fRWNfVNTWUrUB/v+jUaPGtd53pKFDoVAwf/4Mpk9/B2/vkqyH338/TUHBs2UNeeYUgPz8HH78cSPr1w/UaJJRWJiPrq4BX3wxsVwu6s2bfjx48B+3bgn/aqFFQ4NIJK2wxbC5eV+NQEexWIZYXJIC9uDBx2RkXKVFi+810jHFYt2if/VKKc1TkEiMaN58r0ZmRmbmv4SEzMHBYTYuLss1ijoZGHhgbz9D3SWv+BrFYmm563d330hm5j/qV4tIpFvudVN+W+XIyQmtUXvnnJxQQFQmZbN2oVLJuX17EiKRlPT0q9y5M1X9d/36S0RGftqgg05rGwYGpnXSd6QhY+/erwkMvEZhoVxdB2DJkndZtGjWM9e46JlzAejqGjBo0EIGDdLsD+3u7svevRUXIvHxeZEdO+LRQouGBonEGBub1zE3760ub1wcxS6VWmBu/hKXL7fCwMAdW1tB8DRuPIj09EskJBwpEkgTeOGFQDp0uExU1FYyM6/j6Cj4pe3tZ1JYmEZKyu/k58cQHDyLFi0O0K7dn0RGbiEx8ah65ZqVdRtn54XY2U0lN/cB+fkxZGRcITx8NaDCxMRXnfbp4rIcc/MX1cK/UaP2ZGb+i1JZgKXlQExNhSp11tYjSEg4grFxa6ytR6Cv74yLy1IePPikTK0CMSAqNzfW1qOKTPyiIi5RmbWL5jF2dtNJS/u7lGIiKVWXgidOSRWJpHTpcl/74BZBT8/wuQsCnDx5JpMnzyy3fd26Lc/cvVQrDbAu8aRpgFo8GSpKA9TiaT7/oud+DqyshuLp+RlisV5RlkIhYrEMS8tXCA1dTkLCQRwc3sHTcwv37y8jMfEY9vazcHB4m+Tk02Rl3UEkEqGra4+RkQ+XL7fGxWU5Li7vExa2koiI9UilFnh6bsXCoh+3b49TZyY8aRrgk6KqNMBnAbGxd8nKSqmyI2FVeNbTAJ8U9Z0GqFUAtAqAFloF4LmFVgF4MiQkhJGdnUrTpu20CsCzqACoVPWrAMCRemav337TI0XPtwCo98fvOYfoeX/+6lkC9T1bzxNQzxfQp8+GeuV/7733nuvnX5sXp4UWWmihhRZaBUALLeoHu3btwtTUlCtXrjyX/FpooYUWTxvPZCGgq1fvs337WUJD4zE21kdHR4y+vozBgzuQmZmLqakhw4f71hn//atXObt9O/GhoegbGyPW0UGmr0+HwYPJzczE0NQU3+HDtU9XDaCvr4+pqSm6upppYvfu3WPp0qXExMSQk5PDnTt31C03b968SYsWLf5f8GuhhRYlyM/Px9/fn8uXL5OamopEImHRokWYmFRdYyEqKoovvvgCgKZNm9K8eXM6d+7cIF1dOjqwZg00agRz5sDw4TB1KkyYADt3wi+/QHQ0NG8On30Gx48L2zw94dAhzfi5NWvg/fdBpRLOd/QoHDsG27eDUgkmJsLxfn7w8CF07AjvvfeMWQAKCxUsXLif3r0/pFev5vj5fcCpU4s5fnwh27ZN49q1MKZN205KSlad8CsKC9m/cCEf9u5N8169+MDPj8WnTrHw+HGmbdtG2LVrbJ82jayUmpUY7dKlC0ePHi3qLBjBjz/+yOHDh7l69SqHDx+mW7du6rHGxsZMmDCBxMRE0tPT+eabb9R/x48fRy6XI5PJ8PDwYM2aNahUKmJiYjh58iTXrl3jzJkzdOnSpUHxA4wZM4aIiAhatSrpVX/79m3atWtH3759uXTpEoGBgURFRTFkyJBa/27rm///I1xdXdm+fTvHjx9Xb5s7dy6HDx9+Lvi1eHzo6urSs2dPhhctpBQKBefPn3/kcefOnVP/f/To0XTp0qXBxrkUFsKtW3D9OhQUwJUrEBYmCP3QUPjjD0GI/+9/kJ4OISFw4oTwef78kvOYmkLLltCjh/A5IwPu3oXz5wXhDyXHHz0qBH7/8INwnmdKAXjvve/YtOkUBw7MZuzYbkgkJZdvYmLAxx+PY968gXWmAHz33nuc2rSJ2QcO0G3sWMSSkpxiAxMTxn38MQPnzauxAnDx4kX+97//ATBr1iwGDRrEyJEj6datG5GRkZw7d465c+cCkJmZyb59+7hw4QJxcXFMmjRJ/TdkyBDmzp2LkZERISEhvP/++yiVSvbv38/gwYPx9fVFLpfj5+ensXKtb/7KsGnTJqysrJheKlTa2tqaQ4cOaQjqukJ98z8tdOrUibNnz6JSqTh48CAHDx7E39+fwYMHP9F54+LiUCgU6OuXFBY6ffo0O3bsaFD8WjRcmJiYYGpqilgs5sqVK+Tk5FQ6NgGCJ+8AACAASURBVDk5mdjYWLXANzAweKbvvUsXeOONEsFeDCcniIws+dymjWA5GD26+uc+exZ6936GFICAgHt88skpOnf2ZPDgyrt4rVw5gsJCRa3z3wsI4NQnn+DZuTMdqngxjVi5EkVhYY3Pn5eXV+G2BQsW8P3337Nx40batm2r3ldQUFDheXbv3k1GhlAQSaVSqc3VAHK5nM2bN6Orq8vYsWMbFH9FSEhIICYmhpCQEI3tUqmUGTNm1PkzV9/8Twv+/v4cOiS05X399dd5/fXXOXr0KMeOHdOw/tQUOTk5REdHa2wLDg7m7NmzDYpfi4YLkUiEiYkJLVu2pKCggEuXLlU69u+//9ZY8T8rGS6tW8Nrr5VPibx4EfbsgYSEkm2DBsHs2ZoWAE9P6NxZUAyMqlmUUaWCvLxnSAHYuvVXAF57reoWnsbG+syY8VKt8/+6dSsAHV6rukGJvrExL9WycFi1ahUSiYTZs2dXOW7YsGFYWVlRWIUCUpx2l5mZ2WD4o6OjWb16Nc7OzgQElDTA6d+/P3l5eXTp0oWDBzWbzQwYMABra2sAPvnkE3R1dRGJRGzevBmAvXv3YmNjg0gkYty4cdy7d08tbLy8vOjUqZNaONQ3f8MwRxaWU+TEYjGjRo16ovNWt/9GffNr0bDRo2gZfOnSJY1FRTGys7MJDg6mQ4cOz9y9BQYKpv3KauLcuiX49QF+/BHi4wWTP4CPj3DsiRNC3MDIkeWPNzAACwvNbb17C1aAGisAN2/eZNWqVbi4uCASiRCJRDg5OfHhhx9y8+bNOpukc+fuANCsmd0jx1paGtc6/50i35Jds2aPHGtsWbu9oe/evUtSUhK+vpqBjba2tmr/+8mTJ8sJqbLQ09Nj4cKFpKenc+DAgQbDf/v2bc6fP8+DBw80xs+aNYsxY8aQlJTE6NGj8fX1xc/PDwAHBwcaNxZq38+fP585c4RGNn37Ck1rJk6cyOrVqwEYNWoU7u7ugGButrGx4ccff8Te3r5B8DdkFCtqS5cuZePGjXz99decOHECfX19mjZtysmTJwkMDATAy8uLv/76i59++qnCczVr1ozdu3dr+OQbOr8WDQO2trZ4eHiQk5NTYabOxYsXad++PTKZ7Jm5Jx0dYfXv7Q0ymWDKd3EBOztwdYVXXoGhQ2HdOpBIwMsL2rYVLAAffggDB8KCBVBs6EhPF4IJvbwEq8Do0UJA4ebNQiyAlxe8+iqMGAEvvQSLFj2GAuDj48MHH3zA3r171dv27t3LihUr8PHxqZOJUipVxMYKfnULC+On/kWplEpSYmMF4V5WlXpKSE5OxsrKSmNbaR/84MGDWb9+fYXHduzYkWXLlrFz505CQkJo27YtkaWdSPXM369fP1555ZVyx4nFYr799lu+/fZb7O3tuXz5Mn369KFfv37lzPIzZsxAJBLx7bffqrcNHz4ckUjEnj171NuCg4Nxd3dXC++GwN8QMWfOHPLy8ti7dy9eXl68//77LFy4kDfffJNOnTrRp08fwsLCNIRpcHAwv/32W6XnDA0NJSMjQ8Mn31D5tWh46NmzJwDnz5/XsOwUFBRw7do1Onfu/EzdT2GhIMDnzROCAI8cgRdfhJgYePll+PhjIQhw7lxITYWePeHwYcjOhr594aefYOJEiIsTznf2rGAZCA4W9i9bBvv2CdUmi4/fuFHgWbRICBZ8bBeAnV3JStzR0bFOJ0osFiGT6RStCHKf+hclEovRKdIsc2tgOq9NmJmZkZSUVOWYn3/+ucLtV65c4aOPPmLcuHHMnj2bsLCwBsdfNv2uNMaMGUNISAjr16/H1NSUM2fO0L59ew1zvYuLC71792bfvn0oFEIMyIkTJ3Bzc+PUqVPEFf1Kdu3apRHU11D4GwpmzZrFunXrsLKyomPHjgQHBxMaGkq/fv0Qi8W8/PLLqFQqGhXbJMsqy1VUdpTL5SSUdmg2QH4tGi6aNm2Kg4MDaWlpaqsPwNWrV/H29sbQ0FA7STWVrY97oKRUBLxYXPehBG3aCH23796NrZeJcmnTBoDYu3efOrebmxtWVlb8/fffVY4LCAggIiLimeSvKGDnv/9KWjfr6+uzePFiQkNDGTx4MJmZmcycqdmRa9q0acTExHDmzBlUKhVHjhzh0KFDFBYWsmvXLuRyOTdv3qzQT1jf/A0FW7duZcmSJcyYMUPt0issLMTMzIz169cTERFBUlLSYwdYPar089PmD87O5u3gYES//06f69crPS42Px+Znx+W586xJzaWbEXtBBpnB2cT/HYwv4t+53qfyvnzY/Pxk/lxzvIcsXtiUWTXDn9Ozl3u3VvA77+L+OsvEwoLMyodm5V1i99/F/Hnn0ZER2+joODpK1PFsQDnzp1DpVKhVCq5dOnSYwWLisVK1q+HL78UTPBjxgipd/b28Ouv8M47ggn+/feFPPo//hBW7Dt2lA/YW7OmxBTfqJGwGp85E4pFY/Hxy5YJK/KdO6Gsp1gsFszzCoWwSt+9W7gON7eaz9PIkUIqYcniDCp67TwzQYATJwpf/OHD/o8cGxISV/sP3sSJAPhXI4c4rox5+EmxdOlSCgoK1Kl6j8L48eP/X/Dv2LGjXMCPhYUFhw8fxtnZmcDAQI3gsSFDhmBhYaH28w4YMIA2bdrg6+vLjh07OHnyZI1Sy+qbv6GgS5cubNiwgcWLF3Pnzh2NfQqFQmMx8KzxexkastnTEx2RCL+UFG5UYuH7IjoaJdDb3Jw3mjTBsJbu2dDLEM/Nnoh0RKT4pZB5o2L+6C+iQQnmvc1p8kYTJIa1w29g4Im7+yZ0de0pLMwgNnZnpWMjI4UAV1PT7tjbz0Qms37qz2Lz5s2xtLQkISGB4OBgbt68iZ2dHebm5jU+l1Iprvc8fM3rEQR/cjJ88glMngxRUfDddzWfp1OnBEWmGO+8IwQbPrMKwJQpvfH1defChWB27vSrdNyFC8Hcvh1V6/y9p0zB3deX4AsX8NtZ+Y8k+MIFom7frvH5KzJB6+josGLFCsaNG8fUqVM1Xn5SqRSpVFrumL59+2JjY6Ne1Uql0mr5POubvyJkZmZq+NSLIZPJcHBwwNnZGR0dHY3t48eP58cff+SLL75g8uTJAEyfPp3IyEiWLFlSrfTDhsL/NFF8H6Xvpxjt27fHwMAAY2Nj2rZti6WlJQYGBri4uBAfH4+Liwt2dnY0a9aMHj160LhxY/WzURwoXNrSUtHqvT75pSIRvczMMJdK+aSC2JhcpZIr6ek46+khq4PUMpFUhFkvM6TmUiI/Kc+vzFWSfiUdPWc9RLK6SW0zMHDFyKgFkZGfoVKVty7I5Unk5oYhkRgikdRffr1IJFJbAf766y/+/vtv9efaVzzrPg+/YsWktDwTggRritwynvL796GC5IlnRwHQ0ZHw889L6NjRjenTv2bevL1ERpb4pDMyctm69VeuXw9nyJCOtc4v0dFhyc8/49axI19Pn87eefNIKvUU5GZk8OvWrYRfv07HGlaK69q1K4sWLQJg7dq17N27l507d3L27FkcHBxo27Yt+/fvB4RKfNOmTaN37964uLhw5MgRdST+qVOn+Omnnzh16hQeHh6sXbsWsVjMa6+9xpgxYyoU2A2BH0rqEJStLzBr1iwNvzrA999/T0BAAJ9++mm580ydOpWCggJ69+6tVjxGjRqFiYkJ3bt3r9R3/DT4u3TpQufOnenVq1e54x4+fMi7777Lxo0b6dixI+PGjUMul7N8+XJsbGyIiooiICAAExMTPvnkE/UxAwYMwN/fv+g3kKHORihGREQE3bp1Y+DAgaxatYoXX3yR22UU1E6dOjG66O21cOFCHBwcNPYfPXqUrKwsbt26Rfv27fnjjz+YPHky2dnZ+Pn54efnx3///cekSZM4d+4c6enp9OjRAycnJ/r160erVq3o1q0bLi4u9O3bFx8fH42MkvrmBzCQSHjTzo6D8fHE5udr7NsfF8d4W9s6fb9JDCTYvWlH/MF48mM1+eP2x2E73rbO37GOjnPIy3tAYuKx8haI6O3Y27/51N/7FbmM2rRpg7GxMQ8ePEBfX18jHq30MdXtNFqfefiPXHj2LkkPXLdOiPI/dQq6dhVcDdu3Q3FC1fz5QspgWbRrB5cvC8eUk6vPkinS3NyIS5fWsGfPn3z//UXat38PmUwHFxcrXFysmDnzJTp18qgzfiNzc9ZcusSfe/Zw8fvvea99e3RkMqxcXLByceGlmTPx6NSpxue9cOECFy5cqPaqdMeOHdWqZrZkyRKWLFnS4Pl/+eUXdhZZVTZu3IihoSHt2gn9xbOzs5k4cSLz5s3Dzc2NnJwcrKysOHPmjDoquKyJ8KWXXuLtt98utboxYPz48YwbN65e+YcOHcr+/fuJjIxEpVJprES3bt1K165dGTFiBLNmzWLTpk1IpVKWLVvGtm3bkMlk+Pr6Mn78eLUy0rhxY3r27Emnomfu22+/5bvvvmP58uVYFjkYnZ2dadeuHc7OzsyZM4cVK1awYMECTp8+reb29/fnxRdfrPT7iY6OxrvUMuTrr7/W2F/WrVE6G6TsHPWuYNlT3/xqZc/BgU0PHvB5VBTrihyvKuBQQgKnW7dm1WMEz9YEDrMceLDpAVGfR+G2rsjxq4KEQwm0Pt2asFV1y29jM5bQ0CVERn6KtfWIUsJKTlLSKdq3v8CdO1Oe6js/JyeH7Ozsctairl27cvr06XKr/9zcXLXgz87OrlThL43iPHw3N2jfvvz+snn4LVoIJv9Ll0ry8OPjhbS+kSMF372mdQXKGkGL8/ArQ//+0KuXYGnYtAkMDYUMgY4dISVFsExMmSKco7g0zcmTwvayuHatioU1zxgkEjFTp77I1Kkv1gu/WCLhxalTeXHqVLSoHbzyyisVpuEVWxZqiopSwT7//PMGwd+vXz+cnZ3LmaFbt27Nu+++i4GBAQMGDGDChAmAEHw4ZMgQDh48qN5/4MABFi1aRGBgIG2KglOVSiWpqamMGTOG7du3s2zZsgqvLSsriyZNmmgfugrQRFeXUTY2bI+JYbmLC4YSCb8lJ9PLzAzZUwh01m2ii80oG2K2x+Cy3AWJoYTk35Ix62WGWFb3/GKxHnZ2bxIevob0dH9MTATFMiHhCI0bD0EkenriIj8/n2vXrnHt2jWSkpI4efIkHh4eNCuqw+Lr68vdu3fV9TUALl++rGHdOnLkCM2bN+eFF16o0O0kFitp3VoIvqssD9/DA7p1g1WrNPPwT5yALVuEoL333hPOl54OH3wgKAbFefh37wor78WLS/LwfXyEgLwio2uF+PVXKJVkBAjXMWqUoARUkbRULZfAM6sAaFHaImLOvHnz8PDwYGRFJaDqcrXi4MC2bdvo3r07cXFxrF69ukbFhZ4njB49muTkZN555x21O2Dfvn3s2LFD3eBkyJAhKBQK3nrrLZo2bcr27dvVx0+YMIH58+czc+ZMbGxsUCgUBAYG8ueff/Luu+8WrUx+ZNCgQRgYGNCrVy8WL16s4U+/ffs2u3fvplGjRqxYsUL7pVSCuY6OHIiLY09sLLMcHNgeHc2Ox3HCPiYc5zoSdyCO2D2xOMxyIHp7NN47vJ/i7/ptHjz4mAcPPqVlyyMAxMTspGXLo0/1e9DV1aVz586V5vbr6uqWS6d94YUXeOGFF6rNoVSKNYTwkSPCHwh5+MU4dqzYmlSyrajeF6VrThXn4ZfeD0Iuftnji3mqC0ND+P57YdWvUJSs+qvp5ahc6dP+5J9N6Ojo8OKLLzJ48OBqmblqEyKRiF27dnHhwgVmzpzJw4cP2b9/P/37968zzuTkZKZNm8abb77J2LFj8fLy0ljVX716lWnTptGmTRvS09MZPXo0xsbGNGvW7JEVCit+OShZu3Yto0aNYsaMGXh7e/PWW2+RX+QfjoyM5IMPPsDe3p6oqCg+/PBDGjdujL29PcuXL9fIDggNDeXmzZv88MMPXL16lTt37nD27FnulkopjYqKYvjw4dy9e5euXbvSt29fdbGTbt26kZyczObNmxkwYADjx49XF+IqTsE9e/Ys//zzD3///Tfm5uYcPar5wm7evDmTJ09mxYoVT/15eZbQ1tiY7mZmbImK4lZWFlYyGZZVxK7UNozbGmPW3YyoLVFk3cpCZiVDavn0+GUyG6ytR/Hw4XFycyNIT/fH0NATqdTs/+13bmcnmMnnzxcE6/z50KmTsEpPT4dx42DXLpg0qfyxX34pVOkrWZQJVfomTgR/f8GU37o1pKUJ537tNfjqq0dZYjTPCUJAYuPGQivfJk2EMUZGkJlZEu3fqlV5V8Mj5Yj2J/9sorCwkCNHjtCnTx+cnJyeKnerVq1Yt24df/75JyAUvAkJCWHkyJH8+uuvdcI5fvx4cnNz1ZyLFy/mnXfeoU+fPlhbW/PgwQOOHj2KqakpCxcu5KWXXqJHjx4sX76c0aNHo6enx2uP6ONQGps2bWLVqlWkp6ejq6vL6dOneeWVV2jZsiUzZszgxo0bnD17lpiYGD7++GMcHBz4/PPP2bBhAx999BFZWVnqvgBXr15Vn/ejjz7C2dmZpUuXavD98MMPvPHGG5iamrJ69Wr2799PXl4eBgYG6n4Cp06dYtGiRYwbNw53d3d1Y5R///2XAQMGqN0YNjY2rFq16onq6Ds7OzNlyhSWL1/OyZMnCS1KKtbX12fMmDE4OjrWqJ8ECMGmy5cvZ9u2bZw8efKp8vfo0YPPP/8cFxcXLl26xIwZMwgPD69w7DxHR167cYMRN29ytHhJ9xThOM+RG6/d4OaIm7Q8Wg/8jnOJi9tPVNRn5OfH0bRp3VqMbt68ye+//05CQgIuLi7o6uqSkpKCt7c3ffr0KZcZcv36daRSaYWVZ0ufy9bWFktLSxQKBenp6djb29OzZ0/MzDSVmZgYCA+Hc+fgn3+KFDFjQbimpgpBdn/9BTduQGmPYLNmYGsLQ4YIaX0guA0KCmDvXiFYr3VrIcYgLU1wGwD4+VUu+IcNE+r2Dx8upA0+fFh8z8L2334TrA6tWoGDA/z9txAcGBAAW7cKpv4uXYTURF1d6NdPuDc3N+H/ly5pZhnUuwKQkZHL/v1/s3r1DyQkpDN8uC+bNo3Hyakxt25F8dZbO0lMTGfFihF0796M998/xJ49f2JjY8qOHW8ycKAQrBUXl8q7737Db78F8tFHo5k27UXWrDnGyZNXeeEFd3Xr4LCwBM6e/Y8lS4awdq0QeXzTz4/Px42jVb9+yPT0ACjIzeXcvn14dunC+2fP8suWLRz64AMUcjlTvviCXm+8gUxfn8KCAk5u2MDhFSsYMHcufd98k//OnuWH1atJT0jAd/hwxm/aRGMnJ6Ju3WLnW2+RnpjIiBUr6FKTvJFKUFFjjLrGnTt3NKLls7OzuXz5snp1XBdIS0ujS5cuJSu1os6E4eHhNGvWjOHDh/Ppp59y9+5d1qxZoy5b3KRJEwYPHszatWtrpACkpaXh4+OjTo8s5iuuYvjqq6/i7+9PQEAAr732mjqIrWfPnri6urJt2zZWrFih8bKJjo5m3bp1NGrUiOHDh2vULU9OTsbX15cxY8aQn5/PqlWrNNqZTpgwQZ1eaW9vz/Tp02nTpg1xcXHMmTOHNWvWqMdaWlri7+/Pxo0bGTp0KP/++y9RUVG8/vrr6nM8ChEREWzZsoXly5fzzTffcKL47YWQfmVsbFwjAWxmZoZEIqF79+589aglUC3zm5mZ8fbbbzNt2jQMDAz46quvOH78OK1btwZAoVKRUyrL41VLS1z19XHS08O7VHW5ApWK/KK3Z6pczvaYGN6/f582xsYcbdkSBz09voyOZkNEBF94eWGqo8OCe/e4nJ7ONi8vZtjb8yAvjxH//UdHExOWu7gAMlQKFYqcEn7LVy3Rd9VHz0kPQ+8SflWBCmW+wJ8fnc+dqXdI/i0Zj80eOL4rVGNNPp3MfyP+w3OLJyJdEUHTgjD0NqTl4Zbou+ojT5Fza5wQKu611YuKFozGxm0wM+tOTMwOzM17YWjoVencZmX9x61bY7G0HKCOEYiP/x4Q4ev7H7m5oVXuB6G8fEJCAgkJCUyZMgUdHR3Cw8P5+uuvSUtL4/XXX9fgvHTpErq6uhUqAD4+Pjx8+JAzZ84wc+ZM9W8sMzOTQ4cO8b///Y/Jkyfj7Oxc7thOnQRrQKdOJX79Yri5QVE/L41tU6YIefrFCsDp00L9gGbNhHiAP/4QtkskgjXA0lJYuVf0EyiuA1CReyApSYhHKEbpkKaieGWgJCNAsNSW/L+ytiOPrQCUrsWseIKqWI0a6fP22/3o27clHToIs+7kJNRJd3W1xtTUgOPHF6h7AOzePZOHDzO4eDGYbt1KGvPY2prh7m7DtGnz6dtX0JpNTAy4enUdurrSImGpoFOnZbRu7czKlSVRrlnJyaw6fx6bUiWX9rzzDnpGRszatw+Zvj6vvfceEh0d9i9ciKOPD7IiW4uOTIZbx44MXbaMUUXNX2w9PGjZty/vFZVealy0Qrd2dcXA1JQFx4/XW0+B2kBFrYBtbW2rDLR7Uly8eBGRSERqair79+9XWxpKX4tYLMbMzEyjZ8GgQYNwdHTk2rVrNSoas3btWj766CPkcjnHjx/n96JcnLJ8AJ6enuptNjY2DB8+nH379hEYGKiR8mdhYUFBQQEymaxc05I1a9ZoCPGycHNzw63U87llyxb1vJ8ralRVjPbt22ukQJXdXxMrU0U4efJktVOsipGamsq5c+eIj49/6vweHh5MnTpV3ab6rbfe4vfff8fS0pK7OTnsjokhID2dXbGxDLOywlRHh3cdHXEvUsCi8/M5GB9PVF4eOQoF38TGMsLamvecnZGIRHweFUXjou8zS6HglzZtaF6kOPzapg0t/P1RFF2voURCN1NTPil6m+fczSFmdwzpAenE7orFapgVOqY6OL7riIG7gVrYxx+MJy8qD0WOgthvYrEeYU2L71vg7+mP1KLERSCzleG23o0mU4RAz7wHecTti0PPWVjYSM2lGLgZ4LbeDYmBpNRvWrPMt4PDHP77byj29iXZLCqVEqUyD4Uip9QCJJk2bX5FV9euSHG+SETEBtq29UMiMXjkfrUgKrPKd3FxwdHRkRs3bjBs2DB1CnF0dDT6+vrcu3ePhw8fVthTo6JaEsbGxkycOJEtW7bw7bffsmjRonJpyf7+ggUgNbVkm1QqVO4bOVJovlMMKysh7U8iEVb8HTsKhYSSk4VMgpkzhUJAU6cKSoFCIQT2AXTvXvXz6uQE69cLqYXFyVaNGwvVBCtyQ1QEV1fhHDt3ClaDSt0Nj/tCjooqKbYTG/vk5Xk9PGzZtm0aP/wQwMmTgsl07ty9rFnzerkGQF99NQ2lUsWSJSUlksLDE8nOzlcLf4CBA9uqhT/AypWHuXUrigMHZqt7CwA4tmypIfxvnDnDr1u3MmnzZqybNi0537x5uHXsyO5Zs1AUrbwVhYX89c03DHv/fU2B6OHBtG3bCPjhB64WmTv3zp3L62vWPNPCvyJ4eXkRFxenNs/XBfLy8li0aBHz58+nT58+Naqn7+bmhlKprLG1ZNeuXQwbNgxzc3M2bNhQIz6gnEVEX1+f5s2bP5MtS4vRokULOnbsiFwup23btnz//fd8/fXXbNu2jZiYGKZOncq5c+d45513uHjxYrny0U/anvdx+C9fvqwW/sVWnPT0dNLT0/E0MGCDuzsZvXoxpUkTTIuEx2wHB/oX/U7tdXVZ4OSEqk8fknr0YFKpSoDzHB2xkErZEBHB9cxMDMRitfAHMNXRYYunJyvCwkiRy1kTHs4Hpd4pBp4GuG9wp1dGL5pMaYKOqcDvMNsBi/4Cv669Lk4LnOij6kOPpB40mSRUApSaSXFd48r9ZfdR5gnzGrc/DvsZJcs9p/lOqOQqYrbHAJB+OR2TTiZq4Z+Tc4/w8LVkZ9/m/v0PyM6+UyRwBmNpOQALi5fUK/3Q0EWoVArS0s4TE/M1BQUJGBu3VQt3pTKXO3fewMHhbczMuhcJ3qr3Pwo6OjoaSntgYCDjxo3D0tJSoxdHdSCVSunWrRuZmZkaZb7L4u+/BUuAoOAIQvjhQ80gvi5dBJP7iRNCqeC5c4Xt/foJ/372mZAFUFFsdunzV4QHDwSlISpKKDG8Zg28+y788ktNlHdwdNS0AtSKBeDmzZscP35co8PZxIkTmTRpEkOGDHmijoBjxnTl+PErzJy5k1u3oujSxZOWLcv7t+3szPn443HMmLGD0aO70K1bM1auPMLmzZPKCCa7UivIu2zYcJKPPx5H8+aahUbsvEpMXFkpKXz5xhu0HTiQ3lM0c15FYjEzd+9mcdu2nNiwgWHLl3Nq0yb6vf22ulmQhs9zzBiuHD/Ozpkzibp1C88uXXCqB59iXUIkEjF//nymTZtWZxxyuZxevXrh7e3N7qIk25AalFsWiUTY2NigV+TeqQ7mzZvHr7/+yvXr19HT0yMtLa1GfMWrmIpWo6VTl54FjB8/Hl9fX2QyGSNGjFAX7bl16xYikYhu3boxbNgw/v33X44dO8aKFSvo2bMnb7zxhrqeQkPi79q1K1u3bq0V95lEJOILLy/6Xr9OVF4eX1fQLnyYlRVfx8TQ+/p1Nrq7Y6JTe57XJlObEL0tmgebHtCoYyPMe5sj0il564v1xLhvcifozSCsR1uT8H0CHv8rsSUbGLjj4rIUF5elZZ5hMa1bl4S4Gxm1xN19E+7umyq9ltDQpahUSlxd15YS4CZV7q/8XKFERkbSpUsXtaUtOzsbQ0NDdHV16dq1K7/99hv9+/evssBYWRSb/iMjI9XPhq0tODsLJnp7e0FwJiYKvnNLSyFtb9IkwadvZQU3bwo++jNnhM582dlCcN+rrwqr9F9+gW+/FXz0n30mZAZYWAgpg4WFQlrgl18+ysJeftuPP1b/uXjwQKhN8EgFjlUe1AAAIABJREFUq6YPnI+Pj7olcF1g27ZptGgxj2PHLnPtWuWrrmnTXuTgwYtMnfoVCxcOon//1piZVdwNKjMzlwkTttK9ezPmzh1QJf+OGTNQFhYyo5Jyvw7NmzNkyRKOrVmDc6tWpMXH41VRiaXi69y2jXktWnD52DE2VFWR4RnFvHnz2LJlC8nJyXXGcfnyZS5fvqzOja9qRVnWPaFUKgkODmb48OHV5lOpVGzdupXXXnutnNJQ0Qq2LOft27dp0aKFhmug9Auorrtn1jb279+v9sGXLiBUUFBAQkICV65c4c6dO+pS0SkpKZw6dYqQkJAaKWpPg18qlfLqq6/yRkUVUx4TnU1M8DUxIaWwEHElS67lLi70vHat1jMKRGIRnp958u8r/2I73havL8v7662GWhH9RTT/9v0X90/doQ6qCaelXSAqams503519xfj/PnzZGdnk5GRwbBhwzQUuMDAQDp2FKq8tmvXjrNnzxIYGFgji1pxXE3p4kJxcRUXABIUn5L/v/RS6essraxoRt9XlA1tXMqIXaqDdbUxbJjQR0BfX2go5OkppACqVEJsQvHnwkKhqZHwHqsDBaCuoaMjplkze/766zbHj1+ptKyvSCRix44Z+PjM59ChS5w9+36l53z33W9ITs7kzz9XVNlF7Ny+ffgfOcKikycxKeVHLouhy5YR8MMPfDl5Mp+VjQwpA7GODvbNmnH7r7+4cvx4jcsEN2RMmjSJgIAAbpWqP2loaFiucteTojhw7X//+x9OTk6EhYXxQ1HUzYULF4iLi1NX3ouJiSEwMFAd4LVr1y7EYnGNct9FIhHW1tacOnWKvXv3oqury49F6vetW7f4/PPPmVTKGffzzz8ze/ZsQAiQPHXqlIagKg1ra+vHalzSUHD+/HmNGAuVSlXOH1/RtobCv3jxYpYuXVqrz+jFtDRetrDgw7AwziQn81IFLr6TDx8y3c6OWcHBXOjQoVZlsGk3Uwy9DDHxNal0jOM8R+7OvotZ99pP51Mocqo07T9qf2l069atQh++UqkkKChIw91sZGREQEBAjRSA3KKKOKUDbBsqmjQRSv9KpTBtmqAA5OYKsQbDhsELL4CNjVBgqPTnGsnbhnTDKpWK+fP3ceDAbGbN2sXMmTvo0cMbc/OKCyy7ulrj6GhZzqRfGidOXGXPnj/Zt28Wjo6WlY57+OABu2fPptfkybQfNKhqs59UilfXroQEBGBoalrl/eybP5/ZBw6wa9YsdsyciXePHhjVogCQSCRPpR1zWcycOZOmTZsSHx9P//79kclkDB06lBUrVtS6AuDm5sbatWvZuHEjc+bMYe7cuRw4cID27dtz9epV5s2bpyFgv/zyS/T19UlOTkYul3PhwgV1adzqYseOHcyYMYPFixczatQoduzYQXh4OBEREfj4+GBcSqUPCgpi5syZFBQUEBcXx+nTpyttT2pqavpM5+Hn5eURGRmJj4+Pul3vs8I/efJkfvvtN3VKYW0gU6HgxMOHbHR3p1Cl4p27d7nZqRPSUguNr2NiGGNjg6u+Ph6XLrE/Lo4JtdxbQKwrrjKi61H7nwT37y9FpVKVM+0XFMQjk9lUub+6CAoKol+/fhp9IuLi4tiyZQtRUVHl+kdUhgcPHgAVu+caGmJjoSiTmNJVqHNyhOY+GRnCn6Oj5udnVgH46KNjjB3bDTs7c7Ztm4a391xmz97Nt9++81jnS0hIZ9q0rxg+3Jfx4zU1T3//EHXfAJVSyRcTJ2JsYcGk4hkvNl3Fx1OQm4vVYzwwxz76iG5jx2JuZ8e0bduY6+3N7tmzeaeCDnOPg5EjR9KnTx/MzMyYOHEiBw4ceKKMjOpixowZfFnkxFqwYEHJSujiRfUPrLZRUV+BhISECk18ZWvFPw769+9PREREmWem4lbUS5cuxb6yPJtypkBjDA0NnwlhX6xYlrWaubi40K9fP7UAriizorJsi8q6AdY1//Tp0ykoKODhw4c4Oztja2sr9Bd4wud1xf37LCnyK89zdGRnTAwbIyJYWvS+uJmVRVphIW2LFMZ1bm4sunePVy0tMatFd4BKqQLl4+9/XKSlnS8y7f+hYdpPSfFDJrMiJ+delfuri5CQEIaUsZ7a2tpiZWVFQEBAtRSAwsJCzp8/j4WFBS2fsVis4rpetW24aDAKwKFDQlGT3r1bAGBjY8onn0xg8uRtDBzYjtGju1TypSooLFRUovF/iVSqw1dfTStjklLy3XcX1ArAj5s2EXT+PB+eO4e+sWbGwalPPmFEBeZjRWEhykrSlAAuHToEQIui5iOmNjZM+OQTtk2eTLuBA2ulBsDhw4c5fPjwU/+uvvrqq2rlcmtRHvr6+o/dHvlpwtnZmRkzZgBCh77iGgxGRkaMGDGCvn370qJFC3r06IG1tTW9e/fmjz/+oH///ri6ujJu3DguXbpEUFAQIJRuHTp0KE2aNGHo0KHcuXNHoxJiXfK/9dZbfPHFF+U4OnfuLNRYfQxE5uXxXmgod7OzmVeU5vtQLsdKJmNlWBjmUilGEgnz791jZamof4CEggJG3LzJF15ewJO/0VP+SCEnOIek00mY9TRDz1EzbqUgoYDEY4nkx+aT9FMSlgMta+UZUank3LkzGT09B1JSzpCScqbonZxOQsIRunS5z+XLrSvd37VrJPCLWjiDEPBb1gUQHR1daaCfp6cn/v7+9O/fX22Vqyh9NDc3l8OHD5OTk8OUKVOqnQ5cX6hIRx44EP79t1g5LqssV+8c5cao6spZV01ERHzJunXH2bnTj4ULB/HBB8MxMNAlL0/Ohg0nWLnyCPr6MlavHsWbb/bFyEivSIPM4vjxK8yYsQMXFys2bRrPoEElkRy7d//JlCnbaNvWhTZtSlbvcrmCK1dC6dTJg927Z7I52IeFrVphbGFBm1INYVQqFfGhoSRHRbG1TBewy8eOsW/+fFJjY5m+fTsdhwzBwETwvz2MiOD4unX47dzJoIULGf7BB+gaGCDPy+PEhg0cWbkSmb4+o1avpu+bbzKhjMLxvKE2H78OHTqQkJBAZAU93esCCxcuZNOmTdy/f5+mZV7yleHMmTPY29trdLer3xeN6Pl+/sr2gH3K6Hu2niegni+gT58N/Pfff5w9e5aHDx/SuXNnOnbsqI77CQ8P59ixY4jFYl599VWNWhixsbEcO3aM6OhonJyceOWVV8jIyODMmTM8fPgQV1dXzMzM1I2ynJ2d6dq1q4YF7r2yFX8aAJychA6AvXrBp58KGQEWFkKzoldeEfL7X38dBgyA27c1PxcrCC1bCgWFfvpJSCMsXdugQSkAcKSe2UfUK//I5/0FXAuPX3R0NDt27GD9+vXI5XKWLVvGpEmTcHV1rZNrlsvlfPHFF3z66adERUUxcuRIZs+eTdcqskGK8ccff+Dk5FRn16ZVALQKwLOmANQnGqIC8FR//1oFQKsA/H+xAGihVQC0CoBWAdAqADVRAPr0UWl/APWHs/TVvoDqEb///pxLQC200OK5hbYdsBZaaKGFFlpoFQAttNBCCy200OJ5gDrfIkeh4MOwMM6lpSFXKrmTnU1eUdnTzF69MJJI+DY+nh0xMZxLTUVfLMbL0JBcpRJdsZihjRuz0NkZfbGY4OxsjiUmsio8nHylEic9PaxkMhIKCmhjbMx7zs74mmhWrVLkKAj7MIy0c2ko5Uqy72SrG1z0yuyFxEhC/LfxxOyIIfVcKmJ9MYZehihzlYh1xTQe2hjnhc6I9cVkB2eTeCyR8FXhKPOV6DnpIbOSUZBQgHEbY5zfcy5XNUuhyCEs7EPS0s6hVMrJzr6DUpkn8PfKRCIxIj7+W2JidpCaeg6xWB9DQy+UylzEYl0aNx6Ks/NCxGJ9srODSUw8Rnj4KpTKfPT0nJDJrIqaZ7TB2fk9TEx8Nfi181+/86+FFlpo8bxBHQPQ5/p1rGQy9nh7oysWkyyX82ZQEEcTE9UCCOBCWhrd/vmHtx0c2OrpiQrYHBnJvJAQepmZ4deunbrMZZ/r1/FLSeFhjx5YSqVE5+fz0vXrhObk8HObNvQ1N1e7gK/3uY7MSob3Hm/EumLkyXKC3gwi8WiiWgABpF1I459u/+DwtgOeWz1BBZGbIwmZF4JZLzPa+bVT17q+3uc6KX4p9HjYA6mllPzofK6/dJ2c0Bza/NwG877mah/09et9kMms8Pbeg1isi1yeTFDQmyQmHlULIBBqWv/zTzccHN7G03MroCIycjMhIfMwM+tFu3Z+FF/A9et9SEnxo0ePh0illuTnR3P9+kvk5ITSps3PmJv3VccAaOe/fuZfGwOghRZaPK8QA1zNyMAvJYVlLi7oFlUUsJBK+a5FCzzKlB4yLVOkQQTMdXSklbExf6am8ktSUqVj7XV1WevmhlylYmmpcpwZVzNI8UvBZZmLULISkFpIafFdCww8NPmL22WWvgDHuY4YtzIm9c9Ukn5JqnSsrr0ubmvdUMlVhC4txZ9xlZQUP1xcliEW6wr8UgtatPgOAwMPTX6dsqV/RTg6zsXYuBWpqX+SlPRLpWN1de1xc1uLSiUnNLSk+5Z2/ut3/rXQQgstnlsFILGom9n5MtUCZGIx46pZs7pFUXGF8KJmCzUZV5Ao8Kee1+QXy8TYjqsev2EL4by54bk1HldQkCjwp57X5BfLsLUdVz1+Q6GCYW5ueI3Haee/fudfCy200OK5VQB8TUwwkEiYffcua8PDKSyVmz3Cykq9Kq0K93JyAGhuZFT1uCLBU3qcia8JEgMJd2ffJXxtOKrCEn6rEVbqVWlVyLkn8Bs1r5o/915uuXEmJr5IJAbcvTub8PC1qFQlpSStrEaoV6VV8ucIXQGNjJpXzZ9bfpx2/ut3/rXQQgstnlsFwEIqZY+3NyJg2f37tAwI4KciU7KXoaFGZ6uK8FV0NFcyMhhgaUkvs8rbTcYXFLAkNBSpSMS6UiUdpRZSvPd4gwjuL7tPQMsAkn4S+A29DBFJq+aP/iqajCsZWA6wxKxX5fwF8QWELglFJBXhtq4Uv9QCb+89gIj795cRENCSpKSfilaMXohEVTftiI7+ioyMK1haDsDMrFfl/AXxhIYuQSSS4ua2Tr1dO//1O/9aaKGFFs8j1E7akdbWeBgYMC0oiH8yMng1MJC+5uZsb9YMlwqal/inpbHw3j0i8/IoVKn40suL6XZ2FZKsCgtDCYTl5NDJxIRDPj54lvFtW4+0xsDDgKBpQWT8k0Hgq4GY9zWn2fZm6LuU50/zT+PewnvkReahKlTh9aUXdtMr5g9bFQZKyAnLwaSTCT6HfDDwLMNvPRIDAw+CgqaRkfEPgYGvYm7el2bNtqOvX74TYFqaP/fuLSQvLxKVqhAvry+xs5teMX/YKkBJTk4YJiad8PE5hIGBp8YY7fzX7/xroYUWWjy3CgBAa2NjLnfowO7YWJbfv8/ZlBQ6XrmCf4cOuJURGJ1MTdno7l4tkg+aNsWyGq0vjVsb0+FyB2J3x3J/+X1SzqZwpeMVOvh3wMCtTDBcJ1PcN1aPv+kHTZFaVoPfuDUdOlwmNnY39+8vJyXlLFeudKRDB38MDNw0+U074e6+sXr8TT9AKn10By7t/Nfv/GuhhRZaPE8Qg2AaTpbLhQ0iEVPt7Aju3JnBjRuTJJez/P79Or2IgvgC5MkCv0gswm6qHZ2DO9N4cGPkSXLuL69j/oJ45PJkgV8kxs5uKp07B9O48WDk8iTu319ep/za+a/f+ddCCy20eG4VgIjcXPxSUjRXWDo6HPTxwVRHh8DMzDq9iNyIXFL8NPl1THXwOeiDjqkOmYF1zJ8bQUqKnya/jik+PgfR0TElMzOwTvm181+/86+FFlpo8dwqAAB74+LK7dQTi3HQ06NpBT7omnRxq87YuL3l+cV6YvQc9NBv+hT44/aW5xfroafngL5+0zrn185//c6/FlpoocVzqwD8kpTEnJAQshQK9c7v4uO5l5PDylK9y9MLhRSt1MLCR568JmOTfkkiZE4IiqwS/vjv4sm5l4PryhL+wnThXIWpjz5nTcYmJf1CSMgcFIqsEv7478jJuYer68qScxamF/2b+mj+GozVzn/9zr8WWmihxfMGkapPH1VAejqdrl4FwFAiwdvQkAKVisZSKWtcXXmhqG780tBQdsXGqgvXSEQi5jo6ssrVVV2DfktUFF9FR2Oio0OWQoFCpWKgpSVvNGnCUCurchfQ9yykB6RztZPALzGUYOhtiKpAhbSxFNc1rpi8IPAHvRlE3N44lPlCjXqTF0xouqopFi9ZAEI9+4h1EYR/FI5IIgIVqBQqLAda0uSNJlgNLc9P37Okpwdw9WongV9iiKGhNypVAVJpY1xd12Bi8kKRQPqWmJivSU39GwBdXTtkssbqPHWFIpesrP8QiaTY288gJmY7SmUBlpYDadLkDayshpajP0tfKpt/cx0dPA0N+S05WV24x1RHh7TCQowlEla6uqJSqfg8KooHeXmMsbFhvpMTBmIxR4t6ARQolUhFImbY2/OZp2eN5l9iJEHSSELyacE/L2ssQ8dUh5x7OUiMJbiudEVmKyPyk0gyrmVg0smEpu83Rc9Fj8SjRb0ACpSIpCLsZ9jj+ZnnY82/WCzl8uV2SKUWqFQFFBZmIhJJ0NW1o7Awg8LCNGxsRtOixXcARb0AjhIW9gEqlRJLy1do0mRKhfOvLQWshRZaPNcKQE0P2h8Xx4TbtzGTSont1g29UoVqzqelMSs4mHPt25crRVsRatoOPjc8l4CWASiyFHS+17lcdDoq+Nv6b1oebYlpN1Nq/QKAGzcG4+HxKfr6rhrb796dTVTUVlxdV+PiUr3AteJeAFUho7CQnteu8W9mJuvd3Fjs7Kyx/8Xr1xlrY8PkJk3KHXvi4UOG3LjBAienCrMGqnP7wW8HE/1lNDZjbWhxoEW5/ZGbI0k5m0Krk60Q6WjK04cnHnJjyA2cFjhVnDVQjQtITj5DcvKveHh8qrE9L+8B/v4tkEgM6dTpNlKphcb+y5dbk5V1ix49UtDRaVThubUKgBZaaPG84rHaAY+3tWWGvT2pcjnrIyLU23MUCt67d48TrVpVS/g/DvRd9HFbK6SEha8uX8414VACthNtqyf8HxNmZj3KCf/U1HNERX2BsXFbnJ3fq1W+Rjo6HGnZEkOJhI0PHpBUlDEA8E1sLC0MDSsU/gB9zM0xkkgYZW392PzuG9zRc9Qj4XACOSE5mvqWQkXCoQSa7WhWTvgDmPcxR2IkwXrU4/PL5ck4Oy+irKZ3585kFIosvLy+LCf8ARo3HoKFRf9Khb8WWmihhVYBeAxsdHfHUU+PdRERBGVnA/BWcDDznZwqLFxTm7B/255G7RsRtz+OtPNp6u2KbAWRWyJpuqJpnfLb2IzV+KxQZHPnzmREIh2aN9+DSFT7yo+rvj5r3dxIlsuZFxICwO3sbPbExZVb2StVKgbduMHrN29yNzubt+ztkYpEzA8JocOVK9zKyqoRt8RIgufnnqjkKoLfCtbYF7U1CuuR1ug2KSnXq1KquDHoBjdfv0n23Wzs37JHJBURMj+EKx2ukHWrZvzm5r2RyWw0tkVHf0VKyh9YW4/SMO0nJf3ElSsdiYraiqXly1hYvERCwkGuX3+RkJC52l+8FlpoocWTKgBGEgnbmzWjQKlkelAQO2NiMNLRqdDPX9sQiUU0294MkVhE0MwgVHLBixH2Ydj/tXfn0VHV9//4nzczk5kMSSYJJEP2PcQQEYQPi8ivHql6WrFgIaAU27LosQX9Bi3iVywBMRCKCFgsigsVi9Til+rx49HWICiLmiAQlsEMZJJM9oVsZLbMzL2/PzKELJMhGwwtz8c5niOZe+c19/1+vd/33vdd3ojJjOmYuvZ68fXtejZ78eJKWCwGJCT8Ef7+Y65b3GVRUbhLo8H7VVX4uK4Oi3U6vJuWBt9ucwVIAIrMZvxw+TL21tSgzm7Hlw0N+La5GT+aTGjoNILQV6G/CEXorFA0HGhA9QfVANrfH1C7rxbRT0V3PzmHuciMyz9cRs3eGtjr7Gj4sgHN3zbD9KMJ9gb7oMrbYinBhQvPwdc3DKmp23uMFpjNejQ0/Bv19V/AYilGQ8MBtLaehclUyBZPRHRlXzqQewA6+825c9hdVYV0f38cnzixTxPXdDaAS/Ad9Mv1MG41ImlDEkJnhkL/jB7jPh+HG/YDADQ2HsIPP9yLgICxmDgxr99n/325B6CzH00mjP3+e9glCX9NS8NjfZgtcPqJE/jkjjvgL5MNavNt5TYcSzsGmVqGuwrvQuGyQkQ8HoHg/y/Y43onpp/AHZ/c4f7ArN/lL+HEiZ+ioeErjBnzEcLCZve6ZH39Z2hp+QEJCat7XYb3ABARRwAGKCcpCTJBQJnV2vE2uxslcV0iVNEqFK8rxvnHzyPl1ZQbGr/z0H9a2vUZ+u8uddgwLI6IgChJON2Hofx9NTX4qqEBWUPwNkFllBKJ6xLRVtOGgpkFgIBr7vxr9tWg4asGFGUNzdsEy8t3uIb+53rc+QPAhQvPoaRkQ8d0w0RENIQHANklJZin1aLZ4cCywhs7xCrzlyH5T8lwmp1Qj1Jj2G3Dbmj8Cxeeg8VSjPj4VQgIuOOGxCyyWHDOZMJtw4Zhi9GIE9d4S6DMdYIb0oe5APoielk0hqUNQ+PXjUjMTrzm8oKsPb4iZPDxLZZiXLiwEr6+oUhNff3asQUZAAEKRQhbOhHRUB4A/KOmBk5Jwp70dEwPCcE/a2vxz9obe7bll9R+w6EiWHFD4zY2HkR5+Q4EBIxFfPwLNySmTRSxSKfDW7fdhjdvuw2iJOFxnQ5OD2+6S/f3BwCMCwgYkt8gyISO2QH7Uub+6e3xA8YNNr4EnW4xnM5WjBr1ep8m9/H3T4e/f/oNGZkhIrplDgD0ZjP+XFaGLSntw+47UlOh8vHBssLCjjfQ/bdyOluh0y12Df3/1e189SaTbsjj/p/CQjwZGYlktRrTgoKwMCICJy5fxhajsdd1kvz8oPLxQWK32QRv5AGaj8oH6sTBxS8r+wsaGw9Cq82AVpvR43OrtRROZ9dHFIcNS+/xuCYREQ3iAMDsdGLhuXN4Jy2t4yVAyWo1VsXHo9Jmw4oLF/6rC+3q0P8Lbof+7fYGNDTkDmnMPdXVEAE8OvLq43CbkpMR5uuL1UVFuGg2u69gQUCCnx+ilEqvlJXgI8AvwQ/KqIHHt1iKcfFi+9D/qFHuh/4rK9+Dj4+qy9/U6mSoVFFs5UREQ3EAYBFFPHr2LGaEhiKl21nlyrg4hPr64q2KCuy/QZcCOt4333xjRh0aGr5CefkbCAi4A/Hxq3p8LooWFBY+7XYCm4HKbWjAygsXOkZbrghRKPB/4+JgEUX86uxZ2ETR7fpRKhWGyWReK3NVlAqyYQONf+WFPyaMGrUdvr6hPZZobv4WjY0HIAhd09nXN5TX/4mIetGvi6NvVVQgp6QEBosFrU4n7gsJwYTA9resOSQJW43GjuH/X509i6eio7E8Jgbh1+nss+r9KlS8WQEAqP1nLYalD4N2jhbKyOt1tivh/PnFACTY7U04fnxat52/HRZLERyOZiQmrht0tItmM7JLSvBeZSVkgoC/lJfjD7GxuPLcWl5LCz6tr+/4/5/88AOei43t8S6GoTr7N503ofajWjR/3z7Jjn65HuG/DseIGZ6vxw/m7L+y8q9obDwEQZDBaHwVRuOrPUZbzOaLCA9/rGdyyzWQyQLYyomI3Bj0ewAGa5CP4f/H/4D+vgdgIFYVFSE7MdFrm1+0qqj3Jwau4w8wmy+iqekwIiIW9j66wvcAENEtyodF8N8vRO7du+DlId6JL5OpIJOpmQBERDwAuDUFevsAINA78QXBt8eNgURExAOAW4a/TObV+Nd7bobeDwDkPAAgIurt5IxF8N+v86OD3X153434BSOBd735A17p/aP7JC/fA3OfV3PD67fgeL11fHlLF8AtfguW18vf2/cgcQSAiIjoFsQDACIiIh4AEBEREQ8AiIiI6L9Sj5sAW51ObDUacaypCSOVSsgEAYEyGcYHBqLF4cBcrRb7a2uxXK+HBGBaUBAsoohWpxNLo6KwMCICerMZu6uqkF1cjAilEhMCA1FutWK4QoGshARMDQrq9Qc5W50wbjWi6VgTlCOVEGQCZIEyBI4PhKPFAe1cLWr310K/XA9IQNC0IIgWEc5WJ6KWRiFiYQTMejOqdlehOLsYygglAicEwlpuhWK4AglZCQia6iG+sxVG41Y0NR2DUjkSgiCDTBaIwMDxcDhaoNXORW3tfuj1ywFICAqaBlG0wOlsRVTUUkRELITZrEdV1W4UF2dDqYxAYOAEWK3lUCiGIyEhC0FBU3uN7+3y93r8Vie2bjXi2LEmjByphEwmIDBQhvHjA9HS4sDcuVrs31+L5cv1kCRg2rQgWCwiWludWLo0CgsXRkCvN2P37ipkZxcjIkKJCRMCUV5uxfDhCmRlJWDqVE/b34qtxq041nQMI5UjIRNkCJQFYnzgeLQ4WjBXOxf7a/djuX45JEiYFjQNFtGCVmcrlkYtxcKIhdCb9dhdtRvZxdmIUEZgQuAElFvLMVwxHFkJWZjqof69nf/eLn+vt//WVhi3bkXTsWNQjhwJQSaDLDAQgePHw9HSAu3cuajdvx/65csBSULQtGkQLRY4W1sRtXQpIhYuhFmvR9Xu3SjOzoYyIgKBEybAWl4OxfDhSMjKQtDUqTdx/+Pt/L+1y9+rBwBlVivuP3kSD40YgU/Hju2YS76mrQ0zTp3CzNBQhCgUWBIZiT3V1bCIIj4fNw4AsL64GIt0OjgkCY9HRmJdYiI2lJTgsfBw5CQlwS5JmFVQgOknTuD4xIkd09R2Zi2z4uT9JzHioREY++nYjrnk22racGrGKYTODIUiRIHIJZGo3lMN0SJi3Oft8YvXF0O3SAfJISFYnou9AAAgAElEQVTy8UgkrktEyYYShD8WjqScJEh2CQWzCnBi+glMPD6xY5raLvGtZTh58n6MGPEQxo791DWfPNDWVoNTp2YgNHQmFIoQREYuQXX1HoiiBePGfd4ev3g9dLpFkCQHIiMfR2LiOpSUbEB4+GNISsqBJNlRUDALJ05Mx8SJx+Hvn94jvrfL3+vxy6y4//6TeOihEfj007GQueq/pqYNM2acwsyZoQgJUWDJkkjs2VMNi0XE5676X7++GIsW6eBwSHj88UisW5eIDRtK8Nhj4cjJSYLdLmHWrAJMn34Cx49PRHq6u+0vw/0n78dDIx7Cp2M/hcxV/zVtNZhxagZmhs5EiCIESyKXYE/1HlhECz531f/64vVYpFsEh+TA45GPY13iOmwo2YDHwh9DTlIO7JIdswpmYfqJ6Tg+8TjS3dS/t/Pf2+Xv9fZfVoaT99+PEQ89hLGffgrB9fhsW00NTs2YgdCZM6EICUHkkiWo3rMHosWCcZ+72v/69dAtWgTJ4UDk448jcd06lGzYgPDHHkNSTg4kux0Fs2bhxPTpmHj8OPzT02/C/sfb+X9rl79XLwFIAOafPYsguRwbk5M7On8A0Pr6Yv+YMWh1Ojv+pvTpevXgmdhYyAQB71ZWAgAEAIpO36EQBGTGxMAmithTXd3zl0jA2flnIQ+SI3ljckfjBwBfrS/G7B8DZ+vV+D7KrvFjn4mFIBNQ+W57fAiAoLj6HYJCQExmDESbiOo9buJDwtmz8yGXByE5eWNH5QOAr68WY8bsh9PZejW+T9f328fGPgNBkKGy8srzbkKXaYIFQYGYmEyIog3V1Xvcbb5Xy9/r8SVg/vyzCAqSY+PG5I6dDwBotb7Yv38MWjvVv7Jb/T/zTCxkMgHvuupfEABFp/pXKARkZsbAZhOxZ4+77Zcw/+x8BMmDsDF5Y0fn1779Wuwfsx+tnepf2a3+n4l9BjJBhndd9S9AgKJT/SsEBTJjMmETbdjjpv69nf/eLn+vt39Jwtn58yEPCkLyxo0dO5/2+FqM2b8fztZO7b/b/BqxzzwDQSZD5bvvXmnwEBSd2r9CgZjMTIg2G6r37LkJ+x9v5/+tXf5ePwD4prERR5qasDAiAu4eTIxWqTCn2yQznV1ZJ8DDS2c8LdP4TSOajjQhYmEE3P0AVbQKYXN6j39lHVmAbEDLNDZ+g6amI673xvf8ASpVNMLC5uBaX+558pnel/F2+Xs9/jeNOHKkCQsXRsDdk7HR0SrM8VD/V9YJ8FD/npb5pvEbHGk6goURCyG4KYFoVTTmeKj/K+sEeKh/T8t4O/+9Xf5eb//ffIOmI0cQsXAh3BWAKjoaYXM8tH/XOrKAgAEt4/3+x9v5f2uXv9cPAD6/dAkAMN5DAV6Z+c+d9SUlcEoSlkZHu/3cKorYVFoKjVyOBeHhPT6/9Hl7/IDxvccPnNB7/JL1JZCcEqKXuo8vWkWUbiqFXCNH+AI38S997uqcxvceP3BC7/FL1kOSnIiOXuo+vmhFaekmyOUahIcv6PG5t8vf6/Fd9T/eQ/1P8FD/69eXwOmUsLSX+rdaRWzaVAqNRo4FC9xt/+eu7R/vYfsneNj+9XBKTiztpf6tohWbSjdBI9dggZv693b+e7v8vd7+XUPJAeM9tP8JHtr/+vWQnE5EL+2l/VutKN20CXKNBuELFtyE/Y+38//WLn9v6bgHoNxqBdA+x3xfVdlsHdMD20QRB8ePxz3BwV2WyWtuRnZxMc6bTEhRq7EjNRUxqp6vZ7WWt8dXhPQ9vq3KhpKcElgMFog2EeMPjkfwPV3jN+c1ozi7GKbzJqhT1EjdkQpVjJv41nLXUGXf54+32apQUpIDi8UAUbRh/PiDCA6+p2v85jwUF2fDZDoPtToFqak7oFLF9Pgub5e/1+O76j+kH/VfVWVDTk4JDAYLbDYRBw+Oxz3d6j8vrxnZ2cU4f96ElBQ1duxIRUyMu+0vd21/SD+2vwo5JTkwWAywiTYcHH8Q93Sr/7zmPGQXZ+O86TxS1CnYkboDMW7q39v57+3y93r7L3e1/5B+tP+qKpTk5MBiMEC02TD+4EEE39Ot/efloTg7G6bz56FOSUHqjh1QxcTchP2Pt/P/1i5/rx8AqF3Dspc7Xee9lnClEs/HxXlcZqJGg1Xx8df8Lpm6Pb7zct/jK8OViHvec3zNRA3iV/UhvmvWOKfzct/jK8MRF/e85/iaiYiPX3XN7/J2+Xs9vqv+L/ej/sPDlXj+GvU/caIGq1b1ZfvVru2/3I/tD8fz16j/iZqJWNWH+vd2/nu7/L3e/tWu9n+5H+0/PBxxz1+j/U+ciPhVq/4D+h9v5/+tXf7e0nEJ4C6NBgBwoqXFKz9Ec1d7/JYTXoqvuas9fssJr8T3dvl7Pb6r/k+c8Nb23+Xa/hO3ZP57u/y93v7vcrX/E16qf6/3P97O/1u7/L1+ADBXq0WUUont5eVwupkfRZQk7HV39/4Q0c7VQhmlRPn2ckjOnvElUUL13usYXzsXSmUUysu3Q5J6noVIkojq6r3XLb63y9/r8edqERWlxPbt5XC6qX9RlLB37/Xc/rmIUkZhe/l2ON3UvyiJ2Hsd69/b+e/t8vd6+587F8qoKJRv3w7JzSiYJIqo3rv3v7j/8Xb+39rl7/UDALVMhn1jxqDQZMK8M2dQ09bWsVCTw4E1BgOmd7o+YxNFWDwMF0sA7JLkcZmuQ0AyjNk3BqZCE87MO4O2mqvxHU0OGNYYEDL9anzRJsJp8fDdEiDZJc/LdBsCGjNmH0ymQpw5Mw9tbTVX4zuaYDCsQUjI9E4dog1OpwWefoAk2a+xzFXeLn+vx1fLsG/fGBQWmjBv3hnUdKr/piYH1qwxYHqn+rfZRFg81K0kAXa75HGZrtuvxr4x+1BoKsS8M/NQ06n+mxxNWGNYg+md6t8m2mDxULcSJNglu8dlbqb893b5e739q9UYs28fTIWFODNvHtpqOrX/piYY1qxByPRO7d9mg9PioW4lCZLd7nmZm6r/8Xb+39rl7y1dXgQ0WaNBweTJeMlgwKS8PIT5+iJWpUKSWo0VsbEIUSjQYLdjX20t8ltaYBNFbC8rw+ywMIR3ei5TbzZjV2UlREnCx3V1mKTRIEOr7fJcuNthmMkaTC6YDMNLBuRNyoNvmC9UsSqok9SIXRELRYgC9gY7avfVoiW/BaJNRNn2MoTNDoMy/Gp8s96Myl2VkEQJdR/XQTNJA22Gtstzwe6HgSZj8uQCGAwvIS9vEnx9w6BSxUKtTkJs7AooFCGw2xtQW7sPLS35EEUbysq2IyxsNpTKq3cWm816VFbugiSJqKv7GBrNJGi1GV2eC3XH2+Xv9fiTNSgomIyXXjJg0qQ8hIX5IjZWhaQkNVasiEVIiAINDXbs21eL/PwW2Gwitm8vw+zZYQjvVP96vRm7dlVCFCV8/HEdJk3SICND2+W5dPfbPxkFkwvwkuElTMqbhDDfMMSqYpGkTsKK2BUIUYSgwd6AfbX7kN+SD5tow/ay7ZgdNhvhnepfb9ZjV+UuiJKIj+s+xiTNJGRoM7o8F30z5r+3y9/r7X/yZEwuKIDhpZeQN2kSfMPCoIqNhTopCbErVkAREgJ7QwNq9+1DS34+RJsNZdu3I2z2bCg7Pdli1utRuWsXJFFE3ccfQzNpErQZGV2eS785+x9v5/+tXf7eIEg//amX50P3cgl4+Qd8eRPMiO7lAril6/++L++7tYv/Vk/A+9j8buXyz829xlnRjboEQERERLcOtwcAFTYb/lRaCvmBAwg+dAjvVFai2eHosdyhxkY8cuYMhNxcCLm5yNTrUW+3AwC+b27G1Px8BB86hA0lJTD34/EyW4UNpX8qxQH5ARwKPoTKdyrhaO4Zv/qDahyOPIxcIRf5U/PRdLip4zNLiQUFDxfggOwACp8uhK3S1q+CsdkqUFr6Jxw4IMehQ8GorHwHDkez22VPn87A0aNJKCh4GKdPz8Hp03Nw6tSDyM0V8O23oyGK/YutN5ux8sIFKL/6CkJuLh44eRLnTCYAQInFgiU6HeQHDmBZYSEMbq5x9bX+hjLmoNfXm7Fy5QUolV9BEHLxwAMnce6ca/0SC5Ys0UEuP4BlywphMPRc/7vvmjF5cj4EIRcjR36DDz64esOY2ezEqlVFEIRc/PznJ3H8uPs7zQ81HsIjZx6BkCtAyBWQqc9Evb3elc/fY2r+VAQfCsaGkg0wO81uvyPjdAaSjibh4YKHMef0HMw5PQcPnnoQQq6A0d+Ohq2PuTDQ3BZtIip3VXasa3jJAEtR365DfvBBNSIjD0MQcjF1aj4Od4pZUmLBww8XQCY7gKefLkRlp5jNzQ688kopIiLa142PP4ovv2zoKPtXXzVCEHLxs5+d7PKdQ7XNVqMV5359DrlCLnKFXJS+Utqlv6j+oBoHhx3EsdRjqN1f22t80WaDYe1afKVUIlcQoFu0COaLF69u57ff4tu0NBzSaFC6eTPETvfJAIDp3Dl8pVTixH334fScOR3/HY2PR64goOr99/vVD5SVvY6vvx6OU6ce6uhXTp+eg4MH/fHVV2qYzfqe2yDaUFm5C4cPRyI3V4DB8BIslqI+xxxI/urNeqy8sBLKr5QQcgU8cPIBnDOdc7X9EizRLYH8gBzLCpfBYDF4jH86IwNHk5JQ8PDDHeV36sEHkSsI+Hb0aIi2nvGbv/sO+ZMnI1cQ8M3Ikaj+4IOOz5xmM4pWrUKuIODkz3+OluPHr1kGA+3PB1Jf3iZ398dIpRLPxcbi7YoKjFKrsTgiwu3K9wQH457gYEQoldhiNGJCQABGuK6zTNJoMMLXF4dSU3FHQP9efaiMVCL2uVhUvF0B9Sg1Iha7jz9y/kiok9XIn5IPvzg/BE27OsuXX5wfQu4NgWayBnEr4/pdMEplJGJjn0NFxdtQq0chImJxr8uqVNFIT38fPj6qTju0TAiCHKNHv9fjvdHXkqJWY2NyMqYEBeGXBQWIVioxetgwAECcnx9iVCq8kZqKJZGRGEz9DWXMQa+fosbGjcmYMiUIv/xlAaKjlRg92rV+nB9iYlR4441ULFnifv3JkzX497/HIT39O/j4tN/VfoVaLcMjj2hx8mQLPvtsHHobdLsn+B7cE3wPIpQR2GLcggkBEzBCMcKVz5MwwncEDqUewh0Bd/RajtGqaLyf/j5UnXIhU58JuSDHe6Pf6/EO9d4MNLd9lD6IWBiB5m+bUb23GgmrE/qcd/Pnj0RyshpTpuQjLs4P0zrFjIvzw733hmDyZA1Wdoup0cjxhz/EYvbsMEyYkAelUsC99wZ3lP2ECQFYsCAc778/+rpssypGhdG7R8PR4kDdJ3UI/UUo5JqrXZs2Q4vSzaW488s7Pb5oyEepREJWFuQaDfTLl0MzZQrUSUlXt3PKFAwbPRppb7/d8dhaZ2319Ri9eze08+Z1Ojgx4rvbb0fozJkIf+yxfvUDomjCxIk/wM/v6vbW1X2C2tr/h5SULVCrU3pug48SEREL0dz8Laqr9yIhYXW/Yg4kf1PUKdiYvBFTgqbglwW/RLQyGqOHjXa1/TjEqGLwRuobWBK55JrxVdHRSH//ffh0elmYPjMTglyO0e+912MOAKD93oFx//43vktPB3x8oJ07t+MzmVoN7SOPoOXkSYz77DOgDyPuA+3PB1JfN+UIwBW+gtBj0hd3NiYnY0JgIFZevNhxprmptBRztdp+7/w7E3yFHpN+dBf4P4GIWR6Dmr/XoCXv6pmds9WJhi8bEPuH2EEVkCD4XnMHPmLEjC7J0tj4NYzG1xAXt9Lj6yOvZVZoKJ6OicGuqip819w++nCsuRnVbW297kgHUn9DGXPQ688KxdNPx2DXrip8951r/WPNqK5u63Xn35ELgXLs2JGK0lIrtm0zdvksJ6cEb755W1/aPzYmb8SEwAlYeXElml2jPptKN2Gudq7HnT8AzBgxo0vn+XXj13jN+BpWxq30+CrVoc5tH1+fa7Ydd/7nfwKxfHkM/v73GuR1itna6sSXXzbgDx5ixsf74Z130lBYaMarr7aX/6VLdvzpT6XYufO2677NqX9JhTxQDv2zXc+0yl4vQ/yL8X1+y2D0008jcOJEGNasgaPTezFafvgBqqgotzt/ABDkcoTOmnX1D5IE3aJFEBQK3Pbmm/2ui4CACV12JnZ7Pc6ffwJBQdMQHf20547dx7ffJx6Dzd9ZobPwdMzT2FW1C981f+dq+8dQ3Vbdp50/AIyYMaPLzr/x669hfO01xK1c6fFVwPLAQKTu2AFraSmM27Z1+awkJ6e9/Pt4uX2g/flg6uumPABwZ8HZs5h35kyXvykEAbvS0lBvt2PFhQs43NSEEosFvxo5csh/8NkFZ3FmXtf4CWsToIpR4fwT5yE52u9pNKw1IP7F+C6zig0FSbLj6NFElJf/peNvISH3Xu2onK3Q6RbC3/92xMevHnS89YmJiFepsESnQ4XNhuziYmxO6XkkubOiAnFHjsAmijcsprtc6M/6vcZfn4j4eBWWLNGhosKG7OxibN7sJv6Cs5jXLRcefHAEMjK0yMoyoLS0/fWyn35ah3HjAhAdrepTfIWgwK60Xai312PFhRU43HQYJZYS/Grkr9yUwQLMO3P1jO/eTrnQ6mzFQt1C3O5/O1YPMBf6ktutp1vxdcjXuHzy8pDk+Nq1CYiJUeGJJ87D4Yq5dq0BL74Y3zFL4M6dFYiLOwKbTexxAPfooyORlVWECxfMePLJ89i8OQV+fj5Dus0VOytwJO4IxE7xlRFKJG1IQv3/1qP2o/ahflulDc3fNyPs4bC+H/T7+CDtrbfQVluLohdeaG/3oojideuQsHbt1UttO3fiSFxcx7B00NSpXc5Qy15/HQ0HDiD19dfhq9X2ux469ysAcP787+B0mjB69C4IwtXyrKjYiSNH4vp9qdGdvubvzoqdiDsS1+OSwPrE9YhXxWOJbgkqbBXILs7G5pTNfd/mezv1pa2t0C1cCP/bb0f86q7xu5c9AIx48EFoMzJgyMqCtbS0/Qz8008RMG4cVL3MUXKtcvfUn589uwBnOrX9vtbXf/QBQLhSiQg3wzDp/v54MT4eb1dUYHVRUb86/H4NzYcroYzoGl+mliH1L6m4XHAZxi1GtJ5uhWgTETgx8Dr8AhlUqphe3xmt1z8Lq7XcNVTkO+hoapkM76SlQWcyYWp+P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"}],"materials":[{"doubleSided":false,"pbrMetallicRoughness":{"baseColorFactor":[1,1,1,1],"baseColorTexture":{"index":0,"texCoord":0},"metallicFactor":0.4,"roughnessFactor":0.5}},{"doubleSided":true,"pbrMetallicRoughness":{"baseColorFactor":[0,0,0,1],"metallicFactor":1,"roughne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\ No newline at end of file
diff --git a/testdata/match_graph_no_coords.gltf b/testdata/match_graph_no_coords.gltf
index ba8bf609..ab209873 100644
--- a/testdata/match_graph_no_coords.gltf
+++ b/testdata/match_graph_no_coords.gltf
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diff --git a/testdata/match_graph_repetition_code.gltf b/testdata/match_graph_repetition_code.gltf
index 86f4a20b..088fbd43 100644
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diff --git a/testdata/match_graph_surface_code.gltf b/testdata/match_graph_surface_code.gltf
index 5504a89d..d5e1304f 100644
--- a/testdata/match_graph_surface_code.gltf
+++ b/testdata/match_graph_surface_code.gltf
@@ -1 +1 @@
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diff --git a/testdata/measurement_looping.gltf b/testdata/measurement_looping.gltf
index d1dba6bc..5ad842ae 100644
--- a/testdata/measurement_looping.gltf
+++ b/testdata/measurement_looping.gltf
@@ -1 +1 @@
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"}],"materials":[{"doubleSided":false,"pbrMetallicRoughness":{"baseColorFactor":[1,1,1,1],"baseColorTexture":{"index":0,"texCoord":0},"metallicFactor":0.4,"roughnessFactor":0.5}},{"doubleSided":true,"pbrMetallicRoughness":{"baseColorFactor":[0,0,0,1],"metallicFactor":1,"roughnessFactor":1}},{"doubleSided":true,"pbrMetallicRoughness":{"baseColorFactor":[1,0,0,1],"metallicFactor":1,"roughnessFactor":1}}],"meshes":[{"primitives":[{"attributes":{"POSITION":0,"TEXCOORD_0":1},"material":0,"mode":4}]},{"p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\ No newline at end of file
diff --git a/testdata/noise_gates_1.gltf b/testdata/noise_gates_1.gltf
index 1ab94f1e..08b09866 100644
--- a/testdata/noise_gates_1.gltf
+++ b/testdata/noise_gates_1.gltf
@@ -1 +1 @@
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"}],"materials":[{"doubleSided":false,"pbrMetallicRoughness":{"baseColorFactor":[1,1,1,1],"baseColorTexture":{"index":0,"texCoord":0},"metallicFactor":0.4,"roughnessFactor":0.5}},{"doubleSided":true,"pbrMetallicRoughness":{"baseColorFactor":[0,0,0,1],"metallicFactor":1,"roughnessFactor":1}},{"doubleSided":true,"pbrMetallicRoughness":{"baseColorFactor":[1,0,0,1],"metallicFactor":1,"roughnessFactor":1}}],"meshes":[{"primitives":[{"attributes":{"POSITION":0,"TEXCOORD_0":1},"material":0,"mode":4}]},{"primitives":[{"attributes":{"POSITION":0,"TEXCOORD_0":2},"material":0,"mo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\ No newline at end of file
diff --git a/testdata/noise_gates_2.gltf b/testdata/noise_gates_2.gltf
index 33b3ec75..adfd883d 100644
--- a/testdata/noise_gates_2.gltf
+++ b/testdata/noise_gates_2.gltf
@@ -1 +1 @@
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diff --git a/testdata/noise_gates_3.gltf b/testdata/noise_gates_3.gltf
index bab5ebbd..e75ad89a 100644
--- a/testdata/noise_gates_3.gltf
+++ b/testdata/noise_gates_3.gltf
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"}],"materials":[{"doubleSided":false,"pbrMetallicRoughness":{"baseColorFactor":[1,1,1,1],"baseColorTexture":{"index":0,"texCoord":0},"metallicFactor":0.4,"roughnessFactor":0.5}},{"doubleSided":true,"pbrMetallicRoughness":{"baseColorFactor":[0,0,0,1],"metallicFactor":1,"roughnessFactor":1}},{"doubleSided":true,"pbrMetallicRoughness":{"baseColorFactor":[1,0,0,1],"metallicFactor":1,"roughnessFactor":1}}],"meshes":[{"primitives":[{"attributes":{"POSITION":0,"TEXCOORD_0":1},"material":0,"mode":4}]},{"primitives":[{"attributes":{"POSITION":0,"TEXCOORD_0":2},"material":0,"mode":4}]},{"primitives":[{"attributes":{"POSITION":3},"material":1,"mode":1}]},{"primitives":[{"attributes":{"POSITION":4},"material":2,"mode":1}]}],"nodes":[{"mesh":0,"translation":[-0,-0,-0]},{"mesh":0,"translation":[-0,-2,-0]},{"mesh":0,"translation":[-0,-4,-0]},{"mesh":0,"translation":[-0,-6,-0]},{"mesh":1,"translation":[-1,-0,-0]},{"mesh":1,"translation":[-1,-2,-0]},{"mesh":1,"translation":[-1,-4,-0]},{"mesh":1,"translation":[-1,-8,-0]},{"mesh":2,"translation":[0,0,0]},{"mesh":3,"translation":[0,0,0]}],"samplers":[{"magFilter":9728,"minFilter":9728,"wrapS":33071,"wrapT":33071}],"scene":0,"scenes":[{"nodes":[0,1,2,3,4,5,6,7,8,9]}],"textures":[{"sampler":0,"source":0}]}
\ No newline at end of file
diff --git a/testdata/repeat.gltf b/testdata/repeat.gltf
index b43b4148..83ca2d9c 100644
--- a/testdata/repeat.gltf
+++ b/testdata/repeat.gltf
@@ -1 +1 @@
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"}],"materials":[{"doubleSided":false,"pbrMetallicRoughness":{"baseColorFactor":[1,1,1,1],"baseColorTexture":{"index":0,"texCoord":0},"metallicFactor":0.4,"roughnessFactor":0.5}},{"doubleSided":true,"pbrMetallicRoughness":{"baseColorFactor":[0,0,0,1],"metallicFactor":1,"roughnessFactor":1}},{"doubleSided":true,"pbrMetallicRoughness":{"baseColorFactor":[1,0,0,1],"metallicFactor":1,"roughnessFactor":1}}],"meshes":[{"primitives":[{"attributes":{"POSITION":0,"TEXCOORD_0":1},"material":0,"mode":4}]},{"primitives":[{"attributes":{"POSITION":0,"TEXCOORD_0":2},"material":0,"mode":4}]},{"primitives":[{"attributes":{"POSITION":3},"material":1,"mode":1}]},{"primitives":[{"attributes":{"POSITION":4},"material":2,"mode":1}]}],"nodes":[{"mesh":0,"translation":[-0,-0,-0]},{"mesh":0,"translation":[-0,-2,-0]},{"mesh":0,"translation":[-0,-4,-0]},{"mesh":1,"translation":[-1,-4,-0]},{"mesh":0,"translation":[-2,-0,-0]},{"mesh":0,"translation":[-2,-2,-0]},{"mesh":0,"translation":[-2,-6,-0]},{"mesh":0,"translation":[-3,-6,-0]},{"mesh":2,"translation":[0,0,0]},{"mesh":3,"translation":[0,0,0]}],"samplers":[{"magFilter":9728,"minFilter":9728,"wrapS":33071,"wrapT":33071}],"scene":0,"scenes":[{"nodes":[0,1,2,3,4,5,6,7,8,9]}],"textures":[{"sampler":0,"source":0}]}
\ No newline at end of file
diff --git a/testdata/repetition_code.gltf b/testdata/repetition_code.gltf
index bfadd55d..914c1846 100644
--- a/testdata/repetition_code.gltf
+++ b/testdata/repetition_code.gltf
@@ -1 +1 @@
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fv2scL4AVFiRcD4Vjw8PL7ZwY7xY9O/f3+0b98e69atU1ga1q1bh1atWsHX15fdEAWxZ8+eUp2DGeUbNgLAYDC+iXfv3sHV1RW7du3iDkoSigcPHqBbt26IjIwU9Dhwxr/k5OQgODgYJiYmTAAwAcBgMCojSUlJGDlyJNauXQtbW1tBbL5+/Rq+vr6C2mTIEh4eDhcXF1hZWbHCYAKAwWBUVrKzs7F27Vp06NCB9+V4N2/exIkTJzBlyhRu7wcGg8EEAIPBUCCFhYXyWZusYBsMBhMADAaDwWAwKixMSjMYDAaDwQQAg8FgMBgMJgAYDAaDwWAwAcBgMBgMBoMJAAaDwWAwGEwAMBgMBoPBYAKAwWAwGAwGEwAMBoPBYDCYAGAwGAwGg/GjCYD3799j3LhxaNiwIRo1aoQBAwbg1atXgiY8NzcX69evh7m5OTIyMgSzm5OTgxkzZsDS0hJqamowNzfH+PHj8e7dO0HsSyQShISEoF69etDQ0ICFhQXmzJkDsVissEo0bNgwiEQi5ObmCmZz5syZEIlERT7//POPoHlPTk7G3Llz0alTJ4wdOxa7du3i1V7Tpk2Lzbf0Y2FhwXued+/ejZYtW6JDhw5wd3dHy5YtsXv3bsHK/OTJk2jTpg2aNWsGS0tLeHl54dGjR+xpzqgUnDhxAr6+vvD19YW9vT0cHR0RHBwMiUTy9T9GX0lqaio5OjqSt7c3icViIiKaM2cOmZiY0NOnT4lvcnNzafXq1VSnTh0CQADo/fv3JAQFBQXUqlUrUlZWJmtra6pRowaXhjp16lBycjKv9gsLC8nb25vq1atHPj4+5Orqytn39fUlRXDw4EEuDZ8+fRLEZnp6OlWtWpWUlZVlPh07dhQ078uWLSMdHR1avHgx5eXl8W7v1q1bBICUlZWpevXqZGhoKPMRiUQ0btw4XtMwbdo0MjExoUePHnHX7t+/T3p6ejRz5kzey2D58uVkYmJCd+/eJSKiDx8+UIcOHahq1ap07do1YjAqMoGBgeTt7U0FBQVERCQWi2nkyJEEgAYMGPDVv/fVAqBbt26ko6NDGRkZ3LW8vDwyNDQkNzc3Kiws5LUAxGIxvXv3jlJTU0lVVVVQAbBmzRpq3749JSYmctcOHDhAmpqaBIC8vb15tb9//34KDg6WuRYaGsp1wHwLkP+SmJhIderUoapVqwoqAObMmUNLly5VWCOUSCTUp08fUlNToz/++EMwu6NGjaIlS5ZQdnZ2sfcCAP3555+82Y+PjyeRSESrV68uEjZjxgwSiUSUlJTEm/2//vqLlJSUaPv27TLX09LSSFNTkywsLIotG4awbSM8PJxiY2MrTJ5u375NR44c4TpdRZGRkUGqqqq0bNkymes5OTmkp6dHAOjvv//mTwBERUURAPLy8ioS5uvrSwDo+PHjghWImZmZoAJg0KBBlJOTU+T6qlWrCABpaWnxar+km2tra0sA6MGDB4KVvVgsJldXVzp58iSZmpoKJgDevXtHVlZWlJWVpbCGKK3r/+2I+C7vjRs3lhgeHBxMZmZmvArw/fv3E4Bi07Fu3ToCwOtbuKenJwGgx48fFwnz8fEhALR27VrWCyuA9PR0Wrp0KVlaWlL37t3p2bNnFSZvCQkJ9L///Y+srKwoJCRE5uVXSJ4+fUoAqHbt2kXauXQ0ODQ09Kt+86t8AA4ePAgAcHFxKXZuEgDvc6Cfo6amJujci5+fHzQ0NIpc79evHwBALBaDeDxc8aeffir2es2aNeHg4IC6desKVhbz5s1DkyZN0KVLF0HvwerVq5GSkoIePXpg2bJlSE5OFtT+7t27sWPHDrRt2xa+vr6C2VVRUcHo0aNLDD906BD69OkDkUjEWxpMTU0BAJs3b0Z+fr5MWGxsLIyNjdGgQQPe7F+8eBEAYGhoWCSsdevWAIDjx4+zSWIBiYuLw8iRI1G/fn28efMGFy9exLFjxwTxRREKa2trREZG4ujRo4iLi4O1tTXGjRuH+Ph4QdNhaWmJzp07Q0VFBYWFhUX88j5vo7z4ANSuXZsA0L59+4qERUREEAAyNDQUTBFJ/QCEGgEobdhLJBJRw4YNFTIsVLNmTYqJiRHMZmRkJDVu3Jib9xZqBCAjI4OqVavGTXkAoCpVqtD8+fMFGZ77+PEjGRsbEwA6f/58uXlDefLkCQGg69ev82qnsLCQHB0dCQB169aNG26/ffs2VatWjX7//XfebOfm5nL3/MWLF0XCz549SwDI2NhYsBGZVq1akZmZmYw/hFB8+PCBnJycqHbt2vTy5UtBbRcUFNCxY8eobdu2ZGdnRxs2bKCPHz/yZu/kyZOkpaX1VZ+jR4/ylp43b95QUFAQmZiYkIeHB507d06h7T85OZlUVFTIwsKCcnNz+ZkCKCwsJGVlZQJAly5dKnZ4WtpAMzMzK5UAiIuLIwC0atUqQe1mZWVRly5daNu2bYLZTEtLo7p161J8fDx3TSgBkJmZSVeuXKHjx4/TjBkzyNLSkqtzvXr14l0E7Nmzh+tkoqOjydvbm2xsbMja2ppGjRpFqampCql/ixcvJktLS0FsxcfHcyLI2dmZzpw5Qy1btqQbN27wbltLS4sAFPtwP336NAEgbW1twR66IpGIANCuXbsEv+exsbFc3T99+rRgLxshISFUp04d6tSpE/3xxx+8+3yVZ/Ly8mj37t3k4uJC9vb2tHnzZoX4oEybNo2UlZW/6aWkzAIgLS2Nq3DFNfZ79+5x4Xw6ApVHATBnzhyysLAo1j+AD168eEELFy7kfCCUlZVp9uzZgtju1q0b7d69W+aakD4A/30rDAoK4h7EK1eu5NVejx49OAEQFBREsbGxdPv2bW5u2tLSUiEiwNnZmWbMmCGYvcTERHJwcODau1DCt3v37gSAfv755yJhUmfYWrVqCVYOe/bsoaCgIEFWgBRHaGgohYSE8C58k5OTacyYMWRsbEx+fn4y4p/xL5cvX6bevXtTzZo1acaMGZSWliaI3ZSUFNLU1CzVP0guAuDly5dcg79z506pilSoh2B5EACpqamkr69PERERgnZ8iYmJtHfvXnJxceHKfcuWLbzaXbt2LQ0aNKjIdUUJACkrV64kAGRmZsarnbp16xIA2rBhg8z1/Px8sre3JwA0ePBgQfMeHx9PAOjWrVuC2bx+/Tr17t2bAgMDSUlJiQDQ6NGjee+I7ty5w624mTJlCn348IEyMzPpwIED3LOgc+fOrDeSM8+ePSNPT08yMjKigIAASklJYYXyH7KysmjdunVkbm5OP//8syBL4omIJk2aRNOmTfvm75dZAHz8+LHUEYCrV68SABKJRCSRSCqNAOjVqxctXLhQYfYlEgkNGjSIAJC9vT1vdmJjY8nJyalY73tFC4DCwkJq2LAhAaDXr1/zZkdXV7fEIej169cTAKpataqgw6ILFiwgW1tbwexFRESQiYkJt+T0t99+oypVqnAigG+uXbvGiV4lJSWqV68erVu3jqysrAgAbdq0ifVGPPH06VOaPHky1axZk3x8fATzOzp16hTp6up+1Ueo1WiPHz+miRMnkqmpKY0ZM4YePnwo6Iugh4fHd/W3X+UEKH3QF+fsc+rUKcGH4BQtAFasWEEjRoxQeMNMS0sjNTU1UlVV5c2GdOlbWT7STVqEJDAwkADw6hAlnfsurv7fv3+fy/+HDx8Ey7ejoyP5+/sLYuvdu3ekp6dH06dPl7n++++/c3tyCLUZT3p6OjfMeuPGDQJAenp6gpZ9ZX/btbW1pZYtW1J4eLhgL33lhfPnz1PXrl3J2tpaYUsDr169SocOHfqu31D5mhUDrVq1wv79+/HkyZMiYc+ePQMAuLu7V4rlL4cPH8bNmzcRFham8LRUr14dLi4uvG6H2rhxY2RnZ5e4NeWnT5/g5eUFJSUlVKtWTfAyMDExQfXq1WFkZMSbDTs7OyQnJ+PNmzdFwszMzAAA6urq0NbWFiTPjx49wt27d7F//35B7O3fvx/v37+Hq6urzPWOHTsiMDAQs2fPxokTJ7glwXyir6/P/e3v7w8AWLhwIapWrcrW5vGMtrY2/Pz8MHbsWJw5cwZr1qzB1KlT4efnh2HDhimk/QtBTk4O9u7di7Vr18LQ0BDjx49H165doaSkmCN1nj59+v1t7WvUgtTTtrh54OHDhxMAOnXqVIUfATh16hT17NmT8vPzix2SVwT169enfv36KcS2oqcAiIh++eUXmjJlCq821q5dSwBo1KhRRcJevHhRooMan6MeDg4Ogtnz9/cvcQokOTmZAJCfn5+g9126FXXv3r0rtUe6orl37x6NHDmSDAwMaOzYsQpbEcMHb9++penTp5OpqSmNGDGC4uLiykW65FHfv3orYFdXV6pevbrMwz4vL48MDAyoadOmgjZC6b4EQgqAkydPUteuXYtdb/nq1Svy8fHhzXZ+fn6xAuPatWukra1N9+7dq9AC4OnTp3ThwoVi8+/g4MB7PcjJySFzc3PS09Mr4gtx6NAhEolExS6R5Qt7e3sKCgoSzF5kZCQBoKlTpxYJe/jwIQGgEydOCJae69evk6amJnl6eipEfG7fvp3mz5+vkFUA79+/p8mTJ1NQUNBXr/3me2pm6dKl9Ndff1UYAfDnn3/S0qVLKT09vdykKT8/nxYvXvzdS8C/WgA8efKEDA0NuYM/CgoKyM/Pj0xMTOjJkyeCFoL0MJ7nz58LYi8sLIxUVVXJxcWF3NzcuI+rqys5OTmRiooKr0uizM3NSVdXl2bPnk0JCQn07t07OnToENnY2NDZs2cVVhmFEgDS7S5btmxJhw8fpujoaJo/fz41a9ZM5nwGPomJiSEdHR0aMGAAJ8ZevnxJNjY2tGjRIkHfuAAIvgnNiBEjqEqVKhQdHc1dy87Opq5du37TYSTfyq+//ko1atSgRYsWKWSP9mfPnnE+H3v37hXcfkBAAGef7wOgGOWP8PBw7v5/zzMA3/KlxMRE6t27N7m6upKbm5vgQz4bNmyg3r17cwXg6upKS5cu5bUDOnLkCLfevKSPiooKr+UQGBhIpqampKqqSlWrViVnZ2eaOXOmwpflCCUAoqOjycXFhTQ0NEhPT49at25NoaGhxU7F8Mndu3epc+fO5OzsTF5eXtSlSxdBz8CQdgDOzs6C3+vCwkLasmULNWnShDp27EgDBgygLl260ObNm3kf/du3bx9NnTqVunXrRtOmTVPofvO5ubnUtGlTMjMzo4SEBMHtHz9+nHR0dMjFxYVsbGxYj1jJePr0KZmbm1Pjxo2/a/MhERGPm9czGAwGgzeSkpLQr18/XL16lRUG46tRYkXAYDAYPyZ79uzBqFGjWEEwvgkVVgQMBoPxYyGRSLBx40aIxWL4+PiwAmF8E2wKgMFgMH4w/vjjD5iamsLR0ZEVBoMJAAaDwWAwGGWH+QAwGAwGg8EEAIPBYDAYDCYAGAwGg8FgMAHAYDAYDAaDCQAGg8FgMBhMADAYDAaDwWACgMFgMBgMBhMADAaDwWAwmABgMBgMBoPBBACDwWAwGAwh+erDgMRiMU6ePIkzZ85ALBZDXV0dRIScnByoqKjgp59+wuDBg6Gjo8NKl8FgMBiM8gp9Bbt37yY3NzfasGEDZWRkFAkvKCig33//nTw9PemPP/4gvhk7dixdvHiRVxsXLlygmTNnkq6uLgEgAKShoUG2trbk4uJClpaWZGtrSwMGDKCzZ8+SEERFRdHgwYOpTp06ZGxsTA0bNqTmzZvTggUL6Pnz53TlyhVauHDhd9t59OgRLVy4kOrVq8flHQCpqamRsbEx6evrk6WlJbVp04ZmzZpF//zzDy/5PXz4ME2YMIE0NDQIAKmrq5OFhYXMx9TUlNTV1QkA+fv7y9X+gwcPaMGCBWRlZcWVgYmJiYx9fX19LmzBggW83fu3b99SSEgItWjRghwcHMjJyYmaNGlCbdq0oVWrVlFSUhKNGTOG8vLy5Hb/69aty+XN2NiYZsyYQTdu3ODiHTt2jCZMmEBqampcvP/973+0fPlyys7Olmv+79y5Q4GBgWRpacnZMjc3p/nz59OdO3cEaX/S+lC7dm2Z+jBv3jyKiYmRmx1p+depU0em/OfNm8e1tYyMDFq4cCFpa2sTABKJRNSlSxc6cOCA3NJx8OBBGjNmDKmqqnLpcHFxoYCAALp9+7bcy/fhw4c0c+ZMMjIy4uxt3769zN8fOGu29kwAACAASURBVHCgTD0MDg7+6nqYm5tLixYtIjc3N+63+vfvX2L8Z8+e0aJFi8jJyYkAkJOTEy1atIhevHgh17KJiYmhX375hezt7cnW1pYsLCzI3t6eJk6cSE+ePPnq3yuTAMjOzqYePXrQsGHDylSQEomEZsyYQaGhobw1woyMDNLW1qYePXoI0uhXr15NAEhLS6tI2OXLl8nR0ZEAkI+PD4nFYl7SkJmZSX369CFlZWWaPn06JSUlcWE5OTm0a9cuMjU1JWVlZZo0aZJcH7rSRhAeHk4SiYQLi42NpfHjx3MPBx8fH/r48SMv+R89ejQBIDc3txIb7eDBg2nixIm82P/777+5cnjw4EGxD+yGDRvSzJkzebF/6NAh0tHRoRYtWlB0dDQVFhZyYampqTRnzhxSUVEhAHJ98MTGxnL5PnHiRInxpk2bRgCoRo0alJ+fz7sIlqYpMjKSFME///zDpeH06dO82YmJieHsnDx5stg4jo6OZGhoSFFRUbylw9fXlwCQoaGhXATmlzh79iyX73r16snU95J4+fIl9yzS1taWSz2cP38+l47g4OAvihcAlJiYKNeyyM7OJl9fX1JTU6MlS5bQu3fvuLCEhATy9vbmwuQqAHJzc6lFixbf9FY1ZMgQunfvHi+VIyQkhACQsrIyPX/+nPfKeOzYsRIFABFReno6p1hDQkJ4ETz16tUjJSUlOnbsWInxkpKSyMzMjAYPHixX29IG8Pmb3+f8+eefpKenx6luPjqAwMDAUgWA9A15xIgRvNSBly9flioApGJpwoQJcre9YMEC7i2koKCgVJEAgG7duiU322lpaVy+S3vDlbZJR0dH3tvj48ePuTR9y5uPPEhJSeHSEBsbqzA7y5YtI0tLS3r69Cmv+ZV2hE2bNhWkfJOSkkhNTY0bWTp+/PgXvzN16lRuNKROnTpyS4t0BFhJSYl+//33UjtqADIvSd9Lbm4utW7dmgDQoUOHSozn5+dHAGj8+PFl/u0vOgGOGTMGpqamCAoK+urphdWrV8Pf31/u0xaFhYVYv349tLW1UVBQgE2bNvE+VaKsrFxquL6+Pvr27QsA2L17t9ztDx8+HA8ePMDw4cPRvXv3EuPVqlULW7Zswfv37wXLOwC4ubkhLCwMAHDp0iUsXbpU7mWgpPRln9UaNWqga9euCqkDAODo6Fjq/fkWIiIiEBAQACsrK+zcubPUcvDy8sKgQYPw5s0bXvJdmm1pWFnuk1BpqghpKM3Or7/+ig0bNiAyMhJWVlaC5FdFRUWQ8lVVVYWGhgYGDBgAAFi2bFmp8bOysrBt2zYMGzaMl3TWq1cPhYWF6N+/PxISEkptA2V5VpSVCRMmICoqCj169ICXl1eJ8YKDg2FoaIi1a9di7969ZXumlhYYGRmJY8eOYePGjd+UcF1dXdSoUQPPnj2T6404fvw4NDQ0EBgYCADYtm0bcnNzFe5PIb3pqampcv3d33//HeHh4QCAadOmfTG+h4cHzM3NBc9/p06d0LFjRwDAihUrkJWVpZD7wJcAKCtt2rSR22/l5ORg8ODBICJMnz4d6urqX/zO9OnTIZFImINTBWfnzp3w9/fH+fPnYWlpWWHzOXXqVIhEIly5cgVXr14tMV5oaChatWoFOzs7XtKxf/9+mJubIyMjA927dxfk+RYXF4etW7cCAEaPHl1qXE1NTQwcOBAAMGvWLOTl5X2fAAgICMD06dOhr69f7Fv41q1b0bdvX0yaNAmBgYE4deoUWrRogWPHjsl0RufPn5droaxZswbjxo2Dr68vNDU1kZaWhgMHDii0kubk5ODIkSMAgCZNmsj1t0NDQwEANjY2sLa2LtN35s2bp5ByGDRoEAAgMzMTp0+fFtT2/v378c8//ygk32KxGLNmzZL774aFhSE5ORkA0KtXrzJ9x8HBAZ07d2Y9ZAVm3bp18Pf3x4ULF8r8TPhRcXBw4F4sShoFkEgkWLt2LaZOncpbOgwNDXH8+HFoaWnhwYMHGDhwIIiI17yHhYWhsLAQKioqaNGixRfjt27dGgDw8uVLREZGfrsAuH//Pm7cuIGRI0cWCcvLy0OvXr0QHR2NvXv3YtWqVXBwcMCECRNw9epVNG/enItrYWFR4nDJtxAbG4vY2Fj4+PigWrVq8Pb25hqEUBQUFMj8ff36dXTq1AnPnj2Dvr4+Fi9eLFd70hvp4OBQ5u/UqFFDIY21WbNm3N83btwQzO7bt2+xYcMGhYm/xYsX49OnT3L/7ZMnTwIATE1NYWBgwHo+BhYsWIClS5fi4sWLsLW1rRR5lo58njhxAo8ePSoSfvDgQRgbG6Nly5a8psPZ2Rl79uyBSCTCiRMnEBAQwKu9y5cvAwDMzc2hoaHxxfj29vZFvlsaJU6SHD9+HG3atIGenl6Rzq9z5854//49rl69ClVVVQCAra0tnj59ip9++gmGhoZcfC0tLbkOz69ZswbDhg2DlpYWAMDPzw9bt27FrVu38Ndff8mIDz7Izs6GkZERDA0NkZ2djZcvX3LDrV26dMGqVavkqsjT0tLw4cMHhXbqX6uSpaSkpPBi49atWzLDfNnZ2Xj16hXvavxzGjRoAJFIBADIz88HEWHChAlyt/PgwQMAQPXq1UuNt2HDBly5cgXv3r2TSeO4ceNgZmYmt/T06NGjxGkIefqdMIpnxowZOHPmDNzc3OR6X8s7bdq0gYuLC2JiYrB8+XJs27ZNJjwkJARz5swRJC09evTAggULMHfuXCxatAjOzs5lHp37WqSjf9WqVStT/M/jSb/7TSMAN2/eLFZNBQcH4+LFi9ixY4fMg0A6z//focfXr1/D2NhYbm95Bw8ehJ+fH3fNycmJS6cQowBaWlp4+/Yt4uLikJiYiHfv3iE8PBz169fHuXPnMHPmTCQlJcnNXn5+Pvd3WRSgovncSUlNTY0XG40aNcLDhw+5z4sXL/DkyRPUr19fsHzGxsYiNzcXubm5yMnJwdy5c3mxI73/mZmZpcYbO3Ysli9fjqioKJw9exYaGhoIDg6Weydx9OhRmbL//MPHFAijqPBUVlbGlStX0KVLF+Tk5FSavEuH9/fu3SvTuV24cAGZmZno0aOHYGmZM2cO+vfvDyLC4MGDcffuXV7tfT7qXBqfP3PL4ohYogB4/vw56tWrJ3PtyZMnCAwMhKenJxo0aCATdvHixWIFQHx8vNwcVDZv3gx3d/civzd27FgAQHh4OG9vnSWho6ODXr164ebNm3B2dsZvv/2GZs2ayc0LW19fn+tU3759W+4b6ef5NjExEcyulZUVRo0apZA8V6lSBYGBgbzsfikdUXn9+jUKCwtLjWtqaso5fzZs2JD1lhWQAQMGYM+ePVBWVkZkZCS6du3Ky9RTecTLywuWlpbIy8vDmjVruOsrVqzA5MmTBV8NsmPHDjRp0gTZ2dno3r070tPT5W7DyMgIQNlH1z53TDQ1Nf12AfDx48ciww4rV65Efn4+Jk2aVESdHDt2DAYGBnBxcZEJO3PmDDp06PDdBSEWi7Fp0ybExMTAzs5O5uPv7w8VFRWIxWJs2bJFIZVTXV2dm/tPTk7G+vXr5da5SN9s79+/X+4b6d9//8397ebmJqhtd3d3rsEoYuRDulxJnkhHt/Lz8/Hnn39+Mb50So6v0RdG8chz2deX6N+/P/bt2wcVFRVcvHgR3bp1qxQiQFlZmet7Nm/ejKysLNy7dw8xMTEYOnSoQoT/sWPHYGpqisTERPTt27fMb+plRfoMffHixRdHAaUv6VKkDoHfJAA0NTVllhIREQ4dOgRjY+Mi3ohhYWF4/vw5fv75Z25eFPh3/loikXxx/rIsHDp0CDVr1kRSUlKRocf4+HjMmDEDALBlyxaIxWKFVNDPvf/l2Vn369cPAHDnzh0kJiaW6TvZ2dmCzol/Xhekb//t27cX1LaNjY3CBACAIiNm8mDYsGFcm5KWLaP8oaurK6i9Pn36cCLg/Pnz6N69e7lYCi3l4cOHvPzusGHDoK+vjw8fPmDLli1YsWIFxowZo7DpUWNjY5w4cQKampq4cOECpkyZIvcRH2n/WxavfukyyTp16pTJIbJEAWBhYSEznJ6YmIi0tDQZ5yfg3+UG0vlPd3d3md9YvHgxNzz/vaxZs6bUwh07dixUVVWRnJyM3377TSGV4fMhegsLC7n9rnQzJgCYPXt2mUZLZsyYIeM/IASXL1/GiRMnAAALFy5U2FtoXl4epk+fXiE6Fnt7e4wZMwbAv0OOsbGxrLctZ+jo6Mg4vwqFl5cXDhw4ABUVFURERMDT07NciID8/Pwyb0TztWhpaXFTfSEhITh69Kjc+phvpVGjRvj1118hEonkPgLt7OzMvQB+acM7iUSCnTt3AgCWL19epo2QShQAP/30E27duiXzUAWAjIwM7lpKSgqCgoLQtm1bAICrqysXdu7cObx+/Zpbv/k9REdHIykpiSuIkpSYp6cnAGDVqlVyv8llGVUICQn5t1CVlLjdqOT1dnHgwAFoamriwIEDCAoKKvHtPi8vDyNHjsSIESPKtGmMvPIeFxeHvn37orCwECNGjOBlSE46vPalkY358+fzMgf++Ry8vIf6SmPFihXw8PCARCJB//798eLFC0EfcGXNtzRMiLJRxL1IT09H3bp10bVrVxQWFnJ2O3fuzOsUwH+XHX9Or169cPDgQaioqODs2bPo3LkzbxvUSJ8DX3oeBAQEoHHjxt9tTyKRFOv3Mn78eKirqyMlJQX9+/cvsjxWOnL9JZ8ZeaTlczHG15LA0NBQODo64uzZs6WOAs6dOxePHj3C7Nmzy+4QWdIewXFxcWRtbS2zH3GNGjUIAP3yyy80d+5c6tatG71//547fenevXuUl5dHK1asIA8PD+5QmOvXr9Ply5e/aR9ksVhMTZs2JS8vry/G3bJlC7dntjxPwyIiWrFiBQEgTU1N+vDhQ5E94qUH1SgrK/N2CFJUVBRZWFhw++Hv3buX4uPjKSMjgx4/fkxbt26lDh060F9//SVXu7du3eLK9b+nL6akpNDixYtJR0eHtLW1v3hYxvcwbNgwAkCWlpbFnjXw6dMnWrRoEWlpafFyING1a9cEOfylOPLz82nq1KmkpqZGRkZGFBoaSjk5OTJ537VrF+np6ZG6urpc6//nh94cPXq0xHjjxo3jDgPi60AsKZcuXeLSFB0dLcg9uHv3Lmdz7969dP78eapZsybve/DfuHGDs3vq1Kli44wcOZKL4+joyMvZBEOGDCEApKurSwkJCTJnUuTk5ND9+/dp9OjRpKmpKZdTIK9cuUIikajI85aIaPjw4aSkpETx8fElHkqlo6Mjlz35379/X+o5KFIKCwvJy8uL8HWH7JY5DV26dCFVVVUKDg6mzMxMLuzp06fk4+NDGhoatGbNGvkdBtS2bVuZB92ZM2fI1NSUzMzMaP78+ZSbm0tERJGRkWRqakomJibk7u5OYWFhXOUoKCggd3f3Ym/il/j999+pWbNm3BG8o0ePLvEmLFmyRObYUnV1dRoxYkSxFeRruHDhAk2fPp07YAKfHctpZ2dH5ubmpKurSw4ODjRs2DC6e/curw+D7OxsWr9+PbVt25aMjY25o3lbtGhBGzZskKkY38ujR49owYIFZGNjI5N3AwMDcnBwIHt7e7KysqLOnTtTSEgIpaWl8ZLnw4cP09ixY0lJSYlLQ/Xq1alOnTrcx8zMjDsFrG/fvnK1/+DBAwoKCiJra2vOvqGhIQUEBMj1+NeykJiYSIsWLaJWrVpR7dq1ycnJierVq0eWlpbk7u5OK1eupOTkZLne/8/blZGREfn7+xc5DtjPz0/muFghjwO2srKiwMBAQY4DXrBgARkYGJChoSF5e3t/9/OlNO7fv09z5syROYbayMiIZsyYIVPvtmzZQrVq1ZJpo8rKyuTh4UGbN2/+7nQcPHiQRo0aRcrKyjI2Svp07979u+tdUFAQdwyym5sbLV26lN68eSPTJnv16iXzvTNnztCkSZOoSpUqXFo6dOhAy5Yt+6Z6+ObNG1qyZAk1adKEO5Fw4cKF9OjRoxK/k5OTQy4uLrzViYiICPL29iYbGxtycHCgevXqUYMGDWjmzJnfdAKoiEoZT7116xYGDx6MmzdvfvNw8oIFC2BsbIzhw4ezyUIGg8FgMMoJSl9ybvD19cWQIUO+aT4lNDQUycnJrPNnMBgMBuNHEgAAMGnSJDg7O8PT07PMm9t8/PgRfn5+SExMlNt6eAaDwWAwGPKj1CmAz4mOjoa/vz9atmyJQYMGFXsIxePHj7F//35ER0dj9uzZcj0WlcFgMBgMhgIEAPDv8qtz584hPDwcz58/h6qqKpSUlCASiVBQUAArKyt4eXmhVatWMnsFMBgMBoPB+IEFAIPBYDAYjIqBEisCBoPBYDCYAGAwGAwGg8EEAIPBYDAYDCYAGAwGg8FgMAHAYDAYDAaDCQAGg8FgMBhMADAYDAaDwWACgMFgMBgMBhMADAaDwWAwmABgMBgMRjnn3bt3sLa2BttAFrhx4waSkpIqvgCIj4/HwoULYWdnB5FIxH00NDSgp6cHOzs7+Pr6IiYmhvcE379/H+PHj4e9vT3MzMxQvXp1ODk5YebMmXj16pXc7V28eBGzZs2Cnp4el29lZWUYGhqiatWqsLCwgIeHBw4fPixIo7h//z769+8PQ0ND6OrqwsbGBuPGjcOVK1ewcuVKXLp0Se42w8LCZO57WT59+/b9brvnzp3DrFmzUK1aNe53GzZsiCVLluDly5cycRMTE7F48WI4OTlBJBKhVq1amD9/Ph4/fvzN9qOjo9GzZ0+ZfNWrVw+LFy8uNv7p06fRokULLm63bt3w5MmTr7a7bNkymJubc79Tu3ZtrFy5EgAQFxcHX19f7gwOkUiEjh07IiwsTOY3Ll++jJEjR0JJSQmqqqoYMWIEkpOTvyodSUlJmDRpEmrVqiVTBoaGhpgzZw6ys7O5uL/99ht69+7Nxalfvz6CgoK+6/5HRkaiXbt2MrYNDAzg7++PFy9eyMR98uQJRo0axZVL1apVMW3aNLx+/VoubSAqKkqm/Wtraxf5KCsrQyQSQUVFBVevXuXtGZCbmwuRSAQ1NTXUqFGD+/y3TPhAX18ftra2uHbtmkI6rBcvXsjkWU1NDSKRCLm5uYKmQywWY9CgQZg0adKPrWLoK7h58yYBIAB06dIlIiJKT0+nBQsWkLKyMolEIlq1ahXxQUFBAU2fPp1UVVVpypQplJSUREREEomEoqKiyM3NjbS0tGjr1q282F+7di0BoKpVq1J2djYREX369Im2b99O2traBIAGDx5MhYWFxBeXLl0iDQ0NGjBgAL18+ZKIiJKSkmjWrFmkoqJCACgyMlLudjdv3kz29vZ08+ZNysvL4/IurQt//fUXdy8ePXpEnp6e5OHhITf7wcHBnK2UlJRS4yYkJBAAun79utzsz5w5k7N/8eLFUuOKxWJSV1ensWPHfpfNR48ekZKSEgEotk1NmTKFS9OjR49K/J369evTwoULvystubm51K9fP87e1KlTi42XlpZGAGjs2LGUn58vt/KfNm0aZzsgIKDUuM2bNycLCwuKj4+Xaxs4fPgw1a1bl/7+++9i2/i9e/eoSpUqBIBmz55NfCJte+3atSNFsHPnTpowYQKVB37++WcCQJ8+fRLU7rJly8jHx4fq169P58+fpx+VrxIAqampXEO8e/euTNicOXMIAIlEIrk+fImICgsLqU+fPgSANmzYUGycvLw86tixIwGg4OBguRfUsWPHCADp6uoWCdu2bRtXLvv37+flRkkkEjI3N6f69euTRCIpEn7kyBESiUS8CIDly5dznfx/H0KfC4DPw7y8vORmPzw8nACQpqbmF+Pm5eURAHr37p3c7L979460tLQIAK1YsaLUuA8fPiRdXV3KyMj4brvu7u4EgPr06VMk7O3bt6SqqkoA6MiRI8V+Pycnh6pVqyaXshCLxdSqVSsCQDVr1qT3798XiePn50d9+/aVe/0Ti8XUsGFDAkB2dnYkFouLjZeenk56enp07do1uadh48aNdOrUqWLD8vPzufQ1atRIruKnPAqA9+/fk5WVFa8vO+VZALx+/ZrMzMwoJSWFLl68SA4ODiXWyQolAN6+fVuiAHj16hUXNnLkSLkmctGiRQSA/ve//5UaLykpiTQ0NEhJSYnOnTsn1zScPHmyRAEgkUhIXV2dAJCnpycvN+r27dsEgHr37l1inE6dOvEiAPbu3VuksZcmAIiIdu3aJTf7R48eLbHsi+ssAFBWVpZcy2DMmDEEgOzt7UuNN2fOHJo4caLcyh0AaWlp0cePH4uEd+3alQDQwIEDi/3+kSNHqGvXrnIrg5cvX5Kenh4BoCFDhsiE7d+/nxwdHbnRMT7qv3SUa+nSpcXGGTt2LE2ePJkX+0uWLCkxb9IRoipVqtC9e/d4f2grWgAQEXXp0oWio6MrpQAYMGCAzKicl5cXrVmzpnILACLi3pJ+/vlnuSUwJSWFNDU1CQCFh4d/MX7Pnj0JADk5OclVoZYmAIiI6tSpQwCoRYsWvNyoW7ducZ1BSQ+ZHTt28CIASnsIlSQA5El5EADx8fEkEokIAJ09e7bEN0FjY+NSh+S/huzsbG56KSwsrEj4+vXrCQBpa2sX2zn17t2b9u3bJ3cxKL3vZ86cISKiu3fvkrGxsdyH3YsTVwBIQ0ODEhISZMKuXbtGVlZWxQolPrl8+TI3VbN69WpB254iBcCePXvIz8+v0gmA6Ohoql+/vswb//Pnz8nExITevn1beQXAhw8fuLChQ4fKLYHLli3jfrcsQ5nbt2/n4l+9elUQAfDp0ydOpIwaNYqXGyUWi8nU1JRLw6+//lokzqtXr+j58+dMAPAgAKRvPQCoY8eOxYbv37+f2rdvL1ebgwYNIgDF+lT07t2buwf/7egzMzOpRo0avLyR9+jRgwCQmZkZPX/+nGxsbEqchpAnubm5VK9ePW40UCrw8/PzydHRscQher7IzMwkKysrrjMWaki8PAiAzMxMsrS0pIKCgkojACQSCTVo0IAuXLhQJCwoKEjuI98/lADYtGkTF1bSG9L33GBTU9OvelMG8N3OT2UVAAsWLCAApKamxutb0Pnz5zlHIwDUvHlzioqKUkjFqYwC4MKFC5yfy/3794uEt2zZUu4dYUREBAEgFRUVevPmjYzgrlatGicC/isQfv31V/L29ublfqSmplKNGjU4p9hp06YJVu+uXr3KvXFLHX4XLVpUrJ8E3wwdOpQAULVq1ejFixeCtz1FCgAiIk9Pzy86xVYkAbB27doSfZs+ffpE1tbWdOvWrcohAG7cuEFE/3rnHz58mHR0dAiA3OY/pdjZ2REAcnR0LFP8Fy9e8OKLUJwASEpKoilTppBIJKLq1avTiRMneL9h169fp7p163J5BECdO3cutkNiAkD+NGjQoNi6FRcXR7Vq1SrWQfN7KCgo4EZ+1q1bx13fuXMn9ezZk6KioooVCO7u7nT69Gne7snhw4e5+3/y5ElB697EiRO5jjc6OpqMjIwoOTlZ0DRI62RJ0zOVQQDs37+ftxHP8iYA3r59S6ampqWOsB47dozc3NwqhwDo0aMHeXp6UqNGjcjBwYG8vLx4GYIzNzcnAOTi4lKm+Dk5OVwa+/fvL3cBoKKiQu7u7uTg4EAASElJiTZv3sxbh1MceXl5FBwcTLq6ulxeVVVVadGiRUwA8CwAdu7cyc1Dp6WlcddHjx5NCxYs4MWmdBlcs2bNuGsdOnSgo0ePUmFhIVlaWhIAWrt2LRH96zdjaGjIq2fy1atXSVlZmZsK+PDhg2B1Lzs7mxt6V1FRoc2bNwv60ExJSeFGQPhY9fCjCICPHz+ShYWF3EVveR0BqIjI1QmQD1xcXAgA2dralin+u3fvuDSOGTOGtxGAjIwMql27NgGgESNGKOTmpaWl0aRJk7jlYGVZJ/0jCoATJ06UWQDk5uYSAMrJyeFNfBkaGhIATnBlZWVR9erVv7hHwbdy584drqwfP35MKSkpZGBgwO3JMHv2bAJATZo0ISKiNWvW0OjRo3ntAC0tLSk8PJwTocOGDRO07u/Zs4cTYkIvR/Pw8OCmJeW53PRHEwBE//qhREREMAHwg1LutwK2srICALx+/RqFhYVfjP/27Vvu7wYNGvCWLl1dXRw+fBjq6urYunUr9u/fz2s55OTk4P79+zLXqlevjpUrVyI2NhZ169YFACxZsgRpaWkVastNDQ0NrgzoC7stZmdnQ1lZGVWqVOElLWpqahgzZgwAYMOGDRCLxdi9ezfat28PQ0NDXmw6OjpydXnfvn04cOAAevXqBTU1NQCAj48PAODvv//G48ePsW/fPgwYMICXtEgkEvTt2xeTJ09Gr169EBISAgDYvn07zp07J1idqFat2r9bmf7/zn9CsWnTJpw5cwYikQg7d+6Enp5epd4Ot2/fvjh48CDbI7kibwWsSLp06QIA+PjxIx49evTF+LGxsQAAZWVleHh48Jq2Ro0acVu0/vLLL0hISODNVmZmJrZu3VpsWL169XDy5EmoqKhALBbj9u3bFaqS1qxZk9t+MzU19YtbhZqYmPDaKYwePRpVqlTB69evcfDgQWzatAljx47ltQyknXxYWBjCwsIwcOBALszOzg4uLi4AgKCgIKSmpsLNzY2XdEyfPh3GxsYYN24cAGDYsGHo0KEDAGDEiBHIysqqsA/LhIQETJ06FQDg5+fH5bukelgZ6Ny5M86dOweJRMJ6UyYA5E+PHj24DiA8PPyL8Y8dOwYA6NevH8zMzHhP35gxY9C3b19kZWWhT58+vO5JfezYsRJHQWxsbGBnZ8eNTlQkbG1toaWlBQBf3IM8IiICzZo14zU9BgYG8Pb2BgBMmTIFANCyZUtebQ4YMADKysp49OgR0tLSinTwUkGw97mGFQAAIABJREFUZ88e9OvXjxcBdOjQIZw9e7aIEN26dSu0tbWRlJTElUdFQyKRYODAgcjJyYGdnR2Cg4NLjCsWi7Fp06ZK0YFoaGjA1dUV58+fZ73pj8jXzBckJydzc5GxsbGCepsCICMjo1K3WE1ISCB1dXUyNDSUu1ew1BFNR0enSFhmZibZ2NgQAPL19eWlDKRlX5Kj35s3b0hDQ4NsbGwEWZublZXF1YUrV67wbi8gIIDbaKkk57bbt2+Trq4uxcTE8J6euLg4Lv8bN24UpB1Itwb29/cvdl5e6pR3584dudu+ceMGGRgYlLjaRLopEQBBVsNI26OGhoYgZT9v3jzO6VC6AqoktmzZQitXrqwUPgBE/+44+d+dIZkPQAV0Avz777+5Ri70phvSDYF69uxZbAfw/v17cnFxoerVq3+xgX4Lq1ev5taAF+fx/M8//3Br9CdPnix3D+zPxdfAgQM5AVZYWEi3bt2iJk2akL6+viCdHxHRgwcPuPQcPHiQd3u5ubnUq1cvAkCtW7emyMhIyszMpKysLLp16xZNmzaNtLW1aceOHYLVyQ4dOpCOjo5gK0Ckjm8l7TTYsWNHql+/vtztnjx5kvT09Ep19MvLy+PW5+vp6fG+Je66deu4+peens6rrevXr3PbEAcFBZUYLyMjg0JDQ0lLS4vXA2LKmwD49OkTmZubc06pTABUMAHw6NEjCgoKImtra67R1apViwIDAwXrcIj+XWdZq1YtatSoEe3du5fu3LlDN2/epDVr1pCZmRm1a9eOnjx5IlebFy5coBkzZnD7HAAgV1dXCg4OLjLKEBoaysUxNzcnPz8/ev36tdwEwPjx4+nGjRu0YMECcnV1JWNjY6patSpZWFjQqFGjBNmM5NmzZ7Rw4UKqX78+l1cTExOaN28eL8LrcwoKCmjnzp3UoUMHMjQ0JBUVFdLV1SV7e3saM2aM4HshnDlzRq4rTb7Ex48fqW3btiWGh4WF0eLFi+Vm7/Tp09SuXTvuPtesWZPmzp1Lubm5MvGio6NldiXE/29ZPWrUqCJb9n4v0dHRNGvWLO5MAgDUtGlTWrJkCaWmpvJS7p/XdU1NTdLS0iry0dDQkMk/X2kpjwKAiGjgwIGC7wfBBMD3IyIS4BB7OSIWi7Fnzx5MnDiRczhSV1fHhQsXeHN8YjAYjPJCbm4uNDQ00K5du0o/996xY0ecPXsWnz594m3lD3MCLEeoqqrC19cXly5dgqmpKQAgLy8PFy9eZHeTwWAwGIyKKgCkNGrUCHfu3EGfPn0AAIGBgTh16hS7owwGg8FgVGQBAAD6+vo4ePAgoqOj4ebmhl69emHVqlXIz89nd5bBYDAYjFL44XwASuP+/fs4dOgQnj59CltbW7Rv3573NeEMBoMhJFIfADU1NZmdCG/cuIFatWpV6Ly/ePECDRs25P6fmZkJsVhcoX0AYmJi0LRp06/6zpUrV8r0nQolABgMBoPBYJQNJVYEDAaDwWAwAcBgMBgMBoMJAAaDwWAwGEwAMBgMBoPBYAKAwWAwGAwGEwAMBoPBYDCYAGAwGAwGg8EEAIPBYDAYDCYAGAwGg8FgMAHwQxAREYEuXbqgZ8+erDA+IyMjAytWrICFhUWlO55ULBZj6dKl6NChA7S1tdGoUSP89ttvFTKvsbGxGDp0KCwtLctFevr37w9DQ0PExcUpxP7AgQNhZGSEe/fuCWLvxo0bGDJkSLnY7vfly5fw9/eHsbEx/vnnH/YQ/FGhb+DatWukoaFBAOjhw4dU0SksLKQJEyaQubk5AaDu3bsT419u3rxJXl5epKSkRAAoIiKi0uRdIpFQ+/btKTQ0lIiIrl69SlWqVCEAdP78+QqV1/Xr15OzszMBIENDw3KRJktLSwJAR44cUYh96fMgPDycd1s7d+6k9u3bEwCqXr26Qsv9ypUr5OPjQyKRiADQ7du32YPwBwXf+sXDhw+TSCSiZcuWVZrCCg8PZwKgBNzc3CqdANiwYQMBoKysLO7a7t27ycjIiP76668Kl9/Hjx+XKwHw5MkTOn36tMLsx8fH04kTJ6iwsFAQe8nJyeVCAEhxcHBgAuAH55unAHr37o0lS5bg+PHjlWa0pHr16mzIqARq1KhR6fK8f/9+qKqqQltbm7vm4+OD5OTkCnkKZXmr/7Vr14aHh4fC7NvY2KBr164QiUSC2Pv85L/yQHlLD0NgH4AZM2bA0dERb968qRSFpaKiwmoMKxuOhw8fQlVVld1jhiAoKyuz9DDk26a/9wc2bdrESpFRKcnIyIC6ujorCAaDUflGABRBYWEhtm7ditatW6NHjx6oW7cufvrpJ4SFhQmajry8PMycORNGRkbQ0dGBl5cXkpKSBLGdm5uLxYsXw9XVFU2aNEHt2rXxyy+/ICUlRRD758+fh7u7O9q0aQM3Nzf4+vri/fv3gpX9hw8fMHPmTLRo0QJmZmYwNjbG8OHDkZqaKkinb2dnBzs7O0gkEuTk5HD/HzFihCD537lzJ9zd3TFq1Cg4OztDJBLJfAICAgRJx9mzZ/HTTz9BU1MTLVq0wOPHjwWrA4mJiZgzZw6MjY1x8+ZNwZ9DSUlJCAgIgKmpKf7880+FPQ/z8vIwYMAAiEQiGBsbY+bMmYiKiqoQnZNEIsGRI0fw888/cyuvjh49ChcXF2hra6NNmzZcnXv69Ck8PT2ho6MDU1NTbNy4Ue7puXz5Mry9vWFnZwcAuHjxIpo3bw5NTU00b94cCQkJgpTLqVOn0LlzZ3Ts2BE2NjZwc3PD3r17v+3HfjSnhfHjxxMAunfvHhERZWdnk729PQGgP//8k1fbly9fJvwfe+cdFtXxNeB3d+lVEJAqEkSk2nvsGnuLLWJNxN6NLdGfJrbYey8k9hJLjBqj0ajBGnsDCxakWOi9M98fLvsBghplF8t9n4cnZu7unrlz586cOXPOGRAtW7YUX3zxhfj8889FixYtVJ7ftra2IiwsTK11iIuLE9WqVRO+vr4iPT1dCCHE7t27BSCcnJxEfHy8WuWvWrVKGBsbixMnTqjKJk6cKACNOAFGRkaKypUriwMHDgghhMjKyhJLly5V3X90dLTG+qJCoRCGhoYa7f9jx44V2tra4u7du0IIIdLT00XdunUFIHr27CkSExNFZmamWmQnJCSonABXr14t2rZtKxYsWCBatmwpAFGzZk2NtME///wj+vTpo+pzFy5c0OgzOHfunOjfv7/KC97f318jcjMyMgp0Ahw1apRo1KiRRvu+EELUr19frU6A33//vXBzc1M5Xk+ZMkV88803YuHChaJmzZoCEBUqVBAXL14UderUEbNmzRKTJk1SRaj9888/RVaXjRs3iq5duwpAODo6itWrV4tmzZqJ6dOni6ZNmwpAVK5cWe1tPnnyZOHs7CwePXqk6hNDhgwRgPD19dVcFEBxYWxsLLS1tfOUTZo0SQDip59+0ogCUKpUKXH69Ok83sC2trYCED4+PmqtQ48ePYS9vb1ITk5WlaWmpmok/Ozy5ctCS0tLzJs3L095ZmamcHBw0IgC4OPjIyZNmvRSuaenpwDEtGnTPloFICAgQMhkMtGoUaM85QcPHhSAMDMzU6v8HAVAR0dH/Pzzz3mev52dnQBEaGioxtrDy8urWBSAHKpXr17sCsDYsWNF27ZtRWpqqsbvX90KgBD/H3nl6OgoLl++rCpPSkoSJiYmAhDDhw9XLYaEEGLOnDkCEEOHDi3SuoSEhAhA6Ovr5+n/GRkZwsrKSgDi4cOHamuLv/76SwBi27ZtL/WLnPFv/fr1mokCKC5GjRrFuHHj8pQZGxsDkJycrJE61KxZk9q1a6v+38XFhVmzZgHw22+/kZGRoRa5YWFhbN26lS+++AJ9fX1Vua6uLsePH8fPz48GDRqo7b4nT55MZmYmX331VZ5yhUJBlSpV1N7u0dHR7Nixg8OHD9O+ffs8fzo6Ori6umpkG6C4OHPmDEIIrKys8pRXrFgRgJiYGFJTU9VeDzMzM/r06ZPn+bu7u6v6qKYo7qgEc3PzYt0KHTBgAKGhoezevfuj9UUpUaKEqo9XqlRJVW5gYKDqc7169crjjOvl5QVAeHh4kdYlZ56xsrLK0/+1tLSoUKECAE+ePFFbW8ycOROAxo0b5ynX0tJi8ODBAPz000//6Tc/OLfeH3/8EYD09HR27tzJ3r17CQkJUb0UxUXnzp3p3bs3ycnJhIaG4uTkpJYJIDs7u8BMYDVr1lRr6FlSUhKHDx9GV1cXOzu7l65rwiP40qVLZGVlMWrUKLp168anRs6El9/XI2ciMjU1RU9Pr1jqlqOQakIB0WSfex/lCyHo3r07e/fuJSgo6KOOznhVGxsaGqraIzc570BaWprG6mJiYqKal9RBYmIiJ06cKFTxrVevHgBBQUE8ffoUa2vrN/rdDzIVsJ+fHzVq1CAlJYWtW7fSq1evYq+Tnp7eGzf6u6yA1a1lFsajR4/IyMhALi++LpNz/w8ePOBTpFmzZtjZ2XH16lUSEhJU5cHBwQB06dKl2OqWEwtfnEr4p4JMJsPY2FjlAJiZmSk1yntCfmWkqAgNDVX9ds44mBt7e3vVv/+LJfyDUwD69u3LhAkT+P333+nXr997ZfrKzs5GR0cHGxsbtfy+qampyhJQGOrKS56j/aakpBAREVEs7ZuTcGf//v2FfubChQsf7eCir6/PoUOHKFWqFJMmTVL1uRkzZuDu7s7s2bOlEfgTYcmSJVSsWBF/f38mTJggNchHTs7Yn1vhz03OPKilpVWghfajUAD++ecf/Pz8aNWq1XtxIEZuoqKiePbsGc2aNVObGbZq1aoA3Lx5k4MHD750/eTJk2o7GMXJyUll5n3VBKzOFWDOHuD58+fZvn37S9dv3rzJ33///VEPBAYGBhgZGZGcnMzXX3+Nr68v7u7unDt3TsrM9gmhp6fHrl27MDU1Zf78+ezdu1dqlI8YGxsbXFxcAAp81jmLshYtWvynRfEHpQDkOHXcuHFDZQ7Jysri+vXrwP/v+URGRmq8buvXr0dXV5fp06erTUbZsmVp2LChyhJy+vRp1bW//vqLESNG0KZNG7XI1tXVxcfHB3jhDJh/GyIxMRF4EaOvLmxtbWndujUAffr0YdmyZao95/Pnz9O9e3eN+QYIIcjOziYrK0tjfSwlJYWmTZvi4+PD2rVr+fnnn/Hz82PChAkqByV133NRfEaT9fmYyH+/zs7O+Pn5qd6HO3fuFGt9PvZn/CaLG3XWd/z48cCLPCBJSUkvLf7kcvl/twZ9SCGA9+/fF9ra2gIQTZo0EePGjRPVq1cXXbp0EYBwdnYWPXr0UOUIKGpu3rwp9PT0hIGBgVizZo0q9GTXrl3C3Nxc7Nu3TyNtYGNjo4qBdnBwEBYWFkJbW1ucPHlSrbKjoqJEuXLlBCDs7OzEkiVLxO7du0XPnj2Fo6OjAISnp6eYMmWK2g5ICQ0NVckChLa2tjAyMhKAWLt2rUb7Yk4dnj9/rhGZt27dEoBQKBTCyclJuLq6Cjc3N+Hp6Slq1qwpBgwYoMoPoA6Cg4MFIAwNDV96vo0aNdLYyXg5eHt7C0AcOXKkWMajVq1aaTQMMOcwIF1d3Ty5Hvr37y8A4eLiIp4+faqx+885DEidocc5YYD169cvNAzz77//zlP++++/C0DUqVOnSOty584dAYgSJUq81P9zTmpUZ//Pzs4WPXv2VOVFiI2NFUIIcf36dVG6dGkxe/bsjz8PwJYtW4Sjo6MwMDAQbdu2FY8ePRIPHjwQNjY2wsPDQ5w5c0at8h89eiRGjBghnJ2dhbm5uahQoYLo1auXuHPnjsba4PHjx6JHjx7CzMxM6OvriyZNmohz585pRHZkZKQYMGCAsLCwEPr6+qJx48bi33//FZ06dRINGzYUO3fuFBkZGWqtw7Nnz8TAgQOFtbW10NHRERUrVhQ7d+7UWPvPmTNHFYMOiFq1aomFCxeKmzdvql32pEmThK2trbCxsREGBgaqY5hz/szMzMSTJ0+KXO5vv/0m6tWrp5LTqVMncejQIXH9+nUxZswYVVIcNzc34efnp/Z8CMOGDVPVxcvLS/zyyy/FpgDkzgmiLjZt2iQaNmyouueuXbuKP/74Q6SkpIiOHTvmWRDMnDlTNTmog3v37omhQ4eqZHp4eIhly5YVuZzVq1cLFxcXAQi5XC5Gjx4trly5Ii5cuCAGDRqU5/kvWrRICCHEvHnzVIsUuVwuRowYIe7fv//Oddm3b59o0KCBSuZXX30lDh8+LG7cuCHGjh2r6v/ly5cXq1atUqsSsGbNGlG5cmVRsmRJUblyZdG8efO3VoJl4lOzo0lIfKBERETQrVs3du3apYqPziE1NZVHjx4xYMAAfH196dmzp9RgaqZ169YcPHiQa9eu4e3tLTWIxAeHXGoCCYkPAx8fHxo3bvzS5A8vnMLKly9Pw4YNP8mjmYtrT1gul6sl54eEhKQASEhIAHDx4kWOHj2Kv79/ocl2rl27xrlz5/jiiy+kBlMDsbGxeZLLJCYmUrduXY04YEpIqAPpgG8JiQ8ANzc3vL29OXToEE5OTrRu3RpXV1cMDAyIi4vj4sWLREZGsmPHDumcdjWQmprKZ599hra2Ntu2bcPDw4O7d+++MiRWQuJ9R/IBkJD4gCah1atX8+uvv3Lz5k2SkpIwMzOjcuXKqhDIjzktbHHTr18/duzYQXZ2NvXr1+fHH39U5eaQkJAUAAkJCQkJCYkPAskHQEJCQkJCQlIAJCQkJCQkJD4FpA1DCQkJCQmJYkCWc4ymZAGQkJCQkJCQkBQACQkJCQkJCUkBkJCQkJCQkJAUAAkJCQkJCQlJAZCQkJCQkJCQFAAJCQkJCQkJSQGQkJCQ+Ji5f/8+ixcvJikpSWMyfX192bx5s0bvc//+/ezfv5+srCzpoUsKgISEhMSnixCCyZMns3TpUnr37o2hoeFHfb+tW7dGoVDQokULHj16JHWAd0RKBCTxTvj7+1O3bl2pISQkioFZs2Zx+vRpjh079kncr0wmo2XLlqSlpdG0aVOuXLmCkZGR1BEkC4CEprly5Qp+fn5SQ0hIFAPx8fFMmzaNhg0bfnL33qBBA4KCgli1apXUESQFQELTpKamMmDAAKTDJCUkiodLly6RkpJCVFTUJ3fvOffs7+8vdQRJAZDQJGlpafTs2ZMLFy5IjSEhUUzkpJG/evXqJ3fvOfcsl0tTmEYUgOzsbA4ePEj79u1p0aIFQghmzZqFg4MDBgYGNG/enICAAI1U+vLly3Tu3Jnq1atTrlw5atWqxbp166hRowYnTpxQu/wzZ87Qu3dvXFxcEEIwduxYTE1NadOmDdnZ2WqX7+/vT8uWLWnfvj3lypVDJpNRokQJjbS9EII+ffpw8eJFAH7//XcqVqxIxYoVCQ8PV5vcefPm4enpiUwmo2bNmqry06dP07dvX2QyGTKZjNu3b6tF/ooVK7CyslLJ6du3L6Ghoarru3fvxsvLCzMzM9asWVMkMvft24ejo6NK5vTp0wE4dOgQ9evXV5W3bdtWtRLKyspi3LhxyOVyvL29uXHjRpHUZdeuXVStWlUl09vbm1u3bpGWlkanTp2QyWRUrlyZI0eOqKX9p06dir6+PjKZDC0tLSZMmEBcXJzq+qFDh3Bzc0NXV1fVTmoZMOVyzMzM8PLyUvX7ihUrYmJigkwmo3Tp0hqzirm6ugJw8+bNT27iunXrFgDu7u7SLP6OA/obMWPGDFGhQgUBiMaNG4vhw4eLtm3bin79+gkrKysBCHNzcxEcHCzUybp164S1tbU4ceKEqmzz5s1CLpcLQBw/flyt8pcuXSpq1aolAGFnZyd++OEH0a5dO6FQKIRCoRCRkZFqlX/nzh1hbW0twsLChBBCZGdnixkzZghTU1OhSfbu3SsA0bt3b43JPHPmjABEjRo1Xrrm7u4uABEYGKg2+VeuXBEymUwAIjo6+qXrvr6+4ueffy5Smbdu3RJyuVzo6+uLjIwMVXliYqKwsLAQgLh7926e7yQnJ4uSJUuK58+fF2ldUlJSRPXq1QUgvvzyS1X54sWLRc2aNUVSUpJan/+KFSsEIKytrQu83r17dzFx4kS1yc/IyBAeHh4iJSUlT/mNGzeEnp6eUCgU4p9//tHoe2hjYyPMzc1FcdC3b1+xadOmYpH9v//9TwBi9+7d4kPmg1EAhBDir7/+EoCwtLQUW7ZsUZWHhYWJ0qVLC0B89dVXamssf39/oVAoCnzoderU0YgCIIQQwcHBAhB6enpi+fLlqoH61KlTapc9ffp0YW1tLTIzM1Vl2dnZonbt2h+9AhAYGFioApDz/NWpAAghRIsWLQQgNm/e/NKk6+7uLtLT09Um86+//spTPmrUKAGIefPm5SnfvXu3GDhwoFru//79+8LIyEgA4siRIyI0NFS4uLioFFJ1kp2dLby9vQUg/P3981xLTU0VVlZW4vHjx2qTn5ycLKZMmVLgcwfE1KlTNT6BVK5cOY8y9qkoAH///bcAxNmzZyUFQFM+ADnhFl5eXvj4+KjKbW1t+fHHH1Vmy/T0dLVUdvLkyRgZGdG+ffuXrllbW2us0XLM7UZGRgwcOFBliqpTp47aZaenp/P06VP69u1LbGysai9w7NixkjlLAwwbNgyA5cuX5ynfsWMHX375Jdra2kUu85tvvgHgl19+KbDPr127Nk+5n58fvr6+arn/zz77jLlz5wIwZMgQ+vTpw4IFC7C1tdXInveECROAF+Fv+bcoatSogYODg9rk6+vrM3HixDxlI0aMICAggIYNG750Td08ffqUiIiIl9riU6Bhw4Z07dqV48ePa0Te48ePad++PXZ2dtSqVYupU6dy586dAj/r5+fHgwcPPq4tACGEOHv2rGoLID+RkZECEIAICgoqck0pPj5eKBQKUaVKlQKvd+zYUWMWgISEBNUWgKYJCgoSxsbGAhBmZmZi0qRJRW7qlSwAr16Furi4CEBcvHhRVV67dm0REhKiFplpaWnCwsJCGBgYiLi4OCGEEOnp6aJChQqiatWqAhAnT54UQgjx5MmTQt+RoqRp06YCEF988YVG+11mZqZwdnYWgLh69aqqvG7duuLgwYMarcvOnTsFICwsLDRiAcndBv7+/mLQoEEiICCg2FavxWkByNmSGTdunFi6dKmIiopS6ztfv359sWnTJhEYGCj27NkjevbsKYyMjET16tXFkiVLVFvfV69eFY0aNRJZWVkfnwXgVZQsWRJjY2MAMjMzi7yiISEhZGVlqeW3PyScnZ35999/adiwITExMUyfPp2yZcuybt06aXmuAWQyGUOGDAFg6dKlwAunVGtra+zt7dUiU0dHh+7du5OcnMzOnTsB2LJlC+3atWPo0KEAKsfDDRs20KdPH7W3w8iRIwH4+++/VQ6hmkChUKisXTNnzgQgMDCQkJAQmjdvrrF6BAcH079/f2QyGb/88otGLCA5nDp1ioULF9KxY0fc3Nw+2Xcxxxk0JCRErVaQoKAgmjVrRo8ePShfvjwdOnRg48aNPHnyhMGDB7Nv3z6cnZ3R19fnq6++Yv78+R9OdEJRWQCEEMLQ0FDI5XLVKqUouXnzpgCEiYnJJ20ByM2xY8dUTlmadogpDgvA7du3i90CIIQQcXFxwsjISOjp6YmIiAjh6+srjh49qlaZ169fF4D4/PPPRXZ2tqhatap4/vy5SEpKEqampkJPT09ERUWJihUrFuigWNT9v1KlSmLChAkCEB4eHiI1NVVj/SA1NVXY2NgIuVwu7ty5I4YPHy5mzJih0ZVnjiPwqFGjiu39nz17thg9erTIzs7+JC0AFy9eFM2aNRMRERFqlZOSkvKS42d+0tPT36oeH6QFoKBQt4iICJKSkqhWrRomJiZFXlEnJye0tLSIj49n//79n6zWu3r1atLS0gBo1KgRZ8+eZcSIEQBs3Ljxo753HR0dgFceeKKJMEwTExN69epFamoq8+bN48qVKzRu3FitMr28vKhSpQqnTp1i0aJFVKtWDUtLSwwMDOjevTupqakMHjwYDw8PzMzM1FqXIUOGMHz4cH766SeaN2/OrVu3mDJlisb6ga6uLiNHjiQ7O5spU6awfft2+vbtqzH5U6ZM4ezZs1SpUuWllee9e/c0Vo9x48axZ88efv75509uHIyPj6dly5YMHToUCwsLtcrS09NDT0/vlZ/R1tZWez3eGwuAu7v7S9fWrFkjALFr1y61aWLt27cXgHB2dhYPHz5Uld+9e1c4ODho3AJgY2Ojca13/PjxL2ndOfVRZwRGfg4ePCgA0a5dOyHEC29odYeAJiUlCblcLgwMDPKEW27ZskWYmZkV6B2uLgICAgQgZDKZWLx4sUZkLl++XABCW1tb3Lt3T1V+5coVlRXo77//VmsdNm3aJHx8fFT/HxISIoyNjYVCoRBnzpzRWP+Lj48XJUqUEIDo3LmzRq1ucrlcGBsb53kGOfz4448aHQ+8vb2Fp6fnJ2cBWLlypUbfd3XxQSoAgFi3bp2q/N69e8LOzk7069dPrY314MEDVeyzvr6+aNmypWjVqpXw8fER7dq105gCEB4eLgCho6MjEhISNK4AmJqa5ok3PnLkiNDW1tboy5BjjjcwMBCLFy8WnTp1Ek+fPlW73BznM1dXVzF8+HDx+eefi+nTp6u2AKpVqyZmzZqlkTZo0qSJMDQ0FLGxsRqRFxMTI/T09ESnTp1eula1alXh7OysVnPw1atXhY2NjYiJiclTPn36dAGIzz777KVr6mTixIkCEMeOHdOIvIiICGFrayuAPGHQOQQFBYmWLVtqdCtCV1dXGBoafnIKwLhx4wQgVq5cKSkAmlYAatasKQYPHixatWolGjduLKpXry5WrFihkb2ou3fvitatWwsDAwNhb28vZsyYITIzMzXmA7Bz505Rt25dlSJUs2ZNsXXK+OcMAAAgAElEQVTrVo0qADmyK1asKNq3by9atWolzp8/r/HOO3nyZGFkZCS8vLw0kgNBiBc5J5o2bSr09PSEm5ubqu3r168v2rZtK/7880+N7Ynu27dP9O/fX6Nt7uPjU+CzXr16tZg5c6ba5O7atUtYWFgIhUKRJ979xo0befxQPD09xfbt2zXSFhcuXBDlypXTaNvnWGBq1KiR58/Ly0vo6OiIZs2aaaw+OX4hLi4un5wCsGjRIgEIX19fSQF4X5wAixNNOgFKSEgUP99++62YP3/+J3v/hw8f1vgWyPuiAJw8eVIAGrW4fIwKgJYU2CUhIfGhkZiYyPbt27l+/fon2walSpUCPs18+Dnhj5pMAPcx8p8UgByFRbyHR8AK6VhaCYmPmoMHD6Kjo0O9evUYP348Xbt2xdzc/JNtD29vb7y9vUlOTv7k7j0lJQWAnj17Si+GphSAnNO3cp/C9b4QExPz3tZNQkLi3fD396d169bAixP5ypcvz6lTpz7pNpHJZGzatInOnTszYsQI7OzsPpl7nzFjBuPHj6dBgwbSy/EOvFEegNTUVKZMmaKKN7906RL9+vXj5MmTxX4DN27cYMyYMaq6jB8//pPMjS0h8THj6elJtWrVMDU1xcfHh+PHj6s938GHYgU4ePAg06ZNY+7cuaocIR8rJ06cYPDgwdSqVUsa54tCiRSS7VxCQkLigycpKQldXV20tD5e1674+Hi1JJortglYJpNJCoCEhISEhMSntgIvZgVALj0CCQkJCQmJTw8tlM5zxcbRo9JTKE6Up8hJFNMKQOr/xYpo0qR4K9C//3vdPltPncLn88/VJ6C42/8TR7IAfISERUdj3a8fSw4dkhpDQkLircgWgvMaPNxIQlIAJIqA53FxPIuL48bjx1JjSEhIvBWPnj+njJWV1BAfMVImwI+QimXKUNrCgi+rV5ca4wPHWV+fQfb29LCxoZTyOOS07GwWPH7MksePeZqeDsAge3uGOjjgbmhIeFoaP4eHM/3hQ1Lf8Xjk4pYPUM7AgP52dvS3t8dYoQBgTVgYsx894kFKCqZaWgyws2OaszNZwLKQEOYFB/NcWTeJtyMwLAy39zS3wJXr15m5YAEuzs7cCAggMiqKs0eOsPO337h3/z5/nThBjSpVmP3DDwAE3LnDph07SElN5UZAAFvXrqWUpeUn/4xlIjq6eKMApD1QtfDDr7/yv44dUchfY+SRfACK9wV8w/5vpq3N4UqVqGZiwsOUFJxPnyb/izvHxYXqJia0uXqVhKysIq1nccsH8DQy4lTVqphqaVHzwgXO50r6ZaRQEFCrFm2vXeNqQsIb/6bkA1A48/bvp3PNmpwMCGDpn39y8f59tBQKlnz9NYO++AKAPefPM3DtWsyNjJjUsSNtqlRh7bFjLDhwgCcxMZSxtGTNgAE09fYmOS2NVX/9xbcbN9K8YkW+79CBusOGvVXdUlJT6dS7NzGxsaxbsoRLV6/i6uLCsZMn+W7UKGJiY7H38GD7+vU0rFuXph06cPLAAXR0dKjcoAHtWrRgyvjxxf/+m5sXaxSAZAH4iOi5dCmb/f2xMTPDzc6OjvPns//iRUwMDDg8cSLVy5aVGukDJSYjg7ZXr3K1Zk2c9PXpb2fH6rAw1fXKxsZ8YW5Og0uX1DL5Frd8gJuJiQwIDGS7lxezypal4aVLqmvLypdn5N27/2nyz+FucjI/PXrEL+HhzHFxYayj40ufic/MxN7fn5I6OiwqVw4PQ0OWhYay+PFj6pYoQVkDA64nJtLRyooJZcoQk5HBnufPGXD7NmX19albogQBSUl4Ghkxu2xZzLS13/s+9zgyktIWFvSqX58utWvT6McfuXD/Pi0qVVJ9poGHB+729uwbNw5TAwMAxrRpQ8caNag6YQK62to08vQEwEBXl6rOzvSoW5dNbznx56Cvp4e1lRUVvbxwd3XF3dWVIWPHArBo5UoAmjVuTGxcHL8dPIizkxM6SgvW4V270NfXlwYVJB+Aj4ovKlRgRrdu3F28GG9HR3aNHs2RSZP4ukEDSltYSA30gfM0PZ2vb916sTorV44yykHMQlubjZ6edLt5k9jMzI9WPsCOZ8/Y8/w5DczM+MbWFoCvbW2Jz8xkz/Pnb/Wb5QwM+L5MGfTlcpY8fkxGAalR1oWHkykETczNaWdpSVkDA4ba2wMw1dkZP3d3VpYvz8SgIGY+fIi5tja+dnbY6OjQzdqade7uHKpUiT8iI+ly48Z717dCoqJIy8jIU5adnU1OmLqetjbbRozAQEeHIevWvbCeCMGQ9etZ1a+favLPwcnKivWDBnEnPJwFBw4AEJWQwJx9+1gzYEDRrJ5lMnKH0T8ODaV+nTqMHDSIkYMGsWfjRnp27UpwSEieDImWFhYYGRpKA4qkAHxkFoB69fi+QweS09LY/M8/rD56lMZeXizo3RvrEiWkBvoIOBQVxeqwMIwUCvzc3dGWydju5cWPDx4QmJT00csHGHL7NjEZGcxzcaGxuTnf2Noy5h291bVlMrpZW/M0PZ3tT5/muZYlBP/ExOBtbIwiV7lWvhwu1UxM8DQyYluu7+f+jKmWFl9aWXE0OprIfJNtYWxV83kHUQkJjN6wgTazZrH+779fmmBz42hpydyePfnjyhW2njrFrN9+o02VKpQvxE+gfbVqdKtThyk7d3LvyRMGrl3L/F690FeuxIsam1Kl2LVvX56y85cuYWttzYnTp/MoAafPn5cGk9cpAI9DQxk1cSIOnp7IzM1Vf6VcXZk4fTpJuU6h2r1/P51691Z9xrN2babOmfNBNkpWdjZLDh2i4tixGPfqhZWvL42nTmX1X38RGBZGv9Wr32v5N0NCiExI4N+goA+ivZOzspgbHEyzK1eQHT2q+jM6fhzLkycpefIklc6f59u7dwn9yHOdvwnf3r1LUHIyDc3MOFe9OtcSE/n12bNPRv7T9HS+vXcPM21t9lesyNcBAaQXgbOhg54enaysmJ8vemZvRAQd/oM3vPFrUvHKZTIMFYrX/o4mwvC0tbQY07Ytv40bx/wDB0hXWnCexsZiU8BZC/2bNKGxlxdD1q8nJCrqtTkCln7zDcb6+tSaNIkO1avjqrTaFNlYmWu7qVvHjvy6bx/DJ0zgxKlTjP/hBwz09WnTvDlpaWl079+fcxcvMm/ZMuLi41Xfi4mN5bupU5m7dCnVGjcmMSmJ5p060bh9e2Lj4ug5cCAV69UjNDyckLAw6rVqxZNnzzhx6hQLVqygRefObNi2DYC0tDRmLVrEj7Nn07xTJ2JiY1np58fnLVqwZPVqHL296d6/P9lF0F/VrgCUtrdn4YwZBF26xFdffqkq79W1KzMmTcIwl9mnY5s2rF648IWG7uvLlZMnmTxu3Ac5+XeYO5dpu3bxY5cuRPn5EbZ6NWPatGHlkSO4jxrFvSdP3mv5lZycAKis/O/7joFCwVhHRw5VrIiFcm90urMziQ0bElG/PuerVcNSW5sFjx9T4dw5LuV6eT9FkrKy6HXrFllCUNnYmPW59uI/BfkAP4eHE5CUhL5cjms+8/M7KTeOjlxLSOBodLSqbPvTp3QrVeq13z0WHf3CT6GQFfGTtDR2PntGT2tr9OWvN75qIgzPRF8fWzMzylhaUs/NjV9OnABeHQEwp0cPYpOSiHkDi09JY2PGt2tHVEICCcojfIuCS1evcubff9n/559cvnYNgIZ167J87lz27N9P9/798ShfHi93dyxKlmTPpk3cCAigTbduCCFo2bTp/1u1jh6llKUlY4cNY9SgQRgZGvLT5MnExMZSwtSUH8aPJzomBltra3R0dOjfuzemJias37yZ0YMHs3rhQoaMHUt8QgJL1qyhfp06TBk/HlsbGxauXEmzRo24e/8+rb74ghunT+N/9iy7fv/9/VcActDV1WXTqlXUq10bgI07dhBbwLG7P8yeTdcOHVg2Zw7aH4CTS4EDy/Hj7L90ieW+vrSrVg0dLS20FQpaVKrEuZkzqeHi8t7LNzM0pIyl5RsrAOvCwqh38aJq5b3jDVZzyVlZWJw8iezoUT47fZq5wcGEvePqXC6TYa+nB5BnhVTWwIDfK1akrIEB0RkZqsnnUyYyI0PlbLfO3R25hlOKF7f8TlZW3E5KIjU7m5Xly2P0BivqN6GqiQl1S5RgXnAwAP/Gx1PZ2BidV0zYv0VEMP3hQ7Y8fcpvFSrQJ98q90J8PAseP2bS/ft8V6YM69zd36gumg7D+75DB+bs20dmVhaBoaEFyhZCsODAAYa3aMH206fZn8sRsyCiEhI4fecOLSpVYtzmzYRGRb085m3ZQr1WrVTW4zIVKrB5507V9eP+/jRu3x6ZuTl1mjdn74EDVKlYkYBz57h55gyVK1RQfXZw376E3rpFWEAAvb76SlXepH597ly4QMS9e4zN54BYs2pVps2bR99hw2igtGhU8vYmLS2NO0FBXLp2DXMzM06ePs3vhw7RrmVLrt+6RURkJL9s3crf//xD04YNiYqO5tjJk1y7eZNftm6llKUl+np66OjoYGJsjLOTEybGxnRq25YLly9/OAoAgJaWFlvXrsWsRAmeR0QwauLEPNe379nDydOn8Vu27D9X4vSdO3RfsgRZly6Yff01K48cUWmXJ27dou3s2ci6dMF2wADWHTtGYmqq2hrkgPLBeCgdfHKjp63Nkq+/VusDKSr55e3scLGxeaPP+trZcbhyZXSVg9zsR49e+5314eFEKfcxZzg7M9bRETtd3Xe+f0UhE4meXE5v5f0EJCVxMzHxk538jRQKtnl60vbqVYKSk6llasqY0qU/GfllDQwY5uCAz82bzHz4EAc9PWYUYYTLaEdHDkdFcTMxkVWhoQwo4F3MTXtLSyY5OeHn7k7bAmLLq5mYMLp0ada7uzOidOmXfAdepwBsPHmSat99h6xLF7S7dWPlkSOqz+w5fx4rX1/KjxzJZn9/4pKTmbd/P7YDBiDr0gWnIUP46/r1F0p7WhoLDhxA1qULLWbOxD8wMI88FxsbqpUty8Z//iHo6VOcra1fqtOs336jY40aLOjdm8pOTgxau5a4XFvB+ZWFoX5+zOvZk9X9+5MtBIOUDoS5+bp7d04eOIBvz54AuJUrR48uXVTXG9atS4M6dejdrRv+f/xBh9ati7Q/OTo4cOP0aZJTUqhcv75qcevTqRPbd+8m7MkTRgwYwKadO0lITMTYyIjMzEwMDQ3p4+NDHx8f9m7ahK21NZlZWdSpUYM+Pj78NHkyowcPfkmeuZkZJsbGH5YCAGBnY8PS2bMB+GXrVg4pY5hvBgYyeuJEdm/YgMFbhFfUcXVlfq9eAHSuWZNBX3yBmdJLs4GHB3OVHaP755/j27gxRspVojrIORxx3v79BV6vXrYsjmpMIFFU8q1MTSnxHzxd9eVySmpr85m+PlcSEjhcgKaeQ5YQLH78+P+9wNXk1JOfMrme+5s6Ub2O6IwM5gcHIzt6lFZXrxb6ufqXLqF97Bjrw8OJy8xk7/PnlDl1ipInTzIgMJBuN25Q5fx5te+Fy4ANHh4sDw3FPzaWrwMCyBaCqc7OuGvAs7m45evJ5fi5u+MbGEhadjazg4MJTEpiqL09NUxNi0RGWwsLyhoYMObePQwVCkoWkzUzdxie/9Sp1CpXDqDAMLzzM2fSo25dTA0MGNOmDaenTcPcyKjQMLxD339PXTe3l2RO/PJLftq7l5T0dLTzWVWO37pFZEICHapXRyGXs27gQJ7FxTF206YC6z99zx661KqFk5UVDiVL8pOPDwcuXSrQsVEmk7F87lwqeHry57FjHPf3V10LuHOHY//8w5qFC5HLi95vfdfvv2NkaMi2deuo4OnJQ6X1x6dTJ5avX0+VChXo2LYt+/74g3LOzgB4e3hw8vRpft6yhWcREaxYv57klBTq167N4DFjuHf/PjcDA/lV6ZSYmJioGtsD796llTKPwgelAAB079xZpYH1HzmSx6GhfNmrF8vnzsVF2Thv9WIrXzKDAlaRhsoyXQ28iDkv1y8nTtBm9uwCTVaD1Pjwikq+hbGxqk3/y+A+RhkDPesVVoBfnz/H29gYZ6UCoCnj7wPlHqIMimyyMdfW5ltHR5z19TkUGUlAAfualxMSuBAXRxl9ffra2mKqpUUHKysamJnhZmjIajc3tnl50dXamq43bnA6NlZtbTDRyYnn6en8HB4OwKnYWBaHhKArl7PRw+ONV5cfqvylrq6sCg3lnnLVmZ6dzYDAQGQyGevc3F5pqn8VmUKQqRyg5TIZIxwcOBIVxVAHhzyKb2auraecf2e+YjsqM993CuN9CcPzdHDAq3RpIvL52dwJD2fqrl385OOjKqvk5MTApk1Ze+wYf1y5kndSPXeOsOhoOuTKRjq4WTO8HR0Z5udX4Limo6PDz8uWoaWlxYDRo0lNSyMpORnf4cP5edkyVRx/UZOQmEirrl1Zvm4dlStUoKKX14s2dHSkY5s21KtdGxNjY7p26ECzRo0AMDE2ZuPKlfw4Zw4V69allJUVZiVK8O3QodjZ2FClYUO+mzqV9q1aAZCWns68ZctYvm4dtatXz7Nt8UEpAACr5s/HomRJQsPD8apTh/YtWxa5Waa46Ne4sWoSPnDpEuVHjmTyjh3E53JgqalGP4Cikm9pYvJW8nvZ2GCto8OJmBj+LcTZbu6jR4wrIFmKOolIT2eV0tmsj60tNkWw3ZDHClWiBK6GhsxXav+5WRESQldra/LvMuef7HrZ2CCAA5GRammDdpaWtLSwYMTdu3kn5aAg7iQnU8XEhMmffaa2Z1Dc8r93cqK0nh5b84Xp+cfG8ntEBJ5GRsx9i3czKDmZxSEhHIyMVDn/fW1rSw8bG1wNDEjOymLzkycEJCVxLCaGfRERBCUnsyQkBAC/8PCXEhBFZ2SwOiyMJ+npHIyM5EghFrX3MQxvUseOuCm3PZLT0pi6axfVvvuOZ7GxXH74UPW5u0+e8FCZe6HbokXM2bePgNBQ+q9eTdeFC3keF8ejiAjV5/8JCCAlPZ3oxESaTJtWoCWgkrc3Y4YO5d79+0ybO5fBY8YwesgQnNQ43vj27In/H38wxNeXnyZPztPuK+fP//9xYN68PL5tLZs25dG1azy5fZuObdq8WMDq67N9/XriHz9m/7ZtqnwDJc3NGTtsGEN8fRni6/vezHdvpQBYWVqqGiY+IUHlHPgxoJDL+W3sWL5t0waFXE5SWhrTdu/GeehQlhw6RKaaspwVtXxzI6O3kq8rlzNSuZ9bkBXgWHQ0Rlpa1Cwic+vryBCCPyIjqXvxIk/S0uhgZcUSV1e1mLZHli7NlqdPeZYrh3x4WhoKmUyVB/9VxCpXcFZqWKl0LlWKbV5eDAgMfCnkLSU7m2kPHryYjMuUoZUakj4Vp/zG5uYcr1KFGc7OuBoavhSS18nKigrK/j7cwYGtnp5U/Q8KcFkDA5a6unKlRg2amJu/sDoqFGz08HgxqCsU9LCxIalhQx7WqaNKBLTE1RXRpAlbPT2pmG9P11xbmwF2dmQ1bsyVGjX4omTJAmW/j2F4lZ2c6Ne4scoiO7lTJ+I3bCBg4cI8i49yNjYcmDABsXMncRs2MK5dO9zt7VkzYABZO3awZ8wYyuTarmzg4cHdxYsRO3dye9GiQus+Zfx4XMuWZdaiRWgpFHRq2xaJ90gBgBf+AArlHtGgb78l/i1ScL6v6GhpMa9nTy7Nnq3aP4tMSGDEzz9TdcKEPGF4D58/58dff6XkN98g69IFRdeuLDx4kGSlR/ztsDAGrV2LrEsXGk+dyoHXeM3+V/mF8S5+EgPt7THR0uK358+5nc8kPic4WCOr/9VhYTS+fBnzEydodfUqNrq6XKpRgz3e3i95fCdnZbE0JAT3s2dVkQwDAwN5qLSaRGdksPjxY+RHj+J8+jTLQ0IQhVg/jBUKlipXdgArQkMZkssMXBhxmZl8e+8e5Q0NVRnqioIvSpbkSOXK7PTyQl8uZ3Tp0njlU+6alSyJr3IVKJfJ2OPtzcry5V/63IcoP0fpbHjpErKjRylz6hR782X82/X8OU6nT6uevc/Nm1z8QEJF39cwvOLMHKqnq8u0iRPJzs7mZmDgexMz/zZkZ2ez+/ffefrs2XuZfOitzgJ4FhGBT79+7PDzo++wYYSGhzN64kTWLVnyzhU6cu0afZYvzzvAF9OpXhUcHTk2eTKHrlxh3ObN3AwJ4VpwMHUnT+bq3LlYlyiBk5UVUzp3pkvt2tSeNImElBQ616yp8mUob2dH5c8+o3f9+vw8ePBLZr13lV8Yr1sZvApTLS0G2tkxJziYOcHB+CnDlq4nJhKelkbLVwwOG588YWlICBfj49GSyVji6sogpTlxz/PnDLx9G3MtLSY5OdHjFVEKA+zsGFm6NMtCQhh25w4X4+MLDfUyUCgY5uBAH1tbGl+6xIX4eBqYm+Ok9FEw19amuYUFPz95wskqVTAtJFGLvlzOIHt7loeG8n2ZMshkMu4nJ+NtZMTWQuoZmprKmHv3WBsWRm8bG3Z4eRVZSBrAkaioQs3HORyOinql0+aHLP9T4vsOHWgxcybfNGxIYGioSvnPTe4wvCWHDuHz+ee0qVKl0N/MH4bXqnJl7AuxRrwNbWfPxszIiA1DhhTZb6alpbFi/XpaN2vGgcOHWbZ2LcOLKH2wxlfYcjkjBg5kxMCBH4cFIDMzk67ffMPowYPp2KYN86dPB2D95s0cOX783VccFSrwy5Ahef4WKCMENMHF+/dfKmtRqRJX587lR2VoyrO4OGbnSznpZmfHL4MHk5WdzTA/P1X5nfBwfj17ljUDBrzR5P9f5avDAgEvzOG6cjlbnj5VZd+b8+gRYx0dX+n018vGBv+qVaml3CJokWuwaWBmhruhIeerV3/l5J+boQ4OdLKyIjEriy43brzyeFljhYJfvb0poaXFuHv3iFeaU1OysxkUGMheb+9CJ/8chjg4kJSVxc/h4WwID6fXa1bz9np6zHNxob2lJQcjI9/I4UtCoiCKKwzvXcjIyiryFfqoiRMZ+PXXbFixAksLCyZOn87j0FCpg7wPCsC4KVOwKVWKYcpjLPv26EHTBg0A6DdiBAkfeHy23/HjBZrWFHI5kzt1onf9+gAFptltV60afRo04LcLF9h2+jSJqan0X72a9YMGoaOlpRb5ORaIU9OmUcLQEJlMVqgF4uj//kfrV6wWcmOjq0tPGxvSs7NZGBxMSGoqZ+Li6FbAoPSSCU8uZ5uXFwYKBUPu3HkxGPEih/uq8uVfOwnnZ727O2UNDLiWkMBI5e8VhqOeHotcXQlJTWWsMo3qwMBAxjg6qiwCr6KUjg4+1tYsfPyYI9HRNH/D1dLK8uUxVCjoc+sW/0UFMFYo6GVjw/N69Yhr0IBfPDxUf3srVCCjcWN05HJcDAyY5uyMaNKE0Lp12eHlxbHKlblaowaD88Wp1ylRgt3e3ogmTbhaowbbvLz4t3p1TlSpQiPlHndBfKavz8ry5dlboQLr3N1Z4+bG/5yc+PGzz/AwNKSGqSm/eHggmjThZJUqqnpu8vDgdu3azHiHKKAPhdOxsXS8fh3Z0aOYnzzJMaXTYHRGBhOCgjA+fpzZjx6plM//SnGF4b0tA5o25euGDYvs97bv2UN2djZdO3TA3MyM+dOmkZiUxOAxY6TZurgVgJ2//cbhv/9m7eLFecrXLl6MkaEhj0ND+XbSJLVX+klMDF0XLkTWpQt9V64kWql0RCUk8OW8eVT/7jsuKFfS5UaMYPSGDUzavp1J27fT6Mcf0fXx4Waufd7cZAvB3n//LVR2m6pVAV4Ku8lhUZ8+2JcsyTA/P7ovWcL/OnXC4T+Y3N5WflFZIHIz1tERuUzGmrAw/nf/PsMcHNB+w99w1NNjrosLf0RGsvXpU2Y9ekQbS0vKv0X4nomWFju9vNCTy1kdFsb218Ta97axoY2lJWvCwuh96xaOenqv3LYA8lgWRpcuzf2UFJqXLKmydmQVEM6VKYQqI6GBQsFub2+Ox8SoHOLehISsLDY+ecKp2FiepKfT59Yt1V+Ha9cYdfcuRgoF95KT+d/9+2QKwfanT+l64waNL19m+7NnLC9fPo8ScDo2lgXKfPaT7t+n240b1LlwgYSsLA5XqkSFApKQNDY351y1ahyLjqbDtWv4BgTQPzCQQ1FRDHNwwExbm/NxcSxQRkmsDA1V1bPnrVtUOHeOeDU5yMplMvrZ2XGvdm2sNZRzojBylKvu1takZmVRTvkemmtrIwN+q1CB8WXKYKL1dietF2cY3psSER+PRd++KLp2Zckff7Dk0CG0u3XDsm9fnhWQIfZNuX3vHkvXrGHRTz+pynp27UqT+vU5eOQIW3ftei8mzU07dmDt6oqxgwMXle3+7+XLWLq4sHjVKtKLactarQrAxStXGDpuHLs2bHjpKEVHBwdmTZnyQhnYuJH9f/75nyuSs8+fUcAgkqbUpnPiZG3MzNg0bBgeDg7I5XKVx3tJY2NszMw49P33VHN2Jis7m6ldurCgd2+mf/UV37Zpw+3wcCZ36oTnKxy7fti5k6eFxHKfVYZAdatTp8DrpgYGrBs4kKiEBJ7HxdFEGVP6X3hb+e9qgRDKvxzKGRjQ3tKSxKwsfo+MpF8+pySR77/56W9nR2Nzc4bcvk1Iaio+b2A9yJlk8xsVKxkbs0jp/d8vIIBbr3GAWl2+POba2ux89ozRr3BazAnXOhwVxbanT0nJzsbTyAgfa2t6KrcpDkdF8UdkJMGpqfgpEwHtef6c4zEx3EpMZNvTpyRlZeFiYICfuztTHjzg64AAzv6HwTC9kK0Dv/DwPKvJtHzm1qVKh8ZO+XLV5/9chhCsDA1FSyajXb5EUtY6Ouzw8sIvPJxd+RzsLsbHM+j2bdX59YXVMy07m1VvaaZtZ2lJ8Oefc692bRaVK8eicuVY5urKwzp1qGxsRasAACAASURBVGZiQrYQXIiPp6xysv1MX58F5cohmjRhk4eH6ju/entzoGJFjQycq9zcKKWry6DbtwE4GBmJubY2jV9hYXlTijMM700wNTCgT4MGnJ0xg1GtWzPxyy85PmUKfRo0oMRbnssQHBJCm27dmDdtGnr5QnznTp0KwJCxY3nwBllK1U3Prl35bcsW0tLT0VXWNSUlhdk//MCIgQPVlq9AHbyRmnrg8GF6DRrEl61b46bMRpWffr16MXzCBLKzs+k9eDCnDh3C/Q3Dtc7cucMaZVbBfRcuUNnJiS9r1MDM0JB/AgNZd+zYC/PQmTOUt7Oja+3aGOnpsbp/f+pPmUK/xo2pXrYs/oGB1ChblpK5Vjg5K2aAYX5+2JqZMb5du1fWJyQqisrjxzOzWzc61ayJkZ4eSWlprDpyhEUHD9KnQQN61K37yhfEzNCQc/fusf30ab4qRFlQh/xFffpw9MYNhvn5sf306Te2QGQJQWxmJpHp6Xli7MeXKcOe588ZZG//knNbpFJpi3iFxjvHxYUq588T8waZ+7KFIFSZ5jmsgHTPA+zsOBkTw7anT2l++TJ/VKpUqKe5rlyOra4uNxMTmXDvHqsKyHqWs3IbYGf30gEuW3I5YDUrWZJbtWrluf6llRVfFnBQS0crK0STJgDEZmayNCSEsffuMcDOjnnlyhGdkUHH69fpaWOjSm1cGB2trLiUkMCjV3hvm2ppIQOevsE5DCWUSmD+zw6wt6ektjZ+yuQ++dn9/Dmer/DoV8hkDHNwYJHS6lDD1JRRpUsTkZ5OZEYGo0uXpn9gINVMTPi8RAmmPXzIBg8PFjx+zMyHD9kXEUE3a2u0ZDJG5soxsCI0VBVSeT/XPveDlBTWhIUxqnRpZgcH50kLPfg1aXuLCiOFgnVubjS5fJlZjx4RmJTEL8qwwXelspMTFsoxLCcMb3KnTi99LicMLz9rBgwoMNlPThjeu5ITpQTQZvZsbM3MWN2/P5+XL//fF34pKcyYP5+la9aQkJjIxu3bcXRwwFa5WAgJC2PNhg0v3qe4OOq1asXwAQMYN3x4sU6cNatWZUCfPgyfMIF9W7aw58ABFueyXHwUCsAff/3FghUrOHbyJAD7Dx/mfzNnMunbb1WaD4D/2bMsWb1a5QwSExtL9caN6dm1K98OGULZ1yQHqe3qSm1XV34pwJO0npsb9dzc2Dh06MvmOFdXvmnYkIFr13J2+nS2nT7N8r59/39gkstVWQR/v3iRX8+e5dLs2Wi9wkvbWE+PK3PmcO/JE/ZfusTUXbtITksjJT0db0dHNgwZQvdXTP7P4+KYsGULF2fNovakSQzz86ORpydWbxg3/67ycywQzWfMeGMLxOYnT9gXEUFyVhZfBwTQztKSgfb2yIDqJiY0L1mS4bksJn9ERnIwMpLryoH3hwcPiEhPp3OpUtjm6hcCWBAczHAHB5aEhOBjbU2bAtIYJ2dlsTw0lD+jolTnC6xUribrmpnRPtd31ri5cTk+njvJyVQ6f57mJUvStVQp1Wo9R5H4OiCAn93d+S4oiDVhYXQuVapIVmf/hRJaWgxzcMBAoWDR48dkC8HNxETGOToWmDPeRkdHNYmYaWnR0sIClzNnCv39ktrarHZz42l6OtNyrQwLwt3QkGnOzpyLi2NTvjDSFiVLkpiVxd1CnMkyhXgp0c0ge3uaW1ggB2qamnIml7UjITOTCkZGpGVn8+PDh2x79ozozEysdXVx1NfHUU+PJSEheSb1TCFeSqwUkJTEI6UiKAqxFOVngxpP6ixo26SfnR3fBQURWKtWkWbELM4wvP/C5QcPiHyHuhro6zNj0iRmFLJ17GBnx4p581gxb957d+/TJ06kXLVqdOjZk61r1/Ih8koFoGXTpnmOTSyMurVqUTffCklTzO7RA7eRI2k0dSorfX0L3OeOTkxkwJo1rzX9A6pzByqWKUPn/3hPmVlZ9FmxgsVff81npUqxrG9fOi9YwJD16/l19Og3+o13kf+2FogeNjav9Mo/lCv3OEBLCwtaWliw/DUa/6xHj+hoZUVbS0tOxcYy6PZt6pmZveQEmHMc8Ng3yC9gpFBw+zWJpyY/eEArCwuqmpiw1s0Nz3Pn8A0M5EbNmkUaovem9LW15XBUFH0DAqhpaponvWxucnwAcphWiFNdXTMztnl50d7SknVKP4foQiwsfWxtGe3oSL0SJegXGMjmJ0/IyDd52uvpFfr9wlgZGqryxbDS0WF8mTJ5Ju57yckkZmWx9/lzVdy+l6EhDczMWBka+lpHSX25nA5WVi9l/XtdO68PD0dHLmeovT0jSpdm8O3b+Lm7M/7ePSoYG+NqaMjZ2FgGOzhQrQjisi11dLDS0WHKgwfseIvtvg8dz9KlPxhlpagxNTFhqK8vawrYFv9QkH/oD8HM0JBBX3yBQi7Hu5AJZJifH3bm5q81/b8r4zZvpkutWlRQ1qNTzZp0rFGDXefOsfPsWY20R24LRClTU4b5+fH8HRxz3pbjMTFEpqfTwcoKhUzGOnd3nqWnqzzz1cW+iAjC0tLorzTpl9HXZ1bZsjxKSWGcmmW/igXlyrHj2TM8/kNynIOFpBT2j4mh961b3E1Opp6ZGYmvcL77JTycfgEBpGZnU8PU9KXJP8cCY/wOitHz9HQu5Otj2fCSU2A2kJiVVejk72lkxKyyZZnt4oJ/1aqYv8FZFiNLl2ZW2bKscnNjqlJhyhSCf+PjKa2nh5FCwcDbt7mQkMDT9HQqGhlxNDqaSUFBRL+lp37uvmavq8tqNzd2PnvGvlz77Z+MAqB0WvwUiY6JIS09HStLS2bkShksKQAaRldbu9DzyH+7cIFd587xy5AheUz/T2JiirQOyw8f5nFkJH2UIZE5LOvbF22FgoFr1qgcdtRFQRaIyIQEhqxfr9HncSc5makPHvBTriNaKxkbM9DenrVhYfyhplz55+Li+D4oiOX5fE+GODhQwdiYlaGhL2WR0wQC2BAezg4vL/rcukXcG0485+LiCt3/T8/OptetW5Q3MOCH12yx3U9JYYzSD6GglLRn4uIw09ZWne74NmwvglMQbyYmMiEoiPH37tH8ypVX5nzIYdHjx0wICmJgYKAqg2O2EAQrtw72KC0Qt5RJrB6mpnI2Lo714eEkv0PUwoOUFP6MimKQvT3tLS3paGXF4Nu3iX1HpeJDo6y1dZ50v58SPy1cyPgRI1g2Zw4LV6zgXgE5XCQFQANkC0F2ASubqIQEBhZg+o9OTOTojRtFIvufwEBazJzJ0PXrCYuO5nyuVWZ6Zia7zp0jWwhikpJo8MMPLD10qMC6fgwWiOSsLKY+eEC18+d5lp7O5Vz7xneTk1WpebvdvMmc4OB3GoDzD8YDAwOpe/Eiz9PTOZQvxOlARITKxN395k2mPHjAkzdwmisqloWE0MPGhi+trGhlYcHAfOewv46ehWzPXEtI4IcHDxjn6PjasxlWhYZyOCqK9e7uKmfAHBY/fkyWEIwsZGuilI4OTd/Af8JZX5/aRXRGRGRGBif+o5KeO4Ih5+hVkU8RE0Xw7sVmZjLq7t08Bw8tK1+e+MxMhiijAj4VLIyNCw2J/pjx27KFlk2bYmxkRK1q1ejcvj1Dx4374O5D60N/EDdDQvjr+nUCQkM5fO0azXIdszh640aiExNJSElh0vbtqkn5wKVLLC+iE5lynBQLQkdLi6HNmzO0eXO1t0OOBWJB794vWSB+v3iRgWvWUM3ZGacCPNeLCgOFgsmffVbgiXDlDAzUFqL1mb4+q9zcCvX0b2NpWaDzobp5kpbGT48e8Tw9XZUrv4m5OR2vX8dGV5fvnZxUn9WWyQrMsdDU3DxP7LuOXI5eriNv5wQH09bSkm2enlS/cEEVkaGr/Ezuz/YNCOBmrVps8PDgy+vXVTkMriQkMPLuXRaXK0dkRgbzg4NJUa6+yxkY0M3amqnK3AY5ddTOd+yutkzGXBcXvrp5U7Wy0M13PwWVvYqg5GQ8jYzyePm/7vM5R0XHqWklvvnJEyY/eIChQsHDlBRVFMqtxER05XK2Pn2KkULBhDJl3ijx1IeOiYHBO5078qGRnJLCvKVLmb98OX8rs7GmpKZioK/PkePHGfHdd0weO5aSGnY4fltkIjq6eHOXKsP/JN7eAvHT3r38efUqNV1cWNSnDzWUK5P0zEzWHD3KyF9+ISs7m9IWFoxp04YhzZv//5bJmjVSIxbnAHr8OF9ZWzPHxQVTLS12P39OktIyUlJbmy/Mzalw/jxZQtDHxobvnZwISU1lQlAQvz57RoYQuBgYcLVGDZ6lp7MsJITLCQmMKF2a9paW/B0dzU+PHqmOue1ubc1mT0/8Y2NZ/Pgxu3OtmhuamTG2TBncDQ0JTkkhLC2Nf+PjWRISQrYQ1DQ1ZXTp0nQuVYq7ycmqPAfaMhlVTUy4kpDAVzdu0NrCgp+V0QyDb9/m12fPqGhszMry/8feWYdHdXRh/Le72bht3IUYJIQQXIpTqrgVWuzDrUihUKxIW6BI0QLFvVCcAoXiUgoUjRCixN1dNvv9sWFhSQIhBILs+zx5ktw7d2bu3HvnnHnnSE2aGBgwPSSEJeHhSrEKdtWujYZQSPd79xTH9EQivnVwYGZICHoiERlt2mB16RKx+fm4amvzoFkz6vz7Lz5PKAjDra25mJZGZlERkS1aoHn2rKKdrywtGW1jQ9MbNx6zAiUum9WGkoiqb/P8Y6qvX27ioueiuse/ugWwkZGgWttXKQDvOVQKQPVOAKr3n24lKZ41hUJ2xMZSJJOhLhTyqYkJM4KD+T0+nq9tbVnu5sb0kBAOJCQwxsaG0ba2nEhOxj8rC4FAgI2GBp66utS9do0Zjo7MdHRkdmgoCx4+xFgsZpWbGx8ZG/OVn5/CFkSlALwcrgcHY6qvX3lmUaUAqBQAFVQKgEoBUKE6oFIAXg63wsIwNzDAurKUt0oBqFYFQC1VUr0DcLqnahKqVvmvGv9qngFUQ1Cd+PDvapb/b/j4XN51mQ/6Piu1uCMvY/veXvUKViuEqiF495ASncJQi6GcWHFCNRgqqKBCpSArlhF0LUg1ECoFQIW3CekJ6aTHpxPhE6EaDBVUUKFSSHiYgJmDmWog3mGoqYbg3YNDXQdM7Exo1K2RajDeckhaSajxfQ0kbUr26mQQtS6KmA0xZNzMUC43uwaS1hJkRTKi1kQRsTyC3JDct7p9AMMPDHH52QWDpvIYA6nnU3m44CHJJx/He7AaaIXLIhfEJmLidsYRNj+MbL9s1Qv0Eoi+H411Les3sm/3bt9j6U9LcXJxwt/Hn+SkZE5dPcWhvYcICQrh/N/nqd+4PrMXzgbggf8D9mzfQ15uHv4+/qzftR5Tc9P3/hmrGIB3EAKBgDaD2uDVwUs1GG85Ui+kcrPdTeJ2yWPiF6UX8WDMAyXh+6hc8PRgivOLudX+Fg++flAlwre62wdIu5zGzdY3ybwjDywVtzNOSfgDxGyJIe1KGsHTgvH9ylcl/KtIATB3MuePOX/QV6MvvQS9WPO/NcQFP87PEHg1kAnuExhgMICjS44SFxLHqv6r6CXoRS9BL44uPkpO+uOkT5d3XaafTj/G1xzPtQOVz8XgWtOV3JxcLp+/zOyFsxk8ajC3rt8iLCSMb6Z/w/aD21m/aj1/Hf2L7Kxsxg4ey9Q5U/lp2U+kpaaxae0m1QNWMQDvFlb2W8mlHZeQWEqwrmXNku5L+O/of2jrazP95HScGzmrBulthAwCRgZg2MIQTVtNbEbYELk6slQxq/5WhMwMIfVC6rvVPlBcUIz///xpdKMR9t/aE7stluKCx3EENO01ERuJCV8Y/sJ15wTm8HD+Q2K2xODyswv2k0vnFCnKKOKSzSXUjdVxXeaKjocOUavkLIdhC0O0nbXJupeFWXczHKY6UJhaSMKBBAKGB6DlrIVhC0Oy/bPRra2L80JnxBLxG//aJUUkYeFsQc/ve6JtoM3WCVtxbeqKhbPFY0Hc1BVbD1tGbBiBWzN5CO4x28aQm5HLjcM3aNCpAdoGjyMFNu3ZlKNLjjLz75noGulWum+aWpqYWZjhWdcTN3c33NzdmDx6MgBrlq0BoN1H7UhPS+fYoWM4OjmiXhJQa9/JfWi9B0GaVAzAewavDl70+bEPywOXY1/Hnon7JjLj1AxaD2qNiZ2JaoDeYhRlFHF/qDyEsPN8ZzRtlaOv6TfSR7euLhFLI97J9gEyb2cSuSISbRdt7L9VFtKuS1wJnBCIrPjFvZq1XbVxmOaAUEtIxIoIZIWl64jZEIOsSIZReyNMO5ui7ayNzRgbAJzmOuG+yZ2aa2oSPD2YsJ/CEBuJsR5ijbqlOhZ9LHDf4I73CW+Sjifh08vnjXu/kiOTKcxXzghZXFysyK766def4tzImb2z95Kb8ZjZCb0ZirGNsUL4P8KQX4egpa/Ftm+2KR3/a/VfdJ/R/aWE/yMIBAKl7K9REVE0b9WckeNHMnL8SLYd2Ebvfr2JDI8k/4nQ3yamJujo6qgmFZUC8G6hZb+WdJ3WlfycfC7uuMjpdafxbOfJgKUDMLQwVA3QW47kk8nEbI5BpCei1rrHYY8FYgG11tQiYHgAMqnsnW0fIGRWCHmReThOd0TLSb6KM/7EmIKEglLbEi8kTMQCLPpYUBBXQNzvyimIZVIZqRdT0aujB08kTRSoKftw6jfUR7e2LnG748oso2aghlk3M1JOp1CYVLH0y5d3XX6l45mZnMnWiVtZ0HEBZzeeLSVgFX8LBQxfP5yMhAx2TdslH5diGfvn7afXnF6l6pVYSeg7vy83/7zJv/v+BSA1JpXga8E06vpqbJPMLc05vO+w0rGb125iYWXBlfNXlJSAa1euqSaU5ykAl85doku7LhgJjBQ/LqYu/DTzJ6Ijo5XKhoWEMXHERIyFxhgJjLDTt2PW5FnExcRVunOxgbEc+PEA42uOV+wpDbcazs6pOwn577H36Y3DN9gyfotin6qXoBdz2szh6OKj5Oe8eNKXYmkxJ1acYHLdyfTX688QsyHMbTeXv9f9TfT9aNYNXfdKH8rLth/pG0lmUibB14PfipdQmiMlfFE4tz+6zWnBacXPOd1zXDC9wAXjC1zzvkbgN4HkR+W/1x9s4IRA8qPzMf7EGMv+8iRBDlMdSPoricy7me98+9IsKQ/GPkCoKaTmqpoINYXUmFWDkOkvn4lN01YTsx5mRCxRZjESDyZi1rXi1vBqes/eWRUIBYh0np9++XW44amJ1eg0qRPfHvqWP5f8SVGBPIdCWlwaEkvlIDH2dez5fOLnnFpziqBrQZxae4rmfZqjpV82nd5hRAdcm7qy+evN5GbksmvaLvr81Kdq34cnEop179Odw38cZurXU+W2AVNmo6WtxccdPyY/P59hXw7jv3//Y9XiVWSkP1YW01LTmPvdXFYuWkm7hu3Izsqmx8c96NKuC+lp6YzoN4KWdVsSExVDdGQ0n7X8jPjYeC6fv8yvS3+l5yc92b11NwD5+fksW7CMhXMW0uPjHnJ7gzWb+OSDT1i3Yh117Osw7MthFFcg02W1KwAt2rTg0JlDjJ08VnFs8KjBTJs3DWtbZetQRydHlq5dSoMmDbC1t+XczXPMXTQXCyuLSnfO0tWSbtO7MWHvBMWxYeuG8eWCL3Fq4KQ41rBzQwYuG8in4z4FQM9EjxmnZtBxUkc0tDVeWPgu6rqIffP20WtOLzYlb2Jd9Do6TurIqTWnmOA+gdig2Fcq/F+2fUdveZIZx3qOb4VQE2mLsJ9sT90TdRGbyPdGnX5wok1WG1oltqLhtYaITcVELI3gX69/X2ql97ajKL2I+8PlVLzrL65IWkkw72FO2Lyw96J9gMTDiSQeSsT4Y2O8//ImekM0hamFVVK3/Tf2ZN7NJOV0iuJY3O9xmPcxf+61KWdSyPLNwnp42Zbz+bH5xO+Nx6KfBUKt55Ovr8MNT0tfC4mVBFMHU2q1rMX5LeeB8j0Aes7uiZmDGWv+twb/C/40693smYrO8N+Gk5GUwU+f/oSVmxVmjlVzP3du3uH6P9f56+hf3L11VyGvFq1exNEDRxn25TBqetTE3dMdYxNjth/Yjr+PP3069kEmk/Hhpx8q6jp94jSm5qaMnTyWkRNGoqOrw6z5s0hLTcPA0IAps6eQmpKKhZUF6urqDBg2AH0DfXZs3MGoiaP4Zd0vTB49mcyMTH5b8RvNWzVnyvdTsLSyZM0va2j7UVtCAkPo8FkHrvhc4eqlqxzZd+TNVwAeYeZPM6njXQeAg3sPUlROpq3UlFSCAoLYtGcTTi5OVdZJI2ujMv9+Go9obomlBJFYVKm2zm0+x82jNxmyeggNOzdETV0NkViE9yfe/PTvT7g0dnmlD6Qq2teR6GDqYFphBSB6QzT/tfxPsfKO3/P83O7SHCkXTC5wWnCaKzWuEL4onPzol1udC4QCNG3ke8tPrpC0nbWpe6Qu2s7aFKYU4tff75VTzW8yko4lEbs9FrGRmHp/1yNgTADFecXvTfsAAWMDkBXJ0LTRJGZTTJXVq99AH8MWhoQvlhsTZlzPQK+eHkL18qfKxEOJhP0QRtzOOLwOeWE10ErpfMaNDCKWRhAyIwSH7xxw3+Bese/yNbvhdZ3WlcM/H0ZaJCXqflSZbatrqdNrbi+i/KPoMLLDc+u0rW1L6wGtCb4eTKdJnUorRfn5/DjjR6y0rTASGGGpZcniHxaTmpKqxC4P/mIwRgIjatvWZv2q9dStX5d//f/lH99/8KrnpbRA9Yvywz/any/6f6E43qp9K248uEFQYpDSghagQZMGLJ63mLGDx/JBa3nUwzredcjPzyf4QTB3b95FYiThyoUrnDhygk87f4rfPT+SEpPYtWUXF89epM2HbUhJTuHCmQv43vVl15ZdmJqboqmlibq6Onr6ejg6OaKnr0enHp24dePW26MAqKmpsXLTStTU1AgKCGL1ktVllps/az59B/WlfuP6VdtJkVBJSDxLgDyvzPNw60/5g7HxsCl1TqwpZtCKQa/0gVRV+9Y1rbF0saxY2SHW1DtZD6GGfJwfLnz43GtiNsZQmCxfdTn96IT9ZHs0rDVe+v4ForKfnVBTiOUA+f1k+2eT5ZvF+4xHK+78mHzSLqW9d+3nR+VTnF9MYVohVLEuaD/RnuSTyWT5ZhG1Ngqb4TbPLG/axRTHGY64b3LHtFNp33L9hvrYTbTDfaM7duPsStkOPEsBeJ1ueJYuljg3dObitovEBcdh4VQ2e6tnrCdXBjTVK3Qfusa6CIXCMhdlGhoaTP9hOmu2yi33TUxNmDhtIhIjiRK7PH7qeKxtrTl38xxDxwyt0udta2/LFZ8r5Obk0qpeK9LT5Fkue/Ttwf7f9xMbHcvwccPZu30vWZlZ6OrpUlRUhI6ODn0H9qXvwL5sP7gdCysLpEVSGjdvTN+BfZk1fxajJo4q1Z7ESIKevt7bowAAeNb1ZNyUcQAsnLOQ0OBQpfM3r93k7+N/M23utLd6YpWV5Eg/uvhomeedGzljam/6xrdvYGaAjmHFLV2FWkLExmK0amiReTuzlJ+1Uh+lMiKWR6DlIN/7UzdRfy3PRtPhseV5RY2onofClELCl4RzWnCaO5/dKbfczVY3OSM+Q8zGGIrSi0g4mMBlh8tcML7A/eH38enjw7X614j/I/71vKeF1cuAVHf7rxImnUzQdtYmaFIQIh0RYuPqcdl70g3vy4VfApTrhjftxDQ6ftMRCycLxmwbQ8PODeWr2zLc8KxqWvHDPz/QuFvjUm12m96Ng/MPUpBbUGkWtTLo3LMzHbt1JDoyWrGf/iQ2r93M4l8XY2pW9XPvkX1H0NHVYcPuDdT2qk14WLhCAdi4eiNe9b3o1L0Txw8fx8lVzmx71PHgyoUr7Ny8k8T4RDb+upHcnFyatWrGpFGTCAkK4b7vfQ7/ITdKzMrKUsztgfcD6fBZhzfiXX8hL4BJMyfhWsuVvNw8xg8dr7ihwsJCxg0dx8KVC9HW0X6rP37vT7wBOL/lPAs7LiQ5qrQgrAj1Vd3t65noIdZ8wYlLAPaT5O5VDxeUzwIk/JGAXh09hRX260pokxuaq2hPx71q3HjERmLsv7FHy0mLpBNJZPuXDiCTeSuT9BvpaDloYTXYSm7N3dUMSWsJOrV0qLWuFp67PbHobYFPbx/SrqShwlum+BfJkBXJFAyi7Thbkk8lYzvGVknxfVTm0TVP/n5evc/Cm+KGZ1vbFjtPOzISy7ezeWQo+HR/n1W+qLBIIS/Kw4IVC9DV02XOlDlKWwB3b90lKSGJjz7/6JU8+6zMLHp/1psNqzfgVc8Lz7qecibI0Z6O3TvSrGUz9PT16Nq7K20/aiufX/X1WLNtDT/P+ZkWdVtgZm6GocSQMd+MwdLakjb12zD3u7l81uUzAAryC1i1eBUbVm+gUbNGStsWb40CoKGhwcqNKxEKhVw+f5kdG3cAsHLRSlxrub4xWs3LoN3QdgohfPPPm4yvOZ49s/YofXQuTVze+Pb1TfUr1b5lf0vULdRJPZ9KxvWyJ4GHix6W8sN+1ShILCB6rdzzxGqgFRqWGlVav2FzQ3TcdAhfUjqQTOSvkVj0tlByAYPSbmCW/S1BBkl/Jr36ARHwWpWvN619gViAUEuImsHLxzLLCc4hcnkkSceSFMZ/VoOssPzKEm03baQ5UmJ3xJLtn03qmVQSDyfKr1khD4YUsylGEaXwSWYpel00BbEFJB1LIvlU2Yzam+iG131Gd2xqlb3t4XvWl79W/QXAsWXHCLgcUL7yUyzj8q7LXD94HVmxjN9n/P5MA2ZLa0umzZtGUmISc6bOUTCisybN4oelP7yyd6nfkH4cv3ScIaOHMGv+LKVxX7JmieLvxb8uRix+vKj68NMPufvwLgGxAXTs3hEALW0tNv6+kYiMCHYf3a2IN2BkbMTYyWMZMnoICWKRJwAAIABJREFUQ0YPeWPk3Qt/PQ2bNmT418NZs2wNsybPwtnNmd9W/sbF2xdfS4cXdV2EWKPslW126suH/xSKhEw+NJnd03ZzbNkx8rPz2T9vP6fWnKLHzB50GNUBkdqro8aqqv3KBtoQagixG29H8NRgHi54SJ0DdZTOp5xJQU1XDYMmBq9nZVYoI/nvZAInBpIfm49ZVzPcVri9EoFmN96OB+Me4PyTM+rm8m2N/Jh8BCKBwjvhWShMk6+I1M1e/ZbIo/6IjcQIRILXbhRZne0btTXCop8FAqEAbWdtnH5wIuFAApm3KueGqO2sjdtK5XdKpCPCY5uH/G9tEZZfWWL5lbJNjdsKt3LfRbGRGOvh1uV6BCgm4BI3vE+//pS57ebSbkg71NTVnumGd2TxEVr2a0nozdDnuuFd2nGJzV9vxquDV4Xd8BzrOaJnUvYede22tandtnbFPimhgA/6fvCcdMLKGDZ2GH/s+IPtG7bz5aAvCfAL4IM2H2DnYKeiqaqbAXiEGT/OwN7RnvS0dDq37czU2VMxs3g9WaMmH5zMsoBlZf50+a5L1WhF6mr0W9yPhTcXKl72zKRMNo/bzNQGU5W02ODrwcxtN1dhdLN2yFoi/R6HSc3NyGXPzD30EvRilP0ozqw/U6XtlwdNXc1K37/NCBvU9NVIOJRAdoCyUhX+c/hrWf1Hr4vmVrtbnDc6z53P7qBhqUHjm42pc6AOIl1lBSg/Kp/7I+4rvBj+a/kfKX+nKJWJ2RzDeYPznNU+S+jcUApTCstkP0R6IiJXPn5+Ub9GYTva9vk0Z3oRQd8EoVNTB6v/Wb2ycRHpirAdY0vN1TUV/9feURuLvhav5fur7vYBUs6m4D/IX/G8Q2aEVFr4VzfeVDe86oocKhQKWbpuKUKhkHFDx7F943a+/vbrt1bAFhcXc2T/EeLj4t/I4EOVUgC0tLX4bu53Cg12wLAB76R2ZO9lz6wzs/ju+HfY1pYLgfC74cxqMYu0OPk+r3MjZ6afnK5wz2vUtRG2HrZKH/in4z/FyNqI+Tfm025ouyptvzy8iNZdSgExUMN6hDXI5AL/EbLuZZEfk4/Jp2VPDsX5xYTOCeWsxllOC07j/z9/coIfWyCnX03nqvtVzhucJ3xJuFIs96dhPdyaemfq4Txfnr8g47+MUoL/ETRsNKi1thZ2E+WrBIPGBhh9qOwuajXICq0aWnj/5U2NWTUQG5Ve0Qu1hNiMtCFqTRTSHCnFucXkhOSgW6d8NiUvKo+gSUFctruMlpMWjW40qhJaujxIs6RErorkeqPrCgHo08dHkaznVaO623+XUR1ueC+LhZ0WsnrA6iqt06ueF/2G9CPAL4DBowajoaHx1j5ToVDIiHEjiMqKonHzxu+GAgBgYGiguMEn90zedjwZYfARvD/xZtGdRYq9tvT4dA4vfBxyUqQmYtSWUYg1xOycuhNpoVTp+gM/HmDw6sEYmBlUefuvgoEAOR0u1BAStzNOEX3v4c8P5YlSynncQg0hNb6vgfNCudA2aGqAtvNjo1CDpgboeOjgfcIb+2/sn+lbrZjAxthi1sMMaZYUn14+z/Q3d/7RGW03bSJXRz42GCxB4pFEJG0kSFpKnt3eaFuk2VJiNscQszUGq/7PXs1r2mjistgF0y6mJB1LqpDBlwoqlIXqcMN7aYWwUPpKotrVcK4BgKFEFcL8jVQA3lWc23SuTFsCoUhIj1k9aDWglULwKq1Ya1rTfWZ3In0jObTgkOJ42O0wUqJTFG45Vd3+q2IgNCw1sOxnSXFBMeG/hJMXmUf6P+lY9Hk+1Wv7tS36jfQJnR1KUcbjoFEZNzPQtNHEoNmL2Q+4b3RH21mbzLuZPBj/oPyXWVOI+wZ3ivOKuT/s/uNJKlseathp3vODU6mbq2PR14KIXyJIOZWC8cfGFepjzTU1EemI8Bvo90J+6SI9EZb9LWmZ0JLW6a3x2OKh+PE66EW7wnYI1YVou2jjNM+J9rL2tIhqgeceT+qdqUfjO42xGaVssGXY3JA6++vQXtaexnca47nbk0bXG1H/fH2M2pYfSEurhhY119TE66AX7hvcqfVbLRxnOlJjTg10PHQwaGyAxxYP2svaU/9C/cd93e5Bs4BmOP3o9M7PD/F74rlocZHTgtMETQ567I4qg/DFcnfSgFEBlQ5ZXV1ueJXFh8M/pM2gNirBoVIA3g3IimVcP3i93PMNOjYAUPKtfYTOUzpj72XPgR8PEB0QjaxYxo7JOxj4y8BX2n5VMhBPwn6yPQKhgOjfogmZGYLtWFsE4uezPQKhAPf17hQkFBAyLURxX2Hzwqgxp8YLPxM1fTU893oi1BQSvS6a+N/L97U3/MAQm5E2pJxJIWazPEJcyMwQHKY6PDP++pPMgt1EO3JDcuXCv+R2n3YBgxIXrxLjN5G2iDr765B6LpXQeaEVX0FlSondFkva5TQKYgvwG+in+Lnb9S6BEwIR6YrICcohZGYIsiIZcb/H4dPbh1vtbhH/ezw1V9dUUgLSrqQpsvKFzAjBp48PN5rfQJopxfukN3pepQ28jNoZ0fDfhqScSeFu17v4D/Hn/rD7JJ9IxnasLWKJmPRr6YQvlW8JRa2JetzXfn786/Uv0gzpK/kmBUIB1kOtaRbUDHUL9WqdH8x7m+P5uycIQMtJ67FxqAAkLSXYfm1LzV9romFTOdq6Ot3wKoqMxAwGmwymt6g3x1cc58SKE/QR92Gw6WDS49PfafmQl5vHrMmzMBIYMXrgaEXQoOAHwdSxr8P3335PZsbbY49SaQXgUTjg15HU4MkUn8XS8tt7dO5ZZSqCvbP3lrvHHng1EIDmfZqXXs2piRi1aRTSIinrhq7j+PLjNOrWCImV5JW3XyUMhAyl1au2qzamXUyRZklJOpKE9VDr0uWhzBWvbh1d7CbaEbUmivRr6USvjcaijwVq+s/eH1cI2aceoZ63Hm7L5BbX/kP9yfYr3+PDZYELmnaaBE0KIvlEMvmx+Zh8VrbdwiN3reSTycTtjqM4txjd2rpY9LXAsp/c6jv5ZDJJx5PIC88jZlNJIKADCaSeSyXLL4u43XFIs6Vou2jjvsmd0O9D8R/kT/rVik+GsoKyJ+eYTTFKLEpxvvLARK6MBBmY91COVf90OVmhjKg1UQjUBJh2Vg6mom6hjuceT2I2xZCwL0F5sv8vg4CRAYr89eX1szi/mKi1UZX63kw7m/JB+Ac0C2qG6zJXXJe54rbKjeZhzdFvqI+sWEbGjQzFdpJWDS1cl7rSXtYej+0eimvq/FGHun/WfeXzkaS1BOvB1oTMDFFEw0QGEcsjcP7J+aXrry43vIpC20Cb1gNb8+PVH/l8wud0m96N7899T+uBrdE2rJo4MI+S9TyZtOdNgKaWJnMXzaVV+1aI1ESKrXBbB1s+7vgxc36e88ZE+avQ4qqyF8ZExSg0otSUVKXQjVWN5Mhkpb9r1C97FZkULve/TotLQ1okrbS7XnJkMlPqTaHPT31o0qMJmrqa5Gfnc2rtKY4tO0brga1p8VWLMq91rOdIx0kdObzwMLJiGXMvzX1t7Xee0pmrf1zlwI8HaNKzCVauVuyYvIMx28Y8XwBJZRSlFVGQVKDkY+8wxYGEAwnYjLQpZYRXkFQg/51YUGadTrOdSNiXgP///NGtrYvnHs/nKnp5UXny9yo6r9R56+HWpF5IJW53HLc+voX3cW90PUsb6In0RNRaW4vbn97G90tfmvg1KbfN8ty1au987Opk/JExTf2aKp0362aGWbfSFtVm3c1oL2svH5f4Any+8CHxSCKN/m2Ebh1dpJlSfL/0RbeuriLoUnkw625G5s1Mch/mlv8BG6iBAPLjnk85qxnKP/eny9oMt0FsLC43pn7C/gR0a5dvCCkQCbAda0vEMjnrYNDYALsJdhQkFlCYVIjdRDvuD7uPfkN9DD8wJGxeGB5bPYhYGkHYT2EkHk7Eoo8FAjUBgeMDFfVG/RqlcKnMCXlsTJobmkv0b9HYTbAjfGG4Uljop7dDXhVcFrmQ+GciQZOCcN/sTsymGMx7mlcoy9/zUJ1ueBUSGiVeSgALOy5EYiVh2Lph1Pyg5kvXnZqSyoHfD7Bz804AVi9ZTX5ePl8M+AI1NTXeFCxYvoDW9VozYtwI3D3d2bRmE6O/Gf3WMRovPKJXL13l9InTbF67WXGs16e9+KzLZ3z5vy+rNFRjbGAs/+z9h0s7LimOrR+5ntBboTTo1ECREfDG4Rv4nPbh73V/A3KXuR8+/IF6n9Wjw6gOL5QRUFNPk59v/0xsUCw3j95k39x95OfkU5BbgH0de0ZvHU2LL1s8s47WA1tzeOFharao+cJ5CV6m/UcMxHeNvmPd0HU07ta4QgxE7I5YEg8nIs2R4j/IH9POptiMsAEB6DfSx/hjY2y/fmxXkHQ8iaRjSWTdk0+8obNDKUgswLynORpWj8daqCXEaa4Tvl/5KtzGyqTBc6RErY4i+a9kxYoqao18NSlpIcG0y+N3qtZvtci4lUHOgxyueV/D+GNjzHubK1brCqH9iTHq5upoOWhVedCgikLdXB2PLR5cq3+N7AfZcm8CkTw2vOPM0oma1C3l5QHUJGqYfGrCPy7/lK+8GIupta4WBXEFz83Gp+Oug9M8J9L/TSd2e2ypsZJmSckJzCmXlXk60I3NSBtMPjYBIRg0MSD9n8dsR1FmEbpeuhTnFxM2J4z43fEUpRShYaGBlr0WmvaaRK6IVBLqsiJZqcBK2f7Z5D3MK5NlKs/YMnZr7OuZOA3VcFvuhk9vH0w6mpB2OQ33ze5VVn91ueG9KEJvhWKSVHV9lRhJGDxqMINHDX6j79vN3Y0BwwYwfeJ01u9aT1ZmFvaO9rxteGEFoGmLpjRt0ZSZP8185Z2zdLWk+4zudJ/R/ZnlGnZuSMPODfnfyv+9dJv9Fsk1W4e6DjTt2fS1P5CXbb8yDERZQU6ehPcJb+XJ6VMTTD41eaZQf1JIgdxArzw8SgdsP/n5H5BIV0SzgGZvzQcm1BTisdWDu53uImklIXZLrJIypcSolNgAKBiUcowWJS0keO72xLSLKdEbovEb4FdmXAOQR020n2iPYUtD7g+9T+yO2FJx/DVtNMu9vjxErYlS2GKom6njMMVBSXDnBOUgzZKScDCBhIPybQUdTx0krSVy5e4529FCLSFmXc1eyL3QarAVMRtjEKoLsRljg904OwJGBeC+yZ2gKUHoeemh46ZD2tU0bEfZcq3hy/llm/cyJ3ZbLL59fWka0JT3EXa17d4aZaWqMXXOVBq6NmTYl8PYvHfzW3kPKiPAdxCtB7YGqBQDoULVQ7+BPtZDrbnz2R10PXUrHCcg6VjZIYVTL6XiN8CPnMAcJC0lSLPKN76L2RKD/1B/ivOKMWhsUGYSH2mOFJFe5anrgoQC0m88Ze9QTGmjwGJ5HIHyhL9ubV2cFzjjstCFBpcalBmroZQAGm+H8wJnaq2thdNcJwU7kHE9A007TUS6IgJGBJB5I5OCuAJ06+qScjqF4BnBFKUUvfyKtbUEka5IkRjrfcMjo8X3EYYSQ/oM7IO1jbXCFuCdZwBUeD7ys/OVfr/PeBTs52mjtNcBaY4Uabb0jRgHmzE2hC8Jx/gT4wpfk/5v+jPH1a+/Hw2vN6TG7BoETwsut2xuSC5Bk4KouaYmCQcSSsWlT/8nHcsBlmg5aD3T3uBZeJZnRkWR5ZtF8FT5fYhNxJh1eX7UuohlEQobAIeHchZCViwjL1y+dZBwIEGh9OjV0yMvLI/0q+kvZKCpQvmwcLbAyNrovb1/DQ2Nt3qRpWIAqhh3T95l/7z9AFw7cI3zW85XSY6CtxEpZ1OIXBWpmKjTLr+eLHmZdzIJnhqMNFNKtn824YvDyQnKqdaxeJlgWU/bNyju824mobNDsf/W/rm5GaLWRpF8Mhn3je4KY0CFEF0egUwqw3Z82VsT6ubqpSIrlgUtJ60XjvFQHgqTCkk9n/pC1zzpwaBweXuSbZBRZa5wiiormO3vXYWeiV6ZLtHvzQKnuFjJS03FALzn8PrIC6+PqjfV45vCQBi1NXpm4JlXNinV1UOvrh7OC5zfiHdCVigj8ajcyDLxaCKmHUsbygrEgjJjLBh9aKTk+y5UFyrZU4T/HI5pJ1Nq767NjUY3FB4ZQg15mSfL+g/2p6lvUzy2enCv2z1FDIPM25kEjg/EdbkrhUmF8jDNuXLGRttVG4s+FoTODVX0E0AoFpbqv8siF3y/8FUsLQQaglLLjVLHnoGc4Bx0a+sqWfk/r/yjVNFF6UWv/Lmmnk8l4WACRelFRK6MxPwLc9RN1d+r+U5bX/ul8o68zQi8H8jl85fJzMjE546PIo2wSgFQoVoZiFNrTikYiBr1a9Cwc0N0JDqqwamu1b9YgNUgK6wGlQ4rLNITYfGFBUZtjVAzUKPOH3UU2xZiYzFGHYy45nUNbRdtLAdaIhALMO1kSvo/6cT/EY+sUIZffz8a32lMw2sNiVwVSeatTOzGyfdlbUbaUJRWRMrpFPKj8wkYE0DtHbWpf64+EcsjSNgvXzVHrookyy8Lh8kOWA+xJjc8l/zofDKuZ8g9DGRya/9H+RYcZzhi1M5IcX/6DfTJvJ1JcUExJp+bYNhUHsLVvKc58X/Eo1dXD/Oe5mg5aOE4zVGuZDy5LSSkVIhpkZ4I897mcgVA8BST8kj/eOoa62HWpF18zDQJRAKlFbpAVHV0raS1hEbXGr3X77a6lvoLeVm9S3Ct5crJf06+3XNTiiylWvmL05xWSYhqxG/8phqE6nz/Bar336ybPMWzUFMo91IokiFUF2LyqQnBM4KJ/z0e269tcVvuRsj0EHlcijE22I62JflEMln+WQgEAjRsNND11OVa3Ws4znDEcaYjobNDebjgIWJjMW6r3DD+yBi/r/wUngmPYjZUF4Yx7K1+dsHXg9E31a9wlsGn0Z727/W7byQwqlYDApUCoFIAVIOgUgDeW6gUgJdD2K0wDMwNKm0IqFIAqlcBUCNVUr0jcLqnahaqVg1ANf7VC5WbZrXiw7+rt/23QP5furQTZ+eGWFq6ljrnCBDyMhqY6hWsTqi8AN5BpKREM3SoBSdOrFANhgoqqPBSCAz8ByMja9VAqBQAFd4GpKcnkJ4eT0SEj2owVFBBhZdCXl42GhoqI+J3ESovgHcQDg51MTGxo1GjbqrBeIfg7X0CY+OPKSxMpqhIOaaCUKiJhoZ8lRYQMJKoqLXvXPt6evVo3PgmeXmRFBTEKvn0a2u7IBYbERe3G1/fvqqX5R3Gn38eZP36lbRp04HLl88THh7G5cv3iImJYs+ebaSlpXLjxlXmz19Oo0bysOFbtqwjJSWZe/duIxKJWLlyI9raKqVGpQC8gxAIBLRpMwgvrw6qwXiXPlY1fe7e7URi4tFS5zw8tmJp2Z+kpD9fifB9E9pXVzchIuIXAgMnKh3X1LSnaVNfCgriefBgrOpFqUJkZ6eiqys38Ltx4zC//NILN7dmaGs/DviUnp5AYOBVLC1dWbToDurqWnz7rTe5uRnY2LgjFD4OM+3vf5Hs7FRGjtxImzaVy93Stm0HZs2axKVL51i8+FcuXz6HQCBgxoyJbN26HzU1NX75ZT79+nXj3r2H7Nu3i9DQYObOXURubg41ahjTqlU7+vcfqppTVK/4u4OVK/tx6dIOJBJLrK1rsWRJd/777yja2vpMn34SZ+dGqkF6i5GW9k+ZwtfcvBeWlv0pKEjA33/wO9u+WGzMw4c/P63u4u6+CZFIFz+/ARQWJr9wvTk5gTx8OJ+YmC24uPyMvf3kUmWKijK4dMkGdXVjXF2XoaPjQVTUKiIilmNo2AJtbWeysu5hZtYdB4epFBamkpBwgICA4WhpOWNo2ILsbH90dWvj7LwQsVjyVrxzUVH3sbGpBUBmZhLffLOf+vU/Vyozf/6nCARCRo3ajLq6PCeClZUbY8ZsQ03tcWCkwMCr/PffUerW/bjSwl/O9uhgZGRC+/Yf4+johKOjE2fPniQ+Po7161cBkJWViadnXRIS4lm7djk//vgLAFpa2ty8GYSxsalqQlEpAO8WvLw6YGNTi08++Zo9e2by1VeL8Pe/wK1bxzAxsVMN0FuOhIR9pY5patpSq9a6ktXVIAoKEt7Z9lNSzlJQoJxzwMZmBEZGbYmP30NCwoFKChRXHBymERe3h4iIFdjZjUcgUE5EFBOzAZmsCCOj9piadi5pewwREctxcpqLRNKajIwbXL/eGJmsGEfH6VhbDyE0dDYWFn2oUWM2RUXpXL3qQW5uGPXq/f1WvHPR0fextpYrACKRWinhf/bsRm7fPkHHjt/g5vY4S6e39ydKwr+gIJfVqweipaXH8OHrX7pf8oBQjz1oIiPDMTExZeTI8aXKhoeHUVhYoPjfyspGNZmUQGUE+A6hZct+dO06jfz8HC5e3MHp0+vw9GzHgAFLMTS0UA3QW4709GtPTYJCPDy2o6ZmSGTkapKSjr/T7T8t/LW0HHBx+ZmCggQCAsa8pEARY2HRh4KCOOLiflc6J5NJSU29iJ5eHUD0xDXK6yd9/Ybo6tYmLm53mWXU1AwwM+tGSsppCguTKty3S5d2Ehsb+ErH9u7dU0yf3hRf37PlKgCtWg1QOpecHMXWrROxtHSld+95SueeLrt793RiYwMZMOAXjI2rXgBbWFhx+fJ54uNjFcdCQ4OJj4/FysqGkyf/VCp/8eJZ1YTyIgzAlSsX6NixtVxrEArR1Cyd/jIvL5fi4mJEIhHHjl1UGGC8S4iK8mf//nn4+p6loCAPAwMz6tb9mObNvyAo6BqOjvXw8GhdJW0VF0s5eXI1Z89uIj4+BHV1LezsPGnatBfu7i3588+lZWrTkZG+ZGYmERx8nY8+Gv3C7ebkPCAmZgsxMVsoKJDnY3d334iVVcVoOz+/fsTG7gBAImmNickn2NiMQSR68aQhKSlnSUn5m6iotQrDM4FAiFhsglSai1gsQUfHAyurQZib9+B98qu3t5+CRNKK7Oz7BAVNfs/aF1Cr1sYS6n/gCwnU8qCpaYuZWQ8iIpZgadlPcTwx8SBmZl2JilpTsUlVTe85yoYQkajiBmiBgf/QqFGXVyj8TxIZ6UfbtoPZt28utWu3VZzLyEhCT6/sDJZr1w4hLy+L0aO3KKj/svDgwRWOH19eQv0PqrJ+FxdLn1j8tMXIyJguXdozbdpctLS0OXbsEL/8so6+fQcyb9407O0dadasJQcO7KFnzy8V16alpbJixc9IJEYcOrSXI0fOMWBAD4qKCtm6dT9TpozF39+H33//E5lMxrBhX7Jp0x6Cgh5w794tzp37m27dvqBPnwHk5+ezZs0v5Ofnc+PGVTZs2M2BA7/zxx876dKlF6tXL6FJkw9Yu3Y7QmH1r78r3IPk5EScnd04c+Y6iYlFREVlKf2cOXMdsVhO+YwbN+WdFP7+/heYOrUBAoGQhQtvsXVrOt9/fxYtLT1mz27Ntm3fVOnLvWhRV/btm0evXnPYtCmZdeui6dhxEqdOrWHCBHdiY4PKvNbR0bvkd71Kta2t7Yaz83w8PLY+QaMtptxE7k8gPz+auLg9JZShLvXqncLe/ttKCX8AI6O2ODvPx8lpbsnkqk/r1pm0bBlPq1YJ1KjxPWlpl/Dx6YWf36AK9fFdgL5+A5yc5lBcXICv75cUF+e+V+3b2Iwsof73kpCwvwqVmm/IzLxLSsrjCI1xcb9jbt6nAsrqGbKyfLG2Hl7OtxFLfPxeLCz6IRRqVbhPr9oNz8vrIz7/fCJt2/6P1NRY7t+/+NxrzpzZwN27J/n88wm4ujZ9BmuTy6+/Dqoy6h/g3LlTBAc/4ODBvdy7d7uEDdJm797jSCRGjBo1kNWrlzJp0gwARo2ayOjR37B8+UIGD/6CRo2aUaeOt6K+06dPYGpqztixkxk5cgI6OrrMmjWftLRUDAwMmTJlNqmpKVhYWKGurs6AAcPQ1zdgx46NjBo1kV9+WcfkyaPJzMzgt99W0Lx5K6ZM+R5LSyvWrPmFtm0/IiQkkA4dPuPKFR+uXr3EkSP73i4GICkpkR9+WIK3d8NS5woLCxk+/Cvy8/Pw8qrHlCmz37kJt7hYyqpV/TE3d2LMmG0Ky1ZjY1v69PkJJ6eGLFnSvcraO3duMzdvHmXChD00bNhZcdzb+xNq127D7Nnlsww6OhJMTR0qrQA8rqcWQqFcqcvOvk9i4lFMTTs985qIiGUIhRpIpYWoq5uX2kut/OrMTrHye6RMCIWaJayEDH//IcTGbsXE5GPMzb+ocL1FRWnExm4jPHwxeXmR6OnVpXHj28+8Jjc3lH/+cUUmk2Ju3hNz8y/Q1fUkKelPwsJ+oLAwBXV1c7S1XZFKsygsTEZPrx4ODlMwMGjy0mMhEulQu/ZOBAIxwcHfkpl5+7V+C9XdvpaWIy4uCykoSCQgYHSV1q2v3wBDwxaEhy/GyKg9GRnX0dOrp/gOykJi4iHS0i6TmxuKl9ehUt9IRsYNIiKWkpXlh4PDd9jajn4j5ziBQEjXrt+xb988Zs78m4KCXDQ0tMuQBRFs2/YNVlZufPHFD8+sc9eu74iNDWLkyE3PpP6/+WYkW7f+hpOTK/r6Bk/NKQ9JTIzHycmVCxdu0aZNB8LCSqeKrlnTg+PHL5XByKgxe/ZCZs9eWGbbDRo0oV27hvj7+zB9unwro04db/Lz8wkOfoCv710kEiOuXLlAWFgw3bp9gZ/fPZKSEtm1awsAbdp8SEpKMhcunEFXV4+goAeYmpqjqamFuro6enr6ODo6AdCpUw9u3bpBly693h4FICMjnRYt2pR5bv78Wdy7dxsNDU3Wrt2OWFzxST8uLphLl3ayb99cZLJiunadRuvWA7G0dCE6OoCzZzdy9OhiRCI1evacTYsWX2Jq6vDaByoiwoekpAiaNOmh5Na8x+OPAAAgAElEQVTyCI0adaVu3Y+rrL1bt/4sWel4lDonFmsyaNAKduz4ttzrra1rYmnp8nL0kFCMUKiFmVlXYmK2EB7+8zMVAKk0k+joDVhbDyYiYnmpPdKXm5xE5Z6ztBxIQMBoiovziYvb80IKgJqaIba2X6OmZoCf30AyM++QnHwCY+NPyr0mPHwxMpmcfnxkgQ5gZzeB3NwwIiNX4uKyGEvLr0q+nevcvv0x//13DG/v4xgZvVz8U1fXZWhru5Kaep6IiCWv/Vuo3vYFuLvLqX9///9VCfVfmgWYyN27XcnK8iUqai0uLoueWd7UtAsSSetnKBUNsbObWKm+vG43vBYtvuKPP+YQGHgVdXUtrKzcSpV5RP2PGrUZsbj8VMABAZc5cWIl3t6fPJf6z8hI58iRczRr1lLpeEJCHM2beyIWi1m/ftcr8d23tbXnyhUfZsz4hlat6nH9egAGBob06NGX/ft/R19fn+HDx7F373Zq1aqNrq4eRUVF6Ojo0LfvQAD69h1Ifn4+UmkRjRs3x93ds4T1ySc5OVGpPYnESCmGRXWiwlsA48dPRUurtDb477+XWbFC7prz/fcLcHNzf6EOWFg407Pn91haumBp6UKfPj8qBJe1dU369VuEoaEFDg516dZterUIf0DxwG7fPkFUlH+ZZZo06Vnl7R09urjM887OjTA1tS/3egMDM3R0DKtoQpwECEhLu0J6+j/llouK+g2JpCXa2jVf88pFhIaGTQkbVTmBIBYbo6/fAICwsPnPoDQTSEk5g7q6GQKBSCH8H9djVIYAaISDw3fIZIWEhHz/UvdqatoFa+shFBWl4efXH5ms+LWOdXW3b2s7ComkDfHxfxAf/0cZTJF9pbebHsHEpBPa2s4EBU1CJNJBLDautgm6LDe8778/x+TJhxQ/OjqGZbrh/fLLfaZMOaoo17nzFHJy0p/phicSqdGlyxT27ZurZAD4mC7/jXv3/ubzzyeWSf3n5maUCL6cZ1L/j8o9gpubeynhL5PJGDGiP8nJSUybNo+6deu/kjE+cmQfOjq6bNiwm9q1vQgPDwOgR4++bNy4Gi+v+nTq1J3jxw/j5CTPh+DhUYcrVy6wc+dmEhPj2bjxV3Jzc2jWrBWTJo0iJCSI+/d9OXxY/o5mZWUp5vTAwPt06PDZ26UAlIWsrExGjuxPcXExrVq1Y/jwrytdl1isibp62R+uhoYOamrVm3Pazs4TIyNr8vOzmTGjGRcubC1Vpm7djzA3r1El7Xl7y1eg589vYeHCjiQnR5Uq06HDyHKv19MzeaZ2/iLQ0fHA2FjObpT2w370sRYRGbmiRFl4vSguzqOgQG79q6tbu9L1GBo2x9CwOWlpl0hLu1JmmcjIFdjYjHrhrQ0tLecSBSKu0v3T0LDC3X0DAPfvjyAvL7LMcmZmryYCZHW3r6XliLOznPp/8KBsGt3KagDFxXmVULiLkMmKShRKIba240hOPoWt7ZgnykgVZR5d8+Tv59VbGVTUDe/zzydUmRte69aDiIjw4cKFbQrlA+TU//btk0qo/3llfBu+PHx4B5BT/3FxwQwY8EuZeQQuXtyh9H+/fqXjR/z661LOn/+bDz5ozdixr87INCsrk969P2PDhtV4edXD07NuycLHkY4du9OsWUv09PTp2rU3bdt+VDK/6rNmzTZ+/nkOLVrUxczMHENDCWPGfIOlpTVt2tRn7tzv+OyzLiXjn8+qVYvZsGE1jRo1w8urHm8CXkoB+O67cYSHh2FgYMjq1VtKfDPfTYhEaowevRWxWJOcnHRWrx7IjBnNlAxmJBKrKvO3b9duqEIJuHnzT8aPr8mePbOUNGcXlybPoB2rNtCFg4P8A0xMPEJOzoNS5+Pj96ChYYmhYYvX/mzCwxcjleYgFKpXmmp9fJ9TSxSd0iyAVJpFfPwerK2HvHC9qannSt6RNpXlOfDw2IJYbExs7Dbi4/eU/UELtTAx+fRV8CzV3r58u0WHBw/GUFCQWAbr1RSJpN0LsxI5OcFERi4nKemYwvjPymoQlpZfoa3thlSaQ2zsDrKz/UlNPUNi4uGSa+TJtmJiNpGZeUepzsLCFKKj11FQEEtS0jGSk089sw9vkhueWKxBx46TCAi4jJGRjWI1vmbNYPLyssuk/ouKCti16ztsbWtz//5F/vqrfOr//v1LxMUFKx0zN7dU+v/evdvMmzcNQ0PJK7eY79dvCMePX2LIkNHMmjVfSY4tWfLY82Px4l+Vtrc//PBT7t59SEBALB07di9RUrXZuPF3IiIy2L37KDo6cobQyMiYsWMnM2TIaIYMeXNsQCq9SXvs2CF27twMwKJFq9+L4Aqenu2YM+cCq1b1JybmAYGBV/n++1bUq/cZ/fotKkWXvZRmJhQxefIhdu+exrFjy8jPz2b//nmcOrWGHj1m0qHDKESi8h/fo33DqoJE0gZ9/fpkZNzk4cNFipXgYyG8BEfH6a/1eeTlRRIZuZzw8KWIxca4u29GW/vl7B5MTD4rMeg7RlbWPXR16zwxGa/HwuLLF3LhkkpziIxcSWTkKiSSlri4LKxUv+ztJ2Jk9CG5uWHlhrtVVzfDzW0VeXlhVT7W1d2+ldVAJJLWyGRS7OwmllL0xGIjtLWdiY3d/sJ1a2s74+a28imFXwcPj20lf2tjafmVwqbjEdzcVuDmtqIcIWqEtfXwcj0ClIX/m+eG1779MHx8TiuE4cWL2/DxOY2OjoTDhxeWEv4PH94FZOjoGPLrr/9DJpORnZ3GokXK7osZGYkEBv7L0KHlu1Tm5uYwdGhfCgoKWLduhypwz5umACQmxjN+vDyOcteuvenR4/1JvuHs3IjFi+9x7NgyDh78iZycdG7dOsbdu6fo1Ws2XbtOq7qHo6ZOv36LadmyH1u3TsTX9yyZmUls3jyOs2c3MXHiH+Ua+mlq6r4CITAJH58+xMXtwMlpHhoacq09JeUMRUUZmJp2feXjL5Vmc/v2R+TlRZOd7YdAIKRWrTUlgrkq7lmAvf23+Pn14+HDBdSuvatkBVRIVNQ6Gja8UqFaYmO3kJCwl+Tkk4jFJtSrdxqJpDUCwYuvZLS0HHFy+lEhWJo0uVeGwqiBuro5IMDX96sqHfPqbl++yt5MTMzmd3JO8fL6CC+vj5DJijlyZBH371+kVq2Wz7zmkRtex47fvBI3PA0NbQYNWq7EKDzNKgAsXdqTvLxs1q2LVhxbuTL4pcbju+/GExQUwJdfDqJz555v9bMtLi7myJH9xMfHce3aFRo3bv72KwBjxvyP5OQkLC2tlSiSl0VSUgSrVw8sdTwjI+GNimSnpqZO587f0rbtYA4c+JG//lqFVFrI7t3TKSzMp1evOVUseL2YNesMt2+fYMeOb4mM9CU8/C6zZrVg0aI7ZY7NBx9UvVJmZtYTLa3vyM19SGTkcpydF5Ss/hdjbz+xUsLtRSES6eDtfZKionSuXatHbm4oGRk3K7TSqigsLL4gNHQm8fF7cXKah5aWE3FxuzA2/qjCBmGWlgOxtPySW7c+JCXlDIWFiZUen9zcMM6e1ay29726239fUJ1ueGXB3NzpuWWiovzJykqpsjH488+DbNu2nho1nFmwYMVb/0yFQiEjRoxjxIhxb2b/XvSCTZvW8PffxxEIBKxevRlDw6pLamFiYsfo0VtK/ejrm1X7QOXn55Sy/tfTM2bAgKUsXnxX4S5z8OB8MjNf3jUpJOS/Use8vT9h0aI7CgUjPT2+FB33aicoEXZ2E0o+/LVIpZlkZ/uRmXkTK6tBr/V5qKkZUKfOHwiFGkRHr1cKv/ry96mGnd03yGTSEqNHGRERy7C3f9FATwI8PLYjFptw//5w8vLCVVJOhWeiRYuviIsLJjDwKjExD16bG15l0aBBJ6U4JS+D2Nhoxo0bgpqaGr/9tlOxf67CG8IAhIQEMXOm3Mp76NAxtG79Ybllo6Mjsba2fWcGKjc3gzNn1jNgwC+lzllb12LKlKNMnOiOVFpIWNht6tT58KXaO3duExYWTujoSJ7SKEX06DGL+PhQLlzYSnDw9dc6DlZWgwkNnUNhYQpRUevIzvbDxmbUC0U2qyro6dXD1XUpAQGjuX9/OPr6DV7aBuDxMx1MWNhcYmO3oq9fH11dzyeCEVUcGhqWeHhs5s6djvj6fkn9+heUYhqIRHqYmXXFxWUxQqEGiYkHlZQcE5PPOXdOB01Neywt++PoOIP8/GjS0q4gFpsgFhsTHf0bUVG/Kq4zNGyOnd1EzMy6kZl5l5yc+2hpOSGV5hAWNpeUlLLjoGtp1cDefjIaGhYUFiYjkxWTlxeJQKBGfPxe1NR0sbEZiaXlAFJTLz6x1y/CwKAh8fH7CQmZ/s5PmlJpDhcvmmJi0gk1tUf++MXExGxGR6cWjRr998zAQc9muB674bVq1b9cN7yOHSeV64anpaVfITc8LS39lx6Lxo27kZmZ/NL1FBcXM2JEP1JTU5g+/Qfq1WtUZpng4Ae4utZChdfMABQVFTF8+Ffk5ubg4lKz3KhKII8MuGnTmlfW6atX9zJunBu9egk4fly+T5Wdncr69SP49ltvfH3PEhZ2m8mT69Krl4C//lqlsAyOiXnAuHGurF49gKSkiBdq9/r1Q+VaGFtaumBlJfd/fzJIR2UhkxVz/frBZ2jeHausrReboHSwsRkByA3/EhIOYmNTfVatNjajMDfvjVSaiY9Pr0q5gJVN3Wlha/s1xcX5BASMxsFhyos+wSeYrc+xtR1DWtoVQkO/f0qYZBIbu420tMsUFMTi5zdQ8XP3blcCAycgEumSkxNESMhMZLIi4uJ+x8enN7dutSM+/ndq1lyNjc0oRZ1paVeIiFhaorTPwMenDzduNEcqzcTb+yR6el6lemtk1I6GDf8lJeUMd+92xd9/CPfvDyM5+QS2tmMRiyWkp18jPHxpCQO05om+9uPff72QSjNeEfMkxNp6KM2aBaGuXv1bgYWFidSqtR5Pz93UqrWWWrXWoq3tXML4bKu08H+E6nDDexEEBV2jXz9devcWsXPnVE6dWsNXX2nTp486Fy9ur1SdK1cu4tKlczRt2oIJE74rs8zp0ydISUnmTUCPHh/z3Xfj+PHHGfz44wyaNatNs2a1KSwsfDcVgCVLfuDWreuoqamxdu32MpMBPcKuXZsxMXkxN7SCghyk0sJylI98ioryFf83bdqLWbPkFqlFRQUlglAe9Ob7789Su3ZbHB29mTRpP+rqWujoSBT7r6amDri6NmPUqC0v7LKXmPiQQ4cWlHkuIyOR+PgQLC1dcHJqUCUPZ+/e2aSlle03Hhh4FYDmzfu8spdD7sNcWuGxtf0aoVCDgoI4LCz6oK5uWuq6R6uiquzLI8Xoabi7r0db24XMzDuVDg1bVJROUVH6U8rFaEQiPYyNP0ZHx0NJuEulmchkUqTSrKfqSSsREsr7oi4ui9DV9SQs7KcyLdVlsoIy+xUTs4mioownVkH5T036KwFZSSIkyi0nN2Jcg0Cgpkhn+wjq6hZ4eu4hJmZTqZS/GRn/ERAwUpG/vrx+FhfnExW1tlJjb2ramQ8+CKdZsyBcXZfh6roMN7dVNG8ehr5+Q2SyYjIybpQIWTlT4eq6lPbtZXh4bFdcU6fOH9St++ernzSFmkqujtnZfoSEzKJGjVno6dV96fqrww3vRWBsbEPbtv9jwYIbdOs2nfbth7Fo0R06dBiJnZ3nC9d3+/Z//PTTTPT1Dcp1+Xv4MJTp0yfi4VHnjRCc/fsPYf785Uyf/gN9+w7i4cNQli5d+0JRcN8EVGgL4Nat6yxZIrcCnjx5Ft7eDcoRgukcOrSX6dMnsnPn4Qp1IC4umGvX9hMXF4xQKOLgwfk0adJDEQr4+vWDJCdHkZYWz6FDC2je/AtMTR0wNrblf/9byW+/Dadp0578889e2rYdrESZm5s7/Z+98w6Pouza+G93s5tOKimkk0ogdDD0IggKgnTpSBMUlI4UBQEpgr6gKCJNikqRJioKRkVKAggGKQkhJCE9Ib0nm939/phkk00jgYSEj72vKxfszDNzzzw7O+c8pzJs2HIOHFhE+/avoq/fiF9+2cLQoUsfu2bB998vIzo6iEGDFuLk1BKVSkVERCA7dsxAJtNnzpyDtRYMl5wcxeLFbRk9ei2+vsPR0zMiPz+bM2e+4uefN9Oz5yS6dRtXZw9HXl4kCkUWhYUZ6Og0KiUwrLG1HU9s7O4K8+7z8gTLSn5+PCqVosoyvtVFbu6DohVz+euRSIzx8TnC1au+xMbuRkfHFHf3DdUqRVxYmE5CwiGioraSlxeFkZEPFhavYGjohVRqhr39dI3shuTkX0lMPKEWykFB07GyGl6UOnhKLdyjooSeCFZWryGT2SAW6+Hjc5DLl9tz+/ZEEhOPYWs7tsprs7IaRmbmNXJzIyr/AeuYACLy8x9dYEhHx1T9vWgqOm8ilVoQG7u7wuMSE49WWWBJJJLg4DCbyMjNAJiYvICj41wKCh4ilyfh6DiPoKDpNGrUAVPTroSHr6Z5871ERn5KePhaHj48iY3NaEQiHUJCSvq5R0d/iUwmxP/k5Nwv9SyEERPzNY6Oc3nwYANZWbc0LEJ1DSHboUSxunVrAkZGLXF2XlJrHPWZhvcomJvb8cYbQoDeZ5+NJSsrhaVLT2tkDdQE06ePQS6XY2IiY/LkURXKlfDwUJo0scfYuBENAcUFgQAWLHiLYcNex9e36zPnAhClpDy6KHGXLj4EBd0q0r4NKhSeSqWSvLySjmB37ybQuPGjg/d+//3JbmD9+oEkJ0fTu/cUXn65fH6yQiFn0aI2tGjRm4ED53H+/LcMHVpzP2VaWjzHj6+je/fxBAb+SmDgaRITw8nLy8bIyIzWrV9m6NBltdbrev/+hXTrNpa4uHtcu3aK4OAL5OfnUFCQi5NTS/r2nUG3bmOfmOfrr8tvy8kJISHhELGxe8nNvY+paRcsLV+lSZPJ6tV+dnYw9+8vp2XLH0oJx9MkJ58lOnqb2hRvbt4XC4u+Ravpx20HfIaoqC9RKDKLBExnrKwGY2s7QcMkHBOzg6Cg6YDQPKhx40E4Oy9Vpys2RPz+u/Bb8vE5iIXFy+oYAB0dMywtX+HSJXcNBaBXryyio7/i3r0FSKUWNG/+DY0atefatd5kZwepxzVq1J6OHa8SGPgqSUk/YWjoTevWpygoSOTatd4a3fs6dAjAyKg5f/5p/MjrNTT0olOnoFIxAGJMTHxJT7/E7duTisZ407LlUZTKfMLDP8TCoh+JiUextZ2IufmLhIWtRiazJDv7rrqgUIsWBxCL9fjvP01LhkRigEKRg0RiRK9emfz9ty0FBfEYGLjRufM9AgJ8NBQAicQQhSK7BoL2yWqy37//AQ8ebOSFF/7F0LDmJbCnT698X0LC/WpF4tcnZs50ID8/h927H88036cPzzSOHTvIokWzuHw5GAsLy8dQpuq3el61LAAXL95ssF/AmDHrWLCgZaW+cIlEyvTp21m5sicpKbHMnv14PipTUxu1huvq2p5hw5bX6X2NHy80IHF2bk2nTk83F9bAwAMXl/dxcXm/SkFQWvgLpsGXsbB4GQ+PT2vtWszNexe1BF7/yLF2dtOws5v2zL5MimMAiuHqurrCcWZm3fDx+Z7GjV8jJmYnt29PLOdyKEaTJpNwcpqHqWl3goKmERd3AJVK09Wmp2df6fGVITp6GwkJB4tWxFYaMRLZ2XfIybmHQpFFYuJxEhOPFz0zPpiZ9SQ6ehuPattc3IQqPv67al9TkyZTiI3dhVgsw95+Fo6O7xIc/Bbe3ru5d28xxsatMDT0JC3NHweHt7h8ucMTfV8ZGf8QEbEOd/ePH0v4PwoNXfgDODq2RC7P43lEZmYGy5bNY+XKDY8l/BsCdJ71L8HPbycjRqxg3775tG37CsbG5b8IT88ueHl1xd7eu8qKWVpo0ZCQlPRzhdtTU89z//5SOna8iplZ93JxCKURG/sN2dlB+PrewMTkhQqL6SgUOUilj/8CKyhIJD39almbYAVBgcqia61Y+BsZtcDNbT0ikQgzsxeJi/umGgJoDgUFSUilplhavkps7C5UqkIyMq6gp+eIRGJEcPAMcnJC0NW1xtp6FGFhq8nPj6Ww8PHz15XKPG7fnoCpaWccHN59bp9RZ+dWyOX5z+W9r169FGfnpowdO/mZvYdnWgH45ZctdO06Gje3jly//jPffDO30hW+jo5undaT1kKL2kZ6ekAVAqiA27cn0KHDFZo2XUloaOUVKHNz73Pv3gK8vLaRmHisXF369PRL2NpORF/fucp4g6pQbA14EmRl3SI0VOjFIJVaYmX12iOPiYzcrHYBODsL1y6kLwoxI4mJx9QWD2PjtuTlhZOe7k96uv8TXWto6DLy8qJo3fpnjZifwsIM8vNj68Qi0BDRpIkneXnZz91v899//2H//p34+V1Vu8QVCgWpqSk1DoCvTzyzEvHOnXMolQrc3X0RicRMn/41Fy9+zz///FjheJVKiVKpRAstnjXY2o6vcHtm5g3Cwlbi5LQIExPfKs8RHf0Vycm/4e29Sx0MWCJEt6BSKXBwmFPhsTKZNebmj65roa/violJ51q5Z7k8idTUv2p0TOkMhpJ+66WtDapa6cOelnaeqKjNeHhsQl/fRWNfVNTWUrUB/v+jUaPGtd53pKFDoVAwf/4Mpk9/B2/vkqyH338/TUHBs2UNeeYUgPz8HH78cSPr1w/UaJJRWJiPrq4BX3wxsVwu6s2bfjx48B+3bgn/aqFFQ4NIJK2wxbC5eV+NQEexWIZYXJIC9uDBx2RkXKVFi+810jHFYt2if/VKKc1TkEiMaN58r0ZmRmbmv4SEzMHBYTYuLss1ijoZGHhgbz9D3SWv+BrFYmm563d330hm5j/qV4tIpFvudVN+W+XIyQmtUXvnnJxQQFQmZbN2oVLJuX17EiKRlPT0q9y5M1X9d/36S0RGftqgg05rGwYGpnXSd6QhY+/erwkMvEZhoVxdB2DJkndZtGjWM9e46JlzAejqGjBo0EIGDdLsD+3u7svevRUXIvHxeZEdO+LRQouGBonEGBub1zE3760ub1wcxS6VWmBu/hKXL7fCwMAdW1tB8DRuPIj09EskJBwpEkgTeOGFQDp0uExU1FYyM6/j6Cj4pe3tZ1JYmEZKyu/k58cQHDyLFi0O0K7dn0RGbiEx8ah65ZqVdRtn54XY2U0lN/cB+fkxZGRcITx8NaDCxMRXnfbp4rIcc/MX1cK/UaP2ZGb+i1JZgKXlQExNhSp11tYjSEg4grFxa6ytR6Cv74yLy1IePPikTK0CMSAqNzfW1qOKTPyiIi5RmbWL5jF2dtNJS/u7lGIiKVWXgidOSRWJpHTpcl/74BZBT8/wuQsCnDx5JpMnzyy3fd26Lc/cvVQrDbAu8aRpgFo8GSpKA9TiaT7/oud+DqyshuLp+RlisV5RlkIhYrEMS8tXCA1dTkLCQRwc3sHTcwv37y8jMfEY9vazcHB4m+Tk02Rl3UEkEqGra4+RkQ+XL7fGxWU5Li7vExa2koiI9UilFnh6bsXCoh+3b49TZyY8aRrgk6KqNMBnAbGxd8nKSqmyI2FVeNbTAJ8U9Z0GqFUAtAqAFloF4LmFVgF4MiQkhJGdnUrTpu20CsCzqACoVPWrAMCRemav337TI0XPtwCo98fvOYfoeX/+6lkC9T1bzxNQzxfQp8+GeuV/7733nuvnX5sXp4UWWmihhRZaBUALLeoHu3btwtTUlCtXrjyX/FpooYUWTxvPZCGgq1fvs337WUJD4zE21kdHR4y+vozBgzuQmZmLqakhw4f71hn//atXObt9O/GhoegbGyPW0UGmr0+HwYPJzczE0NQU3+HDtU9XDaCvr4+pqSm6upppYvfu3WPp0qXExMSQk5PDnTt31C03b968SYsWLf5f8GuhhRYlyM/Px9/fn8uXL5OamopEImHRokWYmFRdYyEqKoovvvgCgKZNm9K8eXM6d+7cIF1dOjqwZg00agRz5sDw4TB1KkyYADt3wi+/QHQ0NG8On30Gx48L2zw94dAhzfi5NWvg/fdBpRLOd/QoHDsG27eDUgkmJsLxfn7w8CF07AjvvfeMWQAKCxUsXLif3r0/pFev5vj5fcCpU4s5fnwh27ZN49q1MKZN205KSlad8CsKC9m/cCEf9u5N8169+MDPj8WnTrHw+HGmbdtG2LVrbJ82jayUmpUY7dKlC0ePHi3qLBjBjz/+yOHDh7l69SqHDx+mW7du6rHGxsZMmDCBxMRE0tPT+eabb9R/x48fRy6XI5PJ8PDwYM2aNahUKmJiYjh58iTXrl3jzJkzdOnSpUHxA4wZM4aIiAhatSrpVX/79m3atWtH3759uXTpEoGBgURFRTFkyJBa/27rm///I1xdXdm+fTvHjx9Xb5s7dy6HDx9+Lvi1eHzo6urSs2dPhhctpBQKBefPn3/kcefOnVP/f/To0XTp0qXBxrkUFsKtW3D9OhQUwJUrEBYmCP3QUPjjD0GI/+9/kJ4OISFw4oTwef78kvOYmkLLltCjh/A5IwPu3oXz5wXhDyXHHz0qBH7/8INwnmdKAXjvve/YtOkUBw7MZuzYbkgkJZdvYmLAxx+PY968gXWmAHz33nuc2rSJ2QcO0G3sWMSSkpxiAxMTxn38MQPnzauxAnDx4kX+97//ATBr1iwGDRrEyJEj6datG5GRkZw7d465c+cCkJmZyb59+7hw4QJxcXFMmjRJ/TdkyBDmzp2LkZERISEhvP/++yiVSvbv38/gwYPx9fVFLpfj5+ensXKtb/7KsGnTJqysrJheKlTa2tqaQ4cOaQjqukJ98z8tdOrUibNnz6JSqTh48CAHDx7E39+fwYMHP9F54+LiUCgU6OuXFBY6ffo0O3bsaFD8WjRcmJiYYGpqilgs5sqVK+Tk5FQ6NgGCJ+8AACAASURBVDk5mdjYWLXANzAweKbvvUsXeOONEsFeDCcniIws+dymjWA5GD26+uc+exZ6936GFICAgHt88skpOnf2ZPDgyrt4rVw5gsJCRa3z3wsI4NQnn+DZuTMdqngxjVi5EkVhYY3Pn5eXV+G2BQsW8P3337Nx40batm2r3ldQUFDheXbv3k1GhlAQSaVSqc3VAHK5nM2bN6Orq8vYsWMbFH9FSEhIICYmhpCQEI3tUqmUGTNm1PkzV9/8Twv+/v4cOiS05X399dd5/fXXOXr0KMeOHdOw/tQUOTk5REdHa2wLDg7m7NmzDYpfi4YLkUiEiYkJLVu2pKCggEuXLlU69u+//9ZY8T8rGS6tW8Nrr5VPibx4EfbsgYSEkm2DBsHs2ZoWAE9P6NxZUAyMqlmUUaWCvLxnSAHYuvVXAF57reoWnsbG+syY8VKt8/+6dSsAHV6rukGJvrExL9WycFi1ahUSiYTZs2dXOW7YsGFYWVlRWIUCUpx2l5mZ2WD4o6OjWb16Nc7OzgQElDTA6d+/P3l5eXTp0oWDBzWbzQwYMABra2sAPvnkE3R1dRGJRGzevBmAvXv3YmNjg0gkYty4cdy7d08tbLy8vOjUqZNaONQ3f8MwRxaWU+TEYjGjRo16ovNWt/9GffNr0bDRo2gZfOnSJY1FRTGys7MJDg6mQ4cOz9y9BQYKpv3KauLcuiX49QF+/BHi4wWTP4CPj3DsiRNC3MDIkeWPNzAACwvNbb17C1aAGisAN2/eZNWqVbi4uCASiRCJRDg5OfHhhx9y8+bNOpukc+fuANCsmd0jx1paGtc6/50i35Jds2aPHGtsWbu9oe/evUtSUhK+vpqBjba2tmr/+8mTJ8sJqbLQ09Nj4cKFpKenc+DAgQbDf/v2bc6fP8+DBw80xs+aNYsxY8aQlJTE6NGj8fX1xc/PDwAHBwcaNxZq38+fP585c4RGNn37Ck1rJk6cyOrVqwEYNWoU7u7ugGButrGx4ccff8Te3r5B8DdkFCtqS5cuZePGjXz99decOHECfX19mjZtysmTJwkMDATAy8uLv/76i59++qnCczVr1ozdu3dr+OQbOr8WDQO2trZ4eHiQk5NTYabOxYsXad++PTKZ7Jm5Jx0dYfXv7Q0ymWDKd3EBOztwdYVXXoGhQ2HdOpBIwMsL2rYVLAAffggDB8KCBVBs6EhPF4IJvbwEq8Do0UJA4ebNQiyAlxe8+iqMGAEvvQSLFj2GAuDj48MHH3zA3r171dv27t3LihUr8PHxqZOJUipVxMYKfnULC+On/kWplEpSYmMF4V5WlXpKSE5OxsrKSmNbaR/84MGDWb9+fYXHduzYkWXLlrFz505CQkJo27YtkaWdSPXM369fP1555ZVyx4nFYr799lu+/fZb7O3tuXz5Mn369KFfv37lzPIzZsxAJBLx7bffqrcNHz4ckUjEnj171NuCg4Nxd3dXC++GwN8QMWfOHPLy8ti7dy9eXl68//77LFy4kDfffJNOnTrRp08fwsLCNIRpcHAwv/32W6XnDA0NJSMjQ8Mn31D5tWh46NmzJwDnz5/XsOwUFBRw7do1Onfu/EzdT2GhIMDnzROCAI8cgRdfhJgYePll+PhjIQhw7lxITYWePeHwYcjOhr594aefYOJEiIsTznf2rGAZCA4W9i9bBvv2CdUmi4/fuFHgWbRICBZ8bBeAnV3JStzR0bFOJ0osFiGT6RStCHKf+hclEovRKdIsc2tgOq9NmJmZkZSUVOWYn3/+ucLtV65c4aOPPmLcuHHMnj2bsLCwBsdfNv2uNMaMGUNISAjr16/H1NSUM2fO0L59ew1zvYuLC71792bfvn0oFEIMyIkTJ3Bzc+PUqVPEFf1Kdu3apRHU11D4GwpmzZrFunXrsLKyomPHjgQHBxMaGkq/fv0Qi8W8/PLLqFQqGhXbJMsqy1VUdpTL5SSUdmg2QH4tGi6aNm2Kg4MDaWlpaqsPwNWrV/H29sbQ0FA7STWVrY97oKRUBLxYXPehBG3aCH23796NrZeJcmnTBoDYu3efOrebmxtWVlb8/fffVY4LCAggIiLimeSvKGDnv/9KWjfr6+uzePFiQkNDGTx4MJmZmcycqdmRa9q0acTExHDmzBlUKhVHjhzh0KFDFBYWsmvXLuRyOTdv3qzQT1jf/A0FW7duZcmSJcyYMUPt0issLMTMzIz169cTERFBUlLSYwdYPar089PmD87O5u3gYES//06f69crPS42Px+Znx+W586xJzaWbEXtBBpnB2cT/HYwv4t+53qfyvnzY/Pxk/lxzvIcsXtiUWTXDn9Ozl3u3VvA77+L+OsvEwoLMyodm5V1i99/F/Hnn0ZER2+joODpK1PFsQDnzp1DpVKhVCq5dOnSYwWLisVK1q+HL78UTPBjxgipd/b28Ouv8M47ggn+/feFPPo//hBW7Dt2lA/YW7OmxBTfqJGwGp85E4pFY/Hxy5YJK/KdO6Gsp1gsFszzCoWwSt+9W7gON7eaz9PIkUIqYcniDCp67TwzQYATJwpf/OHD/o8cGxISV/sP3sSJAPhXI4c4rox5+EmxdOlSCgoK1Kl6j8L48eP/X/Dv2LGjXMCPhYUFhw8fxtnZmcDAQI3gsSFDhmBhYaH28w4YMIA2bdrg6+vLjh07OHnyZI1Sy+qbv6GgS5cubNiwgcWLF3Pnzh2NfQqFQmMx8KzxexkastnTEx2RCL+UFG5UYuH7IjoaJdDb3Jw3mjTBsJbu2dDLEM/Nnoh0RKT4pZB5o2L+6C+iQQnmvc1p8kYTJIa1w29g4Im7+yZ0de0pLMwgNnZnpWMjI4UAV1PT7tjbz0Qms37qz2Lz5s2xtLQkISGB4OBgbt68iZ2dHebm5jU+l1Iprvc8fM3rEQR/cjJ88glMngxRUfDddzWfp1OnBEWmGO+8IwQbPrMKwJQpvfH1defChWB27vSrdNyFC8Hcvh1V6/y9p0zB3deX4AsX8NtZ+Y8k+MIFom7frvH5KzJB6+josGLFCsaNG8fUqVM1Xn5SqRSpVFrumL59+2JjY6Ne1Uql0mr5POubvyJkZmZq+NSLIZPJcHBwwNnZGR0dHY3t48eP58cff+SLL75g8uTJAEyfPp3IyEiWLFlSrfTDhsL/NFF8H6Xvpxjt27fHwMAAY2Nj2rZti6WlJQYGBri4uBAfH4+Liwt2dnY0a9aMHj160LhxY/WzURwoXNrSUtHqvT75pSIRvczMMJdK+aSC2JhcpZIr6ek46+khq4PUMpFUhFkvM6TmUiI/Kc+vzFWSfiUdPWc9RLK6SW0zMHDFyKgFkZGfoVKVty7I5Unk5oYhkRgikdRffr1IJFJbAf766y/+/vtv9efaVzzrPg+/YsWktDwTggRritwynvL796GC5IlnRwHQ0ZHw889L6NjRjenTv2bevL1ERpb4pDMyctm69VeuXw9nyJCOtc4v0dFhyc8/49axI19Pn87eefNIKvUU5GZk8OvWrYRfv07HGlaK69q1K4sWLQJg7dq17N27l507d3L27FkcHBxo27Yt+/fvB4RKfNOmTaN37964uLhw5MgRdST+qVOn+Omnnzh16hQeHh6sXbsWsVjMa6+9xpgxYyoU2A2BH0rqEJStLzBr1iwNvzrA999/T0BAAJ9++mm580ydOpWCggJ69+6tVjxGjRqFiYkJ3bt3r9R3/DT4u3TpQufOnenVq1e54x4+fMi7777Lxo0b6dixI+PGjUMul7N8+XJsbGyIiooiICAAExMTPvnkE/UxAwYMwN/fv+g3kKHORihGREQE3bp1Y+DAgaxatYoXX3yR22UU1E6dOjG66O21cOFCHBwcNPYfPXqUrKwsbt26Rfv27fnjjz+YPHky2dnZ+Pn54efnx3///cekSZM4d+4c6enp9OjRAycnJ/r160erVq3o1q0bLi4u9O3bFx8fH42MkvrmBzCQSHjTzo6D8fHE5udr7NsfF8d4W9s6fb9JDCTYvWlH/MF48mM1+eP2x2E73rbO37GOjnPIy3tAYuKx8haI6O3Y27/51N/7FbmM2rRpg7GxMQ8ePEBfX18jHq30MdXtNFqfefiPXHj2LkkPXLdOiPI/dQq6dhVcDdu3Q3FC1fz5QspgWbRrB5cvC8eUk6vPkinS3NyIS5fWsGfPn3z//UXat38PmUwHFxcrXFysmDnzJTp18qgzfiNzc9ZcusSfe/Zw8fvvea99e3RkMqxcXLByceGlmTPx6NSpxue9cOECFy5cqPaqdMeOHdWqZrZkyRKWLFnS4Pl/+eUXdhZZVTZu3IihoSHt2gn9xbOzs5k4cSLz5s3Dzc2NnJwcrKysOHPmjDoquKyJ8KWXXuLtt98utboxYPz48YwbN65e+YcOHcr+/fuJjIxEpVJprES3bt1K165dGTFiBLNmzWLTpk1IpVKWLVvGtm3bkMlk+Pr6Mn78eLUy0rhxY3r27Emnomfu22+/5bvvvmP58uVYFjkYnZ2dadeuHc7OzsyZM4cVK1awYMECTp8+reb29/fnxRdfrPT7iY6OxrvUMuTrr7/W2F/WrVE6G6TsHPWuYNlT3/xqZc/BgU0PHvB5VBTrihyvKuBQQgKnW7dm1WMEz9YEDrMceLDpAVGfR+G2rsjxq4KEQwm0Pt2asFV1y29jM5bQ0CVERn6KtfWIUsJKTlLSKdq3v8CdO1Oe6js/JyeH7Ozsctairl27cvr06XKr/9zcXLXgz87OrlThL43iPHw3N2jfvvz+snn4LVoIJv9Ll0ry8OPjhbS+kSMF372mdQXKGkGL8/ArQ//+0KuXYGnYtAkMDYUMgY4dISVFsExMmSKco7g0zcmTwvayuHatioU1zxgkEjFTp77I1Kkv1gu/WCLhxalTeXHqVLSoHbzyyisVpuEVWxZqiopSwT7//PMGwd+vXz+cnZ3LmaFbt27Nu+++i4GBAQMGDGDChAmAEHw4ZMgQDh48qN5/4MABFi1aRGBgIG2KglOVSiWpqamMGTOG7du3s2zZsgqvLSsriyZNmmgfugrQRFeXUTY2bI+JYbmLC4YSCb8lJ9PLzAzZUwh01m2ii80oG2K2x+Cy3AWJoYTk35Ix62WGWFb3/GKxHnZ2bxIevob0dH9MTATFMiHhCI0bD0EkenriIj8/n2vXrnHt2jWSkpI4efIkHh4eNCuqw+Lr68vdu3fV9TUALl++rGHdOnLkCM2bN+eFF16o0O0kFitp3VoIvqssD9/DA7p1g1WrNPPwT5yALVuEoL333hPOl54OH3wgKAbFefh37wor78WLS/LwfXyEgLwio2uF+PVXKJVkBAjXMWqUoARUkbRULZfAM6sAaFHaImLOvHnz8PDwYGRFJaDqcrXi4MC2bdvo3r07cXFxrF69ukbFhZ4njB49muTkZN555x21O2Dfvn3s2LFD3eBkyJAhKBQK3nrrLZo2bcr27dvVx0+YMIH58+czc+ZMbGxsUCgUBAYG8ueff/Luu+8WrUx+ZNCgQRgYGNCrVy8WL16s4U+/ffs2u3fvplGjRqxYsUL7pVSCuY6OHIiLY09sLLMcHNgeHc2Ox3HCPiYc5zoSdyCO2D2xOMxyIHp7NN47vJ/i7/ptHjz4mAcPPqVlyyMAxMTspGXLo0/1e9DV1aVz586V5vbr6uqWS6d94YUXeOGFF6rNoVSKNYTwkSPCHwh5+MU4dqzYmlSyrajeF6VrThXn4ZfeD0Iuftnji3mqC0ND+P57YdWvUJSs+qvp5ahc6dP+5J9N6Ojo8OKLLzJ48OBqmblqEyKRiF27dnHhwgVmzpzJw4cP2b9/P/37968zzuTkZKZNm8abb77J2LFj8fLy0ljVX716lWnTptGmTRvS09MZPXo0xsbGNGvW7JEVCit+OShZu3Yto0aNYsaMGXh7e/PWW2+RX+QfjoyM5IMPPsDe3p6oqCg+/PBDGjdujL29PcuXL9fIDggNDeXmzZv88MMPXL16lTt37nD27FnulkopjYqKYvjw4dy9e5euXbvSt29fdbGTbt26kZyczObNmxkwYADjx49XF+IqTsE9e/Ys//zzD3///Tfm5uYcPar5wm7evDmTJ09mxYoVT/15eZbQ1tiY7mZmbImK4lZWFlYyGZZVxK7UNozbGmPW3YyoLVFk3cpCZiVDavn0+GUyG6ytR/Hw4XFycyNIT/fH0NATqdTs/+13bmcnmMnnzxcE6/z50KmTsEpPT4dx42DXLpg0qfyxX34pVOkrWZQJVfomTgR/f8GU37o1pKUJ537tNfjqq0dZYjTPCUJAYuPGQivfJk2EMUZGkJlZEu3fqlV5V8Mj5Yj2J/9sorCwkCNHjtCnTx+cnJyeKnerVq1Yt24df/75JyAUvAkJCWHkyJH8+uuvdcI5fvx4cnNz1ZyLFy/mnXfeoU+fPlhbW/PgwQOOHj2KqakpCxcu5KWXXqJHjx4sX76c0aNHo6enx2uP6ONQGps2bWLVqlWkp6ejq6vL6dOneeWVV2jZsiUzZszgxo0bnD17lpiYGD7++GMcHBz4/PPP2bBhAx999BFZWVnqvgBXr15Vn/ejjz7C2dmZpUuXavD98MMPvPHGG5iamrJ69Wr2799PXl4eBgYG6n4Cp06dYtGiRYwbNw53d3d1Y5R///2XAQMGqN0YNjY2rFq16onq6Ds7OzNlyhSWL1/OyZMnCS1KKtbX12fMmDE4OjrWqJ8ECMGmy5cvZ9u2bZw8efKp8vfo0YPPP/8cFxcXLl26xIwZMwgPD69w7DxHR167cYMRN29ytHhJ9xThOM+RG6/d4OaIm7Q8Wg/8jnOJi9tPVNRn5OfH0bRp3VqMbt68ye+//05CQgIuLi7o6uqSkpKCt7c3ffr0KZcZcv36daRSaYWVZ0ufy9bWFktLSxQKBenp6djb29OzZ0/MzDSVmZgYCA+Hc+fgn3+KFDFjQbimpgpBdn/9BTduQGmPYLNmYGsLQ4YIaX0guA0KCmDvXiFYr3VrIcYgLU1wGwD4+VUu+IcNE+r2Dx8upA0+fFh8z8L2334TrA6tWoGDA/z9txAcGBAAW7cKpv4uXYTURF1d6NdPuDc3N+H/ly5pZhnUuwKQkZHL/v1/s3r1DyQkpDN8uC+bNo3Hyakxt25F8dZbO0lMTGfFihF0796M998/xJ49f2JjY8qOHW8ycKAQrBUXl8q7737Db78F8tFHo5k27UXWrDnGyZNXeeEFd3Xr4LCwBM6e/Y8lS4awdq0QeXzTz4/Px42jVb9+yPT0ACjIzeXcvn14dunC+2fP8suWLRz64AMUcjlTvviCXm+8gUxfn8KCAk5u2MDhFSsYMHcufd98k//OnuWH1atJT0jAd/hwxm/aRGMnJ6Ju3WLnW2+RnpjIiBUr6FKTvJFKUFFjjLrGnTt3NKLls7OzuXz5snp1XBdIS0ujS5cuJSu1os6E4eHhNGvWjOHDh/Ppp59y9+5d1qxZoy5b3KRJEwYPHszatWtrpACkpaXh4+OjTo8s5iuuYvjqq6/i7+9PQEAAr732mjqIrWfPnri6urJt2zZWrFih8bKJjo5m3bp1NGrUiOHDh2vULU9OTsbX15cxY8aQn5/PqlWrNNqZTpgwQZ1eaW9vz/Tp02nTpg1xcXHMmTOHNWvWqMdaWlri7+/Pxo0bGTp0KP/++y9RUVG8/vrr6nM8ChEREWzZsoXly5fzzTffcKL47YWQfmVsbFwjAWxmZoZEIqF79+589aglUC3zm5mZ8fbbbzNt2jQMDAz46quvOH78OK1btwZAoVKRUyrL41VLS1z19XHS08O7VHW5ApWK/KK3Z6pczvaYGN6/f582xsYcbdkSBz09voyOZkNEBF94eWGqo8OCe/e4nJ7ONi8vZtjb8yAvjxH//UdHExOWu7gAMlQKFYqcEn7LVy3Rd9VHz0kPQ+8SflWBCmW+wJ8fnc+dqXdI/i0Zj80eOL4rVGNNPp3MfyP+w3OLJyJdEUHTgjD0NqTl4Zbou+ojT5Fza5wQKu611YuKFozGxm0wM+tOTMwOzM17YWjoVencZmX9x61bY7G0HKCOEYiP/x4Q4ev7H7m5oVXuB6G8fEJCAgkJCUyZMgUdHR3Cw8P5+uuvSUtL4/XXX9fgvHTpErq6uhUqAD4+Pjx8+JAzZ84wc+ZM9W8sMzOTQ4cO8b///Y/Jkyfj7Oxc7thOnQRrQKdOJX79Yri5QVE/L41tU6YIefrFCsDp00L9gGbNhHiAP/4QtkskgjXA0lJYuVf0EyiuA1CReyApSYhHKEbpkKaieGWgJCNAsNSW/L+ytiOPrQCUrsWseIKqWI0a6fP22/3o27clHToIs+7kJNRJd3W1xtTUgOPHF6h7AOzePZOHDzO4eDGYbt1KGvPY2prh7m7DtGnz6dtX0JpNTAy4enUdurrSImGpoFOnZbRu7czKlSVRrlnJyaw6fx6bUiWX9rzzDnpGRszatw+Zvj6vvfceEh0d9i9ciKOPD7IiW4uOTIZbx44MXbaMUUXNX2w9PGjZty/vFZVealy0Qrd2dcXA1JQFx4/XW0+B2kBFrYBtbW2rDLR7Uly8eBGRSERqair79+9XWxpKX4tYLMbMzEyjZ8GgQYNwdHTk2rVrNSoas3btWj766CPkcjnHjx/n96JcnLJ8AJ6enuptNjY2DB8+nH379hEYGKiR8mdhYUFBQQEymaxc05I1a9ZoCPGycHNzw63U87llyxb1vJ8ralRVjPbt22ukQJXdXxMrU0U4efJktVOsipGamsq5c+eIj49/6vweHh5MnTpV3ab6rbfe4vfff8fS0pK7OTnsjokhID2dXbGxDLOywlRHh3cdHXEvUsCi8/M5GB9PVF4eOQoF38TGMsLamvecnZGIRHweFUXjou8zS6HglzZtaF6kOPzapg0t/P1RFF2voURCN1NTPil6m+fczSFmdwzpAenE7orFapgVOqY6OL7riIG7gVrYxx+MJy8qD0WOgthvYrEeYU2L71vg7+mP1KLERSCzleG23o0mU4RAz7wHecTti0PPWVjYSM2lGLgZ4LbeDYmBpNRvWrPMt4PDHP77byj29iXZLCqVEqUyD4Uip9QCJJk2bX5FV9euSHG+SETEBtq29UMiMXjkfrUgKrPKd3FxwdHRkRs3bjBs2DB1CnF0dDT6+vrcu3ePhw8fVthTo6JaEsbGxkycOJEtW7bw7bffsmjRonJpyf7+ggUgNbVkm1QqVO4bOVJovlMMKysh7U8iEVb8HTsKhYSSk4VMgpkzhUJAU6cKSoFCIQT2AXTvXvXz6uQE69cLqYXFyVaNGwvVBCtyQ1QEV1fhHDt3ClaDSt0Nj/tCjooqKbYTG/vk5Xk9PGzZtm0aP/wQwMmTgsl07ty9rFnzerkGQF99NQ2lUsWSJSUlksLDE8nOzlcLf4CBA9uqhT/AypWHuXUrigMHZqt7CwA4tmypIfxvnDnDr1u3MmnzZqybNi0537x5uHXsyO5Zs1AUrbwVhYX89c03DHv/fU2B6OHBtG3bCPjhB64WmTv3zp3L62vWPNPCvyJ4eXkRFxenNs/XBfLy8li0aBHz58+nT58+Naqn7+bmhlKprLG1ZNeuXQwbNgxzc3M2bNhQIz6gnEVEX1+f5s2bP5MtS4vRokULOnbsiFwup23btnz//fd8/fXXbNu2jZiYGKZOncq5c+d45513uHjxYrny0U/anvdx+C9fvqwW/sVWnPT0dNLT0/E0MGCDuzsZvXoxpUkTTIuEx2wHB/oX/U7tdXVZ4OSEqk8fknr0YFKpSoDzHB2xkErZEBHB9cxMDMRitfAHMNXRYYunJyvCwkiRy1kTHs4Hpd4pBp4GuG9wp1dGL5pMaYKOqcDvMNsBi/4Cv669Lk4LnOij6kOPpB40mSRUApSaSXFd48r9ZfdR5gnzGrc/DvsZJcs9p/lOqOQqYrbHAJB+OR2TTiZq4Z+Tc4/w8LVkZ9/m/v0PyM6+UyRwBmNpOQALi5fUK/3Q0EWoVArS0s4TE/M1BQUJGBu3VQt3pTKXO3fewMHhbczMuhcJ3qr3Pwo6OjoaSntgYCDjxo3D0tJSoxdHdSCVSunWrRuZmZkaZb7L4u+/BUuAoOAIQvjhQ80gvi5dBJP7iRNCqeC5c4Xt/foJ/372mZAFUFFsdunzV4QHDwSlISpKKDG8Zg28+y788ktNlHdwdNS0AtSKBeDmzZscP35co8PZxIkTmTRpEkOGDHmijoBjxnTl+PErzJy5k1u3oujSxZOWLcv7t+3szPn443HMmLGD0aO70K1bM1auPMLmzZPKCCa7UivIu2zYcJKPPx5H8+aahUbsvEpMXFkpKXz5xhu0HTiQ3lM0c15FYjEzd+9mcdu2nNiwgWHLl3Nq0yb6vf22ulmQhs9zzBiuHD/Ozpkzibp1C88uXXCqB59iXUIkEjF//nymTZtWZxxyuZxevXrh7e3N7qIk25AalFsWiUTY2NigV+TeqQ7mzZvHr7/+yvXr19HT0yMtLa1GfMWrmIpWo6VTl54FjB8/Hl9fX2QyGSNGjFAX7bl16xYikYhu3boxbNgw/v33X44dO8aKFSvo2bMnb7zxhrqeQkPi79q1K1u3bq0V95lEJOILLy/6Xr9OVF4eX1fQLnyYlRVfx8TQ+/p1Nrq7Y6JTe57XJlObEL0tmgebHtCoYyPMe5sj0il564v1xLhvcifozSCsR1uT8H0CHv8rsSUbGLjj4rIUF5elZZ5hMa1bl4S4Gxm1xN19E+7umyq9ltDQpahUSlxd15YS4CZV7q/8XKFERkbSpUsXtaUtOzsbQ0NDdHV16dq1K7/99hv9+/evssBYWRSb/iMjI9XPhq0tODsLJnp7e0FwJiYKvnNLSyFtb9IkwadvZQU3bwo++jNnhM582dlCcN+rrwqr9F9+gW+/FXz0n30mZAZYWAgpg4WFQlrgl18+ysJeftuPP1b/uXjwQKhN8EgFjlUe1AAAIABJREFUq6YPnI+Pj7olcF1g27ZptGgxj2PHLnPtWuWrrmnTXuTgwYtMnfoVCxcOon//1piZVdwNKjMzlwkTttK9ezPmzh1QJf+OGTNQFhYyo5Jyvw7NmzNkyRKOrVmDc6tWpMXH41VRiaXi69y2jXktWnD52DE2VFWR4RnFvHnz2LJlC8nJyXXGcfnyZS5fvqzOja9qRVnWPaFUKgkODmb48OHV5lOpVGzdupXXXnutnNJQ0Qq2LOft27dp0aKFhmug9Auorrtn1jb279+v9sGXLiBUUFBAQkICV65c4c6dO+pS0SkpKZw6dYqQkJAaKWpPg18qlfLqq6/yRkUVUx4TnU1M8DUxIaWwEHElS67lLi70vHat1jMKRGIRnp958u8r/2I73havL8v7662GWhH9RTT/9v0X90/doQ6qCaelXSAqams503519xfj/PnzZGdnk5GRwbBhwzQUuMDAQDp2FKq8tmvXjrNnzxIYGFgji1pxXE3p4kJxcRUXABIUn5L/v/RS6essraxoRt9XlA1tXMqIXaqDdbUxbJjQR0BfX2go5OkppACqVEJsQvHnwkKhqZHwHqsDBaCuoaMjplkze/766zbHj1+ptKyvSCRix44Z+PjM59ChS5w9+36l53z33W9ITs7kzz9XVNlF7Ny+ffgfOcKikycxKeVHLouhy5YR8MMPfDl5Mp+VjQwpA7GODvbNmnH7r7+4cvx4jcsEN2RMmjSJgIAAbpWqP2loaFiucteTojhw7X//+x9OTk6EhYXxQ1HUzYULF4iLi1NX3ouJiSEwMFAd4LVr1y7EYnGNct9FIhHW1tacOnWKvXv3oqury49F6vetW7f4/PPPmVTKGffzzz8ze/ZsQAiQPHXqlIagKg1ra+vHalzSUHD+/HmNGAuVSlXOH1/RtobCv3jxYpYuXVqrz+jFtDRetrDgw7AwziQn81IFLr6TDx8y3c6OWcHBXOjQoVZlsGk3Uwy9DDHxNal0jOM8R+7OvotZ99pP51Mocqo07T9qf2l069atQh++UqkkKChIw91sZGREQEBAjRSA3KKKOKUDbBsqmjQRSv9KpTBtmqAA5OYKsQbDhsELL4CNjVBgqPTnGsnbhnTDKpWK+fP3ceDAbGbN2sXMmTvo0cMbc/OKCyy7ulrj6GhZzqRfGidOXGXPnj/Zt28Wjo6WlY57+OABu2fPptfkybQfNKhqs59UilfXroQEBGBoalrl/eybP5/ZBw6wa9YsdsyciXePHhjVogCQSCRPpR1zWcycOZOmTZsSHx9P//79kclkDB06lBUrVtS6AuDm5sbatWvZuHEjc+bMYe7cuRw4cID27dtz9epV5s2bpyFgv/zyS/T19UlOTkYul3PhwgV1adzqYseOHcyYMYPFixczatQoduzYQXh4OBEREfj4+GBcSqUPCgpi5syZFBQUEBcXx+nTpyttT2pqavpM5+Hn5eURGRmJj4+Pul3vs8I/efJkfvvtN3VKYW0gU6HgxMOHbHR3p1Cl4p27d7nZqRPSUguNr2NiGGNjg6u+Ph6XLrE/Lo4JtdxbQKwrrjKi61H7nwT37y9FpVKVM+0XFMQjk9lUub+6CAoKol+/fhp9IuLi4tiyZQtRUVHl+kdUhgcPHgAVu+caGmJjoSiTmNJVqHNyhOY+GRnCn6Oj5udnVgH46KNjjB3bDTs7c7Ztm4a391xmz97Nt9++81jnS0hIZ9q0rxg+3Jfx4zU1T3//EHXfAJVSyRcTJ2JsYcGk4hkvNl3Fx1OQm4vVYzwwxz76iG5jx2JuZ8e0bduY6+3N7tmzeaeCDnOPg5EjR9KnTx/MzMyYOHEiBw4ceKKMjOpixowZfFnkxFqwYEHJSujiRfUPrLZRUV+BhISECk18ZWvFPw769+9PREREmWem4lbUS5cuxb6yPJtypkBjDA0NnwlhX6xYlrWaubi40K9fP7UAriizorJsi8q6AdY1//Tp0ykoKODhw4c4Oztja2sr9Bd4wud1xf37LCnyK89zdGRnTAwbIyJYWvS+uJmVRVphIW2LFMZ1bm4sunePVy0tMatFd4BKqQLl4+9/XKSlnS8y7f+hYdpPSfFDJrMiJ+delfuri5CQEIaUsZ7a2tpiZWVFQEBAtRSAwsJCzp8/j4WFBS2fsVis4rpetW24aDAKwKFDQlGT3r1bAGBjY8onn0xg8uRtDBzYjtGju1TypSooLFRUovF/iVSqw1dfTStjklLy3XcX1ArAj5s2EXT+PB+eO4e+sWbGwalPPmFEBeZjRWEhykrSlAAuHToEQIui5iOmNjZM+OQTtk2eTLuBA2ulBsDhw4c5fPjwU/+uvvrqq2rlcmtRHvr6+o/dHvlpwtnZmRkzZgBCh77iGgxGRkaMGDGCvn370qJFC3r06IG1tTW9e/fmjz/+oH///ri6ujJu3DguXbpEUFAQIJRuHTp0KE2aNGHo0KHcuXNHoxJiXfK/9dZbfPHFF+U4OnfuLNRYfQxE5uXxXmgod7OzmVeU5vtQLsdKJmNlWBjmUilGEgnz791jZamof4CEggJG3LzJF15ewJO/0VP+SCEnOIek00mY9TRDz1EzbqUgoYDEY4nkx+aT9FMSlgMta+UZUank3LkzGT09B1JSzpCScqbonZxOQsIRunS5z+XLrSvd37VrJPCLWjiDEPBb1gUQHR1daaCfp6cn/v7+9O/fX22Vqyh9NDc3l8OHD5OTk8OUKVOqnQ5cX6hIRx44EP79t1g5LqssV+8c5cao6spZV01ERHzJunXH2bnTj4ULB/HBB8MxMNAlL0/Ohg0nWLnyCPr6MlavHsWbb/bFyEivSIPM4vjxK8yYsQMXFys2bRrPoEElkRy7d//JlCnbaNvWhTZtSlbvcrmCK1dC6dTJg927Z7I52IeFrVphbGFBm1INYVQqFfGhoSRHRbG1TBewy8eOsW/+fFJjY5m+fTsdhwzBwETwvz2MiOD4unX47dzJoIULGf7BB+gaGCDPy+PEhg0cWbkSmb4+o1avpu+bbzKhjMLxvKE2H78OHTqQkJBAZAU93esCCxcuZNOmTdy/f5+mZV7yleHMmTPY29trdLer3xeN6Pl+/sr2gH3K6Hu2niegni+gT58N/Pfff5w9e5aHDx/SuXNnOnbsqI77CQ8P59ixY4jFYl599VWNWhixsbEcO3aM6OhonJyceOWVV8jIyODMmTM8fPgQV1dXzMzM1I2ynJ2d6dq1q4YF7r2yFX8aAJychA6AvXrBp58KGQEWFkKzoldeEfL7X38dBgyA27c1PxcrCC1bCgWFfvpJSCMsXdugQSkAcKSe2UfUK//I5/0FXAuPX3R0NDt27GD9+vXI5XKWLVvGpEmTcHV1rZNrlsvlfPHFF3z66adERUUxcuRIZs+eTdcqskGK8ccff+Dk5FRn16ZVALQKwLOmANQnGqIC8FR//1oFQKsA/H+xAGihVQC0CoBWAdAqADVRAPr0UWl/APWHs/TVvoDqEb///pxLQC200OK5hbYdsBZaaKGFFlpoFQAttNBCCy200OJ5gDrfIkeh4MOwMM6lpSFXKrmTnU1eUdnTzF69MJJI+DY+nh0xMZxLTUVfLMbL0JBcpRJdsZihjRuz0NkZfbGY4OxsjiUmsio8nHylEic9PaxkMhIKCmhjbMx7zs74mmhWrVLkKAj7MIy0c2ko5Uqy72SrG1z0yuyFxEhC/LfxxOyIIfVcKmJ9MYZehihzlYh1xTQe2hjnhc6I9cVkB2eTeCyR8FXhKPOV6DnpIbOSUZBQgHEbY5zfcy5XNUuhyCEs7EPS0s6hVMrJzr6DUpkn8PfKRCIxIj7+W2JidpCaeg6xWB9DQy+UylzEYl0aNx6Ks/NCxGJ9srODSUw8Rnj4KpTKfPT0nJDJrIqaZ7TB2fk9TEx8Nfi181+/86+FFlpo8bxBHQPQ5/p1rGQy9nh7oysWkyyX82ZQEEcTE9UCCOBCWhrd/vmHtx0c2OrpiQrYHBnJvJAQepmZ4deunbrMZZ/r1/FLSeFhjx5YSqVE5+fz0vXrhObk8HObNvQ1N1e7gK/3uY7MSob3Hm/EumLkyXKC3gwi8WiiWgABpF1I459u/+DwtgOeWz1BBZGbIwmZF4JZLzPa+bVT17q+3uc6KX4p9HjYA6mllPzofK6/dJ2c0Bza/NwG877mah/09et9kMms8Pbeg1isi1yeTFDQmyQmHlULIBBqWv/zTzccHN7G03MroCIycjMhIfMwM+tFu3Z+FF/A9et9SEnxo0ePh0illuTnR3P9+kvk5ITSps3PmJv3VccAaOe/fuZfGwOghRZaPK8QA1zNyMAvJYVlLi7oFlUUsJBK+a5FCzzKlB4yLVOkQQTMdXSklbExf6am8ktSUqVj7XV1WevmhlylYmmpcpwZVzNI8UvBZZmLULISkFpIafFdCww8NPmL22WWvgDHuY4YtzIm9c9Ukn5JqnSsrr0ubmvdUMlVhC4txZ9xlZQUP1xcliEW6wr8UgtatPgOAwMPTX6dsqV/RTg6zsXYuBWpqX+SlPRLpWN1de1xc1uLSiUnNLSk+5Z2/ut3/rXQQgstnlsFILGom9n5MtUCZGIx46pZs7pFUXGF8KJmCzUZV5Ao8Kee1+QXy8TYjqsev2EL4by54bk1HldQkCjwp57X5BfLsLUdVz1+Q6GCYW5ueI3Haee/fudfCy200OK5VQB8TUwwkEiYffcua8PDKSyVmz3Cykq9Kq0K93JyAGhuZFT1uCLBU3qcia8JEgMJd2ffJXxtOKrCEn6rEVbqVWlVyLkn8Bs1r5o/915uuXEmJr5IJAbcvTub8PC1qFQlpSStrEaoV6VV8ucIXQGNjJpXzZ9bfpx2/ut3/rXQQgstnlsFwEIqZY+3NyJg2f37tAwI4KciU7KXoaFGZ6uK8FV0NFcyMhhgaUkvs8rbTcYXFLAkNBSpSMS6UiUdpRZSvPd4gwjuL7tPQMsAkn4S+A29DBFJq+aP/iqajCsZWA6wxKxX5fwF8QWELglFJBXhtq4Uv9QCb+89gIj795cRENCSpKSfilaMXohEVTftiI7+ioyMK1haDsDMrFfl/AXxhIYuQSSS4ua2Tr1dO//1O/9aaKGFFs8j1E7akdbWeBgYMC0oiH8yMng1MJC+5uZsb9YMlwqal/inpbHw3j0i8/IoVKn40suL6XZ2FZKsCgtDCYTl5NDJxIRDPj54lvFtW4+0xsDDgKBpQWT8k0Hgq4GY9zWn2fZm6LuU50/zT+PewnvkReahKlTh9aUXdtMr5g9bFQZKyAnLwaSTCT6HfDDwLMNvPRIDAw+CgqaRkfEPgYGvYm7el2bNtqOvX74TYFqaP/fuLSQvLxKVqhAvry+xs5teMX/YKkBJTk4YJiad8PE5hIGBp8YY7fzX7/xroYUWWjy3CgBAa2NjLnfowO7YWJbfv8/ZlBQ6XrmCf4cOuJURGJ1MTdno7l4tkg+aNsWyGq0vjVsb0+FyB2J3x3J/+X1SzqZwpeMVOvh3wMCtTDBcJ1PcN1aPv+kHTZFaVoPfuDUdOlwmNnY39+8vJyXlLFeudKRDB38MDNw0+U074e6+sXr8TT9AKn10By7t/Nfv/GuhhRZaPE8Qg2AaTpbLhQ0iEVPt7Aju3JnBjRuTJJez/P79Or2IgvgC5MkCv0gswm6qHZ2DO9N4cGPkSXLuL69j/oJ45PJkgV8kxs5uKp07B9O48WDk8iTu319ep/za+a/f+ddCCy20eG4VgIjcXPxSUjRXWDo6HPTxwVRHh8DMzDq9iNyIXFL8NPl1THXwOeiDjqkOmYF1zJ8bQUqKnya/jik+PgfR0TElMzOwTvm181+/86+FFlpo8dwqAAB74+LK7dQTi3HQ06NpBT7omnRxq87YuL3l+cV6YvQc9NBv+hT44/aW5xfroafngL5+0zrn185//c6/FlpoocVzqwD8kpTEnJAQshQK9c7v4uO5l5PDylK9y9MLhRSt1MLCR568JmOTfkkiZE4IiqwS/vjv4sm5l4PryhL+wnThXIWpjz5nTcYmJf1CSMgcFIqsEv7478jJuYer68qScxamF/2b+mj+GozVzn/9zr8WWmihxfMGkapPH1VAejqdrl4FwFAiwdvQkAKVisZSKWtcXXmhqG780tBQdsXGqgvXSEQi5jo6ssrVVV2DfktUFF9FR2Oio0OWQoFCpWKgpSVvNGnCUCurchfQ9yykB6RztZPALzGUYOhtiKpAhbSxFNc1rpi8IPAHvRlE3N44lPlCjXqTF0xouqopFi9ZAEI9+4h1EYR/FI5IIgIVqBQqLAda0uSNJlgNLc9P37Okpwdw9WongV9iiKGhNypVAVJpY1xd12Bi8kKRQPqWmJivSU39GwBdXTtkssbqPHWFIpesrP8QiaTY288gJmY7SmUBlpYDadLkDayshpajP0tfKpt/cx0dPA0N+S05WV24x1RHh7TCQowlEla6uqJSqfg8KooHeXmMsbFhvpMTBmIxR4t6ARQolUhFImbY2/OZp2eN5l9iJEHSSELyacE/L2ssQ8dUh5x7OUiMJbiudEVmKyPyk0gyrmVg0smEpu83Rc9Fj8SjRb0ACpSIpCLsZ9jj+ZnnY82/WCzl8uV2SKUWqFQFFBZmIhJJ0NW1o7Awg8LCNGxsRtOixXcARb0AjhIW9gEqlRJLy1do0mRKhfOvLQWshRZaPNcKQE0P2h8Xx4TbtzGTSont1g29UoVqzqelMSs4mHPt25crRVsRatoOPjc8l4CWASiyFHS+17lcdDoq+Nv6b1oebYlpN1Nq/QKAGzcG4+HxKfr6rhrb796dTVTUVlxdV+PiUr3AteJeAFUho7CQnteu8W9mJuvd3Fjs7Kyx/8Xr1xlrY8PkJk3KHXvi4UOG3LjBAienCrMGqnP7wW8HE/1lNDZjbWhxoEW5/ZGbI0k5m0Krk60Q6WjK04cnHnJjyA2cFjhVnDVQjQtITj5DcvKveHh8qrE9L+8B/v4tkEgM6dTpNlKphcb+y5dbk5V1ix49UtDRaVThubUKgBZaaPG84rHaAY+3tWWGvT2pcjnrIyLU23MUCt67d48TrVpVS/g/DvRd9HFbK6SEha8uX8414VACthNtqyf8HxNmZj3KCf/U1HNERX2BsXFbnJ3fq1W+Rjo6HGnZEkOJhI0PHpBUlDEA8E1sLC0MDSsU/gB9zM0xkkgYZW392PzuG9zRc9Qj4XACOSE5mvqWQkXCoQSa7WhWTvgDmPcxR2IkwXrU4/PL5ck4Oy+irKZ3585kFIosvLy+LCf8ARo3HoKFRf9Khb8WWmihhVYBeAxsdHfHUU+PdRERBGVnA/BWcDDznZwqLFxTm7B/255G7RsRtz+OtPNp6u2KbAWRWyJpuqJpnfLb2IzV+KxQZHPnzmREIh2aN9+DSFT7yo+rvj5r3dxIlsuZFxICwO3sbPbExZVb2StVKgbduMHrN29yNzubt+ztkYpEzA8JocOVK9zKyqoRt8RIgufnnqjkKoLfCtbYF7U1CuuR1ug2KSnXq1KquDHoBjdfv0n23Wzs37JHJBURMj+EKx2ukHWrZvzm5r2RyWw0tkVHf0VKyh9YW4/SMO0nJf3ElSsdiYraiqXly1hYvERCwkGuX3+RkJC52l+8FlpoocWTKgBGEgnbmzWjQKlkelAQO2NiMNLRqdDPX9sQiUU0294MkVhE0MwgVHLBixH2Ydj/tXfn0VHV9//4nzczk5kMSSYJJEP2PcQQEYQPi8ivHql6WrFgIaAU27LosQX9Bi3iVywBMRCKCFgsigsVi9Til+rx49HWICiLmiAQlsEMZJJM9oVsZLbMzL2/PzKELJMhGwwtz8c5niOZe+c19/1+vd/33vdd3ojJjOmYuvZ68fXtejZ78eJKWCwGJCT8Ef7+Y65b3GVRUbhLo8H7VVX4uK4Oi3U6vJuWBt9ucwVIAIrMZvxw+TL21tSgzm7Hlw0N+La5GT+aTGjoNILQV6G/CEXorFA0HGhA9QfVANrfH1C7rxbRT0V3PzmHuciMyz9cRs3eGtjr7Gj4sgHN3zbD9KMJ9gb7oMrbYinBhQvPwdc3DKmp23uMFpjNejQ0/Bv19V/AYilGQ8MBtLaehclUyBZPRHRlXzqQewA6+825c9hdVYV0f38cnzixTxPXdDaAS/Ad9Mv1MG41ImlDEkJnhkL/jB7jPh+HG/YDADQ2HsIPP9yLgICxmDgxr99n/325B6CzH00mjP3+e9glCX9NS8NjfZgtcPqJE/jkjjvgL5MNavNt5TYcSzsGmVqGuwrvQuGyQkQ8HoHg/y/Y43onpp/AHZ/c4f7ArN/lL+HEiZ+ioeErjBnzEcLCZve6ZH39Z2hp+QEJCat7XYb3ABARRwAGKCcpCTJBQJnV2vE2uxslcV0iVNEqFK8rxvnHzyPl1ZQbGr/z0H9a2vUZ+u8uddgwLI6IgChJON2Hofx9NTX4qqEBWUPwNkFllBKJ6xLRVtOGgpkFgIBr7vxr9tWg4asGFGUNzdsEy8t3uIb+53rc+QPAhQvPoaRkQ8d0w0RENIQHANklJZin1aLZ4cCywhs7xCrzlyH5T8lwmp1Qj1Jj2G3Dbmj8Cxeeg8VSjPj4VQgIuOOGxCyyWHDOZMJtw4Zhi9GIE9d4S6DMdYIb0oe5APoielk0hqUNQ+PXjUjMTrzm8oKsPb4iZPDxLZZiXLiwEr6+oUhNff3asQUZAAEKRQhbOhHRUB4A/KOmBk5Jwp70dEwPCcE/a2vxz9obe7bll9R+w6EiWHFD4zY2HkR5+Q4EBIxFfPwLNySmTRSxSKfDW7fdhjdvuw2iJOFxnQ5OD2+6S/f3BwCMCwgYkt8gyISO2QH7Uub+6e3xA8YNNr4EnW4xnM5WjBr1ep8m9/H3T4e/f/oNGZkhIrplDgD0ZjP+XFaGLSntw+47UlOh8vHBssLCjjfQ/bdyOluh0y12Df3/1e189SaTbsjj/p/CQjwZGYlktRrTgoKwMCICJy5fxhajsdd1kvz8oPLxQWK32QRv5AGaj8oH6sTBxS8r+wsaGw9Cq82AVpvR43OrtRROZ9dHFIcNS+/xuCYREQ3iAMDsdGLhuXN4Jy2t4yVAyWo1VsXHo9Jmw4oLF/6rC+3q0P8Lbof+7fYGNDTkDmnMPdXVEAE8OvLq43CbkpMR5uuL1UVFuGg2u69gQUCCnx+ilEqvlJXgI8AvwQ/KqIHHt1iKcfFi+9D/qFHuh/4rK9+Dj4+qy9/U6mSoVFFs5UREQ3EAYBFFPHr2LGaEhiKl21nlyrg4hPr64q2KCuy/QZcCOt4333xjRh0aGr5CefkbCAi4A/Hxq3p8LooWFBY+7XYCm4HKbWjAygsXOkZbrghRKPB/4+JgEUX86uxZ2ETR7fpRKhWGyWReK3NVlAqyYQONf+WFPyaMGrUdvr6hPZZobv4WjY0HIAhd09nXN5TX/4mIetGvi6NvVVQgp6QEBosFrU4n7gsJwYTA9resOSQJW43GjuH/X509i6eio7E8Jgbh1+nss+r9KlS8WQEAqP1nLYalD4N2jhbKyOt1tivh/PnFACTY7U04fnxat52/HRZLERyOZiQmrht0tItmM7JLSvBeZSVkgoC/lJfjD7GxuPLcWl5LCz6tr+/4/5/88AOei43t8S6GoTr7N503ofajWjR/3z7Jjn65HuG/DseIGZ6vxw/m7L+y8q9obDwEQZDBaHwVRuOrPUZbzOaLCA9/rGdyyzWQyQLYyomI3Bj0ewAGa5CP4f/H/4D+vgdgIFYVFSE7MdFrm1+0qqj3Jwau4w8wmy+iqekwIiIW9j66wvcAENEtyodF8N8vRO7du+DlId6JL5OpIJOpmQBERDwAuDUFevsAINA78QXBt8eNgURExAOAW4a/TObV+Nd7bobeDwDkPAAgIurt5IxF8N+v86OD3X153434BSOBd735A17p/aP7JC/fA3OfV3PD67fgeL11fHlLF8AtfguW18vf2/cgcQSAiIjoFsQDACIiIh4AEBEREQ8AiIiI6L9Sj5sAW51ObDUacaypCSOVSsgEAYEyGcYHBqLF4cBcrRb7a2uxXK+HBGBaUBAsoohWpxNLo6KwMCICerMZu6uqkF1cjAilEhMCA1FutWK4QoGshARMDQrq9Qc5W50wbjWi6VgTlCOVEGQCZIEyBI4PhKPFAe1cLWr310K/XA9IQNC0IIgWEc5WJ6KWRiFiYQTMejOqdlehOLsYygglAicEwlpuhWK4AglZCQia6iG+sxVG41Y0NR2DUjkSgiCDTBaIwMDxcDhaoNXORW3tfuj1ywFICAqaBlG0wOlsRVTUUkRELITZrEdV1W4UF2dDqYxAYOAEWK3lUCiGIyEhC0FBU3uN7+3y93r8Vie2bjXi2LEmjByphEwmIDBQhvHjA9HS4sDcuVrs31+L5cv1kCRg2rQgWCwiWludWLo0CgsXRkCvN2P37ipkZxcjIkKJCRMCUV5uxfDhCmRlJWDqVE/b34qtxq041nQMI5UjIRNkCJQFYnzgeLQ4WjBXOxf7a/djuX45JEiYFjQNFtGCVmcrlkYtxcKIhdCb9dhdtRvZxdmIUEZgQuAElFvLMVwxHFkJWZjqof69nf/eLn+vt//WVhi3bkXTsWNQjhwJQSaDLDAQgePHw9HSAu3cuajdvx/65csBSULQtGkQLRY4W1sRtXQpIhYuhFmvR9Xu3SjOzoYyIgKBEybAWl4OxfDhSMjKQtDUqTdx/+Pt/L+1y9+rBwBlVivuP3kSD40YgU/Hju2YS76mrQ0zTp3CzNBQhCgUWBIZiT3V1bCIIj4fNw4AsL64GIt0OjgkCY9HRmJdYiI2lJTgsfBw5CQlwS5JmFVQgOknTuD4xIkd09R2Zi2z4uT9JzHioREY++nYjrnk22racGrGKYTODIUiRIHIJZGo3lMN0SJi3Oft8YvXF0O3SAfJISFYnou9AAAgAElEQVTy8UgkrktEyYYShD8WjqScJEh2CQWzCnBi+glMPD6xY5raLvGtZTh58n6MGPEQxo791DWfPNDWVoNTp2YgNHQmFIoQREYuQXX1HoiiBePGfd4ev3g9dLpFkCQHIiMfR2LiOpSUbEB4+GNISsqBJNlRUDALJ05Mx8SJx+Hvn94jvrfL3+vxy6y4//6TeOihEfj007GQueq/pqYNM2acwsyZoQgJUWDJkkjs2VMNi0XE5676X7++GIsW6eBwSHj88UisW5eIDRtK8Nhj4cjJSYLdLmHWrAJMn34Cx49PRHq6u+0vw/0n78dDIx7Cp2M/hcxV/zVtNZhxagZmhs5EiCIESyKXYE/1HlhECz531f/64vVYpFsEh+TA45GPY13iOmwo2YDHwh9DTlIO7JIdswpmYfqJ6Tg+8TjS3dS/t/Pf2+Xv9fZfVoaT99+PEQ89hLGffgrB9fhsW00NTs2YgdCZM6EICUHkkiWo3rMHosWCcZ+72v/69dAtWgTJ4UDk448jcd06lGzYgPDHHkNSTg4kux0Fs2bhxPTpmHj8OPzT02/C/sfb+X9rl79XLwFIAOafPYsguRwbk5M7On8A0Pr6Yv+YMWh1Ojv+pvTpevXgmdhYyAQB71ZWAgAEAIpO36EQBGTGxMAmithTXd3zl0jA2flnIQ+SI3ljckfjBwBfrS/G7B8DZ+vV+D7KrvFjn4mFIBNQ+W57fAiAoLj6HYJCQExmDESbiOo9buJDwtmz8yGXByE5eWNH5QOAr68WY8bsh9PZejW+T9f328fGPgNBkKGy8srzbkKXaYIFQYGYmEyIog3V1Xvcbb5Xy9/r8SVg/vyzCAqSY+PG5I6dDwBotb7Yv38MWjvVv7Jb/T/zTCxkMgHvuupfEABFp/pXKARkZsbAZhOxZ4+77Zcw/+x8BMmDsDF5Y0fn1779Wuwfsx+tnepf2a3+n4l9BjJBhndd9S9AgKJT/SsEBTJjMmETbdjjpv69nf/eLn+vt39Jwtn58yEPCkLyxo0dO5/2+FqM2b8fztZO7b/b/BqxzzwDQSZD5bvvXmnwEBSd2r9CgZjMTIg2G6r37LkJ+x9v5/+tXf5ePwD4prERR5qasDAiAu4eTIxWqTCn2yQznV1ZJ8DDS2c8LdP4TSOajjQhYmEE3P0AVbQKYXN6j39lHVmAbEDLNDZ+g6amI673xvf8ASpVNMLC5uBaX+558pnel/F2+Xs9/jeNOHKkCQsXRsDdk7HR0SrM8VD/V9YJ8FD/npb5pvEbHGk6goURCyG4KYFoVTTmeKj/K+sEeKh/T8t4O/+9Xf5eb//ffIOmI0cQsXAh3BWAKjoaYXM8tH/XOrKAgAEt4/3+x9v5f2uXv9cPAD6/dAkAMN5DAV6Z+c+d9SUlcEoSlkZHu/3cKorYVFoKjVyOBeHhPT6/9Hl7/IDxvccPnNB7/JL1JZCcEqKXuo8vWkWUbiqFXCNH+AI38S997uqcxvceP3BC7/FL1kOSnIiOXuo+vmhFaekmyOUahIcv6PG5t8vf6/Fd9T/eQ/1P8FD/69eXwOmUsLSX+rdaRWzaVAqNRo4FC9xt/+eu7R/vYfsneNj+9XBKTiztpf6tohWbSjdBI9dggZv693b+e7v8vd7+XUPJAeM9tP8JHtr/+vWQnE5EL+2l/VutKN20CXKNBuELFtyE/Y+38//WLn9v6bgHoNxqBdA+x3xfVdlsHdMD20QRB8ePxz3BwV2WyWtuRnZxMc6bTEhRq7EjNRUxqp6vZ7WWt8dXhPQ9vq3KhpKcElgMFog2EeMPjkfwPV3jN+c1ozi7GKbzJqhT1EjdkQpVjJv41nLXUGXf54+32apQUpIDi8UAUbRh/PiDCA6+p2v85jwUF2fDZDoPtToFqak7oFLF9Pgub5e/1+O76j+kH/VfVWVDTk4JDAYLbDYRBw+Oxz3d6j8vrxnZ2cU4f96ElBQ1duxIRUyMu+0vd21/SD+2vwo5JTkwWAywiTYcHH8Q93Sr/7zmPGQXZ+O86TxS1CnYkboDMW7q39v57+3y93r7L3e1/5B+tP+qKpTk5MBiMEC02TD+4EEE39Ot/efloTg7G6bz56FOSUHqjh1QxcTchP2Pt/P/1i5/rx8AqF3Dspc7Xee9lnClEs/HxXlcZqJGg1Xx8df8Lpm6Pb7zct/jK8OViHvec3zNRA3iV/UhvmvWOKfzct/jK8MRF/e85/iaiYiPX3XN7/J2+Xs9vqv+L/ej/sPDlXj+GvU/caIGq1b1ZfvVru2/3I/tD8fz16j/iZqJWNWH+vd2/nu7/L3e/tWu9n+5H+0/PBxxz1+j/U+ciPhVq/4D+h9v5/+tXf7e0nEJ4C6NBgBwoqXFKz9Ec1d7/JYTXoqvuas9fssJr8T3dvl7Pb6r/k+c8Nb23+Xa/hO3ZP57u/y93v7vcrX/E16qf6/3P97O/1u7/L1+ADBXq0WUUont5eVwupkfRZQk7HV39/4Q0c7VQhmlRPn2ckjOnvElUUL13usYXzsXSmUUysu3Q5J6noVIkojq6r3XLb63y9/r8edqERWlxPbt5XC6qX9RlLB37/Xc/rmIUkZhe/l2ON3UvyiJ2Hsd69/b+e/t8vd6+587F8qoKJRv3w7JzSiYJIqo3rv3v7j/8Xb+39rl7/UDALVMhn1jxqDQZMK8M2dQ09bWsVCTw4E1BgOmd7o+YxNFWDwMF0sA7JLkcZmuQ0AyjNk3BqZCE87MO4O2mqvxHU0OGNYYEDL9anzRJsJp8fDdEiDZJc/LdBsCGjNmH0ymQpw5Mw9tbTVX4zuaYDCsQUjI9E4dog1OpwWefoAk2a+xzFXeLn+vx1fLsG/fGBQWmjBv3hnUdKr/piYH1qwxYHqn+rfZRFg81K0kAXa75HGZrtuvxr4x+1BoKsS8M/NQ06n+mxxNWGNYg+md6t8m2mDxULcSJNglu8dlbqb893b5e739q9UYs28fTIWFODNvHtpqOrX/piYY1qxByPRO7d9mg9PioW4lCZLd7nmZm6r/8Xb+39rl7y1dXgQ0WaNBweTJeMlgwKS8PIT5+iJWpUKSWo0VsbEIUSjQYLdjX20t8ltaYBNFbC8rw+ywMIR3ei5TbzZjV2UlREnCx3V1mKTRIEOr7fJcuNthmMkaTC6YDMNLBuRNyoNvmC9UsSqok9SIXRELRYgC9gY7avfVoiW/BaJNRNn2MoTNDoMy/Gp8s96Myl2VkEQJdR/XQTNJA22Gtstzwe6HgSZj8uQCGAwvIS9vEnx9w6BSxUKtTkJs7AooFCGw2xtQW7sPLS35EEUbysq2IyxsNpTKq3cWm816VFbugiSJqKv7GBrNJGi1GV2eC3XH2+Xv9fiTNSgomIyXXjJg0qQ8hIX5IjZWhaQkNVasiEVIiAINDXbs21eL/PwW2Gwitm8vw+zZYQjvVP96vRm7dlVCFCV8/HEdJk3SICND2+W5dPfbPxkFkwvwkuElTMqbhDDfMMSqYpGkTsKK2BUIUYSgwd6AfbX7kN+SD5tow/ay7ZgdNhvhnepfb9ZjV+UuiJKIj+s+xiTNJGRoM7o8F30z5r+3y9/r7X/yZEwuKIDhpZeQN2kSfMPCoIqNhTopCbErVkAREgJ7QwNq9+1DS34+RJsNZdu3I2z2bCg7Pdli1utRuWsXJFFE3ccfQzNpErQZGV2eS785+x9v5/+tXf7eIEg//amX50P3cgl4+Qd8eRPMiO7lAril6/++L++7tYv/Vk/A+9j8buXyz829xlnRjboEQERERLcOtwcAFTYb/lRaCvmBAwg+dAjvVFai2eHosdyhxkY8cuYMhNxcCLm5yNTrUW+3AwC+b27G1Px8BB86hA0lJTD34/EyW4UNpX8qxQH5ARwKPoTKdyrhaO4Zv/qDahyOPIxcIRf5U/PRdLip4zNLiQUFDxfggOwACp8uhK3S1q+CsdkqUFr6Jxw4IMehQ8GorHwHDkez22VPn87A0aNJKCh4GKdPz8Hp03Nw6tSDyM0V8O23oyGK/YutN5ux8sIFKL/6CkJuLh44eRLnTCYAQInFgiU6HeQHDmBZYSEMbq5x9bX+hjLmoNfXm7Fy5QUolV9BEHLxwAMnce6ca/0SC5Ys0UEuP4BlywphMPRc/7vvmjF5cj4EIRcjR36DDz64esOY2ezEqlVFEIRc/PznJ3H8uPs7zQ81HsIjZx6BkCtAyBWQqc9Evb3elc/fY2r+VAQfCsaGkg0wO81uvyPjdAaSjibh4YKHMef0HMw5PQcPnnoQQq6A0d+Ohq2PuTDQ3BZtIip3VXasa3jJAEtR365DfvBBNSIjD0MQcjF1aj4Od4pZUmLBww8XQCY7gKefLkRlp5jNzQ688kopIiLa142PP4ovv2zoKPtXXzVCEHLxs5+d7PKdQ7XNVqMV5359DrlCLnKFXJS+Utqlv6j+oBoHhx3EsdRjqN1f22t80WaDYe1afKVUIlcQoFu0COaLF69u57ff4tu0NBzSaFC6eTPETvfJAIDp3Dl8pVTixH334fScOR3/HY2PR64goOr99/vVD5SVvY6vvx6OU6ce6uhXTp+eg4MH/fHVV2qYzfqe2yDaUFm5C4cPRyI3V4DB8BIslqI+xxxI/urNeqy8sBLKr5QQcgU8cPIBnDOdc7X9EizRLYH8gBzLCpfBYDF4jH86IwNHk5JQ8PDDHeV36sEHkSsI+Hb0aIi2nvGbv/sO+ZMnI1cQ8M3Ikaj+4IOOz5xmM4pWrUKuIODkz3+OluPHr1kGA+3PB1Jf3iZ398dIpRLPxcbi7YoKjFKrsTgiwu3K9wQH457gYEQoldhiNGJCQABGuK6zTNJoMMLXF4dSU3FHQP9efaiMVCL2uVhUvF0B9Sg1Iha7jz9y/kiok9XIn5IPvzg/BE27OsuXX5wfQu4NgWayBnEr4/pdMEplJGJjn0NFxdtQq0chImJxr8uqVNFIT38fPj6qTju0TAiCHKNHv9fjvdHXkqJWY2NyMqYEBeGXBQWIVioxetgwAECcnx9iVCq8kZqKJZGRGEz9DWXMQa+fosbGjcmYMiUIv/xlAaKjlRg92rV+nB9iYlR4441ULFnifv3JkzX497/HIT39O/j4tN/VfoVaLcMjj2hx8mQLPvtsHHobdLsn+B7cE3wPIpQR2GLcggkBEzBCMcKVz5MwwncEDqUewh0Bd/RajtGqaLyf/j5UnXIhU58JuSDHe6Pf6/EO9d4MNLd9lD6IWBiB5m+bUb23GgmrE/qcd/Pnj0RyshpTpuQjLs4P0zrFjIvzw733hmDyZA1Wdoup0cjxhz/EYvbsMEyYkAelUsC99wZ3lP2ECQFYsCAc778/+rpssypGhdG7R8PR4kDdJ3UI/UUo5JqrXZs2Q4vSzaW488s7Pb5oyEepREJWFuQaDfTLl0MzZQrUSUlXt3PKFAwbPRppb7/d8dhaZ2319Ri9eze08+Z1Ojgx4rvbb0fozJkIf+yxfvUDomjCxIk/wM/v6vbW1X2C2tr/h5SULVCrU3pug48SEREL0dz8Laqr9yIhYXW/Yg4kf1PUKdiYvBFTgqbglwW/RLQyGqOHjXa1/TjEqGLwRuobWBK55JrxVdHRSH//ffh0elmYPjMTglyO0e+912MOAKD93oFx//43vktPB3x8oJ07t+MzmVoN7SOPoOXkSYz77DOgDyPuA+3PB1JfN+UIwBW+gtBj0hd3NiYnY0JgIFZevNhxprmptBRztdp+7/w7E3yFHpN+dBf4P4GIWR6Dmr/XoCXv6pmds9WJhi8bEPuH2EEVkCD4XnMHPmLEjC7J0tj4NYzG1xAXt9Lj6yOvZVZoKJ6OicGuqip819w++nCsuRnVbW297kgHUn9DGXPQ688KxdNPx2DXrip8951r/WPNqK5u63Xn35ELgXLs2JGK0lIrtm0zdvksJ6cEb755W1/aPzYmb8SEwAlYeXElml2jPptKN2Gudq7HnT8AzBgxo0vn+XXj13jN+BpWxq30+CrVoc5tH1+fa7Ydd/7nfwKxfHkM/v73GuR1itna6sSXXzbgDx5ixsf74Z130lBYaMarr7aX/6VLdvzpT6XYufO2677NqX9JhTxQDv2zXc+0yl4vQ/yL8X1+y2D0008jcOJEGNasgaPTezFafvgBqqgotzt/ABDkcoTOmnX1D5IE3aJFEBQK3Pbmm/2ui4CACV12JnZ7Pc6ffwJBQdMQHf20547dx7ffJx6Dzd9ZobPwdMzT2FW1C981f+dq+8dQ3Vbdp50/AIyYMaPLzr/x669hfO01xK1c6fFVwPLAQKTu2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diff --git a/testdata/single_qubits_gates.gltf b/testdata/single_qubits_gates.gltf
index e4b76e96..7e2836b6 100644
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\ No newline at end of file
diff --git a/testdata/surface_code.gltf b/testdata/surface_code.gltf
index dbe3e626..1ec7ab45 100644
--- a/testdata/surface_code.gltf
+++ b/testdata/surface_code.gltf
@@ -1 +1 @@
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diff --git a/testdata/tick.gltf b/testdata/tick.gltf
index c06bc551..7a0debc4 100644
--- a/testdata/tick.gltf
+++ b/testdata/tick.gltf
@@ -1 +1 @@
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"}],"materials":[{"doubleSided":false,"pbrMetallicRoughness":{"baseColorFactor":[1,1,1,1],"baseColorTexture":{"index":0,"texCoord":0},"metallicFactor":0.4,"roughnessFactor":0.5}},{"doubleSided":true,"pbrMetallicRoughness":{"baseColorFactor":[0,0,0,1],"metallicFactor":1,"roughnessFactor":1}},{"doubleSided":true,"pbrMetallicRoughness":{"baseColorFactor":[1,0,0,1],"metallicFactor":1,"roughnessFactor":1}},{"doubleSided":true,"pbrMetallicRoughness":{"baseColorFactor":[0,0,1,1],"metallicFactor":1,"roughnessFactor":1}}],"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\ No newline at end of file
diff --git a/testdata/two_qubits_gates.gltf b/testdata/two_qubits_gates.gltf
index 96258114..2debb822 100644
--- a/testdata/two_qubits_gates.gltf
+++ b/testdata/two_qubits_gates.gltf
@@ -1 +1 @@
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From 1efe91b6aeebd420f8e7c5bbfa3c2dfff3dfd44e Mon Sep 17 00:00:00 2001
From: Craig Gidney <craiggidney@google.com>
Date: Mon, 5 Aug 2024 03:43:20 -0700
Subject: [PATCH 12/17] Add
 `stim.{CircuitInstruction,DemInstruction}.target_groups` (#812)

Fixes https://github.com/quantumlib/Stim/issues/811
---
 doc/python_api_reference_vDev.md              | 127 +++++++++++++++++-
 doc/stim.pyi                                  | 111 ++++++++++++++-
 file_lists/test_files                         |   1 +
 glue/python/src/stim/__init__.pyi             | 111 ++++++++++++++-
 src/stim/circuit/circuit_instruction.h        |  32 +++++
 .../circuit/circuit_instruction.pybind.cc     |  92 +++++++++++--
 src/stim/circuit/circuit_instruction.pybind.h |   1 +
 src/stim/circuit/circuit_instruction.test.cc  |  78 +++++++++++
 .../circuit_instruction_pybind_test.py        |  19 +++
 src/stim/circuit/gate_target.cc               |   9 +-
 src/stim/circuit/gate_target.pybind.cc        |   2 +-
 src/stim/dem/dem_instruction.h                |  14 ++
 src/stim/dem/dem_instruction.test.cc          |  45 +++++++
 ...detector_error_model_instruction.pybind.cc |  59 ++++++--
 .../detector_error_model_instruction.pybind.h |  17 +--
 ...tor_error_model_instruction_pybind_test.py |  19 +--
 .../diagram/graph/match_graph_3d_drawer.h     |  16 ---
 src/stim/gates/gate_data_annotations.cc       |  14 --
 src/stim/simulators/matched_error.pybind.cc   |  19 +++
 19 files changed, 693 insertions(+), 93 deletions(-)
 create mode 100644 src/stim/circuit/circuit_instruction.test.cc

diff --git a/doc/python_api_reference_vDev.md b/doc/python_api_reference_vDev.md
index 4ba62593..f509eef0 100644
--- a/doc/python_api_reference_vDev.md
+++ b/doc/python_api_reference_vDev.md
@@ -82,6 +82,7 @@ API references for stable versions are kept on the [stim github wiki](https://gi
     - [`stim.CircuitInstruction.gate_args_copy`](#stim.CircuitInstruction.gate_args_copy)
     - [`stim.CircuitInstruction.name`](#stim.CircuitInstruction.name)
     - [`stim.CircuitInstruction.num_measurements`](#stim.CircuitInstruction.num_measurements)
+    - [`stim.CircuitInstruction.target_groups`](#stim.CircuitInstruction.target_groups)
     - [`stim.CircuitInstruction.targets_copy`](#stim.CircuitInstruction.targets_copy)
 - [`stim.CircuitRepeatBlock`](#stim.CircuitRepeatBlock)
     - [`stim.CircuitRepeatBlock.__eq__`](#stim.CircuitRepeatBlock.__eq__)
@@ -123,6 +124,7 @@ API references for stable versions are kept on the [stim github wiki](https://gi
     - [`stim.DemInstruction.__repr__`](#stim.DemInstruction.__repr__)
     - [`stim.DemInstruction.__str__`](#stim.DemInstruction.__str__)
     - [`stim.DemInstruction.args_copy`](#stim.DemInstruction.args_copy)
+    - [`stim.DemInstruction.target_groups`](#stim.DemInstruction.target_groups)
     - [`stim.DemInstruction.targets_copy`](#stim.DemInstruction.targets_copy)
     - [`stim.DemInstruction.type`](#stim.DemInstruction.type)
 - [`stim.DemRepeatBlock`](#stim.DemRepeatBlock)
@@ -3664,6 +3666,25 @@ def instruction_targets(
     """Within the error instruction, which may have hundreds of
     targets, which specific targets were being executed to
     produce the error.
+
+    Examples:
+        >>> import stim
+        >>> err = stim.Circuit('''
+        ...     R 0
+        ...     TICK
+        ...     Y_ERROR(0.125) 0
+        ...     M 0
+        ...     OBSERVABLE_INCLUDE(0) rec[-1]
+        ... ''').shortest_graphlike_error()
+        >>> targets = err[0].circuit_error_locations[0].instruction_targets
+        >>> targets == stim.CircuitTargetsInsideInstruction(
+        ...     gate='Y_ERROR',
+        ...     args=[0.125],
+        ...     target_range_start=0,
+        ...     target_range_end=1,
+        ...     targets_in_range=(stim.GateTargetWithCoords(0, []),),
+        ... )
+        True
     """
 ```
 
@@ -3917,6 +3938,17 @@ def gate_args_copy(
     For noisy gates this typically a list of probabilities.
     For OBSERVABLE_INCLUDE it's a singleton list containing the logical observable
     index.
+
+    Examples:
+        >>> import stim
+        >>> instruction = stim.CircuitInstruction('X_ERROR', [2, 3], [0.125])
+        >>> instruction.gate_args_copy()
+        [0.125]
+
+        >>> instruction.gate_args_copy() == instruction.gate_args_copy()
+        True
+        >>> instruction.gate_args_copy() is instruction.gate_args_copy()
+        False
     """
 ```
 
@@ -3961,6 +3993,52 @@ def num_measurements(
     """
 ```
 
+<a name="stim.CircuitInstruction.target_groups"></a>
+```python
+# stim.CircuitInstruction.target_groups
+
+# (in class stim.CircuitInstruction)
+def target_groups(
+    self,
+) -> List[List[stim.GateTarget]]:
+    """Splits the instruction's targets into groups depending on the type of gate.
+
+    Single qubit gates like H get one group per target.
+    Two qubit gates like CX get one group per pair of targets.
+    Pauli product gates like MPP get one group per combined product.
+
+    Returns:
+        A list of groups of targets.
+
+    Examples:
+        >>> import stim
+        >>> for g in stim.Circuit('H 0 1 2')[0].target_groups():
+        ...     print(repr(g))
+        [stim.GateTarget(0)]
+        [stim.GateTarget(1)]
+        [stim.GateTarget(2)]
+
+        >>> for g in stim.Circuit('CX 0 1 2 3')[0].target_groups():
+        ...     print(repr(g))
+        [stim.GateTarget(0), stim.GateTarget(1)]
+        [stim.GateTarget(2), stim.GateTarget(3)]
+
+        >>> for g in stim.Circuit('MPP X0*Y1*Z2 X5*X6')[0].target_groups():
+        ...     print(repr(g))
+        [stim.target_x(0), stim.target_y(1), stim.target_z(2)]
+        [stim.target_x(5), stim.target_x(6)]
+
+        >>> for g in stim.Circuit('DETECTOR rec[-1] rec[-2]')[0].target_groups():
+        ...     print(repr(g))
+        [stim.target_rec(-1)]
+        [stim.target_rec(-2)]
+
+        >>> for g in stim.Circuit('CORRELATED_ERROR(0.1) X0 Y1')[0].target_groups():
+        ...     print(repr(g))
+        [stim.target_x(0), stim.target_y(1)]
+    """
+```
+
 <a name="stim.CircuitInstruction.targets_copy"></a>
 ```python
 # stim.CircuitInstruction.targets_copy
@@ -3970,6 +4048,17 @@ def targets_copy(
     self,
 ) -> List[stim.GateTarget]:
     """Returns a copy of the targets of the instruction.
+
+    Examples:
+        >>> import stim
+        >>> instruction = stim.CircuitInstruction('X_ERROR', [2, 3], [0.125])
+        >>> instruction.targets_copy()
+        [stim.GateTarget(2), stim.GateTarget(3)]
+
+        >>> instruction.targets_copy() == instruction.targets_copy()
+        True
+        >>> instruction.targets_copy() is instruction.targets_copy()
+        False
     """
 ```
 
@@ -5335,6 +5424,42 @@ def args_copy(
     """
 ```
 
+<a name="stim.DemInstruction.target_groups"></a>
+```python
+# stim.DemInstruction.target_groups
+
+# (in class stim.DemInstruction)
+def target_groups(
+    self,
+) -> List[List[stim.DemTarget]]:
+    """Returns a copy of the instruction's targets, split by target separators.
+
+    When a detector error model instruction contains a suggested decomposition,
+    its targets contain separators (`stim.DemTarget("^")`). This method splits the
+    targets into groups based the separators, similar to how `str.split` works.
+
+    Returns:
+        A list of groups of targets.
+
+    Examples:
+        >>> import stim
+        >>> dem = stim.DetectorErrorModel('''
+        ...     error(0.01) D0 D1 ^ D2
+        ...     error(0.01) D0 L0
+        ...     error(0.01)
+        ... ''')
+
+        >>> dem[0].target_groups()
+        [[stim.DemTarget('D0'), stim.DemTarget('D1')], [stim.DemTarget('D2')]]
+
+        >>> dem[1].target_groups()
+        [[stim.DemTarget('D0'), stim.DemTarget('L0')]]
+
+        >>> dem[2].target_groups()
+        [[]]
+    """
+```
+
 <a name="stim.DemInstruction.targets_copy"></a>
 ```python
 # stim.DemInstruction.targets_copy
@@ -8928,7 +9053,7 @@ class GateTarget:
         >>> circuit[0].targets_copy()[0]
         stim.GateTarget(0)
         >>> circuit[0].targets_copy()[1]
-        stim.GateTarget(stim.target_inv(1))
+        stim.target_inv(1)
     """
 ```
 
diff --git a/doc/stim.pyi b/doc/stim.pyi
index cd232b6a..e20cbea0 100644
--- a/doc/stim.pyi
+++ b/doc/stim.pyi
@@ -2846,6 +2846,25 @@ class CircuitErrorLocation:
         """Within the error instruction, which may have hundreds of
         targets, which specific targets were being executed to
         produce the error.
+
+        Examples:
+            >>> import stim
+            >>> err = stim.Circuit('''
+            ...     R 0
+            ...     TICK
+            ...     Y_ERROR(0.125) 0
+            ...     M 0
+            ...     OBSERVABLE_INCLUDE(0) rec[-1]
+            ... ''').shortest_graphlike_error()
+            >>> targets = err[0].circuit_error_locations[0].instruction_targets
+            >>> targets == stim.CircuitTargetsInsideInstruction(
+            ...     gate='Y_ERROR',
+            ...     args=[0.125],
+            ...     target_range_start=0,
+            ...     target_range_end=1,
+            ...     targets_in_range=(stim.GateTargetWithCoords(0, []),),
+            ... )
+            True
         """
     @property
     def stack_frames(
@@ -3001,6 +3020,17 @@ class CircuitInstruction:
         For noisy gates this typically a list of probabilities.
         For OBSERVABLE_INCLUDE it's a singleton list containing the logical observable
         index.
+
+        Examples:
+            >>> import stim
+            >>> instruction = stim.CircuitInstruction('X_ERROR', [2, 3], [0.125])
+            >>> instruction.gate_args_copy()
+            [0.125]
+
+            >>> instruction.gate_args_copy() == instruction.gate_args_copy()
+            True
+            >>> instruction.gate_args_copy() is instruction.gate_args_copy()
+            False
         """
     @property
     def name(
@@ -3029,10 +3059,60 @@ class CircuitInstruction:
             >>> stim.CircuitInstruction('HERALDED_ERASE', [0]).num_measurements
             1
         """
+    def target_groups(
+        self,
+    ) -> List[List[stim.GateTarget]]:
+        """Splits the instruction's targets into groups depending on the type of gate.
+
+        Single qubit gates like H get one group per target.
+        Two qubit gates like CX get one group per pair of targets.
+        Pauli product gates like MPP get one group per combined product.
+
+        Returns:
+            A list of groups of targets.
+
+        Examples:
+            >>> import stim
+            >>> for g in stim.Circuit('H 0 1 2')[0].target_groups():
+            ...     print(repr(g))
+            [stim.GateTarget(0)]
+            [stim.GateTarget(1)]
+            [stim.GateTarget(2)]
+
+            >>> for g in stim.Circuit('CX 0 1 2 3')[0].target_groups():
+            ...     print(repr(g))
+            [stim.GateTarget(0), stim.GateTarget(1)]
+            [stim.GateTarget(2), stim.GateTarget(3)]
+
+            >>> for g in stim.Circuit('MPP X0*Y1*Z2 X5*X6')[0].target_groups():
+            ...     print(repr(g))
+            [stim.target_x(0), stim.target_y(1), stim.target_z(2)]
+            [stim.target_x(5), stim.target_x(6)]
+
+            >>> for g in stim.Circuit('DETECTOR rec[-1] rec[-2]')[0].target_groups():
+            ...     print(repr(g))
+            [stim.target_rec(-1)]
+            [stim.target_rec(-2)]
+
+            >>> for g in stim.Circuit('CORRELATED_ERROR(0.1) X0 Y1')[0].target_groups():
+            ...     print(repr(g))
+            [stim.target_x(0), stim.target_y(1)]
+        """
     def targets_copy(
         self,
     ) -> List[stim.GateTarget]:
         """Returns a copy of the targets of the instruction.
+
+        Examples:
+            >>> import stim
+            >>> instruction = stim.CircuitInstruction('X_ERROR', [2, 3], [0.125])
+            >>> instruction.targets_copy()
+            [stim.GateTarget(2), stim.GateTarget(3)]
+
+            >>> instruction.targets_copy() == instruction.targets_copy()
+            True
+            >>> instruction.targets_copy() is instruction.targets_copy()
+            False
         """
 class CircuitRepeatBlock:
     """A REPEAT block from a circuit.
@@ -4172,6 +4252,35 @@ class DemInstruction:
             >>> instruction.args_copy() is instruction.args_copy()
             False
         """
+    def target_groups(
+        self,
+    ) -> List[List[stim.DemTarget]]:
+        """Returns a copy of the instruction's targets, split by target separators.
+
+        When a detector error model instruction contains a suggested decomposition,
+        its targets contain separators (`stim.DemTarget("^")`). This method splits the
+        targets into groups based the separators, similar to how `str.split` works.
+
+        Returns:
+            A list of groups of targets.
+
+        Examples:
+            >>> import stim
+            >>> dem = stim.DetectorErrorModel('''
+            ...     error(0.01) D0 D1 ^ D2
+            ...     error(0.01) D0 L0
+            ...     error(0.01)
+            ... ''')
+
+            >>> dem[0].target_groups()
+            [[stim.DemTarget('D0'), stim.DemTarget('D1')], [stim.DemTarget('D2')]]
+
+            >>> dem[1].target_groups()
+            [[stim.DemTarget('D0'), stim.DemTarget('L0')]]
+
+            >>> dem[2].target_groups()
+            [[]]
+        """
     def targets_copy(
         self,
     ) -> List[Union[int, stim.DemTarget]]:
@@ -6976,7 +7085,7 @@ class GateTarget:
         >>> circuit[0].targets_copy()[0]
         stim.GateTarget(0)
         >>> circuit[0].targets_copy()[1]
-        stim.GateTarget(stim.target_inv(1))
+        stim.target_inv(1)
     """
     def __eq__(
         self,
diff --git a/file_lists/test_files b/file_lists/test_files
index b57d0093..b00287c5 100644
--- a/file_lists/test_files
+++ b/file_lists/test_files
@@ -1,5 +1,6 @@
 src/stim.test.cc
 src/stim/circuit/circuit.test.cc
+src/stim/circuit/circuit_instruction.test.cc
 src/stim/circuit/gate_decomposition.test.cc
 src/stim/circuit/gate_target.test.cc
 src/stim/cmd/command_analyze_errors.test.cc
diff --git a/glue/python/src/stim/__init__.pyi b/glue/python/src/stim/__init__.pyi
index cd232b6a..e20cbea0 100644
--- a/glue/python/src/stim/__init__.pyi
+++ b/glue/python/src/stim/__init__.pyi
@@ -2846,6 +2846,25 @@ class CircuitErrorLocation:
         """Within the error instruction, which may have hundreds of
         targets, which specific targets were being executed to
         produce the error.
+
+        Examples:
+            >>> import stim
+            >>> err = stim.Circuit('''
+            ...     R 0
+            ...     TICK
+            ...     Y_ERROR(0.125) 0
+            ...     M 0
+            ...     OBSERVABLE_INCLUDE(0) rec[-1]
+            ... ''').shortest_graphlike_error()
+            >>> targets = err[0].circuit_error_locations[0].instruction_targets
+            >>> targets == stim.CircuitTargetsInsideInstruction(
+            ...     gate='Y_ERROR',
+            ...     args=[0.125],
+            ...     target_range_start=0,
+            ...     target_range_end=1,
+            ...     targets_in_range=(stim.GateTargetWithCoords(0, []),),
+            ... )
+            True
         """
     @property
     def stack_frames(
@@ -3001,6 +3020,17 @@ class CircuitInstruction:
         For noisy gates this typically a list of probabilities.
         For OBSERVABLE_INCLUDE it's a singleton list containing the logical observable
         index.
+
+        Examples:
+            >>> import stim
+            >>> instruction = stim.CircuitInstruction('X_ERROR', [2, 3], [0.125])
+            >>> instruction.gate_args_copy()
+            [0.125]
+
+            >>> instruction.gate_args_copy() == instruction.gate_args_copy()
+            True
+            >>> instruction.gate_args_copy() is instruction.gate_args_copy()
+            False
         """
     @property
     def name(
@@ -3029,10 +3059,60 @@ class CircuitInstruction:
             >>> stim.CircuitInstruction('HERALDED_ERASE', [0]).num_measurements
             1
         """
+    def target_groups(
+        self,
+    ) -> List[List[stim.GateTarget]]:
+        """Splits the instruction's targets into groups depending on the type of gate.
+
+        Single qubit gates like H get one group per target.
+        Two qubit gates like CX get one group per pair of targets.
+        Pauli product gates like MPP get one group per combined product.
+
+        Returns:
+            A list of groups of targets.
+
+        Examples:
+            >>> import stim
+            >>> for g in stim.Circuit('H 0 1 2')[0].target_groups():
+            ...     print(repr(g))
+            [stim.GateTarget(0)]
+            [stim.GateTarget(1)]
+            [stim.GateTarget(2)]
+
+            >>> for g in stim.Circuit('CX 0 1 2 3')[0].target_groups():
+            ...     print(repr(g))
+            [stim.GateTarget(0), stim.GateTarget(1)]
+            [stim.GateTarget(2), stim.GateTarget(3)]
+
+            >>> for g in stim.Circuit('MPP X0*Y1*Z2 X5*X6')[0].target_groups():
+            ...     print(repr(g))
+            [stim.target_x(0), stim.target_y(1), stim.target_z(2)]
+            [stim.target_x(5), stim.target_x(6)]
+
+            >>> for g in stim.Circuit('DETECTOR rec[-1] rec[-2]')[0].target_groups():
+            ...     print(repr(g))
+            [stim.target_rec(-1)]
+            [stim.target_rec(-2)]
+
+            >>> for g in stim.Circuit('CORRELATED_ERROR(0.1) X0 Y1')[0].target_groups():
+            ...     print(repr(g))
+            [stim.target_x(0), stim.target_y(1)]
+        """
     def targets_copy(
         self,
     ) -> List[stim.GateTarget]:
         """Returns a copy of the targets of the instruction.
+
+        Examples:
+            >>> import stim
+            >>> instruction = stim.CircuitInstruction('X_ERROR', [2, 3], [0.125])
+            >>> instruction.targets_copy()
+            [stim.GateTarget(2), stim.GateTarget(3)]
+
+            >>> instruction.targets_copy() == instruction.targets_copy()
+            True
+            >>> instruction.targets_copy() is instruction.targets_copy()
+            False
         """
 class CircuitRepeatBlock:
     """A REPEAT block from a circuit.
@@ -4172,6 +4252,35 @@ class DemInstruction:
             >>> instruction.args_copy() is instruction.args_copy()
             False
         """
+    def target_groups(
+        self,
+    ) -> List[List[stim.DemTarget]]:
+        """Returns a copy of the instruction's targets, split by target separators.
+
+        When a detector error model instruction contains a suggested decomposition,
+        its targets contain separators (`stim.DemTarget("^")`). This method splits the
+        targets into groups based the separators, similar to how `str.split` works.
+
+        Returns:
+            A list of groups of targets.
+
+        Examples:
+            >>> import stim
+            >>> dem = stim.DetectorErrorModel('''
+            ...     error(0.01) D0 D1 ^ D2
+            ...     error(0.01) D0 L0
+            ...     error(0.01)
+            ... ''')
+
+            >>> dem[0].target_groups()
+            [[stim.DemTarget('D0'), stim.DemTarget('D1')], [stim.DemTarget('D2')]]
+
+            >>> dem[1].target_groups()
+            [[stim.DemTarget('D0'), stim.DemTarget('L0')]]
+
+            >>> dem[2].target_groups()
+            [[]]
+        """
     def targets_copy(
         self,
     ) -> List[Union[int, stim.DemTarget]]:
@@ -6976,7 +7085,7 @@ class GateTarget:
         >>> circuit[0].targets_copy()[0]
         stim.GateTarget(0)
         >>> circuit[0].targets_copy()[1]
-        stim.GateTarget(stim.target_inv(1))
+        stim.target_inv(1)
     """
     def __eq__(
         self,
diff --git a/src/stim/circuit/circuit_instruction.h b/src/stim/circuit/circuit_instruction.h
index 9cc510ed..33caeb81 100644
--- a/src/stim/circuit/circuit_instruction.h
+++ b/src/stim/circuit/circuit_instruction.h
@@ -109,6 +109,38 @@ struct CircuitInstruction {
     /// Raises:
     ///     std::invalid_argument: Validation failed.
     void validate() const;
+
+    template <typename CALLBACK>
+    inline void for_combined_target_groups(CALLBACK callback) const {
+        auto flags = GATE_DATA[gate_type].flags;
+        size_t start = 0;
+        while (start < targets.size()) {
+            size_t end;
+            if (flags & stim::GateFlags::GATE_TARGETS_COMBINERS) {
+                end = start + 1;
+                while (end < targets.size() && targets[end].is_combiner()) {
+                    end += 2;
+                }
+            } else if (flags & stim::GateFlags::GATE_IS_SINGLE_QUBIT_GATE) {
+                end = start + 1;
+            } else if (flags & stim::GateFlags::GATE_TARGETS_PAIRS) {
+                end = start + 2;
+            } else if ((flags & stim::GateFlags::GATE_TARGETS_PAULI_STRING) && !(flags & stim::GateFlags::GATE_TARGETS_COMBINERS)) {
+                // like CORRELATED_ERROR
+                end = targets.size();
+            } else if (flags & stim::GateFlags::GATE_ONLY_TARGETS_MEASUREMENT_RECORD) {
+                // like DETECTOR
+                end = start + 1;
+            } else if (gate_type == GateType::MPAD || gate_type == GateType::QUBIT_COORDS) {
+                end = start + 1;
+            } else {
+                throw std::invalid_argument("Not implemented: splitting " + str());
+            }
+            std::span<const GateTarget> group = targets.sub(start, end);
+            callback(group);
+            start = end;
+        }
+    }
 };
 
 }  // namespace stim
diff --git a/src/stim/circuit/circuit_instruction.pybind.cc b/src/stim/circuit/circuit_instruction.pybind.cc
index adef47c2..5c1cce13 100644
--- a/src/stim/circuit/circuit_instruction.pybind.cc
+++ b/src/stim/circuit/circuit_instruction.pybind.cc
@@ -1,17 +1,3 @@
-// Copyright 2021 Google LLC
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
 #include "stim/circuit/circuit_instruction.pybind.h"
 
 #include "stim/circuit/gate_target.pybind.h"
@@ -90,6 +76,19 @@ std::vector<GateTarget> PyCircuitInstruction::targets_copy() const {
 std::vector<double> PyCircuitInstruction::gate_args_copy() const {
     return gate_args;
 }
+std::vector<std::vector<stim::GateTarget>> PyCircuitInstruction::target_groups() const {
+    std::vector<std::vector<stim::GateTarget>> results;
+    as_operation_ref().for_combined_target_groups([&](std::span<const GateTarget> group) {
+        std::vector<stim::GateTarget> copy;
+        for (auto g : group) {
+            if (!g.is_combiner()) {
+                copy.push_back(g);
+            }
+        }
+        results.push_back(std::move(copy));
+    });
+    return results;
+}
 
 pybind11::class_<PyCircuitInstruction> stim_pybind::pybind_circuit_instruction(pybind11::module &m) {
     return pybind11::class_<PyCircuitInstruction>(
@@ -142,11 +141,65 @@ void stim_pybind::pybind_circuit_instruction_methods(pybind11::module &m, pybind
         )DOC")
             .data());
 
+    c.def(
+        "target_groups",
+        &PyCircuitInstruction::target_groups,
+        clean_doc_string(R"DOC(
+            @signature def target_groups(self) -> List[List[stim.GateTarget]]:
+            Splits the instruction's targets into groups depending on the type of gate.
+
+            Single qubit gates like H get one group per target.
+            Two qubit gates like CX get one group per pair of targets.
+            Pauli product gates like MPP get one group per combined product.
+
+            Returns:
+                A list of groups of targets.
+
+            Examples:
+                >>> import stim
+                >>> for g in stim.Circuit('H 0 1 2')[0].target_groups():
+                ...     print(repr(g))
+                [stim.GateTarget(0)]
+                [stim.GateTarget(1)]
+                [stim.GateTarget(2)]
+
+                >>> for g in stim.Circuit('CX 0 1 2 3')[0].target_groups():
+                ...     print(repr(g))
+                [stim.GateTarget(0), stim.GateTarget(1)]
+                [stim.GateTarget(2), stim.GateTarget(3)]
+
+                >>> for g in stim.Circuit('MPP X0*Y1*Z2 X5*X6')[0].target_groups():
+                ...     print(repr(g))
+                [stim.target_x(0), stim.target_y(1), stim.target_z(2)]
+                [stim.target_x(5), stim.target_x(6)]
+
+                >>> for g in stim.Circuit('DETECTOR rec[-1] rec[-2]')[0].target_groups():
+                ...     print(repr(g))
+                [stim.target_rec(-1)]
+                [stim.target_rec(-2)]
+
+                >>> for g in stim.Circuit('CORRELATED_ERROR(0.1) X0 Y1')[0].target_groups():
+                ...     print(repr(g))
+                [stim.target_x(0), stim.target_y(1)]
+        )DOC")
+            .data());
+
     c.def(
         "targets_copy",
         &PyCircuitInstruction::targets_copy,
         clean_doc_string(R"DOC(
             Returns a copy of the targets of the instruction.
+
+            Examples:
+                >>> import stim
+                >>> instruction = stim.CircuitInstruction('X_ERROR', [2, 3], [0.125])
+                >>> instruction.targets_copy()
+                [stim.GateTarget(2), stim.GateTarget(3)]
+
+                >>> instruction.targets_copy() == instruction.targets_copy()
+                True
+                >>> instruction.targets_copy() is instruction.targets_copy()
+                False
         )DOC")
             .data());
 
@@ -159,6 +212,17 @@ void stim_pybind::pybind_circuit_instruction_methods(pybind11::module &m, pybind
             For noisy gates this typically a list of probabilities.
             For OBSERVABLE_INCLUDE it's a singleton list containing the logical observable
             index.
+
+            Examples:
+                >>> import stim
+                >>> instruction = stim.CircuitInstruction('X_ERROR', [2, 3], [0.125])
+                >>> instruction.gate_args_copy()
+                [0.125]
+
+                >>> instruction.gate_args_copy() == instruction.gate_args_copy()
+                True
+                >>> instruction.gate_args_copy() is instruction.gate_args_copy()
+                False
         )DOC")
             .data());
 
diff --git a/src/stim/circuit/circuit_instruction.pybind.h b/src/stim/circuit/circuit_instruction.pybind.h
index 48873543..a1a36bf2 100644
--- a/src/stim/circuit/circuit_instruction.pybind.h
+++ b/src/stim/circuit/circuit_instruction.pybind.h
@@ -39,6 +39,7 @@ struct PyCircuitInstruction {
     std::vector<stim::GateTarget> targets_copy() const;
     std::vector<double> gate_args_copy() const;
     std::vector<uint32_t> raw_targets() const;
+    std::vector<std::vector<stim::GateTarget>> target_groups() const;
     bool operator==(const PyCircuitInstruction &other) const;
     bool operator!=(const PyCircuitInstruction &other) const;
 
diff --git a/src/stim/circuit/circuit_instruction.test.cc b/src/stim/circuit/circuit_instruction.test.cc
new file mode 100644
index 00000000..e0339977
--- /dev/null
+++ b/src/stim/circuit/circuit_instruction.test.cc
@@ -0,0 +1,78 @@
+#include "stim/circuit/circuit_instruction.h"
+
+#include "gtest/gtest.h"
+
+#include "stim/circuit/circuit.h"
+#include "stim/circuit/circuit.test.h"
+
+using namespace stim;
+
+TEST(circuit_instruction, for_combined_targets) {
+    Circuit circuit(R"CIRCUIT(
+        X
+        CX
+        S 1
+        H 0 2
+        TICK
+        CX 0 1 2 3
+        CY 3 5
+        SPP
+        MPP X0*X1*Z2 Z7 X5*X9
+        SPP Z5
+    )CIRCUIT");
+    auto get_k = [&](size_t k) {
+        std::vector<std::vector<GateTarget>> results;
+        circuit.operations[k].for_combined_target_groups([&](std::span<const GateTarget> group) {
+            std::vector<GateTarget> items;
+            for (auto g : group) {
+                items.push_back(g);
+            }
+            results.push_back(items);
+        });
+        return results;
+    };
+    ASSERT_EQ(get_k(0), (std::vector<std::vector<GateTarget>>{
+                       }));
+    ASSERT_EQ(get_k(1), (std::vector<std::vector<GateTarget>>{
+                       }));
+    ASSERT_EQ(get_k(2), (std::vector<std::vector<GateTarget>>{
+                           {GateTarget::qubit(1)},
+                       }));
+    ASSERT_EQ(get_k(3), (std::vector<std::vector<GateTarget>>{
+                           {GateTarget::qubit(0)},
+                           {GateTarget::qubit(2)},
+                       }));
+    ASSERT_EQ(get_k(4), (std::vector<std::vector<GateTarget>>{
+                       }));
+    ASSERT_EQ(get_k(5), (std::vector<std::vector<GateTarget>>{
+                            {GateTarget::qubit(0), GateTarget::qubit(1)},
+                            {GateTarget::qubit(2), GateTarget::qubit(3)},
+                       }));
+    ASSERT_EQ(get_k(6), (std::vector<std::vector<GateTarget>>{
+                            {GateTarget::qubit(3), GateTarget::qubit(5)},
+                       }));
+    ASSERT_EQ(get_k(7), (std::vector<std::vector<GateTarget>>{
+                       }));
+    ASSERT_EQ(get_k(8), (std::vector<std::vector<GateTarget>>{
+                            {GateTarget::x(0), GateTarget::combiner(), GateTarget::x(1), GateTarget::combiner(), GateTarget::z(2)},
+                            {GateTarget::z(7)},
+                            {GateTarget::x(5), GateTarget::combiner(), GateTarget::x(9)},
+                       }));
+    ASSERT_EQ(get_k(9), (std::vector<std::vector<GateTarget>>{
+                            {GateTarget::z(5)},
+                       }));
+}
+
+TEST(circuit_instruction, for_combined_targets_works_on_all) {
+    Circuit c = generate_test_circuit_with_all_operations();
+    size_t count = 0;
+    for (const auto &e : c.operations) {
+        if (e.gate_type == GateType::REPEAT) {
+            continue;
+        }
+        e.for_combined_target_groups([&](std::span<const GateTarget> group) {
+            count += group.size();
+        });
+    }
+    ASSERT_TRUE(count > 0);
+}
diff --git a/src/stim/circuit/circuit_instruction_pybind_test.py b/src/stim/circuit/circuit_instruction_pybind_test.py
index d0592713..1939d70b 100644
--- a/src/stim/circuit/circuit_instruction_pybind_test.py
+++ b/src/stim/circuit/circuit_instruction_pybind_test.py
@@ -57,3 +57,22 @@ def test_num_measurements():
     assert stim.CircuitInstruction("MXX", [1, 2]).num_measurements == 1
     assert stim.CircuitInstruction("M", [1, 2]).num_measurements == 2
     assert stim.CircuitInstruction("MPAD", [0, 1, 0]).num_measurements == 3
+
+
+def test_target_groups():
+    assert stim.CircuitInstruction("MPAD", [0, 1, 0]).target_groups() == [
+        [stim.GateTarget(0)],
+        [stim.GateTarget(1)],
+        [stim.GateTarget(0)],
+    ]
+    assert stim.CircuitInstruction("H", []).target_groups() == []
+    assert stim.CircuitInstruction("H", [1]).target_groups() == [[stim.GateTarget(1)]]
+    assert stim.CircuitInstruction("H", [2, 3]).target_groups() == [[stim.GateTarget(2)], [stim.GateTarget(3)]]
+    assert stim.CircuitInstruction("CX", []).target_groups() == []
+    assert stim.CircuitInstruction("CX", [0, 1]).target_groups() == [[stim.GateTarget(0), stim.GateTarget(1)]]
+    assert stim.CircuitInstruction("CX", [2, 3, 5, 7]).target_groups() == [[stim.GateTarget(2), stim.GateTarget(3)], [stim.GateTarget(5), stim.GateTarget(7)]]
+    assert stim.CircuitInstruction("DETECTOR", []).target_groups() == []
+    assert stim.CircuitInstruction("CORRELATED_ERROR", [], [0.001]).target_groups() == []
+    assert stim.CircuitInstruction("MPP", []).target_groups() == []
+    assert stim.CircuitInstruction("MPAD", []).target_groups() == []
+    assert stim.CircuitInstruction("QUBIT_COORDS", [1, 2]).target_groups() == [[stim.GateTarget(1)], [stim.GateTarget(2)]]
diff --git a/src/stim/circuit/gate_target.cc b/src/stim/circuit/gate_target.cc
index a7918ff6..e961bab8 100644
--- a/src/stim/circuit/gate_target.cc
+++ b/src/stim/circuit/gate_target.cc
@@ -178,9 +178,14 @@ std::string GateTarget::str() const {
 
 std::string GateTarget::repr() const {
     std::stringstream ss;
-    ss << "stim.GateTarget(";
+    bool need_wrap = is_qubit_target() && !is_inverted_result_target();
+    if (need_wrap) {
+        ss << "stim.GateTarget(";
+    }
     ss << *this;
-    ss << ")";
+    if (need_wrap) {
+        ss << ")";
+    }
     return ss.str();
 }
 
diff --git a/src/stim/circuit/gate_target.pybind.cc b/src/stim/circuit/gate_target.pybind.cc
index 9278ae4b..ba7db7f4 100644
--- a/src/stim/circuit/gate_target.pybind.cc
+++ b/src/stim/circuit/gate_target.pybind.cc
@@ -52,7 +52,7 @@ pybind11::class_<stim::GateTarget> stim_pybind::pybind_circuit_gate_target(pybin
                 >>> circuit[0].targets_copy()[0]
                 stim.GateTarget(0)
                 >>> circuit[0].targets_copy()[1]
-                stim.GateTarget(stim.target_inv(1))
+                stim.target_inv(1)
         )DOC")
             .data());
 }
diff --git a/src/stim/dem/dem_instruction.h b/src/stim/dem/dem_instruction.h
index 42361112..39423b58 100644
--- a/src/stim/dem/dem_instruction.h
+++ b/src/stim/dem/dem_instruction.h
@@ -61,6 +61,20 @@ struct DemInstruction {
     uint64_t repeat_block_rep_count() const;
     const DetectorErrorModel &repeat_block_body(const DetectorErrorModel &host) const;
     DetectorErrorModel &repeat_block_body(DetectorErrorModel &host) const;
+
+    template <typename CALLBACK>
+    inline void for_separated_targets(CALLBACK callback) const {
+        size_t start = 0;
+        do {
+            size_t end = start + 1;
+            while (end < target_data.size() && !target_data[end].is_separator()) {
+                end++;
+            }
+            std::span<const DemTarget> group = target_data.sub(start, std::min(end, target_data.size()));
+            callback(group);
+            start = end + 1;
+        } while (start < target_data.size());
+    }
 };
 
 std::ostream &operator<<(std::ostream &out, const DemInstructionType &type);
diff --git a/src/stim/dem/dem_instruction.test.cc b/src/stim/dem/dem_instruction.test.cc
index 53865e46..f14c2f1e 100644
--- a/src/stim/dem/dem_instruction.test.cc
+++ b/src/stim/dem/dem_instruction.test.cc
@@ -2,6 +2,8 @@
 
 #include "gtest/gtest.h"
 
+#include "stim/dem/detector_error_model.h"
+
 using namespace stim;
 
 TEST(dem_instruction, from_str) {
@@ -29,3 +31,46 @@ TEST(dem_instruction, from_str) {
     ASSERT_THROW({ DemTarget::from_text("'"); }, std::invalid_argument);
     ASSERT_THROW({ DemTarget::from_text(" "); }, std::invalid_argument);
 }
+
+TEST(dem_instruction, for_separated_targets) {
+    DetectorErrorModel dem("error(0.1) D0 ^ D2 L0 ^ D1 D2 D3");
+    std::vector<std::vector<DemTarget>> results;
+    dem.instructions[0].for_separated_targets([&](std::span<const DemTarget> group) {
+        std::vector<DemTarget> items;
+        for (auto g : group) {
+            items.push_back(g);
+        }
+        results.push_back(items);
+    });
+    ASSERT_EQ(results, (std::vector<std::vector<DemTarget>>{
+                           {DemTarget::relative_detector_id(0)},
+                           {DemTarget::relative_detector_id(2), DemTarget::observable_id(0)},
+                           {DemTarget::relative_detector_id(1), DemTarget::relative_detector_id(2), DemTarget::relative_detector_id(3)},
+                       }));
+
+    dem = DetectorErrorModel("error(0.1) D0");
+    results.clear();
+    dem.instructions[0].for_separated_targets([&](std::span<const DemTarget> group) {
+        std::vector<DemTarget> items;
+        for (auto g : group) {
+            items.push_back(g);
+        }
+        results.push_back(items);
+    });
+    ASSERT_EQ(results, (std::vector<std::vector<DemTarget>>{
+                           {DemTarget::relative_detector_id(0)},
+                       }));
+
+    dem = DetectorErrorModel("error(0.1)");
+    results.clear();
+    dem.instructions[0].for_separated_targets([&](std::span<const DemTarget> group) {
+        std::vector<DemTarget> items;
+        for (auto g : group) {
+            items.push_back(g);
+        }
+        results.push_back(items);
+    });
+    ASSERT_EQ(results, (std::vector<std::vector<DemTarget>>{
+                           {},
+                       }));
+}
diff --git a/src/stim/dem/detector_error_model_instruction.pybind.cc b/src/stim/dem/detector_error_model_instruction.pybind.cc
index a9f0a5ca..ceac444d 100644
--- a/src/stim/dem/detector_error_model_instruction.pybind.cc
+++ b/src/stim/dem/detector_error_model_instruction.pybind.cc
@@ -1,17 +1,3 @@
-// Copyright 2021 Google LLC
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
 #include "stim/dem/detector_error_model_instruction.pybind.h"
 
 #include "stim/dem/detector_error_model_target.pybind.h"
@@ -21,6 +7,18 @@
 using namespace stim;
 using namespace stim_pybind;
 
+std::vector<std::vector<ExposedDemTarget>> ExposedDemInstruction::target_groups() const {
+    std::vector<std::vector<ExposedDemTarget>> result;
+    as_dem_instruction().for_separated_targets([&](std::span<const DemTarget> group) {
+        std::vector<ExposedDemTarget> copy;
+        for (auto e : group) {
+            copy.push_back(e);
+        }
+        result.push_back(copy);
+    });
+    return result;
+}
+
 DemInstruction ExposedDemInstruction::as_dem_instruction() const {
     return DemInstruction{arguments, targets, type};
 }
@@ -205,6 +203,39 @@ void stim_pybind::pybind_detector_error_model_instruction_methods(
         )DOC")
             .data());
 
+    c.def(
+        "target_groups",
+        &ExposedDemInstruction::target_groups,
+        clean_doc_string(R"DOC(
+            @signature def target_groups(self) -> List[List[stim.DemTarget]]:
+            Returns a copy of the instruction's targets, split by target separators.
+
+            When a detector error model instruction contains a suggested decomposition,
+            its targets contain separators (`stim.DemTarget("^")`). This method splits the
+            targets into groups based the separators, similar to how `str.split` works.
+
+            Returns:
+                A list of groups of targets.
+
+            Examples:
+                >>> import stim
+                >>> dem = stim.DetectorErrorModel('''
+                ...     error(0.01) D0 D1 ^ D2
+                ...     error(0.01) D0 L0
+                ...     error(0.01)
+                ... ''')
+
+                >>> dem[0].target_groups()
+                [[stim.DemTarget('D0'), stim.DemTarget('D1')], [stim.DemTarget('D2')]]
+
+                >>> dem[1].target_groups()
+                [[stim.DemTarget('D0'), stim.DemTarget('L0')]]
+
+                >>> dem[2].target_groups()
+                [[]]
+        )DOC")
+            .data());
+
     c.def(
         "targets_copy",
         &ExposedDemInstruction::targets_copy,
diff --git a/src/stim/dem/detector_error_model_instruction.pybind.h b/src/stim/dem/detector_error_model_instruction.pybind.h
index facaa8e0..247a713a 100644
--- a/src/stim/dem/detector_error_model_instruction.pybind.h
+++ b/src/stim/dem/detector_error_model_instruction.pybind.h
@@ -1,23 +1,9 @@
-// Copyright 2021 Google LLC
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
 #ifndef _STIM_DEM_DETECTOR_ERROR_MODEL_INSTRUCTION_PYBIND_H
 #define _STIM_DEM_DETECTOR_ERROR_MODEL_INSTRUCTION_PYBIND_H
 
 #include <pybind11/pybind11.h>
 
-#include "stim/dem/detector_error_model.h"
+#include "stim/dem/detector_error_model_target.pybind.h"
 
 namespace stim_pybind {
 
@@ -26,6 +12,7 @@ struct ExposedDemInstruction {
     std::vector<stim::DemTarget> targets;
     stim::DemInstructionType type;
 
+    std::vector<std::vector<stim_pybind::ExposedDemTarget>> target_groups() const;
     std::vector<double> args_copy() const;
     std::vector<pybind11::object> targets_copy() const;
     stim::DemInstruction as_dem_instruction() const;
diff --git a/src/stim/dem/detector_error_model_instruction_pybind_test.py b/src/stim/dem/detector_error_model_instruction_pybind_test.py
index 46c8aa3a..2afbe0e6 100644
--- a/src/stim/dem/detector_error_model_instruction_pybind_test.py
+++ b/src/stim/dem/detector_error_model_instruction_pybind_test.py
@@ -1,17 +1,3 @@
-# Copyright 2021 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
 import stim
 import pytest
 
@@ -87,3 +73,8 @@ def test_hashable():
     c = stim.DemInstruction("error", [0.25], [stim.target_relative_detector_id(3)])
     assert hash(a) == hash(c)
     assert len({a, b, c}) == 2
+
+
+def test_target_groups():
+    dem = stim.DetectorErrorModel("detector D0")
+    assert dem[0].target_groups() == [[stim.DemTarget("D0")]]
diff --git a/src/stim/diagram/graph/match_graph_3d_drawer.h b/src/stim/diagram/graph/match_graph_3d_drawer.h
index 9d033018..535307ea 100644
--- a/src/stim/diagram/graph/match_graph_3d_drawer.h
+++ b/src/stim/diagram/graph/match_graph_3d_drawer.h
@@ -1,19 +1,3 @@
-/*
- * Copyright 2021 Google LLC
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
 #ifndef _STIM_DIAGRAM_GRAPH_MATCH_GRAPH_DRAWER_H
 #define _STIM_DIAGRAM_GRAPH_MATCH_GRAPH_DRAWER_H
 
diff --git a/src/stim/gates/gate_data_annotations.cc b/src/stim/gates/gate_data_annotations.cc
index b71301cc..c95027a0 100644
--- a/src/stim/gates/gate_data_annotations.cc
+++ b/src/stim/gates/gate_data_annotations.cc
@@ -1,17 +1,3 @@
-// Copyright 2021 Google LLC
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
 #include "stim/gates/gates.h"
 
 using namespace stim;
diff --git a/src/stim/simulators/matched_error.pybind.cc b/src/stim/simulators/matched_error.pybind.cc
index a85efef3..6587750a 100644
--- a/src/stim/simulators/matched_error.pybind.cc
+++ b/src/stim/simulators/matched_error.pybind.cc
@@ -624,6 +624,25 @@ void stim_pybind::pybind_circuit_error_location_methods(
             Within the error instruction, which may have hundreds of
             targets, which specific targets were being executed to
             produce the error.
+
+            Examples:
+                >>> import stim
+                >>> err = stim.Circuit('''
+                ...     R 0
+                ...     TICK
+                ...     Y_ERROR(0.125) 0
+                ...     M 0
+                ...     OBSERVABLE_INCLUDE(0) rec[-1]
+                ... ''').shortest_graphlike_error()
+                >>> targets = err[0].circuit_error_locations[0].instruction_targets
+                >>> targets == stim.CircuitTargetsInsideInstruction(
+                ...     gate='Y_ERROR',
+                ...     args=[0.125],
+                ...     target_range_start=0,
+                ...     target_range_end=1,
+                ...     targets_in_range=(stim.GateTargetWithCoords(0, []),),
+                ... )
+                True
         )DOC")
             .data());
 

From da89d9ed241cc855f3befea39df277f70e8fcda7 Mon Sep 17 00:00:00 2001
From: Craig Gidney <craiggidney@google.com>
Date: Wed, 7 Aug 2024 12:08:23 -0700
Subject: [PATCH 13/17] Update circuit_data_references.md (#813)

---
 doc/circuit_data_references.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/doc/circuit_data_references.md b/doc/circuit_data_references.md
index 3e26d485..ea041097 100644
--- a/doc/circuit_data_references.md
+++ b/doc/circuit_data_references.md
@@ -26,3 +26,4 @@
 ## 2024
 
 - [arXiv:2405.15854](https://arxiv.org/abs/2405.15854) → [Stim circuits for 'Accommodating Fabrication Defects on Floquet Codes with Minimal Hardware Requirements' manuscript](https://zenodo.org/records/11241876)
+- [arXiv:2408.00758](https://arxiv.org/abs/2408.00758) → [Stim circuits for ``To reset, or not to reset -- that is the question" manuscript](https://zenodo.org/records/13152440)

From 8a23f4cf9cee29c73d8c6e3ac8646e5177b762c9 Mon Sep 17 00:00:00 2001
From: Craig Gidney <craiggidney@google.com>
Date: Mon, 26 Aug 2024 15:40:01 -0700
Subject: [PATCH 14/17] Update circuit_data_references.md (#816)

---
 doc/circuit_data_references.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/circuit_data_references.md b/doc/circuit_data_references.md
index ea041097..3eaf5ae8 100644
--- a/doc/circuit_data_references.md
+++ b/doc/circuit_data_references.md
@@ -26,4 +26,6 @@
 ## 2024
 
 - [arXiv:2405.15854](https://arxiv.org/abs/2405.15854) → [Stim circuits for 'Accommodating Fabrication Defects on Floquet Codes with Minimal Hardware Requirements' manuscript](https://zenodo.org/records/11241876)
+- [arXiv:2407.13826 ](https://arxiv.org/abs/2407.13826) → [Stim circuits for 'Designing fault-tolerant circuits using detector error models'](https://github.com/peter-janderks/short_measurement_schedules_simulations/tree/main/stim_circuits)
 - [arXiv:2408.00758](https://arxiv.org/abs/2408.00758) → [Stim circuits for ``To reset, or not to reset -- that is the question" manuscript](https://zenodo.org/records/13152440)
+- [arXiv:2408.11894](https://arxiv.org/abs/2408.11894) → [Stim circuits for 'Automated Synthesis of Fault-Tolerant State Preparation Circuits for Quantum Error Correction Codes'](https://github.com/cda-tum/mqt-qecc/tree/main/src/mqt/qecc/ft_stateprep/eval/circuits)

From 64cf7e11f5a8407e65eb194d5fa9638e6d42342a Mon Sep 17 00:00:00 2001
From: Paul Conner <97137029+qec-pconner@users.noreply.github.com>
Date: Fri, 6 Sep 2024 20:30:25 -0700
Subject: [PATCH 15/17] Fixing Extra Semicolon (#821)

* Removing unnecessary `;` on `JsonObj::JsonObj(std::vector<JsonObj>)`.
  * Trips off `-Wpedantic` and other warnings.
* Fixing newlines around function definition.
---
 src/stim/diagram/json_obj.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/stim/diagram/json_obj.cc b/src/stim/diagram/json_obj.cc
index d59bc578..71e44d5d 100644
--- a/src/stim/diagram/json_obj.cc
+++ b/src/stim/diagram/json_obj.cc
@@ -48,8 +48,8 @@ JsonObj::JsonObj(std::map<std::string, JsonObj> map) : map(map), type(JsonTypeMa
 }
 
 JsonObj::JsonObj(std::vector<JsonObj> arr) : arr(arr), type(JsonTypeArray) {
+}
 
-};
 JsonObj::JsonObj(bool boolean) : val_boolean(boolean), type(JsonTypeBool) {
 }
 

From 07d5232f74036fcbd6282dcf4fc26e0de33907e1 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 10 Sep 2024 03:59:06 +0000
Subject: [PATCH 16/17] Bump actions/download-artifact from 2 to 4.1.7 in
 /.github/workflows (#818)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Bumps
[actions/download-artifact](https://github.com/actions/download-artifact)
from 2 to 4.1.7.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/actions/download-artifact/releases">actions/download-artifact's
releases</a>.</em></p>
<blockquote>
<h2>v4.1.7</h2>
<h2>What's Changed</h2>
<ul>
<li>Update <code>@​actions/artifact</code> dependency by <a
href="https://github.com/bethanyj28"><code>@​bethanyj28</code></a> in <a
href="https://redirect.github.com/actions/download-artifact/pull/325">actions/download-artifact#325</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/actions/download-artifact/compare/v4.1.6...v4.1.7">https://github.com/actions/download-artifact/compare/v4.1.6...v4.1.7</a></p>
<h2>v4.1.6</h2>
<h2>What's Changed</h2>
<ul>
<li>updating <code>@actions/artifact</code> dependency to v2.1.6 by <a
href="https://github.com/eggyhead"><code>@​eggyhead</code></a> in <a
href="https://redirect.github.com/actions/download-artifact/pull/324">actions/download-artifact#324</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/actions/download-artifact/compare/v4.1.5...v4.1.6">https://github.com/actions/download-artifact/compare/v4.1.5...v4.1.6</a></p>
<h2>v4.1.5</h2>
<h2>What's Changed</h2>
<ul>
<li>Update readme with v3/v2/v1 deprecation notice by <a
href="https://github.com/robherley"><code>@​robherley</code></a> in <a
href="https://redirect.github.com/actions/download-artifact/pull/322">actions/download-artifact#322</a></li>
<li>Update dependencies <code>@actions/core</code> to v1.10.1 and
<code>@actions/artifact</code> to v2.1.5</li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/actions/download-artifact/compare/v4.1.4...v4.1.5">https://github.com/actions/download-artifact/compare/v4.1.4...v4.1.5</a></p>
<h2>v4.1.4</h2>
<h2>What's Changed</h2>
<ul>
<li>Update <code>@​actions/artifact</code> by <a
href="https://github.com/bethanyj28"><code>@​bethanyj28</code></a> in <a
href="https://redirect.github.com/actions/download-artifact/pull/307">actions/download-artifact#307</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/actions/download-artifact/compare/v4...v4.1.4">https://github.com/actions/download-artifact/compare/v4...v4.1.4</a></p>
<h2>v4.1.3</h2>
<h2>What's Changed</h2>
<ul>
<li>Update release-new-action-version.yml by <a
href="https://github.com/konradpabjan"><code>@​konradpabjan</code></a>
in <a
href="https://redirect.github.com/actions/download-artifact/pull/292">actions/download-artifact#292</a></li>
<li>Update toolkit dependency with updated unzip logic by <a
href="https://github.com/bethanyj28"><code>@​bethanyj28</code></a> in <a
href="https://redirect.github.com/actions/download-artifact/pull/299">actions/download-artifact#299</a></li>
<li>Update <code>@​actions/artifact</code> by <a
href="https://github.com/bethanyj28"><code>@​bethanyj28</code></a> in <a
href="https://redirect.github.com/actions/download-artifact/pull/303">actions/download-artifact#303</a></li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a
href="https://github.com/bethanyj28"><code>@​bethanyj28</code></a> made
their first contribution in <a
href="https://redirect.github.com/actions/download-artifact/pull/299">actions/download-artifact#299</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/actions/download-artifact/compare/v4...v4.1.3">https://github.com/actions/download-artifact/compare/v4...v4.1.3</a></p>
<h2>v4.1.2</h2>
<ul>
<li>Bump <code>@​actions/artifacts</code> to latest version to include
<a href="https://redirect.github.com/actions/toolkit/pull/1648">updated
GHES host check</a></li>
</ul>
<h2>v4.1.1</h2>
<ul>
<li>Fix transient request timeouts <a
href="https://redirect.github.com/actions/download-artifact/issues/249">actions/download-artifact#249</a></li>
<li>Bump <code>@actions/artifacts</code> to latest version</li>
</ul>
<h2>v4.1.0</h2>
<h2>What's Changed</h2>
<ul>
<li>Some cleanup by <a
href="https://github.com/robherley"><code>@​robherley</code></a> in <a
href="https://redirect.github.com/actions/download-artifact/pull/247">actions/download-artifact#247</a></li>
<li>Fix default for run-id by <a
href="https://github.com/stchr"><code>@​stchr</code></a> in <a
href="https://redirect.github.com/actions/download-artifact/pull/252">actions/download-artifact#252</a></li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/actions/download-artifact/commit/65a9edc5881444af0b9093a5e628f2fe47ea3b2e"><code>65a9edc</code></a>
Merge pull request <a
href="https://redirect.github.com/actions/download-artifact/issues/325">#325</a>
from bethanyj28/main</li>
<li><a
href="https://github.com/actions/download-artifact/commit/fdd1595981c1a29187d3de99c28c28a166bc38f7"><code>fdd1595</code></a>
licensed</li>
<li><a
href="https://github.com/actions/download-artifact/commit/c13dba102f4bb92b3f679fa086db9e2973960ca7"><code>c13dba1</code></a>
update <code>@​actions/artifact</code> dependency</li>
<li><a
href="https://github.com/actions/download-artifact/commit/0daa75ebeac4617faeb127496dbd716b8bcce26e"><code>0daa75e</code></a>
Merge pull request <a
href="https://redirect.github.com/actions/download-artifact/issues/324">#324</a>
from actions/eggyhead/use-artifact-v2.1.6</li>
<li><a
href="https://github.com/actions/download-artifact/commit/9c19ed7fe5d278cd354c7dfd5d3b88589c7e2395"><code>9c19ed7</code></a>
Merge branch 'main' into eggyhead/use-artifact-v2.1.6</li>
<li><a
href="https://github.com/actions/download-artifact/commit/3d3ea8741ef44e86f7392b41e391bde3c36219bd"><code>3d3ea87</code></a>
updating license</li>
<li><a
href="https://github.com/actions/download-artifact/commit/89af5db8211998d3ca691103a86b0b9362a94286"><code>89af5db</code></a>
updating artifact package v2.1.6</li>
<li><a
href="https://github.com/actions/download-artifact/commit/b4aefff88e83a2676a730654e1ce3dce61880379"><code>b4aefff</code></a>
Merge pull request <a
href="https://redirect.github.com/actions/download-artifact/issues/323">#323</a>
from actions/eggyhead/update-artifact-v215</li>
<li><a
href="https://github.com/actions/download-artifact/commit/8caf195ad4b1dee92908e23f56eeb0696f1dd42d"><code>8caf195</code></a>
package lock update</li>
<li><a
href="https://github.com/actions/download-artifact/commit/d7a2ec411d177e8ca679ac5969b70be59c322700"><code>d7a2ec4</code></a>
updating package version</li>
<li>Additional commits viewable in <a
href="https://github.com/actions/download-artifact/compare/v2...v4.1.7">compare
view</a></li>
</ul>
</details>
<br />


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---
 .github/workflows/ci.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 3c6709c6..b22c5327 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -185,7 +185,7 @@ jobs:
     if: github.ref == 'refs/heads/main'
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/download-artifact@v2
+      - uses: actions/download-artifact@v4.1.7
         with:
           name: dist
           path: dist

From 5e4f8b2497b2bf128aac8144098c4572ec83ecbd Mon Sep 17 00:00:00 2001
From: Craig Gidney <craiggidney@google.com>
Date: Mon, 9 Sep 2024 21:25:33 -0700
Subject: [PATCH 17/17] Add custom samplers, better collection, and better
 plotting to sinter (#804)

- Add `sinter.Sampler` and `sinter.CompiledSampler` classes
- They can go anywhere a Decoder would go, but they are responsible for
all parts of the sampling instead of only prediction
- Add a new default sampler `perfectionist`, which discards anything
with detection events and predicts the observables are not flipped
- Improved layout of the progress printouts when collect is running
- Sinter decoders can now flag that they want to discard shots by adding
an extra byte to the returned observable data, with 0 meaning keep and
not-0 meaning discard
- Change how `sinter collect` distributes work
- Workers are now distributed as widely as possible, instead of all on
one task
- Workers are now never switched between tasks until their current task
is done
- Add `sinter plot --point_label_func` argument for drawing text next to
data points
- Augment `sinter plot --group_func` to support dictionaries with
special keys controlling precise grouping behaviors
- If group_func returns a dict with a `"color"` key, all items with the
same `"color"` value are drawn with the same color
- If group_func returns a dict with a `"linestyle"` key, all items with
the same `"linestyle"` value are drawn with the same linestyle
- If group_func returns a dict with a `"marker"` key, all items with the
same `"marker"` value are drawn with the same marker
- If group_func returns a dict with a `"label"` key, this forces the
label shown in the legend
- If group_func returns a dict with an `"order"` key, this takes
priority for ordering the legend
- `sinter collect --processes` is no longer required (defaults to
`"auto"`)
- `sinter plot --show` is no longer required (defaults to showing,
unless `--out` is specified, unless `--show` is specified)
- Group some of sinter's code into private subpackages
- Show traditional error bars instead of a filled region for high/low
fit when only one data point is present
- Add `sinter plot --preprocess_stats_func`
- Add `sinter.TaskStats.with_edits`
- Add safety error when adding stats that have equal strong ids but
differing identifying information (json_metadata or decoder)

Some of the sampler design is adapted from @inmzhang's design in
https://github.com/quantumlib/Stim/pull/735

Fixes https://github.com/quantumlib/Stim/issues/774

Fixes https://github.com/quantumlib/Stim/issues/682

Fixes https://github.com/quantumlib/Stim/issues/392

---------

Co-authored-by: Matt McEwen <mmcewen@google.com>
---
 dev/gen_sinter_api_reference.py               |  24 +-
 dev/util_gen_stub_file.py                     |  13 +-
 doc/python_api_reference_vDev.md              |   1 +
 doc/sinter_api.md                             | 179 +++++-
 doc/stim.pyi                                  |   1 +
 glue/python/src/stim/__init__.pyi             |   1 +
 glue/sample/setup.py                          |   2 +-
 glue/sample/src/sinter/__init__.py            |  33 +-
 .../sample/src/sinter/_collection/__init__.py |  10 +
 .../sinter/{ => _collection}/_collection.py   | 102 ++--
 .../sinter/_collection/_collection_manager.py | 577 ++++++++++++++++++
 .../_collection/_collection_manager_test.py   | 287 +++++++++
 .../{ => _collection}/_collection_test.py     |  64 ++
 .../_collection/_collection_worker_loop.py    |  35 ++
 .../_collection/_collection_worker_state.py   | 256 ++++++++
 .../_collection/_collection_worker_test.py    | 214 +++++++
 .../src/sinter/_collection/_mux_sampler.py    |  56 ++
 .../src/sinter/{ => _collection}/_printer.py  |   0
 .../_collection/_sampler_ramp_throttled.py    |  66 ++
 .../_collection_tracker_for_single_task.py    | 230 -------
 .../src/sinter/_collection_work_manager.py    | 275 ---------
 glue/sample/src/sinter/_command/__init__.py   |   0
 .../sample/src/sinter/{ => _command}/_main.py |   8 +-
 .../sinter/{ => _command}/_main_collect.py    |  20 +-
 .../{ => _command}/_main_collect_test.py      |  38 +-
 .../sinter/{ => _command}/_main_combine.py    |   3 +-
 .../{ => _command}/_main_combine_test.py      |   2 +-
 .../src/sinter/{ => _command}/_main_plot.py   | 222 ++++---
 .../sinter/{ => _command}/_main_plot_test.py  |   4 +-
 .../sinter/{ => _command}/_main_predict.py    |   0
 .../{ => _command}/_main_predict_test.py      |   2 +-
 glue/sample/src/sinter/_data/__init__.py      |  20 +
 .../sinter/{ => _data}/_anon_task_stats.py    |  23 +-
 .../{ => _data}/_anon_task_stats_test.py      |   0
 .../sinter/{ => _data}/_collection_options.py |   0
 .../{ => _data}/_collection_options_test.py   |   0
 .../sample/src/sinter/{ => _data}/_csv_out.py |   0
 .../src/sinter/{ => _data}/_existing_data.py  |  11 +-
 .../sinter/{ => _data}/_existing_data_test.py |   0
 glue/sample/src/sinter/{ => _data}/_task.py   |   2 +-
 .../src/sinter/{ => _data}/_task_stats.py     |  96 ++-
 .../sinter/{ => _data}/_task_stats_test.py    |  51 ++
 .../src/sinter/{ => _data}/_task_test.py      |   0
 glue/sample/src/sinter/_decoding/__init__.py  |  16 +
 .../src/sinter/{ => _decoding}/_decoding.py   |   6 +-
 .../_decoding_all_built_in_decoders.py        |  20 +
 .../_decoding_decoder_class.py                |   0
 .../_decoding_fusion_blossom.py               |   2 +-
 .../{ => _decoding}/_decoding_pymatching.py   |   2 +-
 .../sinter/{ => _decoding}/_decoding_test.py  |   6 +-
 .../{ => _decoding}/_decoding_vacuous.py      |   2 +-
 .../_decoding/_perfectionist_sampler.py       |  38 ++
 glue/sample/src/sinter/_decoding/_sampler.py  |  72 +++
 .../_decoding/_stim_then_decode_sampler.py    | 222 +++++++
 .../_stim_then_decode_sampler_test.py         | 192 ++++++
 .../sinter/_decoding_all_built_in_decoders.py |  12 -
 glue/sample/src/sinter/_plotting.py           | 294 ++++++---
 glue/sample/src/sinter/_plotting_test.py      |   3 +
 glue/sample/src/sinter/_predict.py            |   4 +-
 glue/sample/src/sinter/_probability_util.py   |   7 +-
 glue/sample/src/sinter/_worker.py             | 212 -------
 glue/sample/src/sinter/_worker_test.py        | 134 ----
 src/stim/circuit/circuit.pybind.cc            |   2 +-
 src/stim/util_bot/probability_util.h          |  10 +
 64 files changed, 2998 insertions(+), 1186 deletions(-)
 create mode 100644 glue/sample/src/sinter/_collection/__init__.py
 rename glue/sample/src/sinter/{ => _collection}/_collection.py (87%)
 create mode 100644 glue/sample/src/sinter/_collection/_collection_manager.py
 create mode 100644 glue/sample/src/sinter/_collection/_collection_manager_test.py
 rename glue/sample/src/sinter/{ => _collection}/_collection_test.py (78%)
 create mode 100644 glue/sample/src/sinter/_collection/_collection_worker_loop.py
 create mode 100644 glue/sample/src/sinter/_collection/_collection_worker_state.py
 create mode 100644 glue/sample/src/sinter/_collection/_collection_worker_test.py
 create mode 100755 glue/sample/src/sinter/_collection/_mux_sampler.py
 rename glue/sample/src/sinter/{ => _collection}/_printer.py (100%)
 create mode 100755 glue/sample/src/sinter/_collection/_sampler_ramp_throttled.py
 delete mode 100644 glue/sample/src/sinter/_collection_tracker_for_single_task.py
 delete mode 100644 glue/sample/src/sinter/_collection_work_manager.py
 create mode 100644 glue/sample/src/sinter/_command/__init__.py
 rename glue/sample/src/sinter/{ => _command}/_main.py (83%)
 rename glue/sample/src/sinter/{ => _command}/_main_collect.py (96%)
 rename glue/sample/src/sinter/{ => _command}/_main_collect_test.py (92%)
 rename glue/sample/src/sinter/{ => _command}/_main_combine.py (97%)
 rename glue/sample/src/sinter/{ => _command}/_main_combine_test.py (99%)
 rename glue/sample/src/sinter/{ => _command}/_main_plot.py (82%)
 rename glue/sample/src/sinter/{ => _command}/_main_plot_test.py (99%)
 rename glue/sample/src/sinter/{ => _command}/_main_predict.py (100%)
 rename glue/sample/src/sinter/{ => _command}/_main_predict_test.py (95%)
 create mode 100644 glue/sample/src/sinter/_data/__init__.py
 rename glue/sample/src/sinter/{ => _data}/_anon_task_stats.py (84%)
 rename glue/sample/src/sinter/{ => _data}/_anon_task_stats_test.py (100%)
 rename glue/sample/src/sinter/{ => _data}/_collection_options.py (100%)
 rename glue/sample/src/sinter/{ => _data}/_collection_options_test.py (100%)
 rename glue/sample/src/sinter/{ => _data}/_csv_out.py (100%)
 rename glue/sample/src/sinter/{ => _data}/_existing_data.py (96%)
 rename glue/sample/src/sinter/{ => _data}/_existing_data_test.py (100%)
 rename glue/sample/src/sinter/{ => _data}/_task.py (99%)
 rename glue/sample/src/sinter/{ => _data}/_task_stats.py (62%)
 rename glue/sample/src/sinter/{ => _data}/_task_stats_test.py (53%)
 rename glue/sample/src/sinter/{ => _data}/_task_test.py (100%)
 create mode 100644 glue/sample/src/sinter/_decoding/__init__.py
 rename glue/sample/src/sinter/{ => _decoding}/_decoding.py (98%)
 create mode 100644 glue/sample/src/sinter/_decoding/_decoding_all_built_in_decoders.py
 rename glue/sample/src/sinter/{ => _decoding}/_decoding_decoder_class.py (100%)
 rename glue/sample/src/sinter/{ => _decoding}/_decoding_fusion_blossom.py (99%)
 rename glue/sample/src/sinter/{ => _decoding}/_decoding_pymatching.py (97%)
 rename glue/sample/src/sinter/{ => _decoding}/_decoding_test.py (98%)
 rename glue/sample/src/sinter/{ => _decoding}/_decoding_vacuous.py (94%)
 create mode 100755 glue/sample/src/sinter/_decoding/_perfectionist_sampler.py
 create mode 100644 glue/sample/src/sinter/_decoding/_sampler.py
 create mode 100755 glue/sample/src/sinter/_decoding/_stim_then_decode_sampler.py
 create mode 100755 glue/sample/src/sinter/_decoding/_stim_then_decode_sampler_test.py
 delete mode 100644 glue/sample/src/sinter/_decoding_all_built_in_decoders.py
 delete mode 100644 glue/sample/src/sinter/_worker.py
 delete mode 100644 glue/sample/src/sinter/_worker_test.py

diff --git a/dev/gen_sinter_api_reference.py b/dev/gen_sinter_api_reference.py
index c3e348af..a4b907f0 100644
--- a/dev/gen_sinter_api_reference.py
+++ b/dev/gen_sinter_api_reference.py
@@ -48,6 +48,25 @@ def main():
 ```
 '''.strip())
 
+    replace_rules = []
+    for package in ['stim', 'sinter']:
+        p = __import__(package)
+        for name in dir(p):
+            x = getattr(p, name)
+            if isinstance(x, type) and 'class' in str(x):
+                desired_name = f'{package}.{name}'
+                if '._' in str(x):
+                    bad_name = str(x).split("'")[1]
+                    replace_rules.append((bad_name, desired_name))
+                lonely_name = desired_name.split(".")[-1]
+                for q in ['"', "'"]:
+                    replace_rules.append(('ForwardRef(' + q + lonely_name + q + ')', desired_name))
+                    replace_rules.append(('ForwardRef(' + q + desired_name + q + ')', desired_name))
+                    replace_rules.append((q + desired_name + q, desired_name))
+                    replace_rules.append((q + lonely_name + q, desired_name))
+                replace_rules.append(('ForwardRef(' + desired_name + ')', desired_name))
+                replace_rules.append(('ForwardRef(' + lonely_name + ')', desired_name))
+
     for obj in objects:
         print()
         print(f'<a name="{obj.full_name}"></a>')
@@ -58,7 +77,10 @@ def main():
             print(f'# (in class {".".join(obj.full_name.split(".")[:-1])})')
         else:
             print(f'# (at top-level in the sinter module)')
-        print('\n'.join(obj.lines))
+        for line in obj.lines:
+            for a, b in replace_rules:
+                line = line.replace(a, b)
+            print(line)
         print("```")
 
 
diff --git a/dev/util_gen_stub_file.py b/dev/util_gen_stub_file.py
index 26a34530..2c651999 100644
--- a/dev/util_gen_stub_file.py
+++ b/dev/util_gen_stub_file.py
@@ -1,5 +1,4 @@
 import dataclasses
-import sys
 import types
 from typing import Any
 from typing import Optional, Iterator, List
@@ -9,6 +8,7 @@
 
 keep = {
     "__add__",
+    "__radd__",
     "__eq__",
     "__call__",
     "__ge__",
@@ -224,17 +224,6 @@ def print_doc(*, full_name: str, parent: object, obj: object, level: int) -> Opt
             text += '@abc.abstractmethod\n'
         sig_name = f'{term_name}{inspect.signature(obj)}'
         text += "\n".join(splay_signature(f"def {sig_name}:"))
-        text = text.replace('''ForwardRef('sinter.TaskStats')''', 'sinter.TaskStats')
-        text = text.replace('''ForwardRef('sinter.Task')''', 'sinter.Task')
-        text = text.replace('''ForwardRef('sinter.Progress')''', 'sinter.Progress')
-        text = text.replace('''ForwardRef('sinter.Decoder')''', 'sinter.Decoder')
-        text = text.replace("'AnonTaskStats'", "sinter.AnonTaskStats")
-        text = text.replace('sinter._decoding_decoder_class.CompiledDecoder', 'sinter.CompiledDecoder')
-        text = text.replace("'AnonTaskStats'", "sinter.AnonTaskStats")
-        text = text.replace("'stim.Circuit'", "stim.Circuit")
-        text = text.replace("'stim.DetectorErrorModel'", "stim.DetectorErrorModel")
-        text = text.replace("'sinter.CollectionOptions'", "sinter.CollectionOptions")
-        text = text.replace("'sinter.Fit'", 'sinter.Fit')
 
         # Replace default value lambdas with their source.
         if 'lambda' in str(text):
diff --git a/doc/python_api_reference_vDev.md b/doc/python_api_reference_vDev.md
index f509eef0..3b7e34cf 100644
--- a/doc/python_api_reference_vDev.md
+++ b/doc/python_api_reference_vDev.md
@@ -1610,6 +1610,7 @@ def diagram(
     *,
     tick: Union[None, int, range] = None,
     filter_coords: Iterable[Union[Iterable[float], stim.DemTarget]] = ((),),
+    rows: int | None = None,
 ) -> 'stim._DiagramHelper':
     """Returns a diagram of the circuit, from a variety of options.
 
diff --git a/doc/sinter_api.md b/doc/sinter_api.md
index f940813f..3093e41a 100644
--- a/doc/sinter_api.md
+++ b/doc/sinter_api.md
@@ -12,11 +12,16 @@ API references for stable versions are kept on the [stim github wiki](https://gi
     - [`sinter.CollectionOptions.combine`](#sinter.CollectionOptions.combine)
 - [`sinter.CompiledDecoder`](#sinter.CompiledDecoder)
     - [`sinter.CompiledDecoder.decode_shots_bit_packed`](#sinter.CompiledDecoder.decode_shots_bit_packed)
+- [`sinter.CompiledSampler`](#sinter.CompiledSampler)
+    - [`sinter.CompiledSampler.handles_throttling`](#sinter.CompiledSampler.handles_throttling)
+    - [`sinter.CompiledSampler.sample`](#sinter.CompiledSampler.sample)
 - [`sinter.Decoder`](#sinter.Decoder)
     - [`sinter.Decoder.compile_decoder_for_dem`](#sinter.Decoder.compile_decoder_for_dem)
     - [`sinter.Decoder.decode_via_files`](#sinter.Decoder.decode_via_files)
 - [`sinter.Fit`](#sinter.Fit)
 - [`sinter.Progress`](#sinter.Progress)
+- [`sinter.Sampler`](#sinter.Sampler)
+    - [`sinter.Sampler.compiled_sampler_for_task`](#sinter.Sampler.compiled_sampler_for_task)
 - [`sinter.Task`](#sinter.Task)
     - [`sinter.Task.__init__`](#sinter.Task.__init__)
     - [`sinter.Task.strong_id`](#sinter.Task.strong_id)
@@ -26,6 +31,7 @@ API references for stable versions are kept on the [stim github wiki](https://gi
 - [`sinter.TaskStats`](#sinter.TaskStats)
     - [`sinter.TaskStats.to_anon_stats`](#sinter.TaskStats.to_anon_stats)
     - [`sinter.TaskStats.to_csv_line`](#sinter.TaskStats.to_csv_line)
+    - [`sinter.TaskStats.with_edits`](#sinter.TaskStats.with_edits)
 - [`sinter.better_sorted_str_terms`](#sinter.better_sorted_str_terms)
 - [`sinter.collect`](#sinter.collect)
 - [`sinter.comma_separated_key_values`](#sinter.comma_separated_key_values)
@@ -257,6 +263,73 @@ def decode_shots_bit_packed(
     """
 ```
 
+<a name="sinter.CompiledSampler"></a>
+```python
+# sinter.CompiledSampler
+
+# (at top-level in the sinter module)
+class CompiledSampler(metaclass=abc.ABCMeta):
+    """A sampler that has been configured for efficiently sampling some task.
+    """
+```
+
+<a name="sinter.CompiledSampler.handles_throttling"></a>
+```python
+# sinter.CompiledSampler.handles_throttling
+
+# (in class sinter.CompiledSampler)
+def handles_throttling(
+    self,
+) -> bool:
+    """Return True to disable sinter wrapping samplers with throttling.
+
+    By default, sinter will wrap samplers so that they initially only do
+    a small number of shots then slowly ramp up. Sometimes this behavior
+    is not desired (e.g. in unit tests). Override this method to return True
+    to disable it.
+    """
+```
+
+<a name="sinter.CompiledSampler.sample"></a>
+```python
+# sinter.CompiledSampler.sample
+
+# (in class sinter.CompiledSampler)
+@abc.abstractmethod
+def sample(
+    self,
+    suggested_shots: int,
+) -> sinter.AnonTaskStats:
+    """Samples shots and returns statistics.
+
+    Args:
+        suggested_shots: The number of shots being requested. The sampler
+            may perform more shots or fewer shots than this, so technically
+            this argument can just be ignored. If a sampler is optimized for
+            a specific batch size, it can simply return one batch per call
+            regardless of this parameter.
+
+            However, this parameter is a useful hint about the amount of
+            work being done. The sampler can use this to optimize its
+            behavior. For example, it could adjust its batch size downward
+            if the suggested shots is very small. Whereas if the suggested
+            shots is very high, the sampler should focus entirely on
+            achieving the best possible throughput.
+
+            Note that, in typical workloads, the sampler will be called
+            repeatedly with the same value of suggested_shots. Therefore it
+            is reasonable to allocate buffers sized to accomodate the
+            current suggested_shots, expecting them to be useful again for
+            the next shot.
+
+    Returns:
+        A sinter.AnonTaskStats saying how many shots were actually taken,
+        how many errors were seen, etc.
+
+        The returned stats must have at least one shot.
+    """
+```
+
 <a name="sinter.Decoder"></a>
 ```python
 # sinter.Decoder
@@ -385,9 +458,9 @@ class Fit:
             of the best fit's square error, or whose likelihood was within some
             maximum Bayes factor of the max likelihood hypothesis.
     """
-    low: float
-    best: float
-    high: float
+    low: Optional[float]
+    best: Optional[float]
+    high: Optional[float]
 ```
 
 <a name="sinter.Progress"></a>
@@ -409,10 +482,45 @@ class Progress:
             collection status, such as the number of tasks left and the
             estimated time to completion for each task.
     """
-    new_stats: Tuple[sinter._task_stats.TaskStats, ...]
+    new_stats: Tuple[sinter.TaskStats, ...]
     status_message: str
 ```
 
+<a name="sinter.Sampler"></a>
+```python
+# sinter.Sampler
+
+# (at top-level in the sinter module)
+class Sampler(metaclass=abc.ABCMeta):
+    """A strategy for producing stats from tasks.
+
+    Call `sampler.compiled_sampler_for_task(task)` to get a compiled sampler for
+    a task, then call `compiled_sampler.sample(shots)` to collect statistics.
+
+    A sampler differs from a `sinter.Decoder` because the sampler is responsible
+    for the full sampling process (e.g. simulating the circuit), whereas a
+    decoder can do nothing except predict observable flips from detection event
+    data. This prevents the decoders from cheating, but makes them less flexible
+    overall. A sampler can do things like use simulators other than stim, or
+    really anything at all as long as it ends with returning statistics about
+    shot counts, error counts, and etc.
+    """
+```
+
+<a name="sinter.Sampler.compiled_sampler_for_task"></a>
+```python
+# sinter.Sampler.compiled_sampler_for_task
+
+# (in class sinter.Sampler)
+@abc.abstractmethod
+def compiled_sampler_for_task(
+    self,
+    task: sinter.Task,
+) -> sinter.CompiledSampler:
+    """Creates, configures, and returns an object for sampling the task.
+    """
+```
+
 <a name="sinter.Task"></a>
 ```python
 # sinter.Task
@@ -475,9 +583,9 @@ class Task:
 def __init__(
     self,
     *,
-    circuit: Optional[ForwardRef(stim.Circuit)] = None,
+    circuit: Optional[stim.Circuit] = None,
     decoder: Optional[str] = None,
-    detector_error_model: Optional[ForwardRef(stim.DetectorErrorModel)] = None,
+    detector_error_model: Optional[stim.DetectorErrorModel] = None,
     postselection_mask: Optional[np.ndarray] = None,
     postselected_observables_mask: Optional[np.ndarray] = None,
     json_metadata: Any = None,
@@ -699,7 +807,7 @@ class TaskStats:
 # (in class sinter.TaskStats)
 def to_anon_stats(
     self,
-) -> sinter._anon_task_stats.AnonTaskStats:
+) -> sinter.AnonTaskStats:
     """Returns a `sinter.AnonTaskStats` with the same statistics.
 
     Examples:
@@ -745,6 +853,25 @@ def to_csv_line(
     """
 ```
 
+<a name="sinter.TaskStats.with_edits"></a>
+```python
+# sinter.TaskStats.with_edits
+
+# (in class sinter.TaskStats)
+def with_edits(
+    self,
+    *,
+    strong_id: Optional[str] = None,
+    decoder: Optional[str] = None,
+    json_metadata: Optional[Any] = None,
+    shots: Optional[int] = None,
+    errors: Optional[int] = None,
+    discards: Optional[int] = None,
+    seconds: Optional[float] = None,
+    custom_counts: Optional[Counter[str]] = None,
+) -> sinter.TaskStats:
+```
+
 <a name="sinter.better_sorted_str_terms"></a>
 ```python
 # sinter.better_sorted_str_terms
@@ -809,7 +936,7 @@ def collect(
     start_batch_size: Optional[int] = None,
     print_progress: bool = False,
     hint_num_tasks: Optional[int] = None,
-    custom_decoders: Optional[Dict[str, sinter.Decoder]] = None,
+    custom_decoders: Optional[Dict[str, Union[sinter.Decoder, sinter.Sampler]]] = None,
     custom_error_count_key: Optional[str] = None,
     allowed_cpu_affinity_ids: Optional[Iterable[int]] = None,
 ) -> List[sinter.TaskStats]:
@@ -1124,7 +1251,7 @@ def iter_collect(
     num_workers: int,
     tasks: Union[Iterator[sinter.Task], Iterable[sinter.Task]],
     hint_num_tasks: Optional[int] = None,
-    additional_existing_data: Optional[sinter._existing_data.ExistingData] = None,
+    additional_existing_data: Union[NoneType, Dict[str, sinter.TaskStats], Iterable[sinter.TaskStats]] = None,
     max_shots: Optional[int] = None,
     max_errors: Optional[int] = None,
     decoders: Optional[Iterable[str]] = None,
@@ -1133,7 +1260,7 @@ def iter_collect(
     start_batch_size: Optional[int] = None,
     count_observable_error_combos: bool = False,
     count_detection_events: bool = False,
-    custom_decoders: Optional[Dict[str, sinter.Decoder]] = None,
+    custom_decoders: Optional[Dict[str, Union[sinter.Decoder, sinter.Sampler]]] = None,
     custom_error_count_key: Optional[str] = None,
     allowed_cpu_affinity_ids: Optional[Iterable[int]] = None,
 ) -> Iterator[sinter.Progress]:
@@ -1337,6 +1464,7 @@ def plot_discard_rate(
     filter_func: Callable[[sinter.TaskStats], Any] = lambda _: True,
     plot_args_func: Callable[[int, ~TCurveId, List[sinter.TaskStats]], Dict[str, Any]] = lambda index, group_key, group_stats: dict(),
     highlight_max_likelihood_factor: Optional[float] = 1000.0,
+    point_label_func: Callable[[sinter.TaskStats], Any] = lambda _: None,
 ) -> None:
     """Plots discard rates in curves with uncertainty highlights.
 
@@ -1353,11 +1481,21 @@ def plot_discard_rate(
         group_func: Optional. When specified, multiple curves will be plotted instead of one curve.
             The statistics are grouped into curves based on whether or not they get the same result
             out of this function. For example, this could be `group_func=lambda stat: stat.decoder`.
+            If the result of the function is a dictionary, then optional keys in the dictionary will
+            also control the plotting of each curve. Available keys are:
+                'label': the label added to the legend for the curve
+                'color': the color used for plotting the curve
+                'marker': the marker used for the curve
+                'linestyle': the linestyle used for the curve
+                'sort': the order in which the curves will be plotted and added to the legend
+            e.g. if two curves (with different resulting dictionaries from group_func) share the same
+            value for key 'marker', they will be plotted with the same marker.
+            Colors, markers and linestyles are assigned in order, sorted by the values for those keys.
         filter_func: Optional. When specified, some curves will not be plotted.
             The statistics are filtered and only plotted if filter_func(stat) returns True.
             For example, `filter_func=lambda s: s.json_metadata['basis'] == 'x'` would plot only stats
             where the saved metadata indicates the basis was 'x'.
-        plot_args_func: Optional. Specifies additional arguments to give the the underlying calls to
+        plot_args_func: Optional. Specifies additional arguments to give the underlying calls to
             `plot` and `fill_between` used to do the actual plotting. For example, this can be used
             to specify markers and colors. Takes the index of the curve in sorted order and also a
             curve_id (these will be 0 and None respectively if group_func is not specified). For example,
@@ -1370,6 +1508,7 @@ def plot_discard_rate(
         highlight_max_likelihood_factor: Controls how wide the uncertainty highlight region around curves is.
             Must be 1 or larger. Hypothesis probabilities at most that many times as unlikely as the max likelihood
             hypothesis will be highlighted.
+        point_label_func: Optional. Specifies text to draw next to data points.
     """
 ```
 
@@ -1390,6 +1529,7 @@ def plot_error_rate(
     plot_args_func: Callable[[int, ~TCurveId, List[sinter.TaskStats]], Dict[str, Any]] = lambda index, group_key, group_stats: dict(),
     highlight_max_likelihood_factor: Optional[float] = 1000.0,
     line_fits: Optional[Tuple[Literal['linear', 'log', 'sqrt'], Literal['linear', 'log', 'sqrt']]] = None,
+    point_label_func: Callable[[sinter.TaskStats], Any] = lambda _: None,
 ) -> None:
     """Plots error rates in curves with uncertainty highlights.
 
@@ -1410,11 +1550,21 @@ def plot_error_rate(
         group_func: Optional. When specified, multiple curves will be plotted instead of one curve.
             The statistics are grouped into curves based on whether or not they get the same result
             out of this function. For example, this could be `group_func=lambda stat: stat.decoder`.
+            If the result of the function is a dictionary, then optional keys in the dictionary will
+            also control the plotting of each curve. Available keys are:
+                'label': the label added to the legend for the curve
+                'color': the color used for plotting the curve
+                'marker': the marker used for the curve
+                'linestyle': the linestyle used for the curve
+                'sort': the order in which the curves will be plotted and added to the legend
+            e.g. if two curves (with different resulting dictionaries from group_func) share the same
+            value for key 'marker', they will be plotted with the same marker.
+            Colors, markers and linestyles are assigned in order, sorted by the values for those keys.
         filter_func: Optional. When specified, some curves will not be plotted.
             The statistics are filtered and only plotted if filter_func(stat) returns True.
             For example, `filter_func=lambda s: s.json_metadata['basis'] == 'x'` would plot only stats
             where the saved metadata indicates the basis was 'x'.
-        plot_args_func: Optional. Specifies additional arguments to give the the underlying calls to
+        plot_args_func: Optional. Specifies additional arguments to give the underlying calls to
             `plot` and `fill_between` used to do the actual plotting. For example, this can be used
             to specify markers and colors. Takes the index of the curve in sorted order and also a
             curve_id (these will be 0 and None respectively if group_func is not specified). For example,
@@ -1430,6 +1580,7 @@ def plot_error_rate(
         line_fits: Defaults to None. Set this to a tuple (x_scale, y_scale) to include a dashed line
             fit to every curve. The scales determine how to transform the coordinates before
             performing the fit, and can be set to 'linear', 'sqrt', or 'log'.
+        point_label_func: Optional. Specifies text to draw next to data points.
     """
 ```
 
@@ -1712,11 +1863,11 @@ def read_stats_from_csv_files(
 
 # (at top-level in the sinter module)
 def shot_error_rate_to_piece_error_rate(
-    shot_error_rate: Union[float, ForwardRef(sinter.Fit)],
+    shot_error_rate: Union[float, sinter.Fit],
     *,
     pieces: float,
     values: float = 1,
-) -> Union[float, ForwardRef(sinter.Fit)]:
+) -> Union[float, sinter.Fit]:
     """Convert from total error rate to per-piece error rate.
 
     Args:
diff --git a/doc/stim.pyi b/doc/stim.pyi
index e20cbea0..2ffe7d47 100644
--- a/doc/stim.pyi
+++ b/doc/stim.pyi
@@ -1010,6 +1010,7 @@ class Circuit:
         *,
         tick: Union[None, int, range] = None,
         filter_coords: Iterable[Union[Iterable[float], stim.DemTarget]] = ((),),
+        rows: int | None = None,
     ) -> 'stim._DiagramHelper':
         """Returns a diagram of the circuit, from a variety of options.
 
diff --git a/glue/python/src/stim/__init__.pyi b/glue/python/src/stim/__init__.pyi
index e20cbea0..2ffe7d47 100644
--- a/glue/python/src/stim/__init__.pyi
+++ b/glue/python/src/stim/__init__.pyi
@@ -1010,6 +1010,7 @@ class Circuit:
         *,
         tick: Union[None, int, range] = None,
         filter_coords: Iterable[Union[Iterable[float], stim.DemTarget]] = ((),),
+        rows: int | None = None,
     ) -> 'stim._DiagramHelper':
         """Returns a diagram of the circuit, from a variety of options.
 
diff --git a/glue/sample/setup.py b/glue/sample/setup.py
index f6c5da08..7fc73a6d 100644
--- a/glue/sample/setup.py
+++ b/glue/sample/setup.py
@@ -37,6 +37,6 @@
     install_requires=requirements,
     tests_require=['pytest', 'pymatching'],
     entry_points={
-        'console_scripts': ['sinter=sinter._main:main'],
+        'console_scripts': ['sinter=sinter._command._main:main'],
     },
 )
diff --git a/glue/sample/src/sinter/__init__.py b/glue/sample/src/sinter/__init__.py
index 1237b79b..a8ccc678 100644
--- a/glue/sample/src/sinter/__init__.py
+++ b/glue/sample/src/sinter/__init__.py
@@ -1,26 +1,27 @@
 __version__ = '1.14.dev0'
 
-from sinter._anon_task_stats import (
-    AnonTaskStats,
-)
 from sinter._collection import (
     collect,
     iter_collect,
     post_selection_mask_from_4th_coord,
     Progress,
 )
-from sinter._collection_options import (
+from sinter._data import (
+    AnonTaskStats,
     CollectionOptions,
-)
-from sinter._csv_out import (
     CSV_HEADER,
-)
-from sinter._decoding_all_built_in_decoders import (
-    BUILT_IN_DECODERS,
-)
-from sinter._existing_data import (
     read_stats_from_csv_files,
     stats_from_csv_files,
+    Task,
+    TaskStats,
+)
+from sinter._decoding import (
+    CompiledDecoder,
+    Decoder,
+    BUILT_IN_DECODERS,
+    BUILT_IN_SAMPLERS,
+    Sampler,
+    CompiledSampler,
 )
 from sinter._probability_util import (
     comma_separated_key_values,
@@ -38,19 +39,9 @@
     plot_error_rate,
     group_by,
 )
-from sinter._task import (
-    Task,
-)
-from sinter._task_stats import (
-    TaskStats,
-)
 from sinter._predict import (
     predict_discards_bit_packed,
     predict_observables_bit_packed,
     predict_on_disk,
     predict_observables,
 )
-from sinter._decoding_decoder_class import (
-    CompiledDecoder,
-    Decoder,
-)
diff --git a/glue/sample/src/sinter/_collection/__init__.py b/glue/sample/src/sinter/_collection/__init__.py
new file mode 100644
index 00000000..271e17c7
--- /dev/null
+++ b/glue/sample/src/sinter/_collection/__init__.py
@@ -0,0 +1,10 @@
+from sinter._collection._collection import (
+    collect,
+    iter_collect,
+    post_selection_mask_from_4th_coord,
+    post_selection_mask_from_predicate,
+    Progress,
+)
+from sinter._collection._printer import (
+    ThrottledProgressPrinter,
+)
diff --git a/glue/sample/src/sinter/_collection.py b/glue/sample/src/sinter/_collection/_collection.py
similarity index 87%
rename from glue/sample/src/sinter/_collection.py
rename to glue/sample/src/sinter/_collection/_collection.py
index 40bfceef..54f875ba 100644
--- a/glue/sample/src/sinter/_collection.py
+++ b/glue/sample/src/sinter/_collection/_collection.py
@@ -1,19 +1,15 @@
 import contextlib
 import dataclasses
 import pathlib
-from typing import Any
-from typing import Callable, Iterator, Optional, Union, Iterable, List, TYPE_CHECKING, Tuple, Dict
+from typing import Any, Callable, Iterator, Optional, Union, Iterable, List, TYPE_CHECKING, Tuple, Dict
 
 import math
 import numpy as np
 import stim
 
-from sinter._collection_options import CollectionOptions
-from sinter._csv_out import CSV_HEADER
-from sinter._collection_work_manager import CollectionWorkManager
-from sinter._existing_data import ExistingData
-from sinter._printer import ThrottledProgressPrinter
-from sinter._task_stats import TaskStats
+from sinter._data import CSV_HEADER, ExistingData, TaskStats, CollectionOptions, Task
+from sinter._collection._collection_manager import CollectionManager
+from sinter._collection._printer import ThrottledProgressPrinter
 
 if TYPE_CHECKING:
     import sinter
@@ -42,7 +38,7 @@ def iter_collect(*,
                  tasks: Union[Iterator['sinter.Task'],
                               Iterable['sinter.Task']],
                  hint_num_tasks: Optional[int] = None,
-                 additional_existing_data: Optional[ExistingData] = None,
+                 additional_existing_data: Union[None, dict[str, 'TaskStats'], Iterable['TaskStats']] = None,
                  max_shots: Optional[int] = None,
                  max_errors: Optional[int] = None,
                  decoders: Optional[Iterable[str]] = None,
@@ -51,7 +47,7 @@ def iter_collect(*,
                  start_batch_size: Optional[int] = None,
                  count_observable_error_combos: bool = False,
                  count_detection_events: bool = False,
-                 custom_decoders: Optional[Dict[str, 'sinter.Decoder']] = None,
+                 custom_decoders: Optional[Dict[str, Union['sinter.Decoder', 'sinter.Sampler']]] = None,
                  custom_error_count_key: Optional[str] = None,
                  allowed_cpu_affinity_ids: Optional[Iterable[int]] = None,
                  ) -> Iterator['sinter.Progress']:
@@ -156,6 +152,19 @@ def iter_collect(*,
         >>> print(total_shots)
         200
     """
+    existing_data: dict[str, TaskStats]
+    if isinstance(additional_existing_data, ExistingData):
+        existing_data = additional_existing_data.data
+    elif isinstance(additional_existing_data, dict):
+        existing_data = additional_existing_data
+    elif additional_existing_data is None:
+        existing_data = {}
+    else:
+        acc = ExistingData()
+        for stat in additional_existing_data:
+            acc.add_sample(stat)
+        existing_data = acc.data
+
     if isinstance(decoders, str):
         decoders = [decoders]
 
@@ -166,50 +175,65 @@ def iter_collect(*,
         except TypeError:
             pass
 
-    with CollectionWorkManager(
-        tasks_iter=iter(tasks),
-        global_collection_options=CollectionOptions(
+    if decoders is not None:
+        old_tasks = tasks
+        tasks = (
+            Task(
+                circuit=task.circuit,
+                decoder=decoder,
+                detector_error_model=task.detector_error_model,
+                postselection_mask=task.postselection_mask,
+                postselected_observables_mask=task.postselected_observables_mask,
+                json_metadata=task.json_metadata,
+                collection_options=task.collection_options,
+                circuit_path=task.circuit_path,
+            )
+            for task in old_tasks
+            for decoder in (decoders if task.decoder is None else [task.decoder])
+        )
+
+    progress_log: list[Optional[TaskStats]] = []
+    def log_progress(e: Optional[TaskStats]):
+        progress_log.append(e)
+    with CollectionManager(
+        num_workers=num_workers,
+        tasks=tasks,
+        collection_options=CollectionOptions(
             max_shots=max_shots,
             max_errors=max_errors,
             max_batch_seconds=max_batch_seconds,
             start_batch_size=start_batch_size,
             max_batch_size=max_batch_size,
         ),
-        decoders=decoders,
+        existing_data=existing_data,
         count_observable_error_combos=count_observable_error_combos,
         count_detection_events=count_detection_events,
-        additional_existing_data=additional_existing_data,
-        custom_decoders=custom_decoders,
         custom_error_count_key=custom_error_count_key,
+        custom_decoders=custom_decoders or {},
         allowed_cpu_affinity_ids=allowed_cpu_affinity_ids,
+        worker_flush_period=max_batch_seconds or 120,
+        progress_callback=log_progress,
     ) as manager:
         try:
             yield Progress(
                 new_stats=(),
                 status_message=f"Starting {num_workers} workers..."
             )
-            manager.start_workers(num_workers)
-
-            yield Progress(
-                new_stats=(),
-                status_message="Finding work..."
-            )
-            manager.fill_work_queue()
-            yield Progress(
-                new_stats=(),
-                status_message=manager.status(num_circuits=hint_num_tasks)
-            )
-
-            while manager.fill_work_queue():
-                # Wait for a worker to finish a job.
-                sample = manager.wait_for_next_sample()
-                manager.fill_work_queue()
+            manager.start_workers()
+            manager.start_distributing_work()
+
+            while manager.task_states:
+                manager.process_message()
+                if progress_log:
+                    vals = list(progress_log)
+                    progress_log.clear()
+                    for e in vals:
+                        if e is not None:
+                            yield Progress(
+                                new_stats=(e,),
+                                status_message=manager.status_message(),
+                            )
 
-                # Report the incremental results.
-                yield Progress(
-                    new_stats=(sample,) if sample.shots > 0 else (),
-                    status_message=manager.status(num_circuits=hint_num_tasks),
-                )
         except KeyboardInterrupt:
             yield Progress(
                 new_stats=(),
@@ -234,7 +258,7 @@ def collect(*,
             start_batch_size: Optional[int] = None,
             print_progress: bool = False,
             hint_num_tasks: Optional[int] = None,
-            custom_decoders: Optional[Dict[str, 'sinter.Decoder']] = None,
+            custom_decoders: Optional[Dict[str, Union['sinter.Decoder', 'sinter.Sampler']]] = None,
             custom_error_count_key: Optional[str] = None,
             allowed_cpu_affinity_ids: Optional[Iterable[int]] = None,
             ) -> List['sinter.TaskStats']:
@@ -356,7 +380,7 @@ def collect(*,
     progress_printer = ThrottledProgressPrinter(
         outs=[],
         print_progress=print_progress,
-        min_progress_delay=1,
+        min_progress_delay=0.1,
     )
     with contextlib.ExitStack() as exit_stack:
         # Open save/resume file.
diff --git a/glue/sample/src/sinter/_collection/_collection_manager.py b/glue/sample/src/sinter/_collection/_collection_manager.py
new file mode 100644
index 00000000..3c331355
--- /dev/null
+++ b/glue/sample/src/sinter/_collection/_collection_manager.py
@@ -0,0 +1,577 @@
+import collections
+import contextlib
+import math
+import multiprocessing
+import os
+import pathlib
+import queue
+import tempfile
+import threading
+from typing import Any, Optional, List, Dict, Iterable, Callable, Tuple
+from typing import Union
+from typing import cast
+
+from sinter._collection._collection_worker_loop import collection_worker_loop
+from sinter._collection._mux_sampler import MuxSampler
+from sinter._collection._sampler_ramp_throttled import RampThrottledSampler
+from sinter._data import CollectionOptions, Task, AnonTaskStats, TaskStats
+from sinter._decoding import Sampler, Decoder
+
+
+class _ManagedWorkerState:
+    def __init__(self, worker_id: int, *, cpu_pin: Optional[int] = None):
+        self.worker_id: int = worker_id
+        self.process: Union[multiprocessing.Process, threading.Thread, None] = None
+        self.input_queue: Optional[multiprocessing.Queue[Tuple[str, Any]]] = None
+        self.assigned_work_key: Any = None
+        self.asked_to_drop_shots: int = 0
+        self.cpu_pin = cpu_pin
+
+        # Shots transfer into this field when manager sends shot requests to workers.
+        # Shots transfer out of this field when clients flush results or respond to work return requests.
+        self.assigned_shots: int = 0
+
+    def send_message(self, message: Any):
+        self.input_queue.put(message)
+
+    def ask_to_return_all_shots(self):
+        if self.asked_to_drop_shots == 0 and self.assigned_shots > 0:
+            self.send_message((
+                'return_shots',
+                (
+                    self.assigned_work_key,
+                    self.assigned_shots,
+                ),
+            ))
+            self.asked_to_drop_shots = self.assigned_shots
+
+    def has_returned_all_shots(self) -> bool:
+        return self.assigned_shots == 0 and self.asked_to_drop_shots == 0
+
+    def is_available_to_reassign(self) -> bool:
+        return self.assigned_work_key is None
+
+
+class _ManagedTaskState:
+    def __init__(self, *, partial_task: Task, strong_id: str, shots_left: int, errors_left: int):
+        self.partial_task = partial_task
+        self.strong_id = strong_id
+        self.shots_left = shots_left
+        self.errors_left = errors_left
+        self.shots_unassigned = shots_left
+        self.shot_return_requests = 0
+        self.assigned_soft_error_flush_threshold: int = errors_left
+        self.workers_assigned: list[int] = []
+
+    def is_completed(self) -> bool:
+        return self.shots_left <= 0 or self.errors_left <= 0
+
+
+class CollectionManager:
+    def __init__(
+            self,
+            *,
+            existing_data: Dict[Any, TaskStats],
+            collection_options: CollectionOptions,
+            custom_decoders: dict[str, Union[Decoder, Sampler]],
+            num_workers: int,
+            worker_flush_period: float,
+            tasks: Iterable[Task],
+            progress_callback: Callable[[Optional[TaskStats]], None],
+            allowed_cpu_affinity_ids: Optional[Iterable[int]],
+            count_observable_error_combos: bool = False,
+            count_detection_events: bool = False,
+            custom_error_count_key: Optional[str] = None,
+            use_threads_for_debugging: bool = False,
+    ):
+        assert isinstance(custom_decoders, dict)
+        self.existing_data = existing_data
+        self.num_workers: int = num_workers
+        self.custom_decoders = custom_decoders
+        self.worker_flush_period: float = worker_flush_period
+        self.progress_callback = progress_callback
+        self.collection_options = collection_options
+        self.partial_tasks: list[Task] = list(tasks)
+        self.task_strong_ids: List[Optional[str]] = [None] * len(self.partial_tasks)
+        self.allowed_cpu_affinity_ids = None if allowed_cpu_affinity_ids is None else sorted(set(allowed_cpu_affinity_ids))
+        self.count_observable_error_combos = count_observable_error_combos
+        self.count_detection_events = count_detection_events
+        self.custom_error_count_key = custom_error_count_key
+        self.use_threads_for_debugging = use_threads_for_debugging
+
+        self.shared_worker_output_queue: Optional[multiprocessing.SimpleQueue[Tuple[str, int, Any]]] = None
+        self.task_states: Dict[Any, _ManagedTaskState] = {}
+        self.started: bool = False
+        self.total_collected = {k: v.to_anon_stats() for k, v in existing_data.items()}
+
+        if self.allowed_cpu_affinity_ids is None:
+            cpus = range(os.cpu_count())
+        else:
+            num_cpus = os.cpu_count()
+            cpus = [e for e in self.allowed_cpu_affinity_ids if e < num_cpus]
+        self.worker_states: List[_ManagedWorkerState] = []
+        for index in range(num_workers):
+            cpu_pin = None if len(cpus) == 0 else cpus[index % len(cpus)]
+            self.worker_states.append(_ManagedWorkerState(index, cpu_pin=cpu_pin))
+        self.tmp_dir: Optional[pathlib.Path] = None
+
+    def __enter__(self):
+        self.exit_stack = contextlib.ExitStack().__enter__()
+        self.tmp_dir = pathlib.Path(self.exit_stack.enter_context(tempfile.TemporaryDirectory()))
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.hard_stop()
+        self.exit_stack.__exit__(exc_type, exc_val, exc_tb)
+        self.exit_stack = None
+        self.tmp_dir = None
+
+    def start_workers(self, *, actually_start_worker_processes: bool = True):
+        assert not self.started
+
+        sampler = RampThrottledSampler(
+            sub_sampler=MuxSampler(
+                custom_decoders=self.custom_decoders,
+                count_observable_error_combos=self.count_observable_error_combos,
+                count_detection_events=self.count_detection_events,
+                tmp_dir=self.tmp_dir,
+            ),
+            target_batch_seconds=1,
+            max_batch_shots=1024,
+        )
+
+        self.started = True
+        current_method = multiprocessing.get_start_method()
+        try:
+            # To ensure the child processes do not accidentally share ANY state
+            # related to random number generation, we use 'spawn' instead of 'fork'.
+            multiprocessing.set_start_method('spawn', force=True)
+            # Create queues after setting start method to work around a deadlock
+            # bug that occurs otherwise.
+            self.shared_worker_output_queue = multiprocessing.SimpleQueue()
+
+            for worker_id in range(self.num_workers):
+                worker_state = self.worker_states[worker_id]
+                worker_state.input_queue = multiprocessing.Queue()
+                worker_state.input_queue.cancel_join_thread()
+                worker_state.assigned_work_key = None
+                args = (
+                    self.worker_flush_period,
+                    worker_id,
+                    sampler,
+                    worker_state.input_queue,
+                    self.shared_worker_output_queue,
+                    worker_state.cpu_pin,
+                    self.custom_error_count_key,
+                )
+                if self.use_threads_for_debugging:
+                    worker_state.process = threading.Thread(
+                        target=collection_worker_loop,
+                        args=args,
+                    )
+                else:
+                    worker_state.process = multiprocessing.Process(
+                        target=collection_worker_loop,
+                        args=args,
+                    )
+
+                if actually_start_worker_processes:
+                    worker_state.process.start()
+        finally:
+            multiprocessing.set_start_method(current_method, force=True)
+
+    def start_distributing_work(self):
+        self._compute_task_ids()
+        self._distribute_work()
+
+    def _compute_task_ids(self):
+        idle_worker_ids = list(range(self.num_workers))
+        unknown_task_ids = list(range(len(self.partial_tasks)))
+        worker_to_task_map = {}
+        while worker_to_task_map or unknown_task_ids:
+            while idle_worker_ids and unknown_task_ids:
+                worker_id = idle_worker_ids.pop()
+                unknown_task_id = unknown_task_ids.pop()
+                worker_to_task_map[worker_id] = unknown_task_id
+                self.worker_states[worker_id].send_message(('compute_strong_id', self.partial_tasks[unknown_task_id]))
+
+            try:
+                message = self.shared_worker_output_queue.get()
+                message_type, worker_id, message_body = message
+                if message_type == 'computed_strong_id':
+                    assert worker_id in worker_to_task_map
+                    assert isinstance(message_body, str)
+                    self.task_strong_ids[worker_to_task_map.pop(worker_id)] = message_body
+                    idle_worker_ids.append(worker_id)
+                elif message_type == 'stopped_due_to_exception':
+                    cur_task, cur_shots_left, unflushed_work_done, traceback, ex = message_body
+                    raise ValueError(f'Worker failed: traceback={traceback}') from ex
+                else:
+                    raise NotImplementedError(f'{message_type=}')
+                self.progress_callback(None)
+            except queue.Empty:
+                pass
+
+        assert len(idle_worker_ids) == self.num_workers
+        seen = set()
+        for k in range(len(self.partial_tasks)):
+            options = self.partial_tasks[k].collection_options.combine(self.collection_options)
+            key: str = self.task_strong_ids[k]
+            if key in seen:
+                raise ValueError(f'Same task given twice: {self.partial_tasks[k]!r}')
+            seen.add(key)
+
+            shots_left = options.max_shots
+            errors_left = options.max_errors
+            if errors_left is None:
+                errors_left = shots_left
+            errors_left = min(errors_left, shots_left)
+            if key in self.existing_data:
+                val = self.existing_data[key]
+                shots_left -= val.shots
+                if self.custom_error_count_key is None:
+                    errors_left -= val.errors
+                else:
+                    errors_left -= val.custom_counts[self.custom_error_count_key]
+            if shots_left <= 0:
+                continue
+            self.task_states[key] = _ManagedTaskState(
+                partial_task=self.partial_tasks[k],
+                strong_id=key,
+                shots_left=shots_left,
+                errors_left=errors_left,
+            )
+            if self.task_states[key].is_completed():
+                del self.task_states[key]
+
+    def hard_stop(self):
+        if not self.started:
+            return
+
+        removed_workers = [state.process for state in self.worker_states]
+        for state in self.worker_states:
+            if isinstance(state.process, threading.Thread):
+                state.send_message('stop')
+            state.process = None
+            state.assigned_work_key = None
+            state.input_queue = None
+        self.shared_worker_output_queue = None
+        self.started = False
+        self.task_states.clear()
+
+        # SIGKILL everything.
+        for w in removed_workers:
+            if isinstance(w, multiprocessing.Process):
+                w.kill()
+        # Wait for them to be done.
+        for w in removed_workers:
+            w.join()
+
+    def _handle_task_progress(self, task_id: Any):
+        task_state = self.task_states[task_id]
+        if task_state.is_completed():
+            workers_ready = all(self.worker_states[worker_id].has_returned_all_shots() for worker_id in task_state.workers_assigned)
+            if workers_ready:
+                # Task is fully completed and can be forgotten entirely. Re-assign the workers.
+                del self.task_states[task_id]
+                for worker_id in task_state.workers_assigned:
+                    w = self.worker_states[worker_id]
+                    assert w.assigned_shots <= 0
+                    assert w.asked_to_drop_shots == 0
+                    w.assigned_work_key = None
+                self._distribute_work()
+            else:
+                # Task is sufficiently sampled, but some workers are still running.
+                for worker_id in task_state.workers_assigned:
+                    self.worker_states[worker_id].ask_to_return_all_shots()
+            self.progress_callback(None)
+        else:
+            self._distribute_unassigned_workers_to_jobs()
+            self._distribute_work_within_a_job(task_state)
+
+    def state_summary(self) -> str:
+        lines = []
+        for worker_id, worker in enumerate(self.worker_states):
+            lines.append(f'worker {worker_id}:'
+                         f' asked_to_drop_shots={worker.asked_to_drop_shots}'
+                         f' assigned_shots={worker.assigned_shots}'
+                         f' assigned_work_key={worker.assigned_work_key}')
+        for task in self.task_states.values():
+            lines.append(f'task {task.strong_id=}:\n'
+                         f'    workers_assigned={task.workers_assigned}\n'
+                         f'    shot_return_requests={task.shot_return_requests}\n'
+                         f'    shots_left={task.shots_left}\n'
+                         f'    errors_left={task.errors_left}\n'
+                         f'    shots_unassigned={task.shots_unassigned}')
+        return '\n' + '\n'.join(lines) + '\n'
+
+    def process_message(self) -> bool:
+        try:
+            message = self.shared_worker_output_queue.get()
+        except queue.Empty:
+            return False
+
+        message_type, worker_id, message_body = message
+        worker_state = self.worker_states[worker_id]
+
+        if message_type == 'flushed_results':
+            task_strong_id, anon_stat = message_body
+            assert isinstance(anon_stat, AnonTaskStats)
+            assert worker_state.assigned_work_key == task_strong_id
+            task_state = self.task_states[task_strong_id]
+
+            worker_state.assigned_shots -= anon_stat.shots
+            task_state.shots_left -= anon_stat.shots
+            if worker_state.assigned_shots < 0:
+                # Worker over-achieved. Correct the imbalance by giving them the shots.
+                extra_shots = abs(worker_state.assigned_shots)
+                worker_state.assigned_shots += extra_shots
+                task_state.shots_unassigned -= extra_shots
+                worker_state.send_message((
+                    'accept_shots',
+                    (task_state.strong_id, extra_shots),
+                ))
+
+            if self.custom_error_count_key is None:
+                task_state.errors_left -= anon_stat.errors
+            else:
+                task_state.errors_left -= anon_stat.custom_counts[self.custom_error_count_key]
+
+            stat = TaskStats(
+                strong_id=task_state.strong_id,
+                decoder=task_state.partial_task.decoder,
+                json_metadata=task_state.partial_task.json_metadata,
+                shots=anon_stat.shots,
+                discards=anon_stat.discards,
+                seconds=anon_stat.seconds,
+                errors=anon_stat.errors,
+                custom_counts=anon_stat.custom_counts,
+            )
+
+            self._handle_task_progress(task_strong_id)
+
+            if stat.strong_id not in self.total_collected:
+                self.total_collected[stat.strong_id] = AnonTaskStats()
+            self.total_collected[stat.strong_id] += stat.to_anon_stats()
+            self.progress_callback(stat)
+
+        elif message_type == 'changed_job':
+            pass
+
+        elif message_type == 'accepted_shots':
+            pass
+
+        elif message_type == 'returned_shots':
+            task_key, shots_returned = message_body
+            assert isinstance(shots_returned, int)
+            assert shots_returned >= 0
+            assert worker_state.assigned_work_key == task_key
+            assert worker_state.asked_to_drop_shots or worker_state.asked_to_drop_errors
+            task_state = self.task_states[task_key]
+            task_state.shot_return_requests -= 1
+            worker_state.asked_to_drop_shots = 0
+            worker_state.asked_to_drop_errors = 0
+            task_state.shots_unassigned += shots_returned
+            worker_state.assigned_shots -= shots_returned
+            assert worker_state.assigned_shots >= 0
+            self._handle_task_progress(task_key)
+
+        elif message_type == 'stopped_due_to_exception':
+            cur_task, cur_shots_left, unflushed_work_done, traceback, ex = message_body
+            raise RuntimeError(f'Worker failed: traceback={traceback}') from ex
+
+        else:
+            raise NotImplementedError(f'{message_type=}')
+
+        return True
+
+    def run_until_done(self) -> bool:
+        try:
+            while self.task_states:
+                self.process_message()
+            return True
+
+        except KeyboardInterrupt:
+            return False
+
+        finally:
+            self.hard_stop()
+
+    def _distribute_unassigned_workers_to_jobs(self):
+        idle_workers = [
+            w
+            for w in range(self.num_workers)[::-1]
+            if self.worker_states[w].is_available_to_reassign()
+        ]
+        if not idle_workers or not self.started:
+            return
+
+        groups = collections.defaultdict(list)
+        for work_state in self.task_states.values():
+            if not work_state.is_completed():
+                groups[len(work_state.workers_assigned)].append(work_state)
+        for k in groups.keys():
+            groups[k] = groups[k][::-1]
+        if not groups:
+            return
+        min_assigned = min(groups.keys(), default=0)
+
+        # Distribute workers to unfinished jobs with the fewest workers.
+        while idle_workers:
+            task_state: _ManagedTaskState = groups[min_assigned].pop()
+            groups[min_assigned + 1].append(task_state)
+            if not groups[min_assigned]:
+                min_assigned += 1
+
+            worker_id = idle_workers.pop()
+            task_state.workers_assigned.append(worker_id)
+            worker_state = self.worker_states[worker_id]
+            worker_state.assigned_work_key = task_state.strong_id
+            worker_state.send_message((
+                'change_job',
+                (task_state.partial_task, CollectionOptions(max_errors=task_state.errors_left), task_state.assigned_soft_error_flush_threshold),
+            ))
+
+    def _distribute_unassigned_work_to_workers_within_a_job(self, task_state: _ManagedTaskState):
+        if not self.started or not task_state.workers_assigned or task_state.shots_left <= 0:
+            return
+
+        num_task_workers = len(task_state.workers_assigned)
+        expected_shots_per_worker = (task_state.shots_left + num_task_workers - 1) // num_task_workers
+
+        # Give unassigned shots to idle workers.
+        for worker_id in sorted(task_state.workers_assigned, key=lambda wid: self.worker_states[wid].assigned_shots):
+            worker_state = self.worker_states[worker_id]
+            if worker_state.assigned_shots < expected_shots_per_worker:
+                shots_to_assign = min(expected_shots_per_worker - worker_state.assigned_shots,
+                                      task_state.shots_unassigned)
+                if shots_to_assign > 0:
+                    task_state.shots_unassigned -= shots_to_assign
+                    worker_state.assigned_shots += shots_to_assign
+                    worker_state.send_message((
+                        'accept_shots',
+                        (task_state.strong_id, shots_to_assign),
+                    ))
+
+    def status_message(self) -> str:
+        num_known_tasks_ids = sum(e is not None for e in self.task_strong_ids)
+        if num_known_tasks_ids < len(self.task_strong_ids):
+            return f"Analyzed {num_known_tasks_ids}/{len(self.task_strong_ids)} tasks..."
+        max_errors = self.collection_options.max_errors
+        max_shots = self.collection_options.max_shots
+
+        tasks_left = 0
+        lines = []
+        skipped_lines = []
+        for k, strong_id in enumerate(self.task_strong_ids):
+            if strong_id not in self.task_states:
+                continue
+            c = self.total_collected.get(strong_id, AnonTaskStats())
+            tasks_left += 1
+            w = len(self.task_states[strong_id].workers_assigned)
+            dt = None
+            if max_shots is not None and c.shots:
+                dt = (max_shots - c.shots) * c.seconds / c.shots
+            c_errors = c.custom_counts[self.custom_error_count_key] if self.custom_error_count_key is not None else c.errors
+            if max_errors is not None and c_errors and c.seconds:
+                dt2 = (max_errors - c_errors) * c.seconds / c_errors
+                if dt is None:
+                    dt = dt2
+                else:
+                    dt = min(dt, dt2)
+            if dt is not None:
+                dt /= 60
+            if dt is not None and w > 0:
+                dt /= w
+            line = [
+                f'{w}',
+                self.partial_tasks[k].decoder,
+                ("?" if dt is None or dt == 0 else "[draining]" if dt <= 0 else "<1m" if dt < 1 else str(round(dt)) + 'm') + ('·∞' if w == 0 else ''),
+                f'{max_shots - c.shots}' if max_shots is not None else f'{c.shots}',
+                f'{max_errors - c_errors}' if max_errors is not None else f'{c_errors}',
+                ",".join(
+                    [f"{k}={v}" for k, v in self.partial_tasks[k].json_metadata.items()]
+                    if isinstance(self.partial_tasks[k].json_metadata, dict)
+                    else str(self.partial_tasks[k].json_metadata)
+                )
+            ]
+            if w == 0:
+                skipped_lines.append(line)
+            else:
+                lines.append(line)
+        if len(lines) < 50 and skipped_lines:
+            missing_lines = 50 - len(lines)
+            lines.extend(skipped_lines[:missing_lines])
+            skipped_lines = skipped_lines[missing_lines:]
+
+        if lines:
+            lines.insert(0, [
+                'workers',
+                'decoder',
+                'eta',
+                'shots_left' if max_shots is not None else 'shots_taken',
+                'errors_left' if max_errors is not None else 'errors_seen',
+                'json_metadata'])
+            justs = cast(list[Callable[[str, int], str]], [str.rjust, str.rjust, str.rjust, str.rjust, str.rjust, str.ljust])
+            cols = len(lines[0])
+            lengths = [
+                max(len(lines[row][col]) for row in range(len(lines)))
+                for col in range(cols)
+            ]
+            lines = [
+                "  " + " ".join(justs[col](row[col], lengths[col]) for col in range(cols))
+                for row in lines
+            ]
+        if skipped_lines:
+            lines.append('        ... (' + str(len(skipped_lines)) + ' more tasks) ...')
+        return f'{tasks_left} tasks left:\n' + '\n'.join(lines)
+
+    def _update_soft_error_threshold_for_a_job(self, task_state: _ManagedTaskState):
+        if task_state.errors_left <= len(task_state.workers_assigned):
+            desired_threshold = 1
+        elif task_state.errors_left <= task_state.assigned_soft_error_flush_threshold * self.num_workers:
+            desired_threshold = max(1, math.ceil(task_state.errors_left * 0.5 / self.num_workers))
+        else:
+            return
+
+        if task_state.assigned_soft_error_flush_threshold != desired_threshold:
+            task_state.assigned_soft_error_flush_threshold = desired_threshold
+            for wid in task_state.workers_assigned:
+                self.worker_states[wid].send_message(('set_soft_error_flush_threshold', desired_threshold))
+
+    def _take_work_if_unsatisfied_workers_within_a_job(self, task_state: _ManagedTaskState):
+        if not self.started or not task_state.workers_assigned or task_state.shots_left <= 0:
+            return
+
+        if all(self.worker_states[w].assigned_shots > 0 for w in task_state.workers_assigned):
+            return
+
+        w = len(task_state.workers_assigned)
+        expected_shots_per_worker = (task_state.shots_left + w - 1) // w
+
+        # There are idle workers that couldn't be given any shots. Take shots from other workers.
+        for worker_id in sorted(task_state.workers_assigned, key=lambda w: self.worker_states[w].assigned_shots, reverse=True):
+            worker_state = self.worker_states[worker_id]
+            if worker_state.asked_to_drop_shots or worker_state.assigned_shots <= expected_shots_per_worker:
+                continue
+            shots_to_take = worker_state.assigned_shots - expected_shots_per_worker
+            assert shots_to_take > 0
+            worker_state.asked_to_drop_shots = shots_to_take
+            task_state.shot_return_requests += 1
+            worker_state.send_message((
+                'return_shots',
+                (
+                    task_state.strong_id,
+                    shots_to_take,
+                ),
+            ))
+
+    def _distribute_work_within_a_job(self, t: _ManagedTaskState):
+        self._distribute_unassigned_work_to_workers_within_a_job(t)
+        self._take_work_if_unsatisfied_workers_within_a_job(t)
+
+    def _distribute_work(self):
+        self._distribute_unassigned_workers_to_jobs()
+        for w in self.task_states.values():
+            if not w.is_completed():
+                self._distribute_work_within_a_job(w)
diff --git a/glue/sample/src/sinter/_collection/_collection_manager_test.py b/glue/sample/src/sinter/_collection/_collection_manager_test.py
new file mode 100644
index 00000000..c4cb359b
--- /dev/null
+++ b/glue/sample/src/sinter/_collection/_collection_manager_test.py
@@ -0,0 +1,287 @@
+import multiprocessing
+import time
+from typing import Any, List, Union
+
+import sinter
+import stim
+
+from sinter._collection._collection_manager import CollectionManager
+
+
+def _assert_drain_queue(q: multiprocessing.Queue, expected_contents: List[Any]):
+    for v in expected_contents:
+        assert q.get(timeout=0.1) == v
+    if not q.empty():
+        assert False, f'queue had another item: {q.get()=}'
+
+
+def _put_wait_not_empty(q: Union[multiprocessing.Queue, multiprocessing.SimpleQueue], item: Any):
+    q.put(item)
+    while q.empty():
+        time.sleep(0.0001)
+
+
+def test_manager():
+    log = []
+    t0 = sinter.Task(
+        circuit=stim.Circuit('H 0'),
+        detector_error_model=stim.DetectorErrorModel(),
+        decoder='fusion_blossom',
+        collection_options=sinter.CollectionOptions(max_shots=100_000_000, max_errors=100),
+        json_metadata={'a': 3},
+    )
+    t1 = sinter.Task(
+        circuit=stim.Circuit('M 0'),
+        detector_error_model=stim.DetectorErrorModel(),
+        decoder='pymatching',
+        collection_options=sinter.CollectionOptions(max_shots=10_000_000),
+        json_metadata=None,
+    )
+    manager = CollectionManager(
+        num_workers=3,
+        worker_flush_period=30,
+        tasks=[t0, t1],
+        progress_callback=log.append,
+        existing_data={},
+        collection_options=sinter.CollectionOptions(),
+        custom_decoders={},
+        allowed_cpu_affinity_ids=None,
+    )
+
+    assert manager.state_summary() == """
+worker 0: asked_to_drop_shots=0 assigned_shots=0 assigned_work_key=None
+worker 1: asked_to_drop_shots=0 assigned_shots=0 assigned_work_key=None
+worker 2: asked_to_drop_shots=0 assigned_shots=0 assigned_work_key=None
+"""
+
+    manager.start_workers(actually_start_worker_processes=False)
+    manager.shared_worker_output_queue.put(('computed_strong_id', 2, 'c03f7852e4579e2a99cefac80eeb6b09556907540ab3d7787a3d07309c3333aa'))
+    manager.shared_worker_output_queue.put(('computed_strong_id', 1, 'a9165b6e4ab1053c04c017d0739a7bfff0910d62091fc9ee81716833eda7f604'))
+    manager.start_distributing_work()
+
+    assert manager.state_summary() == """
+worker 0: asked_to_drop_shots=0 assigned_shots=100000000 assigned_work_key=a9165b6e4ab1053c04c017d0739a7bfff0910d62091fc9ee81716833eda7f604
+worker 1: asked_to_drop_shots=0 assigned_shots=5000000 assigned_work_key=c03f7852e4579e2a99cefac80eeb6b09556907540ab3d7787a3d07309c3333aa
+worker 2: asked_to_drop_shots=0 assigned_shots=5000000 assigned_work_key=c03f7852e4579e2a99cefac80eeb6b09556907540ab3d7787a3d07309c3333aa
+task task.strong_id='a9165b6e4ab1053c04c017d0739a7bfff0910d62091fc9ee81716833eda7f604':
+    workers_assigned=[0]
+    shot_return_requests=0
+    shots_left=100000000
+    errors_left=100
+    shots_unassigned=0
+task task.strong_id='c03f7852e4579e2a99cefac80eeb6b09556907540ab3d7787a3d07309c3333aa':
+    workers_assigned=[1, 2]
+    shot_return_requests=0
+    shots_left=10000000
+    errors_left=10000000
+    shots_unassigned=0
+"""
+
+    _assert_drain_queue(manager.worker_states[0].input_queue, [
+        (
+            'change_job',
+            (t0, sinter.CollectionOptions(max_errors=100), 100),
+        ),
+        (
+            'accept_shots',
+            (t0.strong_id(), 100_000_000),
+        ),
+    ])
+    _assert_drain_queue(manager.worker_states[1].input_queue, [
+        ('compute_strong_id', t0),
+        (
+            'change_job',
+            (t1, sinter.CollectionOptions(max_errors=10000000), 10000000),
+        ),
+        (
+            'accept_shots',
+            (t1.strong_id(), 5_000_000),
+        ),
+    ])
+    _assert_drain_queue(manager.worker_states[2].input_queue, [
+        ('compute_strong_id', t1),
+        (
+            'change_job',
+            (t1, sinter.CollectionOptions(max_errors=10000000), 10000000),
+        ),
+        (
+            'accept_shots',
+            (t1.strong_id(), 5_000_000),
+        ),
+    ])
+
+    assert manager.shared_worker_output_queue.empty()
+    assert log.pop() is None
+    assert log.pop() is None
+    assert not log
+    _put_wait_not_empty(manager.shared_worker_output_queue, (
+        'flushed_results',
+        2,
+        (t1.strong_id(), sinter.AnonTaskStats(
+            shots=5_000_000,
+            errors=123,
+            discards=0,
+            seconds=1,
+        )),
+    ))
+
+    assert manager.process_message()
+    assert manager.state_summary() == """
+worker 0: asked_to_drop_shots=0 assigned_shots=100000000 assigned_work_key=a9165b6e4ab1053c04c017d0739a7bfff0910d62091fc9ee81716833eda7f604
+worker 1: asked_to_drop_shots=2500000 assigned_shots=5000000 assigned_work_key=c03f7852e4579e2a99cefac80eeb6b09556907540ab3d7787a3d07309c3333aa
+worker 2: asked_to_drop_shots=0 assigned_shots=0 assigned_work_key=c03f7852e4579e2a99cefac80eeb6b09556907540ab3d7787a3d07309c3333aa
+task task.strong_id='a9165b6e4ab1053c04c017d0739a7bfff0910d62091fc9ee81716833eda7f604':
+    workers_assigned=[0]
+    shot_return_requests=0
+    shots_left=100000000
+    errors_left=100
+    shots_unassigned=0
+task task.strong_id='c03f7852e4579e2a99cefac80eeb6b09556907540ab3d7787a3d07309c3333aa':
+    workers_assigned=[1, 2]
+    shot_return_requests=1
+    shots_left=5000000
+    errors_left=9999877
+    shots_unassigned=0
+"""
+
+    assert log.pop() == sinter.TaskStats(
+        strong_id=t1.strong_id(),
+        decoder=t1.decoder,
+        json_metadata=t1.json_metadata,
+        shots=5_000_000,
+        errors=123,
+        discards=0,
+        seconds=1,
+    )
+    assert not log
+
+    _assert_drain_queue(manager.worker_states[0].input_queue, [])
+    _assert_drain_queue(manager.worker_states[1].input_queue, [
+        (
+            'return_shots',
+            (t1.strong_id(), 2_500_000),
+        ),
+    ])
+    _assert_drain_queue(manager.worker_states[2].input_queue, [])
+
+    _put_wait_not_empty(manager.shared_worker_output_queue, (
+        'returned_shots',
+        1,
+        (t1.strong_id(), 2_000_000),
+    ))
+    assert manager.process_message()
+    assert manager.state_summary() == """
+worker 0: asked_to_drop_shots=0 assigned_shots=100000000 assigned_work_key=a9165b6e4ab1053c04c017d0739a7bfff0910d62091fc9ee81716833eda7f604
+worker 1: asked_to_drop_shots=0 assigned_shots=3000000 assigned_work_key=c03f7852e4579e2a99cefac80eeb6b09556907540ab3d7787a3d07309c3333aa
+worker 2: asked_to_drop_shots=0 assigned_shots=2000000 assigned_work_key=c03f7852e4579e2a99cefac80eeb6b09556907540ab3d7787a3d07309c3333aa
+task task.strong_id='a9165b6e4ab1053c04c017d0739a7bfff0910d62091fc9ee81716833eda7f604':
+    workers_assigned=[0]
+    shot_return_requests=0
+    shots_left=100000000
+    errors_left=100
+    shots_unassigned=0
+task task.strong_id='c03f7852e4579e2a99cefac80eeb6b09556907540ab3d7787a3d07309c3333aa':
+    workers_assigned=[1, 2]
+    shot_return_requests=0
+    shots_left=5000000
+    errors_left=9999877
+    shots_unassigned=0
+"""
+
+    _assert_drain_queue(manager.worker_states[0].input_queue, [])
+    _assert_drain_queue(manager.worker_states[1].input_queue, [])
+    _assert_drain_queue(manager.worker_states[2].input_queue, [
+        (
+            'accept_shots',
+            (t1.strong_id(), 2_000_000),
+        ),
+    ])
+
+    _put_wait_not_empty(manager.shared_worker_output_queue, (
+        'flushed_results',
+        1,
+        (t1.strong_id(), sinter.AnonTaskStats(
+            shots=3_000_000,
+            errors=444,
+            discards=1,
+            seconds=2,
+        ))
+    ))
+    assert manager.process_message()
+    assert manager.state_summary() == """
+worker 0: asked_to_drop_shots=0 assigned_shots=100000000 assigned_work_key=a9165b6e4ab1053c04c017d0739a7bfff0910d62091fc9ee81716833eda7f604
+worker 1: asked_to_drop_shots=0 assigned_shots=0 assigned_work_key=c03f7852e4579e2a99cefac80eeb6b09556907540ab3d7787a3d07309c3333aa
+worker 2: asked_to_drop_shots=1000000 assigned_shots=2000000 assigned_work_key=c03f7852e4579e2a99cefac80eeb6b09556907540ab3d7787a3d07309c3333aa
+task task.strong_id='a9165b6e4ab1053c04c017d0739a7bfff0910d62091fc9ee81716833eda7f604':
+    workers_assigned=[0]
+    shot_return_requests=0
+    shots_left=100000000
+    errors_left=100
+    shots_unassigned=0
+task task.strong_id='c03f7852e4579e2a99cefac80eeb6b09556907540ab3d7787a3d07309c3333aa':
+    workers_assigned=[1, 2]
+    shot_return_requests=1
+    shots_left=2000000
+    errors_left=9999433
+    shots_unassigned=0
+"""
+
+    _put_wait_not_empty(manager.shared_worker_output_queue, (
+        'flushed_results',
+        2,
+        (t1.strong_id(), sinter.AnonTaskStats(
+            shots=2_000_000,
+            errors=555,
+            discards=2,
+            seconds=2.5,
+        ))
+    ))
+    assert manager.process_message()
+    assert manager.state_summary() == """
+worker 0: asked_to_drop_shots=0 assigned_shots=100000000 assigned_work_key=a9165b6e4ab1053c04c017d0739a7bfff0910d62091fc9ee81716833eda7f604
+worker 1: asked_to_drop_shots=0 assigned_shots=0 assigned_work_key=c03f7852e4579e2a99cefac80eeb6b09556907540ab3d7787a3d07309c3333aa
+worker 2: asked_to_drop_shots=1000000 assigned_shots=0 assigned_work_key=c03f7852e4579e2a99cefac80eeb6b09556907540ab3d7787a3d07309c3333aa
+task task.strong_id='a9165b6e4ab1053c04c017d0739a7bfff0910d62091fc9ee81716833eda7f604':
+    workers_assigned=[0]
+    shot_return_requests=0
+    shots_left=100000000
+    errors_left=100
+    shots_unassigned=0
+task task.strong_id='c03f7852e4579e2a99cefac80eeb6b09556907540ab3d7787a3d07309c3333aa':
+    workers_assigned=[1, 2]
+    shot_return_requests=1
+    shots_left=0
+    errors_left=9998878
+    shots_unassigned=0
+"""
+
+    assert manager.shared_worker_output_queue.empty()
+    _put_wait_not_empty(manager.shared_worker_output_queue, (
+        'returned_shots',
+        2,
+        (t1.strong_id(), 0)
+    ))
+    assert manager.process_message()
+    assert manager.shared_worker_output_queue.empty()
+    assert manager.state_summary() == """
+worker 0: asked_to_drop_shots=66666666 assigned_shots=100000000 assigned_work_key=a9165b6e4ab1053c04c017d0739a7bfff0910d62091fc9ee81716833eda7f604
+worker 1: asked_to_drop_shots=0 assigned_shots=0 assigned_work_key=a9165b6e4ab1053c04c017d0739a7bfff0910d62091fc9ee81716833eda7f604
+worker 2: asked_to_drop_shots=0 assigned_shots=0 assigned_work_key=a9165b6e4ab1053c04c017d0739a7bfff0910d62091fc9ee81716833eda7f604
+task task.strong_id='a9165b6e4ab1053c04c017d0739a7bfff0910d62091fc9ee81716833eda7f604':
+    workers_assigned=[0, 1, 2]
+    shot_return_requests=1
+    shots_left=100000000
+    errors_left=100
+    shots_unassigned=0
+"""
+
+    _assert_drain_queue(manager.worker_states[0].input_queue, [
+        ('return_shots', (t0.strong_id(), 66666666)),
+    ])
+    _assert_drain_queue(manager.worker_states[1].input_queue, [
+        ('change_job', (t0, sinter.CollectionOptions(max_errors=100), 100)),
+    ])
+    _assert_drain_queue(manager.worker_states[2].input_queue, [
+        ('return_shots', (t1.strong_id(), 1000000)),
+        ('change_job', (t0, sinter.CollectionOptions(max_errors=100), 100)),
+    ])
diff --git a/glue/sample/src/sinter/_collection_test.py b/glue/sample/src/sinter/_collection/_collection_test.py
similarity index 78%
rename from glue/sample/src/sinter/_collection_test.py
rename to glue/sample/src/sinter/_collection/_collection_test.py
index 3ca72a5e..2956d419 100644
--- a/glue/sample/src/sinter/_collection_test.py
+++ b/glue/sample/src/sinter/_collection/_collection_test.py
@@ -1,6 +1,9 @@
 import collections
+import math
 import pathlib
+import sys
 import tempfile
+import time
 
 import pytest
 import stim
@@ -208,3 +211,64 @@ def test_iter_collect_worker_fails():
                 ),
             ]),
         ))
+
+
+class FixedSizeSampler(sinter.Sampler, sinter.CompiledSampler):
+    def compiled_sampler_for_task(self, task: sinter.Task) -> sinter.CompiledSampler:
+        return self
+
+    def sample(self, suggested_shots: int) -> 'sinter.AnonTaskStats':
+        return sinter.AnonTaskStats(
+            shots=1024,
+            errors=5,
+        )
+
+
+def test_fixed_size_sampler():
+    results = sinter.collect(
+        num_workers=2,
+        tasks=[
+            sinter.Task(
+                circuit=stim.Circuit(),
+                decoder='fixed_size_sampler',
+                json_metadata={},
+                collection_options=sinter.CollectionOptions(
+                    max_shots=100_000,
+                    max_errors=1_000,
+                ),
+            )
+        ],
+        custom_decoders={'fixed_size_sampler': FixedSizeSampler()}
+    )
+    assert 100_000 <= results[0].shots <= 100_000 + 3000
+
+
+class MockTimingSampler(sinter.Sampler, sinter.CompiledSampler):
+    def compiled_sampler_for_task(self, task: sinter.Task) -> sinter.CompiledSampler:
+        return self
+
+    def sample(self, suggested_shots: int) -> 'sinter.AnonTaskStats':
+        actual_shots = -(-suggested_shots // 1024) * 1024
+        time.sleep(actual_shots * 0.00001)
+        return sinter.AnonTaskStats(
+            shots=actual_shots,
+            errors=5,
+            seconds=actual_shots * 0.00001,
+        )
+
+
+def test_mock_timing_sampler():
+    results = sinter.collect(
+        num_workers=12,
+        tasks=[
+            sinter.Task(
+                circuit=stim.Circuit(),
+                decoder='MockTimingSampler',
+                json_metadata={},
+            )
+        ],
+        max_shots=1_000_000,
+        max_errors=10_000,
+        custom_decoders={'MockTimingSampler': MockTimingSampler()},
+    )
+    assert 1_000_000 <= results[0].shots <= 1_000_000 + 12000
diff --git a/glue/sample/src/sinter/_collection/_collection_worker_loop.py b/glue/sample/src/sinter/_collection/_collection_worker_loop.py
new file mode 100644
index 00000000..1467315f
--- /dev/null
+++ b/glue/sample/src/sinter/_collection/_collection_worker_loop.py
@@ -0,0 +1,35 @@
+import os
+from typing import Optional, TYPE_CHECKING
+
+from sinter._decoding import Sampler
+from sinter._collection._collection_worker_state import CollectionWorkerState
+
+if TYPE_CHECKING:
+    import multiprocessing
+
+
+def collection_worker_loop(
+    flush_period: float,
+    worker_id: int,
+    sampler: Sampler,
+    inp: 'multiprocessing.Queue',
+    out: 'multiprocessing.Queue',
+    core_affinity: Optional[int],
+    custom_error_count_key: Optional[str],
+) -> None:
+    try:
+        if core_affinity is not None and hasattr(os, 'sched_setaffinity'):
+            os.sched_setaffinity(0, {core_affinity})
+    except:
+        # If setting the core affinity fails, we keep going regardless.
+        pass
+
+    worker = CollectionWorkerState(
+        flush_period=flush_period,
+        worker_id=worker_id,
+        sampler=sampler,
+        inp=inp,
+        out=out,
+        custom_error_count_key=custom_error_count_key,
+    )
+    worker.run_message_loop()
diff --git a/glue/sample/src/sinter/_collection/_collection_worker_state.py b/glue/sample/src/sinter/_collection/_collection_worker_state.py
new file mode 100644
index 00000000..ba8967e6
--- /dev/null
+++ b/glue/sample/src/sinter/_collection/_collection_worker_state.py
@@ -0,0 +1,256 @@
+import queue
+import time
+from typing import Any
+from typing import Optional
+from typing import TYPE_CHECKING
+
+import stim
+
+from sinter._data import AnonTaskStats
+from sinter._data import CollectionOptions
+from sinter._data import Task
+from sinter._decoding import CompiledSampler
+from sinter._decoding import Sampler
+
+if TYPE_CHECKING:
+    import multiprocessing
+
+
+def _fill_in_task(task: Task) -> Task:
+    changed = False
+    circuit = task.circuit
+    if circuit is None:
+        circuit = stim.Circuit.from_file(task.circuit_path)
+        changed = True
+    dem = task.detector_error_model
+    if dem is None:
+        try:
+            dem = circuit.detector_error_model(decompose_errors=True, approximate_disjoint_errors=True)
+        except ValueError:
+            dem = circuit.detector_error_model(approximate_disjoint_errors=True)
+        changed = True
+    if not changed:
+        return task
+    return Task(
+        circuit=circuit,
+        decoder=task.decoder,
+        detector_error_model=dem,
+        postselection_mask=task.postselection_mask,
+        postselected_observables_mask=task.postselected_observables_mask,
+        json_metadata=task.json_metadata,
+        collection_options=task.collection_options,
+    )
+
+
+class CollectionWorkerState:
+    def __init__(
+            self,
+            *,
+            flush_period: float,
+            worker_id: int,
+            inp: 'multiprocessing.Queue',
+            out: 'multiprocessing.Queue',
+            sampler: Sampler,
+            custom_error_count_key: Optional[str],
+    ):
+        assert isinstance(flush_period, (int, float))
+        assert isinstance(sampler, Sampler)
+        self.max_flush_period = flush_period
+        self.cur_flush_period = 0.01
+        self.inp = inp
+        self.out = out
+        self.sampler = sampler
+        self.compiled_sampler: CompiledSampler | None = None
+        self.worker_id = worker_id
+
+        self.current_task: Task | None = None
+        self.current_error_cutoff: int | None = None
+        self.custom_error_count_key = custom_error_count_key
+        self.current_task_shots_left: int = 0
+        self.unflushed_results: AnonTaskStats = AnonTaskStats()
+        self.last_flush_message_time = time.monotonic()
+        self.soft_error_flush_threshold: int = 1
+
+    def _send_message_to_manager(self, message: Any):
+        self.out.put(message)
+
+    def state_summary(self) -> str:
+        lines = [
+            f'Worker(id={self.worker_id}) [',
+            f'    max_flush_period={self.max_flush_period}',
+            f'    cur_flush_period={self.cur_flush_period}',
+            f'    sampler={self.sampler}',
+            f'    compiled_sampler={self.compiled_sampler}',
+            f'    current_task={self.current_task}',
+            f'    current_error_cutoff={self.current_error_cutoff}',
+            f'    custom_error_count_key={self.custom_error_count_key}',
+            f'    current_task_shots_left={self.current_task_shots_left}',
+            f'    unflushed_results={self.unflushed_results}',
+            f'    last_flush_message_time={self.last_flush_message_time}',
+            f'    soft_error_flush_threshold={self.soft_error_flush_threshold}',
+            f']',
+        ]
+        return '\n' + '\n'.join(lines) + '\n'
+
+    def flush_results(self):
+        if self.unflushed_results.shots > 0:
+            self.last_flush_message_time = time.monotonic()
+            self.cur_flush_period = min(self.cur_flush_period * 1.4, self.max_flush_period)
+            self._send_message_to_manager((
+                'flushed_results',
+                self.worker_id,
+                (self.current_task.strong_id(), self.unflushed_results),
+            ))
+            self.unflushed_results = AnonTaskStats()
+            return True
+        return False
+
+    def accept_shots(self, *, shots_delta: int):
+        assert shots_delta >= 0
+        self.current_task_shots_left += shots_delta
+        self._send_message_to_manager((
+            'accepted_shots',
+            self.worker_id,
+            (self.current_task.strong_id(), shots_delta),
+        ))
+
+    def return_shots(self, *, requested_shots: int):
+        assert requested_shots >= 0
+        returned_shots = max(0, min(requested_shots, self.current_task_shots_left))
+        self.current_task_shots_left -= returned_shots
+        if self.current_task_shots_left <= 0:
+            self.flush_results()
+        self._send_message_to_manager((
+            'returned_shots',
+            self.worker_id,
+            (self.current_task.strong_id(), returned_shots),
+        ))
+
+    def compute_strong_id(self, *, new_task: Task):
+        strong_id = _fill_in_task(new_task).strong_id()
+        self._send_message_to_manager((
+            'computed_strong_id',
+            self.worker_id,
+            strong_id,
+        ))
+
+    def change_job(self, *, new_task: Task, new_collection_options: CollectionOptions):
+        self.flush_results()
+
+        self.current_task = _fill_in_task(new_task)
+        self.current_error_cutoff = new_collection_options.max_errors
+        self.compiled_sampler = self.sampler.compiled_sampler_for_task(self.current_task)
+        assert self.current_task.strong_id() is not None
+        self.current_task_shots_left = 0
+        self.last_flush_message_time = time.monotonic()
+
+        self._send_message_to_manager((
+            'changed_job',
+            self.worker_id,
+            (self.current_task.strong_id(),),
+        ))
+
+    def process_messages(self) -> int:
+        num_processed = 0
+        while True:
+            try:
+                message = self.inp.get_nowait()
+            except queue.Empty:
+                return num_processed
+
+            num_processed += 1
+            message_type, message_body = message
+
+            if message_type == 'stop':
+                return -1
+
+            elif message_type == 'flush_results':
+                self.flush_results()
+
+            elif message_type == 'compute_strong_id':
+                assert isinstance(message_body, Task)
+                self.compute_strong_id(new_task=message_body)
+
+            elif message_type == 'change_job':
+                new_task, new_collection_options, soft_error_flush_threshold = message_body
+                self.cur_flush_period = 0.01
+                self.soft_error_flush_threshold = soft_error_flush_threshold
+                assert isinstance(new_task, Task)
+                self.change_job(new_task=new_task, new_collection_options=new_collection_options)
+
+            elif message_type == 'set_soft_error_flush_threshold':
+                soft_error_flush_threshold = message_body
+                self.soft_error_flush_threshold = soft_error_flush_threshold
+
+            elif message_type == 'accept_shots':
+                job_key, shots_delta = message_body
+                assert isinstance(shots_delta, int)
+                assert job_key == self.current_task.strong_id()
+                self.accept_shots(shots_delta=shots_delta)
+
+            elif message_type == 'return_shots':
+                job_key, requested_shots = message_body
+                assert isinstance(requested_shots, int)
+                assert job_key == self.current_task.strong_id()
+                self.return_shots(requested_shots=requested_shots)
+
+            else:
+                raise NotImplementedError(f'{message_type=}')
+
+    def num_unflushed_errors(self) -> int:
+        if self.custom_error_count_key is not None:
+            return self.unflushed_results.custom_counts[self.custom_error_count_key]
+        return self.unflushed_results.errors
+
+    def do_some_work(self) -> bool:
+        did_some_work = False
+
+        # Sample some stats.
+        if self.current_task_shots_left > 0:
+            # Don't keep sampling if we've exceeded the number of errors needed.
+            if self.current_error_cutoff is not None and self.current_error_cutoff <= 0:
+                return self.flush_results()
+
+            some_work_done = self.compiled_sampler.sample(self.current_task_shots_left)
+            if some_work_done.shots < 1:
+                raise ValueError(f"Sampler didn't do any work. It returned statistics with shots == 0: {some_work_done}.")
+            assert isinstance(some_work_done, AnonTaskStats)
+            self.current_task_shots_left -= some_work_done.shots
+            if self.current_error_cutoff is not None:
+                errors_done = some_work_done.custom_counts[self.custom_error_count_key] if self.custom_error_count_key is not None else some_work_done.errors
+                self.current_error_cutoff -= errors_done
+            self.unflushed_results += some_work_done
+            did_some_work = True
+
+        # Report them periodically.
+        should_flush = False
+        if self.num_unflushed_errors() >= self.soft_error_flush_threshold:
+            should_flush = True
+        if self.unflushed_results.shots > 0:
+            if self.current_task_shots_left <= 0 or self.last_flush_message_time + self.cur_flush_period < time.monotonic():
+                should_flush = True
+        if should_flush:
+            did_some_work |= self.flush_results()
+
+        return did_some_work
+
+    def run_message_loop(self):
+        try:
+            while True:
+                num_messages_processed = self.process_messages()
+                if num_messages_processed == -1:
+                    break
+                did_some_work = self.do_some_work()
+                if not did_some_work and num_messages_processed == 0:
+                    time.sleep(0.01)
+
+        except KeyboardInterrupt:
+            pass
+
+        except BaseException as ex:
+            import traceback
+            self._send_message_to_manager((
+                'stopped_due_to_exception',
+                self.worker_id,
+                (None if self.current_task is None else self.current_task.strong_id(), self.current_task_shots_left, self.unflushed_results, traceback.format_exc(), ex),
+            ))
diff --git a/glue/sample/src/sinter/_collection/_collection_worker_test.py b/glue/sample/src/sinter/_collection/_collection_worker_test.py
new file mode 100644
index 00000000..db0518dc
--- /dev/null
+++ b/glue/sample/src/sinter/_collection/_collection_worker_test.py
@@ -0,0 +1,214 @@
+import collections
+import multiprocessing
+import time
+from typing import Any, List
+
+import sinter
+import stim
+
+from sinter._collection._collection_worker_state import CollectionWorkerState
+
+
+class MockWorkHandler(sinter.Sampler, sinter.CompiledSampler):
+    def __init__(self):
+        self.expected_task = None
+        self.expected = collections.deque()
+
+    def compiled_sampler_for_task(self, task: sinter.Task) -> sinter.CompiledSampler:
+        assert task == self.expected_task
+        return self
+
+    def handles_throttling(self) -> bool:
+        return True
+
+    def sample(self, shots: int) -> sinter.AnonTaskStats:
+        assert self.expected
+        expected_shots, response = self.expected.popleft()
+        assert shots == expected_shots
+        return response
+
+
+def _assert_drain_queue(q: multiprocessing.Queue, expected_contents: List[Any]):
+    for v in expected_contents:
+        assert q.get(timeout=0.1) == v
+    assert q.empty()
+
+
+def _put_wait_not_empty(q: multiprocessing.Queue, item: Any):
+    q.put(item)
+    while q.empty():
+        time.sleep(0.0001)
+
+
+def test_worker_stop():
+    handler = MockWorkHandler()
+
+    inp = multiprocessing.Queue()
+    out = multiprocessing.Queue()
+    inp.cancel_join_thread()
+    out.cancel_join_thread()
+
+    worker = CollectionWorkerState(
+        flush_period=-1,
+        worker_id=5,
+        sampler=handler,
+        inp=inp,
+        out=out,
+        custom_error_count_key=None,
+    )
+
+    assert worker.process_messages() == 0
+    _assert_drain_queue(out, [])
+
+    t0 = sinter.Task(
+        circuit=stim.Circuit('H 0'),
+        detector_error_model=stim.DetectorErrorModel(),
+        decoder='mock',
+        collection_options=sinter.CollectionOptions(max_shots=100_000_000),
+        json_metadata={'a': 3},
+    )
+    handler.expected_task = t0
+
+    _put_wait_not_empty(inp, ('change_job', (t0, sinter.CollectionOptions(max_errors=100_000_000), 100_000_000)))
+    assert worker.process_messages() == 1
+    _assert_drain_queue(out, [('changed_job', 5, (t0.strong_id(),))])
+
+    _put_wait_not_empty(inp, ('stop', None))
+    assert worker.process_messages() == -1
+
+
+def test_worker_skip_work():
+    handler = MockWorkHandler()
+
+    inp = multiprocessing.Queue()
+    out = multiprocessing.Queue()
+    inp.cancel_join_thread()
+    out.cancel_join_thread()
+
+    worker = CollectionWorkerState(
+        flush_period=-1,
+        worker_id=5,
+        sampler=handler,
+        inp=inp,
+        out=out,
+        custom_error_count_key=None,
+    )
+
+    assert worker.process_messages() == 0
+    _assert_drain_queue(out, [])
+
+    t0 = sinter.Task(
+        circuit=stim.Circuit('H 0'),
+        detector_error_model=stim.DetectorErrorModel(),
+        decoder='mock',
+        collection_options=sinter.CollectionOptions(max_shots=100_000_000),
+        json_metadata={'a': 3},
+    )
+    handler.expected_task = t0
+    _put_wait_not_empty(inp, ('change_job', (t0, sinter.CollectionOptions(max_errors=100_000_000), 100_000_000)))
+    assert worker.process_messages() == 1
+    _assert_drain_queue(out, [('changed_job', 5, (t0.strong_id(),))])
+
+    _put_wait_not_empty(inp, ('accept_shots', (t0.strong_id(), 10000)))
+    assert worker.process_messages() == 1
+    _assert_drain_queue(out, [('accepted_shots', 5, (t0.strong_id(), 10000))])
+
+    assert worker.current_task == t0
+    assert worker.current_task_shots_left == 10000
+    assert worker.process_messages() == 0
+    _assert_drain_queue(out, [])
+
+    _put_wait_not_empty(inp, ('return_shots', (t0.strong_id(), 2000)))
+    assert worker.process_messages() == 1
+    _assert_drain_queue(out, [
+        ('returned_shots', 5, (t0.strong_id(), 2000)),
+    ])
+
+    _put_wait_not_empty(inp, ('return_shots', (t0.strong_id(), 20000000)))
+    assert worker.process_messages() == 1
+    _assert_drain_queue(out, [
+        ('returned_shots', 5, (t0.strong_id(), 8000)),
+    ])
+
+    assert not worker.do_some_work()
+
+
+def test_worker_finish_work():
+    handler = MockWorkHandler()
+
+    inp = multiprocessing.Queue()
+    out = multiprocessing.Queue()
+    inp.cancel_join_thread()
+    out.cancel_join_thread()
+
+    worker = CollectionWorkerState(
+        flush_period=-1,
+        worker_id=5,
+        sampler=handler,
+        inp=inp,
+        out=out,
+        custom_error_count_key=None,
+    )
+
+    assert worker.process_messages() == 0
+    _assert_drain_queue(out, [])
+
+    ta = sinter.Task(
+        circuit=stim.Circuit('H 0'),
+        detector_error_model=stim.DetectorErrorModel(),
+        decoder='mock',
+        collection_options=sinter.CollectionOptions(max_shots=100_000_000),
+        json_metadata={'a': 3},
+    )
+    handler.expected_task = ta
+    _put_wait_not_empty(inp, ('change_job', (ta, sinter.CollectionOptions(max_errors=100_000_000), 100_000_000)))
+    _put_wait_not_empty(inp, ('accept_shots', (ta.strong_id(), 10000)))
+    assert worker.process_messages() == 2
+    _assert_drain_queue(out, [
+        ('changed_job', 5, (ta.strong_id(),)),
+        ('accepted_shots', 5, (ta.strong_id(), 10000)),
+    ])
+
+    assert worker.current_task == ta
+    assert worker.current_task_shots_left == 10000
+    assert worker.process_messages() == 0
+    _assert_drain_queue(out, [])
+
+    handler.expected.append((
+        10000,
+        sinter.AnonTaskStats(
+            shots=1000,
+            errors=23,
+            discards=0,
+            seconds=1,
+        ),
+    ))
+
+    assert worker.do_some_work()
+    worker.flush_results()
+    _assert_drain_queue(out, [
+        ('flushed_results', 5, (ta.strong_id(), sinter.AnonTaskStats(shots=1000, errors=23, discards=0, seconds=1)))])
+
+    handler.expected.append((
+        9000,
+        sinter.AnonTaskStats(
+            shots=9000,
+            errors=13,
+            discards=0,
+            seconds=1,
+        ),
+    ))
+
+    assert worker.do_some_work()
+    worker.flush_results()
+    _assert_drain_queue(out, [
+        ('flushed_results', 5, (ta.strong_id(), sinter.AnonTaskStats(
+            shots=9000,
+            errors=13,
+            discards=0,
+            seconds=1,
+        ))),
+    ])
+    assert not worker.do_some_work()
+    worker.flush_results()
+    _assert_drain_queue(out, [])
diff --git a/glue/sample/src/sinter/_collection/_mux_sampler.py b/glue/sample/src/sinter/_collection/_mux_sampler.py
new file mode 100755
index 00000000..d0db3caa
--- /dev/null
+++ b/glue/sample/src/sinter/_collection/_mux_sampler.py
@@ -0,0 +1,56 @@
+import pathlib
+from typing import Optional
+from typing import Union
+
+from sinter._data import Task
+from sinter._decoding._decoding_all_built_in_decoders import BUILT_IN_SAMPLERS
+from sinter._decoding._decoding_decoder_class import Decoder
+from sinter._decoding._sampler import CompiledSampler
+from sinter._decoding._sampler import Sampler
+from sinter._decoding._stim_then_decode_sampler import StimThenDecodeSampler
+
+
+class MuxSampler(Sampler):
+    """Looks up the sampler to use for a task, by the task's decoder name."""
+
+    def __init__(
+        self,
+        *,
+        custom_decoders: Union[dict[str, Union[Decoder, Sampler]], None],
+        count_observable_error_combos: bool,
+        count_detection_events: bool,
+        tmp_dir: Optional[pathlib.Path],
+    ):
+        self.custom_decoders = custom_decoders
+        self.count_observable_error_combos = count_observable_error_combos
+        self.count_detection_events = count_detection_events
+        self.tmp_dir = tmp_dir
+
+    def compiled_sampler_for_task(self, task: Task) -> CompiledSampler:
+        return self._resolve_sampler(task.decoder).compiled_sampler_for_task(task)
+
+    def _resolve_sampler(self, name: str) -> Sampler:
+        sub_sampler: Union[Decoder, Sampler]
+
+        if name in self.custom_decoders:
+            sub_sampler = self.custom_decoders[name]
+        elif name in BUILT_IN_SAMPLERS:
+            sub_sampler = BUILT_IN_SAMPLERS[name]
+        else:
+            raise NotImplementedError(f'Not a recognized decoder or sampler: {name=}. Did you forget to specify custom_decoders?')
+
+        if isinstance(sub_sampler, Sampler):
+            if self.count_detection_events:
+                raise NotImplementedError("'count_detection_events' not supported when using a custom Sampler (instead of a custom Decoder).")
+            if self.count_observable_error_combos:
+                raise NotImplementedError("'count_observable_error_combos' not supported when using a custom Sampler (instead of a custom Decoder).")
+            return sub_sampler
+        elif isinstance(sub_sampler, Decoder) or hasattr(sub_sampler, 'compile_decoder_for_dem'):
+            return StimThenDecodeSampler(
+                decoder=sub_sampler,
+                count_detection_events=self.count_detection_events,
+                count_observable_error_combos=self.count_observable_error_combos,
+                tmp_dir=self.tmp_dir,
+            )
+        else:
+            raise NotImplementedError(f"Don't know how to turn this into a Sampler: {sub_sampler!r}")
diff --git a/glue/sample/src/sinter/_printer.py b/glue/sample/src/sinter/_collection/_printer.py
similarity index 100%
rename from glue/sample/src/sinter/_printer.py
rename to glue/sample/src/sinter/_collection/_printer.py
diff --git a/glue/sample/src/sinter/_collection/_sampler_ramp_throttled.py b/glue/sample/src/sinter/_collection/_sampler_ramp_throttled.py
new file mode 100755
index 00000000..5f8ae056
--- /dev/null
+++ b/glue/sample/src/sinter/_collection/_sampler_ramp_throttled.py
@@ -0,0 +1,66 @@
+import time
+
+from sinter._decoding import Sampler, CompiledSampler
+from sinter._data import Task, AnonTaskStats
+
+
+class RampThrottledSampler(Sampler):
+    """Wraps a sampler to adjust requested shots to hit a target time.
+
+    This sampler will initially only take 1 shot per call. If the time taken
+    significantly undershoots the target time, the maximum number of shots per
+    call is increased by a constant factor. If it exceeds the target time, the
+    maximum is reduced by a constant factor. The result is that the sampler
+    "ramps up" how many shots it does per call until it takes roughly the target
+    time, and then dynamically adapts to stay near it.
+    """
+
+    def __init__(self, sub_sampler: Sampler, target_batch_seconds: float, max_batch_shots: int):
+        self.sub_sampler = sub_sampler
+        self.target_batch_seconds = target_batch_seconds
+        self.max_batch_shots = max_batch_shots
+
+    def __str__(self) -> str:
+        return f'CompiledRampThrottledSampler({self.sub_sampler})'
+
+    def compiled_sampler_for_task(self, task: Task) -> CompiledSampler:
+        compiled_sub_sampler = self.sub_sampler.compiled_sampler_for_task(task)
+        if compiled_sub_sampler.handles_throttling():
+            return compiled_sub_sampler
+
+        return CompiledRampThrottledSampler(
+            sub_sampler=compiled_sub_sampler,
+            target_batch_seconds=self.target_batch_seconds,
+            max_batch_shots=self.max_batch_shots,
+        )
+
+
+class CompiledRampThrottledSampler(CompiledSampler):
+    def __init__(self, sub_sampler: CompiledSampler, target_batch_seconds: float, max_batch_shots: int):
+        self.sub_sampler = sub_sampler
+        self.target_batch_seconds = target_batch_seconds
+        self.batch_shots = 1
+        self.max_batch_shots = max_batch_shots
+
+    def __str__(self) -> str:
+        return f'CompiledRampThrottledSampler({self.sub_sampler})'
+
+    def sample(self, max_shots: int) -> AnonTaskStats:
+        t0 = time.monotonic()
+        actual_shots = min(max_shots, self.batch_shots)
+        result = self.sub_sampler.sample(actual_shots)
+        dt = time.monotonic() - t0
+
+        # Rebalance number of shots.
+        if self.batch_shots > 1 and dt > self.target_batch_seconds * 1.3:
+            self.batch_shots //= 2
+        if result.shots * 2 >= actual_shots:
+            for _ in range(4):
+                if self.batch_shots * 2 > self.max_batch_shots:
+                    break
+                if dt > self.target_batch_seconds * 0.3:
+                    break
+                self.batch_shots *= 2
+                dt *= 2
+
+        return result
diff --git a/glue/sample/src/sinter/_collection_tracker_for_single_task.py b/glue/sample/src/sinter/_collection_tracker_for_single_task.py
deleted file mode 100644
index 0932b55e..00000000
--- a/glue/sample/src/sinter/_collection_tracker_for_single_task.py
+++ /dev/null
@@ -1,230 +0,0 @@
-import math
-from typing import Iterator
-from typing import Optional
-
-import stim
-
-from sinter._anon_task_stats import AnonTaskStats
-from sinter._existing_data import ExistingData
-from sinter._task import Task
-from sinter._worker import WorkIn
-from sinter._worker import WorkOut
-
-
-DEFAULT_MAX_BATCH_SECONDS = 120
-
-
-class CollectionTrackerForSingleTask:
-    def __init__(
-            self,
-            *,
-            task: Task,
-            count_observable_error_combos: bool,
-            count_detection_events: bool,
-            circuit_path: str,
-            dem_path: str,
-            existing_data: ExistingData,
-            custom_error_count_key: Optional[str],
-    ):
-        self.unfilled_task = task
-        self.count_observable_error_combos = count_observable_error_combos
-        self.count_detection_events = count_detection_events
-        self.task_strong_id = None
-        self.circuit_path = circuit_path
-        self.dem_path = dem_path
-        self.finished_stats = AnonTaskStats()
-        self.existing_data = existing_data
-        self.deployed_shots = 0
-        self.waiting_for_worker_computing_dem_and_strong_id = False
-        self.deployed_processes = 0
-        self.custom_error_count_key = custom_error_count_key
-
-        task.circuit.to_file(circuit_path)
-        if task.detector_error_model is not None:
-            task.detector_error_model.to_file(dem_path)
-            self.task_strong_id = task.strong_id()
-            existing = self.existing_data.data.get(self.task_strong_id)
-            if existing is not None:
-                self.finished_stats += existing.to_anon_stats()
-        if self.copts.max_shots is None and self.copts.max_errors is None:
-            raise ValueError('Neither the task nor the collector specified max_shots or max_errors. Must specify one.')
-
-    @property
-    def copts(self):
-        return self.unfilled_task.collection_options
-
-    def expected_shots_remaining(
-            self, *, safety_factor_on_shots_per_error: float = 1) -> float:
-        """Doesn't include deployed shots."""
-        result: float = float('inf')
-
-        if self.copts.max_shots is not None:
-            result = self.copts.max_shots - self.finished_stats.shots
-
-        errs = self._seen_errors()
-        if errs and self.copts.max_errors is not None:
-            shots_per_error = self.finished_stats.shots / errs
-            errors_left = self.copts.max_errors - errs
-            result = min(result, errors_left * shots_per_error * safety_factor_on_shots_per_error)
-
-        return result
-
-    def _seen_errors(self) -> int:
-        if self.custom_error_count_key is not None:
-            return self.finished_stats.custom_counts.get(self.custom_error_count_key, 0)
-        return self.finished_stats.errors
-
-    def expected_time_per_shot(self) -> Optional[float]:
-        if self.finished_stats.shots == 0:
-            return None
-        return self.finished_stats.seconds / self.finished_stats.shots
-
-    def expected_errors_per_shot(self) -> Optional[float]:
-        return (self._seen_errors() + 1) / (self.finished_stats.shots + 1)
-
-    def expected_time_remaining(self) -> Optional[float]:
-        dt = self.expected_time_per_shot()
-        n = self.expected_shots_remaining()
-        if dt is None or n == float('inf'):
-            return None
-        return dt * n
-
-    def work_completed(self, result: WorkOut) -> None:
-        if self.waiting_for_worker_computing_dem_and_strong_id:
-            self.task_strong_id = result.strong_id
-            existing = self.existing_data.data.get(self.task_strong_id)
-            if existing is not None:
-                self.finished_stats += existing.to_anon_stats()
-            self.waiting_for_worker_computing_dem_and_strong_id = False
-        else:
-            self.deployed_shots -= result.stats.shots
-            self.finished_stats += result.stats
-        self.deployed_processes -= 1
-
-    def is_done(self) -> bool:
-        if self.task_strong_id is None or self.waiting_for_worker_computing_dem_and_strong_id:
-            return False
-        enough_shots = False
-        if self.copts.max_shots is not None and self.finished_stats.shots >= self.copts.max_shots:
-            enough_shots = True
-        if self.copts.max_errors is not None and self._seen_errors() >= self.copts.max_errors:
-            enough_shots = True
-        return enough_shots and self.deployed_shots == 0
-
-    def iter_batch_size_limits(self, *, desperate: bool) -> Iterator[float]:
-        if self.finished_stats.shots == 0:
-            if self.deployed_shots > 0:
-                yield 0
-            elif self.copts.start_batch_size is None:
-                yield 100
-            else:
-                yield self.copts.start_batch_size
-            return
-
-        # Do exponential ramp-up of batch sizes.
-        yield self.finished_stats.shots * 2
-
-        # Don't go super parallel before reaching other maximums.
-        if not desperate:
-            yield self.finished_stats.shots * 5 - self.deployed_shots
-
-        # Don't take more shots than requested.
-        if self.copts.max_shots is not None:
-            yield self.copts.max_shots - self.finished_stats.shots - self.deployed_shots
-
-        # Don't take more errors than requested.
-        if self.copts.max_errors is not None:
-            errors_left = self.copts.max_errors - self._seen_errors()
-            errors_left += 2  # oversample once count gets low
-            de = self.expected_errors_per_shot()
-            yield errors_left / de - self.deployed_shots
-
-        # Don't exceed max batch size.
-        if self.copts.max_batch_size is not None:
-            yield self.copts.max_batch_size
-
-        # If no maximum on batch size is specified, default to 30s maximum.
-        max_batch_seconds = self.copts.max_batch_seconds
-        if max_batch_seconds is None and self.copts.max_batch_size is None:
-            max_batch_seconds = DEFAULT_MAX_BATCH_SECONDS
-
-        # Try not to exceed max batch duration.
-        if max_batch_seconds is not None:
-            dt = self.expected_time_per_shot()
-            if dt is not None and dt > 0:
-                yield max(1, math.floor(max_batch_seconds / dt))
-
-    def next_shot_count(self, *, desperate: bool) -> int:
-        return math.ceil(min(self.iter_batch_size_limits(desperate=desperate)))
-
-    def provide_more_work(self, *, desperate: bool) -> Optional[WorkIn]:
-        if self.task_strong_id is None:
-            if self.waiting_for_worker_computing_dem_and_strong_id:
-                return None
-            self.waiting_for_worker_computing_dem_and_strong_id = True
-            self.deployed_processes += 1
-            return WorkIn(
-                work_key=None,
-                circuit_path=self.circuit_path,
-                dem_path=self.dem_path,
-                decoder=self.unfilled_task.decoder,
-                postselection_mask=self.unfilled_task.postselection_mask,
-                postselected_observables_mask=self.unfilled_task.postselected_observables_mask,
-                json_metadata=self.unfilled_task.json_metadata,
-                strong_id=None,
-                num_shots=-1,
-                count_observable_error_combos=self.count_observable_error_combos,
-                count_detection_events=self.count_detection_events,
-            )
-
-        # Wait to have *some* data before starting to sample in parallel.
-        num_shots = self.next_shot_count(desperate=desperate)
-        if num_shots <= 0:
-            return None
-
-        self.deployed_shots += num_shots
-        self.deployed_processes += 1
-        return WorkIn(
-            work_key=None,
-            strong_id=self.task_strong_id,
-            circuit_path=self.circuit_path,
-            dem_path=self.dem_path,
-            decoder=self.unfilled_task.decoder,
-            postselection_mask=self.unfilled_task.postselection_mask,
-            postselected_observables_mask=self.unfilled_task.postselected_observables_mask,
-            json_metadata=self.unfilled_task.json_metadata,
-            num_shots=num_shots,
-            count_observable_error_combos=self.count_observable_error_combos,
-            count_detection_events=self.count_detection_events,
-        )
-
-    def status(self) -> str:
-        t = self.expected_time_remaining()
-        if t is not None:
-            t /= 60
-            t = math.ceil(t)
-            t = f'{t}'
-        terms = [
-            f'{self.unfilled_task.decoder} '.rjust(22),
-            f'processes={self.deployed_processes}'.ljust(13),
-            f'~core_mins_left={t}'.ljust(24),
-        ]
-        if self.task_strong_id is None:
-            terms.append(f'(initializing...) ')
-        else:
-            if self.copts.max_shots is not None:
-                terms.append(f'shots_left={max(0, self.copts.max_shots - self.finished_stats.shots)}'.ljust(20))
-            if self.copts.max_errors is not None:
-                terms.append(f'errors_left={max(0, self.copts.max_errors - self._seen_errors())}'.ljust(20))
-        if isinstance(self.unfilled_task.json_metadata, dict):
-            keys = self.unfilled_task.json_metadata.keys()
-            try:
-                keys = sorted(keys)
-            except:
-                keys = list(keys)
-            meta_desc = '{' + ','.join(f'{k}={self.unfilled_task.json_metadata[k]}' for k in keys) + '}'
-        else:
-            meta_desc = f'{self.unfilled_task.json_metadata}'
-        terms.append(meta_desc)
-
-        return ''.join(terms)
diff --git a/glue/sample/src/sinter/_collection_work_manager.py b/glue/sample/src/sinter/_collection_work_manager.py
deleted file mode 100644
index d32db698..00000000
--- a/glue/sample/src/sinter/_collection_work_manager.py
+++ /dev/null
@@ -1,275 +0,0 @@
-import os
-
-import contextlib
-import multiprocessing
-import pathlib
-import tempfile
-import stim
-from typing import cast, Iterable, Optional, Iterator, Tuple, Dict, List
-
-from sinter._decoding_decoder_class import Decoder
-from sinter._collection_options import CollectionOptions
-from sinter._existing_data import ExistingData
-from sinter._task_stats import TaskStats
-from sinter._task import Task
-from sinter._anon_task_stats import AnonTaskStats
-from sinter._collection_tracker_for_single_task import CollectionTrackerForSingleTask
-from sinter._worker import worker_loop, WorkIn, WorkOut
-
-
-class CollectionWorkManager:
-    def __init__(
-        self,
-        *,
-        tasks_iter: Iterator[Task],
-        global_collection_options: CollectionOptions,
-        additional_existing_data: Optional[ExistingData],
-        count_observable_error_combos: bool,
-        count_detection_events: bool,
-        decoders: Optional[Iterable[str]],
-        custom_decoders: Dict[str, Decoder],
-        custom_error_count_key: Optional[str],
-        allowed_cpu_affinity_ids: Optional[List[int]],
-    ):
-        self.custom_decoders = custom_decoders
-        self.queue_from_workers: Optional[multiprocessing.Queue] = None
-        self.queue_to_workers: Optional[multiprocessing.Queue] = None
-        self.additional_existing_data = ExistingData() if additional_existing_data is None else additional_existing_data
-        self.tmp_dir: Optional[pathlib.Path] = None
-        self.exit_stack: Optional[contextlib.ExitStack] = None
-        self.custom_error_count_key = custom_error_count_key
-        self.allowed_cpu_affinity_ids = None if allowed_cpu_affinity_ids is None else sorted(set(allowed_cpu_affinity_ids))
-
-        self.global_collection_options = global_collection_options
-        self.decoders: Optional[Tuple[str, ...]] = None if decoders is None else tuple(decoders)
-        self.did_work = False
-
-        self.workers: List[multiprocessing.Process] = []
-        self.active_collectors: Dict[int, CollectionTrackerForSingleTask] = {}
-        self.next_collector_key: int = 0
-        self.finished_count = 0
-        self.deployed_jobs: Dict[int, WorkIn] = {}
-        self.next_job_id = 0
-        self.count_observable_error_combos = count_observable_error_combos
-        self.count_detection_events = count_detection_events
-
-        self.tasks_with_decoder_iter: Iterator[Task] = _iter_tasks_with_assigned_decoders(
-            tasks_iter=tasks_iter,
-            default_decoders=self.decoders,
-            global_collections_options=self.global_collection_options)
-
-    def start_workers(self, num_workers: int) -> None:
-        assert self.tmp_dir is not None
-        current_method = multiprocessing.get_start_method()
-        try:
-            # To ensure the child processes do not accidentally share ANY state
-            # related to, we use 'spawn' instead of 'fork'.
-            multiprocessing.set_start_method('spawn', force=True)
-            # Create queues after setting start method to work around a deadlock
-            # bug that occurs otherwise.
-            self.queue_from_workers = multiprocessing.Queue()
-            self.queue_to_workers = multiprocessing.Queue()
-            self.queue_from_workers.cancel_join_thread()
-            self.queue_to_workers.cancel_join_thread()
-
-            if self.allowed_cpu_affinity_ids is None:
-                cpus = range(os.cpu_count())
-            else:
-                num_cpus = os.cpu_count()
-                cpus = [e for e in self.allowed_cpu_affinity_ids if e < num_cpus]
-            for index in range(num_workers):
-                cpu_pin = None if len(cpus) == 0 else cpus[index % len(cpus)]
-                w = multiprocessing.Process(
-                    target=worker_loop,
-                    args=(self.tmp_dir, self.queue_to_workers, self.queue_from_workers, self.custom_decoders, cpu_pin))
-                w.start()
-                self.workers.append(w)
-        finally:
-            multiprocessing.set_start_method(current_method, force=True)
-
-    def __enter__(self):
-        self.exit_stack = contextlib.ExitStack().__enter__()
-        self.tmp_dir = pathlib.Path(self.exit_stack.enter_context(tempfile.TemporaryDirectory()))
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.shut_down_workers()
-        self.exit_stack.__exit__(exc_type, exc_val, exc_tb)
-        self.exit_stack = None
-        self.tmp_dir = None
-
-    def shut_down_workers(self) -> None:
-        removed_workers = self.workers
-        self.workers = []
-
-        # SIGKILL everything.
-        for w in removed_workers:
-            # This is supposed to be safe because all state on disk was put
-            # in the specified tmp directory which we will handle deleting.
-            w.kill()
-        # Wait for them to be done.
-        for w in removed_workers:
-            w.join()
-
-    def fill_work_queue(self) -> bool:
-        while len(self.deployed_jobs) < len(self.workers):
-            work = self.provide_more_work()
-            if work is None:
-                break
-            self.did_work = True
-            self.queue_to_workers.put(work.with_work_key((self.next_job_id, work.work_key)))
-            self.deployed_jobs[self.next_job_id] = work
-            self.next_job_id += 1
-        return bool(self.deployed_jobs)
-
-    def wait_for_next_sample(self,
-                             *,
-                             timeout: Optional[float] = None,
-                             ) -> TaskStats:
-        result = self.queue_from_workers.get(timeout=timeout)
-        assert isinstance(result, WorkOut)
-        if result.msg_error is not None:
-            msg, error = result.msg_error
-            if isinstance(error, KeyboardInterrupt):
-                raise KeyboardInterrupt()
-            raise RuntimeError(f"Worker failed: {msg}") from error
-
-        else:
-            job_id, sub_key = result.work_key
-            stats = result.stats
-            work_in = self.deployed_jobs[job_id]
-
-            self.work_completed(WorkOut(
-                work_key=sub_key,
-                stats=stats,
-                strong_id=result.strong_id,
-                msg_error=result.msg_error,
-            ))
-            del self.deployed_jobs[job_id]
-            if stats is None:
-                stats = AnonTaskStats()
-            return TaskStats(
-                strong_id=result.strong_id,
-                decoder=work_in.decoder,
-                json_metadata=work_in.json_metadata,
-                shots=stats.shots,
-                errors=stats.errors,
-                custom_counts=stats.custom_counts,
-                discards=stats.discards,
-                seconds=stats.seconds,
-            )
-
-    def _iter_draw_collectors(self, *, prefer_started: bool) -> Iterator[Tuple[int, CollectionTrackerForSingleTask]]:
-        if prefer_started:
-            yield from self.active_collectors.items()
-        while True:
-            key = self.next_collector_key
-            try:
-                task = next(self.tasks_with_decoder_iter)
-            except StopIteration:
-                break
-            collector = CollectionTrackerForSingleTask(
-                task=task,
-                circuit_path=str((self.tmp_dir / f'circuit_{self.next_collector_key}.stim').absolute()),
-                dem_path=str((self.tmp_dir / f'dem_{self.next_collector_key}.dem').absolute()),
-                existing_data=self.additional_existing_data,
-                count_detection_events=self.count_detection_events,
-                count_observable_error_combos=self.count_observable_error_combos,
-                custom_error_count_key=self.custom_error_count_key,
-            )
-            if collector.is_done():
-                self.finished_count += 1
-                continue
-            self.next_collector_key += 1
-            self.active_collectors[key] = collector
-            yield key, collector
-        if not prefer_started:
-            yield from self.active_collectors.items()
-
-    def is_done(self) -> bool:
-        return len(self.active_collectors) == 0
-
-    def work_completed(self, result: WorkOut):
-        assert isinstance(result.work_key, int)
-        collector_index = cast(int, result.work_key)
-        collector = self.active_collectors[collector_index]
-        collector.work_completed(result)
-        if collector.is_done():
-            self.finished_count += 1
-            del self.active_collectors[collector_index]
-
-    def provide_more_work(self) -> Optional[WorkIn]:
-        iter_collectors = self._iter_draw_collectors(
-                prefer_started=len(self.active_collectors) >= 2)
-        for desperate in False, True:
-            for collector_index, collector in iter_collectors:
-                w = collector.provide_more_work(desperate=desperate)
-                if w is not None:
-                    assert w.work_key is None
-                    return w.with_work_key(collector_index)
-        return None
-
-    def status(self, *, num_circuits: Optional[int]) -> str:
-        if self.is_done():
-            if self.did_work:
-                main_status = 'Done collecting'
-            else:
-                main_status = 'There was nothing additional to collect'
-        elif num_circuits is not None:
-            main_status = f'{num_circuits - self.finished_count} cases left:'
-        else:
-            main_status = "Running..."
-        collector_statuses = [
-            collector.status()
-            for collector in self.active_collectors.values()
-        ]
-        if len(collector_statuses) > 24:
-            collector_statuses = collector_statuses[:24] + ['\n...']
-
-        min_indent = 0
-        while collector_statuses and all(min_indent < len(c) and c[min_indent] == ' ' for c in collector_statuses):
-            min_indent += 1
-        if min_indent > 4:
-            collector_statuses = [c[min_indent - 4:] for c in collector_statuses]
-        collector_statuses = ['\n' + c for c in collector_statuses]
-
-        return main_status + ''.join(collector_statuses)
-
-
-def _iter_tasks_with_assigned_decoders(
-    *,
-    tasks_iter: Iterator[Task],
-    default_decoders: Optional[Iterable[str]],
-    global_collections_options: CollectionOptions,
-) -> Iterator[Task]:
-    for task in tasks_iter:
-        if task.circuit is None:
-            task = Task(
-                circuit=stim.Circuit.from_file(task.circuit_path),
-                decoder=task.decoder,
-                detector_error_model=task.detector_error_model,
-                postselection_mask=task.postselection_mask,
-                postselected_observables_mask=task.postselected_observables_mask,
-                json_metadata=task.json_metadata,
-                collection_options=task.collection_options,
-                circuit_path=task.circuit_path,
-            )
-
-        if task.decoder is None and default_decoders is None:
-            raise ValueError("Decoders to use was not specified. decoders is None and task.decoder is None")
-        task_decoders = []
-        if default_decoders is not None:
-            task_decoders.extend(default_decoders)
-        if task.decoder is not None and task.decoder not in task_decoders:
-            task_decoders.append(task.decoder)
-        for decoder in task_decoders:
-            yield Task(
-                circuit=task.circuit,
-                decoder=decoder,
-                detector_error_model=task.detector_error_model,
-                postselection_mask=task.postselection_mask,
-                postselected_observables_mask=task.postselected_observables_mask,
-                json_metadata=task.json_metadata,
-                collection_options=task.collection_options.combine(global_collections_options),
-                skip_validation=True,
-            )
diff --git a/glue/sample/src/sinter/_command/__init__.py b/glue/sample/src/sinter/_command/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/glue/sample/src/sinter/_main.py b/glue/sample/src/sinter/_command/_main.py
similarity index 83%
rename from glue/sample/src/sinter/_main.py
rename to glue/sample/src/sinter/_command/_main.py
index ef5f3c35..30488ca3 100644
--- a/glue/sample/src/sinter/_main.py
+++ b/glue/sample/src/sinter/_command/_main.py
@@ -8,16 +8,16 @@ def main(*, command_line_args: Optional[List[str]] = None):
 
     mode = command_line_args[0] if command_line_args else None
     if mode == 'combine':
-        from sinter._main_combine import main_combine
+        from sinter._command._main_combine import main_combine
         return main_combine(command_line_args=command_line_args[1:])
     if mode == 'collect':
-        from sinter._main_collect import main_collect
+        from sinter._command._main_collect import main_collect
         return main_collect(command_line_args=command_line_args[1:])
     if mode == 'plot':
-        from sinter._main_plot import main_plot
+        from sinter._command._main_plot import main_plot
         return main_plot(command_line_args=command_line_args[1:])
     if mode == 'predict':
-        from sinter._main_predict import main_predict
+        from sinter._command._main_predict import main_predict
         return main_predict(command_line_args=command_line_args[1:])
 
     want_help = mode in ['help', 'h', '--help', '-help', '-h', '--h']
diff --git a/glue/sample/src/sinter/_main_collect.py b/glue/sample/src/sinter/_command/_main_collect.py
similarity index 96%
rename from glue/sample/src/sinter/_main_collect.py
rename to glue/sample/src/sinter/_command/_main_collect.py
index b53dc392..5f008ae7 100644
--- a/glue/sample/src/sinter/_main_collect.py
+++ b/glue/sample/src/sinter/_command/_main_collect.py
@@ -8,12 +8,11 @@
 import numpy as np
 import stim
 
-import sinter
-from sinter._printer import ThrottledProgressPrinter
-from sinter._task import Task
+from sinter._collection import ThrottledProgressPrinter
+from sinter._data import Task
 from sinter._collection import collect, Progress, post_selection_mask_from_predicate
-from sinter._decoding_all_built_in_decoders import BUILT_IN_DECODERS
-from sinter._main_combine import ExistingData, CSV_HEADER
+from sinter._command._main_combine import ExistingData, CSV_HEADER
+from sinter._decoding._decoding_all_built_in_decoders import BUILT_IN_SAMPLERS
 
 
 def iter_file_paths_into_goals(circuit_paths: Iterator[str],
@@ -82,7 +81,7 @@ def parse_args(args: List[str]) -> Any:
                         default=None,
                         help='Sampling of a circuit will stop if this many errors have been seen.')
     parser.add_argument('--processes',
-                        required=True,
+                        default='auto',
                         type=str,
                         help='Number of processes to use for simultaneous sampling and decoding. '
                              'Must be either a number or "auto" which sets it to the number of '
@@ -203,6 +202,7 @@ def parse_args(args: List[str]) -> Any:
                              '''    --metadata_func "auto"\n'''
                              '''    --metadata_func "{'n': circuit.num_qubits, 'p': float(path.split('/')[-1].split('.')[0])}"\n'''
                         )
+    import sinter
     a = parser.parse_args(args=args)
     if a.metadata_func == 'auto':
         a.metadata_func = "sinter.comma_separated_key_values(path)"
@@ -243,9 +243,9 @@ def parse_args(args: List[str]) -> Any:
     else:
         a.custom_decoders = None
     for decoder in a.decoders:
-        if decoder not in BUILT_IN_DECODERS and (a.custom_decoders is None or decoder not in a.custom_decoders):
-            message = f"Not a recognized decoder: {decoder=}.\n"
-            message += f"Available built-in decoders: {sorted(e for e in BUILT_IN_DECODERS.keys() if 'internal' not in e)}.\n"
+        if decoder not in BUILT_IN_SAMPLERS and (a.custom_decoders is None or decoder not in a.custom_decoders):
+            message = f"Not a recognized decoder or sampler: {decoder=}.\n"
+            message += f"Available built-in decoders and samplers: {sorted(e for e in BUILT_IN_SAMPLERS.keys() if 'internal' not in e)}.\n"
             if a.custom_decoders is None:
                 message += f"No custom decoders are available. --custom_decoders_module_function wasn't specified."
             else:
@@ -296,7 +296,7 @@ def main_collect(*, command_line_args: List[str]):
     printer = ThrottledProgressPrinter(
         outs=[],
         print_progress=not args.quiet,
-        min_progress_delay=0.03 if args.also_print_results_to_stdout else 0.2,
+        min_progress_delay=0.03 if args.also_print_results_to_stdout else 0.1,
     )
     if print_to_stdout:
         printer.outs.append(sys.stdout)
diff --git a/glue/sample/src/sinter/_main_collect_test.py b/glue/sample/src/sinter/_command/_main_collect_test.py
similarity index 92%
rename from glue/sample/src/sinter/_main_collect_test.py
rename to glue/sample/src/sinter/_command/_main_collect_test.py
index 09f45d89..408fde65 100644
--- a/glue/sample/src/sinter/_main_collect_test.py
+++ b/glue/sample/src/sinter/_command/_main_collect_test.py
@@ -6,8 +6,8 @@
 
 import pytest
 import sinter
-from sinter._main import main
-from sinter._main_combine import ExistingData
+from sinter._command._main import main
+from sinter._command._main_combine import ExistingData
 from sinter._plotting import split_by
 
 
@@ -144,7 +144,7 @@ def test_main_collect_with_custom_decoder():
                 "--decoders",
                 "NOTEXIST",
                 "--custom_decoders_module_function",
-                "sinter._main_collect_test:_make_custom_decoders",
+                "sinter._command._main_collect_test:_make_custom_decoders",
                 "--processes",
                 "2",
                 "--quiet",
@@ -162,7 +162,7 @@ def test_main_collect_with_custom_decoder():
             "--decoders",
             "alternate",
             "--custom_decoders_module_function",
-            "sinter._main_collect_test:_make_custom_decoders",
+            "sinter._command._main_collect_test:_make_custom_decoders",
             "--processes",
             "2",
             "--quiet",
@@ -450,3 +450,33 @@ def test_auto_processes():
         ])
         data = sinter.stats_from_csv_files(d / "out.csv")
         assert len(data) == 1
+
+
+def test_implicit_auto_processes():
+    with tempfile.TemporaryDirectory() as d:
+        d = pathlib.Path(d)
+        stim.Circuit.generated(
+            'repetition_code:memory',
+            rounds=5,
+            distance=3,
+            after_clifford_depolarization=0.1,
+        ).to_file(d / 'a=3.stim')
+
+        # Collects requested stats.
+        main(command_line_args=[
+            "collect",
+            "--circuits",
+            str(d / 'a=3.stim'),
+            "--max_shots",
+            "200",
+            "--quiet",
+            "--metadata_func",
+            "auto",
+            "--decoders",
+            "perfectionist",
+            "--save_resume_filepath",
+            str(d / "out.csv"),
+        ])
+        data = sinter.stats_from_csv_files(d / "out.csv")
+        assert len(data) == 1
+        assert data[0].discards > 0
diff --git a/glue/sample/src/sinter/_main_combine.py b/glue/sample/src/sinter/_command/_main_combine.py
similarity index 97%
rename from glue/sample/src/sinter/_main_combine.py
rename to glue/sample/src/sinter/_command/_main_combine.py
index 86c5e0cf..216815e9 100644
--- a/glue/sample/src/sinter/_main_combine.py
+++ b/glue/sample/src/sinter/_command/_main_combine.py
@@ -6,8 +6,7 @@
 from typing import List, Any
 
 import sinter
-from sinter._csv_out import CSV_HEADER
-from sinter._existing_data import ExistingData
+from sinter._data import CSV_HEADER, ExistingData
 from sinter._plotting import better_sorted_str_terms
 
 
diff --git a/glue/sample/src/sinter/_main_combine_test.py b/glue/sample/src/sinter/_command/_main_combine_test.py
similarity index 99%
rename from glue/sample/src/sinter/_main_combine_test.py
rename to glue/sample/src/sinter/_command/_main_combine_test.py
index f89ae463..6419c7d2 100644
--- a/glue/sample/src/sinter/_main_combine_test.py
+++ b/glue/sample/src/sinter/_command/_main_combine_test.py
@@ -3,7 +3,7 @@
 import pathlib
 import tempfile
 
-from sinter._main import main
+from sinter._command._main import main
 
 
 def test_main_combine():
diff --git a/glue/sample/src/sinter/_main_plot.py b/glue/sample/src/sinter/_command/_main_plot.py
similarity index 82%
rename from glue/sample/src/sinter/_main_plot.py
rename to glue/sample/src/sinter/_command/_main_plot.py
index c6defeb9..4c1c38da 100644
--- a/glue/sample/src/sinter/_main_plot.py
+++ b/glue/sample/src/sinter/_command/_main_plot.py
@@ -4,11 +4,12 @@
 import argparse
 
 import matplotlib.pyplot as plt
+import numpy as np
 
-from sinter import shot_error_rate_to_piece_error_rate
-from sinter._main_combine import ExistingData
+from sinter._command._main_combine import ExistingData
 from sinter._plotting import plot_discard_rate, plot_custom
 from sinter._plotting import plot_error_rate
+from sinter._probability_util import shot_error_rate_to_piece_error_rate, Fit
 
 if TYPE_CHECKING:
     import sinter
@@ -32,6 +33,16 @@ def parse_args(args: List[str]) -> Any:
                              'Examples:\n'
                              '''    --filter_func "decoder=='pymatching'"\n'''
                              '''    --filter_func "0.001 < metadata['p'] < 0.005"\n''')
+    parser.add_argument('--preprocess_stats_func',
+                        type=str,
+                        default=None,
+                        help='An expression that operates on a `stats` value, returning a new list of stats to plot.\n'
+                             'For example, this could double add a field to json_metadata or merge stats together.\n'
+                             'Examples:\n'
+                             '''    --preprocess_stats_func "[stat for stat in stats if stat.errors > 0]\n'''
+                             '''    --preprocess_stats_func "[stat.with_edits(errors=stat.custom_counts['severe_errors']) for stat in stats]\n'''
+                             '''    --preprocess_stats_func "__import__('your_custom_module').your_custom_function(stats)"\n'''
+                             )
     parser.add_argument('--x_func',
                         type=str,
                         default="1",
@@ -49,6 +60,21 @@ def parse_args(args: List[str]) -> Any:
                              '''    --x_func m.p\n'''
                              '''    --x_func "metadata['path'].split('/')[-1].split('.')[0]"\n'''
                         )
+    parser.add_argument('--point_label_func',
+                        type=str,
+                        default="None",
+                        help='A python expression that determines text to put next to data points.\n'
+                             'Values available to the python expression:\n'
+                             '    metadata: The parsed value from the json_metadata for the data point.\n'
+                             '    m: `m.key` is a shorthand for `metadata.get("key", None)`.\n'
+                             '    decoder: The decoder that decoded the data for the data point.\n'
+                             '    strong_id: The cryptographic hash of the case that was sampled for the data point.\n'
+                             '    stat: The sinter.TaskStats object for the data point.\n'
+                             'Expected expression type:\n'
+                             '    Something Falsy (no label), or something that can be given to `str` to get a string.\n'
+                             'Examples:\n'
+                             '''    --point_label_func "f'p={m.p}'"\n'''
+                        )
     parser.add_argument('--y_func',
                         type=str,
                         default=None,
@@ -62,7 +88,8 @@ def parse_args(args: List[str]) -> Any:
                              '    strong_id: The cryptographic hash of the case that was sampled for the data point.\n'
                              '    stat: The sinter.TaskStats object for the data point.\n'
                              'Expected expression type:\n'
-                             '    Something that can be given to `float` to get a float.\n'
+                             '    A `sinter.Fit` specifying an uncertainty region,.\n'
+                             '    or else something that can be given to `float` to get a float.\n'
                              'Examples:\n'
                              '''    --x_func "metadata['p']"\n'''
                              '''    --x_func "metadata['path'].split('/')[-1].split('.')[0]"\n'''
@@ -76,6 +103,7 @@ def parse_args(args: List[str]) -> Any:
                         type=str,
                         default="'all data (use -group_func and -x_func to group into curves)'",
                         help='A python expression that determines how points are grouped into curves.\n'
+                             'If this evaluates to a dict, different keys control different groupings (e.g. "color" and "marker")\n'
                              'Values available to the python expression:\n'
                              '    metadata: The parsed value from the json_metadata for the data point.\n'
                              '    m: `m.key` is a shorthand for `metadata.get("key", None)`.\n'
@@ -83,10 +111,16 @@ def parse_args(args: List[str]) -> Any:
                              '    strong_id: The cryptographic hash of the case that was sampled for the data point.\n'
                              '    stat: The sinter.TaskStats object for the data point.\n'
                              'Expected expression type:\n'
-                             '    Something that can be given to `str` to get a useful string.\n'
+                             '    A dict, or something that can be given to `str` to get a useful string.\n'
+                             'Recognized dict keys:\n'
+                             '    "color": controls color grouping\n'
+                             '    "marker": controls marker grouping\n'
+                             '    "linestyle": controls linestyle grouping\n'
+                             '    "order": controls ordering in the legend\n'
+                             '    "label": the text shown in the legend\n'
                              'Examples:\n'
-                             '''    --group_func "(decoder, metadata['d'])"\n'''
-                             '''    --group_func m.d\n'''
+                             '''    --group_func "(decoder, m.d)"\n'''
+                             '''    --group_func "{'color': decoder, 'marker': m.d, 'label': (decoder, m.d)}"\n'''
                              '''    --group_func "metadata['path'].split('/')[-2]"\n'''
                         )
     parser.add_argument('--failure_unit_name',
@@ -151,7 +185,7 @@ def parse_args(args: List[str]) -> Any:
                         )
     parser.add_argument('--plot_args_func',
                         type=str,
-                        default='''{'marker': 'ov*sp^<>8P+xXhHDd|'[index % 18]}''',
+                        default='''{}''',
                         help='A python expression used to customize the look of curves.\n'
                              'Values available to the python expression:\n'
                              '    index: A unique integer identifying the curve.\n'
@@ -181,9 +215,8 @@ def parse_args(args: List[str]) -> Any:
     parser.add_argument('--out',
                         type=str,
                         default=None,
-                        help='Output file to write the plot to.\n'
-                             'The file extension determines the type of image.\n'
-                             'Either this or --show must be specified.')
+                        help='Write the plot to a file instead of showing it.\n'
+                             '(Use --show to still show the plot.)')
     parser.add_argument('--xaxis',
                         type=str,
                         default='[log]',
@@ -207,8 +240,7 @@ def parse_args(args: List[str]) -> Any:
                              "stats.")
     parser.add_argument('--show',
                         action='store_true',
-                        help='Displays the plot in a window.\n'
-                             'Either this or --out must be specified.')
+                        help='Displays the plot in a window even when --out is specified.')
     parser.add_argument('--xmin',
                         default=None,
                         type=float,
@@ -246,8 +278,6 @@ def parse_args(args: List[str]) -> Any:
                         help='Adds dashed line fits to every curve.')
 
     a = parser.parse_args(args=args)
-    if not a.show and a.out is None:
-        raise ValueError("Must specify '--out file' or '--show'.")
     if 'custom_y' in a.type and a.y_func is None:
         raise ValueError("--type custom_y requires --y_func.")
     if a.y_func is not None and a.type and 'custom_y' not in a.type:
@@ -265,35 +295,51 @@ def parse_args(args: List[str]) -> Any:
         a.failure_values_func = "1"
     if a.failure_unit_name is None:
         a.failure_unit_name = 'shot'
-    a.x_func = eval(compile(
-        f'lambda *, stat, decoder, metadata, m, strong_id: {a.x_func}',
-        filename='x_func:command_line_arg',
+
+    def _compile_argument_into_func(arg_name: str, arg_val: Any = ()):
+        if arg_val == ():
+            arg_val = getattr(a, arg_name)
+        raw_func = eval(compile(
+            f'lambda *, stat, decoder, metadata, m, strong_id, sinter, math, np: {arg_val}',
+            filename=f'{arg_name}:command_line_arg',
+            mode='eval',
+        ))
+        import sinter
+        return lambda stat: raw_func(
+            sinter=sinter,
+            math=math,
+            np=np,
+            stat=stat,
+            decoder=stat.decoder,
+            metadata=stat.json_metadata,
+            m=_FieldToMetadataWrapper(stat.json_metadata),
+            strong_id=stat.strong_id)
+
+    a.preprocess_stats_func = None if a.preprocess_stats_func is None else eval(compile(
+        f'lambda *, stats: {a.preprocess_stats_func}',
+        filename='preprocess_stats_func:command_line_arg',
         mode='eval'))
+    a.x_func = _compile_argument_into_func('x_func', a.x_func)
     if a.y_func is not None:
-        a.y_func = eval(compile(
-            f'lambda *, stat, decoder, metadata, m, strong_id: {a.y_func}',
-            filename='x_func:command_line_arg',
-            mode='eval'))
-    a.group_func = eval(compile(
-        f'lambda *, stat, decoder, metadata, m, strong_id: {a.group_func}',
-        filename='group_func:command_line_arg',
-        mode='eval'))
-    a.filter_func = eval(compile(
-        f'lambda *, stat, decoder, metadata, m, strong_id: {a.filter_func}',
-        filename='filter_func:command_line_arg',
-        mode='eval'))
-    a.failure_units_per_shot_func = eval(compile(
-        f'lambda *, stat, decoder, metadata, m, strong_id: {a.failure_units_per_shot_func}',
-        filename='failure_units_per_shot_func:command_line_arg',
-        mode='eval'))
-    a.failure_values_func = eval(compile(
-        f'lambda *, stat, decoder, metadata, m, strong_id: {a.failure_values_func}',
-        filename='failure_values_func:command_line_arg',
-        mode='eval'))
-    a.plot_args_func = eval(compile(
+        a.y_func = _compile_argument_into_func('y_func')
+    a.point_label_func = _compile_argument_into_func('point_label_func')
+    a.group_func = _compile_argument_into_func('group_func')
+    a.filter_func = _compile_argument_into_func('filter_func')
+    a.failure_units_per_shot_func = _compile_argument_into_func('failure_units_per_shot_func')
+    a.failure_values_func = _compile_argument_into_func('failure_values_func')
+    raw_plot_args_func = eval(compile(
         f'lambda *, index, key, stats, stat, decoder, metadata, m, strong_id: {a.plot_args_func}',
         filename='plot_args_func:command_line_arg',
         mode='eval'))
+    a.plot_args_func = lambda index, group_key, stats: raw_plot_args_func(
+            index=index,
+            key=group_key,
+            stats=stats,
+            stat=stats[0],
+            decoder=stats[0].decoder,
+            metadata=stats[0].json_metadata,
+            m=_FieldToMetadataWrapper(stats[0].json_metadata),
+            strong_id=stats[0].strong_id)
     return a
 
 
@@ -361,12 +407,21 @@ def _pick_min_max(
         want_strictly_positive: bool,
 ) -> Tuple[float, float]:
     assert default_max >= default_min
-    vs = [
-        v
-        for stat in plotted_stats
-        if (v := v_func(stat)) is not None
-        if v > 0 or not want_positive
-    ]
+    vs = []
+    for stat in plotted_stats:
+        v = v_func(stat)
+        if isinstance(v, (int, float)):
+            vs.append(v)
+        elif isinstance(v, Fit):
+            for e in [v.low, v.best, v.high]:
+                if e is not None:
+                    vs.append(e)
+        elif v is None:
+            pass
+        else:
+            raise NotImplementedError(f'{v=}')
+    if want_positive:
+        vs = [v for v in vs if v > 0]
 
     min_v = min(vs, default=default_min)
     max_v = max(vs, default=default_max)
@@ -429,16 +484,24 @@ def _set_axis_scale_label_ticks(
     elif scale_name == 'log':
         set_scale('log')
         min_v, max_v, major_ticks, minor_ticks = _log_ticks(min_v, max_v)
+        if forced_min_v is not None:
+            min_v = forced_min_v
+        if forced_max_v is not None:
+            max_v = forced_max_v
         set_ticks(major_ticks)
         set_ticks(minor_ticks, minor=True)
         set_lim(min_v, max_v)
     elif scale_name == 'sqrt':
         from matplotlib.scale import FuncScale
         min_v, max_v, major_ticks, minor_ticks = _sqrt_ticks(min_v, max_v)
-        set_lim(min_v, max_v)
+        if forced_min_v is not None:
+            min_v = forced_min_v
+        if forced_max_v is not None:
+            max_v = forced_max_v
         set_scale(FuncScale(ax, (lambda e: e**0.5, lambda e: e**2)))
         set_ticks(major_ticks)
         set_ticks(minor_ticks, minor=True)
+        set_lim(min_v, max_v)
     else:
         raise NotImplemented(f'{scale_name=}')
     return scale_name
@@ -468,6 +531,7 @@ def _plot_helper(
     samples: Union[Iterable['sinter.TaskStats'], ExistingData],
     group_func: Callable[['sinter.TaskStats'], Any],
     filter_func: Callable[['sinter.TaskStats'], Any],
+    preprocess_stats_func: Optional[Callable],
     failure_units_per_shot_func: Callable[['sinter.TaskStats'], Any],
     failure_values_func: Callable[['sinter.TaskStats'], Any],
     x_func: Callable[['sinter.TaskStats'], Any],
@@ -486,6 +550,7 @@ def _plot_helper(
     fig_size: Optional[Tuple[int, int]],
     plot_args_func: Callable[[int, Any, List['sinter.TaskStats']], Dict[str, Any]],
     line_fits: bool,
+    point_label_func: Callable[['sinter.TaskStats'], Any] = lambda _: None,
 ) -> Tuple[plt.Figure, List[plt.Axes]]:
     if isinstance(samples, ExistingData):
         total = samples
@@ -497,6 +562,12 @@ def _plot_helper(
                   for k, v in total.data.items()
                   if bool(filter_func(v))}
 
+    if preprocess_stats_func is not None:
+        processed_stats = preprocess_stats_func(stats=list(total.data.values()))
+        total.data = {}
+        for stat in processed_stats:
+            total.add_sample(stat)
+
     if not plot_types:
         if y_func is not None:
             plot_types = ['custom_y']
@@ -529,7 +600,6 @@ def _plot_helper(
     plotted_stats: List['sinter.TaskStats'] = [
         stat
         for stat in total.data.values()
-        if filter_func(stat)
     ]
 
     def stat_to_err_rate(stat: 'sinter.TaskStats') -> Optional[float]:
@@ -581,6 +651,7 @@ def stat_to_err_rate(stat: 'sinter.TaskStats') -> Optional[float]:
             highlight_max_likelihood_factor=highlight_max_likelihood_factor,
             plot_args_func=plot_args_func,
             line_fits=None if not line_fits else (x_scale_name, y_scale_name),
+            point_label_func=point_label_func,
         )
         ax_err.grid(which='major', color='#000000')
         ax_err.grid(which='minor', color='#DDDDDD')
@@ -595,6 +666,7 @@ def stat_to_err_rate(stat: 'sinter.TaskStats') -> Optional[float]:
             x_func=x_func,
             highlight_max_likelihood_factor=highlight_max_likelihood_factor,
             plot_args_func=plot_args_func,
+            point_label_func=point_label_func,
         )
         ax_dis.set_yticks([p / 10 for p in range(11)], labels=[f'{10*p}%' for p in range(11)])
         ax_dis.set_ylim(0, 1)
@@ -626,9 +698,9 @@ def stat_to_err_rate(stat: 'sinter.TaskStats') -> Optional[float]:
             x_func=x_func,
             y_func=y_func,
             group_func=group_func,
-            filter_func=filter_func,
             plot_args_func=plot_args_func,
             line_fits=None if not line_fits else (x_scale_name, y_scale_name),
+            point_label_func=point_label_func,
         )
         ax_cus.grid(which='major', color='#000000')
         ax_cus.grid(which='minor', color='#DDDDDD')
@@ -710,51 +782,14 @@ def main_plot(*, command_line_args: List[str]):
 
     fig, _ = _plot_helper(
         samples=total,
-        group_func=lambda stat: args.group_func(
-            stat=stat,
-            decoder=stat.decoder,
-            metadata=stat.json_metadata,
-            m=_FieldToMetadataWrapper(stat.json_metadata),
-            strong_id=stat.strong_id),
-        x_func=lambda stat: args.x_func(
-            stat=stat,
-            decoder=stat.decoder,
-            metadata=stat.json_metadata,
-            m=_FieldToMetadataWrapper(stat.json_metadata),
-            strong_id=stat.strong_id),
-        y_func=None if args.y_func is None else lambda stat: args.y_func(
-            stat=stat,
-            decoder=stat.decoder,
-            metadata=stat.json_metadata,
-            m=_FieldToMetadataWrapper(stat.json_metadata),
-            strong_id=stat.strong_id),
-        filter_func=lambda stat: args.filter_func(
-            stat=stat,
-            decoder=stat.decoder,
-            metadata=stat.json_metadata,
-            m=_FieldToMetadataWrapper(stat.json_metadata),
-            strong_id=stat.strong_id),
-        failure_units_per_shot_func=lambda stat: args.failure_units_per_shot_func(
-            stat=stat,
-            decoder=stat.decoder,
-            metadata=stat.json_metadata,
-            m=_FieldToMetadataWrapper(stat.json_metadata),
-            strong_id=stat.strong_id),
-        failure_values_func=lambda stat: args.failure_values_func(
-            stat=stat,
-            decoder=stat.decoder,
-            metadata=stat.json_metadata,
-            m=_FieldToMetadataWrapper(stat.json_metadata),
-            strong_id=stat.strong_id),
-        plot_args_func=lambda index, group_key, stats: args.plot_args_func(
-            index=index,
-            key=group_key,
-            stats=stats,
-            stat=stats[0],
-            decoder=stats[0].decoder,
-            metadata=stats[0].json_metadata,
-            m=_FieldToMetadataWrapper(stats[0].json_metadata),
-            strong_id=stats[0].strong_id),
+        group_func=args.group_func,
+        x_func=args.x_func,
+        point_label_func=args.point_label_func,
+        y_func=args.y_func,
+        filter_func=args.filter_func,
+        failure_units_per_shot_func=args.failure_units_per_shot_func,
+        failure_values_func=args.failure_values_func,
+        plot_args_func=args.plot_args_func,
         failure_unit=args.failure_unit_name,
         plot_types=args.type,
         xaxis=args.xaxis,
@@ -768,8 +803,9 @@ def main_plot(*, command_line_args: List[str]):
         title=args.title,
         subtitle=args.subtitle,
         line_fits=args.line_fits,
+        preprocess_stats_func=args.preprocess_stats_func,
     )
     if args.out is not None:
         fig.savefig(args.out)
-    if args.show:
+    if args.show or args.out is None:
         plt.show()
diff --git a/glue/sample/src/sinter/_main_plot_test.py b/glue/sample/src/sinter/_command/_main_plot_test.py
similarity index 99%
rename from glue/sample/src/sinter/_main_plot_test.py
rename to glue/sample/src/sinter/_command/_main_plot_test.py
index 689199a5..3408a08c 100644
--- a/glue/sample/src/sinter/_main_plot_test.py
+++ b/glue/sample/src/sinter/_command/_main_plot_test.py
@@ -4,8 +4,8 @@
 import tempfile
 
 import pytest
-from sinter._main import main
-from sinter._main_plot import _log_ticks, _sqrt_ticks
+from sinter._command._main import main
+from sinter._command._main_plot import _log_ticks, _sqrt_ticks
 
 
 def test_main_plot():
diff --git a/glue/sample/src/sinter/_main_predict.py b/glue/sample/src/sinter/_command/_main_predict.py
similarity index 100%
rename from glue/sample/src/sinter/_main_predict.py
rename to glue/sample/src/sinter/_command/_main_predict.py
diff --git a/glue/sample/src/sinter/_main_predict_test.py b/glue/sample/src/sinter/_command/_main_predict_test.py
similarity index 95%
rename from glue/sample/src/sinter/_main_predict_test.py
rename to glue/sample/src/sinter/_command/_main_predict_test.py
index 2dec070c..4e819668 100644
--- a/glue/sample/src/sinter/_main_predict_test.py
+++ b/glue/sample/src/sinter/_command/_main_predict_test.py
@@ -1,7 +1,7 @@
 import pathlib
 import tempfile
 
-from sinter._main import main
+from sinter._command._main import main
 
 
 def test_main_predict():
diff --git a/glue/sample/src/sinter/_data/__init__.py b/glue/sample/src/sinter/_data/__init__.py
new file mode 100644
index 00000000..41a0194a
--- /dev/null
+++ b/glue/sample/src/sinter/_data/__init__.py
@@ -0,0 +1,20 @@
+from sinter._data._anon_task_stats import (
+    AnonTaskStats,
+)
+from sinter._data._collection_options import (
+    CollectionOptions,
+)
+from sinter._data._csv_out import (
+    CSV_HEADER,
+)
+from sinter._data._existing_data import (
+    read_stats_from_csv_files,
+    stats_from_csv_files,
+    ExistingData,
+)
+from sinter._data._task import (
+    Task,
+)
+from sinter._data._task_stats import (
+    TaskStats,
+)
diff --git a/glue/sample/src/sinter/_anon_task_stats.py b/glue/sample/src/sinter/_data/_anon_task_stats.py
similarity index 84%
rename from glue/sample/src/sinter/_anon_task_stats.py
rename to glue/sample/src/sinter/_data/_anon_task_stats.py
index d5e62f95..60fee918 100644
--- a/glue/sample/src/sinter/_anon_task_stats.py
+++ b/glue/sample/src/sinter/_data/_anon_task_stats.py
@@ -1,6 +1,9 @@
 import collections
 import dataclasses
-from typing import Counter
+from typing import Counter, Union, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from sinter._data._task_stats import TaskStats
 
 
 @dataclasses.dataclass(frozen=True)
@@ -74,13 +77,13 @@ def __add__(self, other: 'AnonTaskStats') -> 'AnonTaskStats':
             >>> a + b
             sinter.AnonTaskStats(shots=1100, errors=220)
         """
-        if not isinstance(other, AnonTaskStats):
-            return NotImplemented
+        if isinstance(other, AnonTaskStats):
+            return AnonTaskStats(
+                shots=self.shots + other.shots,
+                errors=self.errors + other.errors,
+                discards=self.discards + other.discards,
+                seconds=self.seconds + other.seconds,
+                custom_counts=self.custom_counts + other.custom_counts,
+            )
 
-        return AnonTaskStats(
-            shots=self.shots + other.shots,
-            errors=self.errors + other.errors,
-            discards=self.discards + other.discards,
-            seconds=self.seconds + other.seconds,
-            custom_counts=self.custom_counts + other.custom_counts,
-        )
+        return NotImplemented
diff --git a/glue/sample/src/sinter/_anon_task_stats_test.py b/glue/sample/src/sinter/_data/_anon_task_stats_test.py
similarity index 100%
rename from glue/sample/src/sinter/_anon_task_stats_test.py
rename to glue/sample/src/sinter/_data/_anon_task_stats_test.py
diff --git a/glue/sample/src/sinter/_collection_options.py b/glue/sample/src/sinter/_data/_collection_options.py
similarity index 100%
rename from glue/sample/src/sinter/_collection_options.py
rename to glue/sample/src/sinter/_data/_collection_options.py
diff --git a/glue/sample/src/sinter/_collection_options_test.py b/glue/sample/src/sinter/_data/_collection_options_test.py
similarity index 100%
rename from glue/sample/src/sinter/_collection_options_test.py
rename to glue/sample/src/sinter/_data/_collection_options_test.py
diff --git a/glue/sample/src/sinter/_csv_out.py b/glue/sample/src/sinter/_data/_csv_out.py
similarity index 100%
rename from glue/sample/src/sinter/_csv_out.py
rename to glue/sample/src/sinter/_data/_csv_out.py
diff --git a/glue/sample/src/sinter/_existing_data.py b/glue/sample/src/sinter/_data/_existing_data.py
similarity index 96%
rename from glue/sample/src/sinter/_existing_data.py
rename to glue/sample/src/sinter/_data/_existing_data.py
index 925b2110..077c01c6 100644
--- a/glue/sample/src/sinter/_existing_data.py
+++ b/glue/sample/src/sinter/_data/_existing_data.py
@@ -3,9 +3,9 @@
 import pathlib
 from typing import Any, Dict, List, TYPE_CHECKING
 
-from sinter._task_stats import TaskStats
-from sinter._task import Task
-from sinter._decoding import AnonTaskStats
+from sinter._data._task_stats import TaskStats
+from sinter._data._task import Task
+from sinter._data._anon_task_stats import AnonTaskStats
 
 if TYPE_CHECKING:
     import sinter
@@ -26,8 +26,9 @@ def stats_for(self, case: Task) -> AnonTaskStats:
 
     def add_sample(self, sample: TaskStats) -> None:
         k = sample.strong_id
-        if k in self.data:
-            self.data[k] += sample
+        current = self.data.get(k)
+        if current is not None:
+            self.data[k] = current + sample
         else:
             self.data[k] = sample
 
diff --git a/glue/sample/src/sinter/_existing_data_test.py b/glue/sample/src/sinter/_data/_existing_data_test.py
similarity index 100%
rename from glue/sample/src/sinter/_existing_data_test.py
rename to glue/sample/src/sinter/_data/_existing_data_test.py
diff --git a/glue/sample/src/sinter/_task.py b/glue/sample/src/sinter/_data/_task.py
similarity index 99%
rename from glue/sample/src/sinter/_task.py
rename to glue/sample/src/sinter/_data/_task.py
index c358bde3..4f19b034 100644
--- a/glue/sample/src/sinter/_task.py
+++ b/glue/sample/src/sinter/_data/_task.py
@@ -8,7 +8,7 @@
 
 import numpy as np
 
-from sinter._collection_options import CollectionOptions
+from sinter._data._collection_options import CollectionOptions
 
 if TYPE_CHECKING:
     import sinter
diff --git a/glue/sample/src/sinter/_task_stats.py b/glue/sample/src/sinter/_data/_task_stats.py
similarity index 62%
rename from glue/sample/src/sinter/_task_stats.py
rename to glue/sample/src/sinter/_data/_task_stats.py
index 73a2dd16..4d846e89 100644
--- a/glue/sample/src/sinter/_task_stats.py
+++ b/glue/sample/src/sinter/_data/_task_stats.py
@@ -1,9 +1,27 @@
 import collections
 import dataclasses
 from typing import Counter, List, Any
+from typing import Optional
+from typing import Union
+from typing import overload
 
-from sinter._anon_task_stats import AnonTaskStats
-from sinter._csv_out import csv_line
+from sinter._data._anon_task_stats import AnonTaskStats
+from sinter._data._csv_out import csv_line
+
+
+def _is_equal_json_values(json1: Any, json2: Any):
+    if json1 == json2:
+        return True
+
+    if type(json1) == type(json2):
+        if isinstance(json1, dict):
+            return json1.keys() == json2.keys() and all(_is_equal_json_values(json1[k], json2[k]) for k in json1.keys())
+        elif isinstance(json1, (list, tuple)):
+            return len(json1) == len(json2) and all(_is_equal_json_values(a, b) for a, b in zip(json1, json2))
+    elif isinstance(json1, (list, tuple)) and isinstance(json2, (list, tuple)):
+        return _is_equal_json_values(tuple(json1), tuple(json2))
+
+    return False
 
 
 @dataclasses.dataclass(frozen=True)
@@ -67,22 +85,70 @@ def __post_init__(self):
         assert self.shots >= self.errors + self.discards
         assert all(isinstance(k, str) and isinstance(v, int) for k, v in self.custom_counts.items())
 
-    def __add__(self, other: 'TaskStats') -> 'TaskStats':
-        if self.strong_id != other.strong_id:
-            raise ValueError(f'{self.strong_id=} != {other.strong_id=}')
-        total = self.to_anon_stats() + other.to_anon_stats()
-
+    def with_edits(
+        self,
+        *,
+        strong_id: Optional[str] = None,
+        decoder: Optional[str] = None,
+        json_metadata: Optional[Any] = None,
+        shots: Optional[int] = None,
+        errors: Optional[int] = None,
+        discards: Optional[int] = None,
+        seconds: Optional[float] = None,
+        custom_counts: Optional[Counter[str]] = None,
+    ) -> 'TaskStats':
         return TaskStats(
-            decoder=self.decoder,
-            strong_id=self.strong_id,
-            json_metadata=self.json_metadata,
-            shots=total.shots,
-            errors=total.errors,
-            discards=total.discards,
-            seconds=total.seconds,
-            custom_counts=total.custom_counts,
+            strong_id=self.strong_id if strong_id is None else strong_id,
+            decoder=self.decoder if decoder is None else decoder,
+            json_metadata=self.json_metadata if json_metadata is None else json_metadata,
+            shots=self.shots if shots is None else shots,
+            errors=self.errors if errors is None else errors,
+            discards=self.discards if discards is None else discards,
+            seconds=self.seconds if seconds is None else seconds,
+            custom_counts=self.custom_counts if custom_counts is None else custom_counts,
         )
 
+    @overload
+    def __add__(self, other: AnonTaskStats) -> AnonTaskStats:
+        pass
+    @overload
+    def __add__(self, other: 'TaskStats') -> 'TaskStats':
+        pass
+    def __add__(self, other: Union[AnonTaskStats, 'TaskStats']) -> Union[AnonTaskStats, 'TaskStats']:
+        if isinstance(other, AnonTaskStats):
+            return self.to_anon_stats() + other
+
+        if isinstance(other, TaskStats):
+            if self.strong_id != other.strong_id:
+                raise ValueError(f'{self.strong_id=} != {other.strong_id=}')
+            if not _is_equal_json_values(self.json_metadata, other.json_metadata) or self.decoder != other.decoder:
+                raise ValueError(
+                    "A stat had the same strong id as another, but their other identifying information (json_metadata, decoder) differed.\n"
+                    "The strong id is supposed to be a cryptographic hash that uniquely identifies what was sampled, so this is an error.\n"
+                    "\n"
+                    "This failure can occur when post-processing data (e.g. combining X basis stats and Z basis stats into synthetic both-basis stats).\n"
+                    "To fix it, ensure any post-processing sets the strong id of the synthetic data in some cryptographically secure way.\n"
+                    "\n"
+                    "In some cases this can be caused by attempting to add a value that has gone through JSON serialization+parsing to one\n"
+                    "that hasn't, which causes things like tuples transforming into lists.\n"
+                    "\n"
+                    f"The two stats:\n1. {self!r}\n2. {other!r}")
+
+            total = self.to_anon_stats() + other.to_anon_stats()
+            return TaskStats(
+                decoder=self.decoder,
+                strong_id=self.strong_id,
+                json_metadata=self.json_metadata,
+                shots=total.shots,
+                errors=total.errors,
+                discards=total.discards,
+                seconds=total.seconds,
+                custom_counts=total.custom_counts,
+            )
+
+        return NotImplemented
+    __radd__ = __add__
+
     def to_anon_stats(self) -> AnonTaskStats:
         """Returns a `sinter.AnonTaskStats` with the same statistics.
 
diff --git a/glue/sample/src/sinter/_task_stats_test.py b/glue/sample/src/sinter/_data/_task_stats_test.py
similarity index 53%
rename from glue/sample/src/sinter/_task_stats_test.py
rename to glue/sample/src/sinter/_data/_task_stats_test.py
index 0847b003..4060c2f1 100644
--- a/glue/sample/src/sinter/_task_stats_test.py
+++ b/glue/sample/src/sinter/_data/_task_stats_test.py
@@ -3,6 +3,7 @@
 import pytest
 
 import sinter
+from sinter._data._task_stats import _is_equal_json_values
 
 
 def test_repr():
@@ -87,3 +88,53 @@ def test_add():
             seconds=52,
             custom_counts=collections.Counter({'a': 11, 'b': 20, 'c': 3}),
         )
+
+
+def test_with_edits():
+    v = sinter.TaskStats(
+        decoder='pymatching',
+        json_metadata={'a': 2},
+        strong_id='abcdefDIFFERENT',
+        shots=270,
+        errors=34,
+        discards=43,
+        seconds=52,
+        custom_counts=collections.Counter({'a': 11, 'b': 20, 'c': 3}),
+    )
+    assert v.with_edits(json_metadata={'b': 3}) == sinter.TaskStats(
+        decoder='pymatching',
+        json_metadata={'b': 3},
+        strong_id='abcdefDIFFERENT',
+        shots=270,
+        errors=34,
+        discards=43,
+        seconds=52,
+        custom_counts=collections.Counter({'a': 11, 'b': 20, 'c': 3}),
+    )
+    assert v == sinter.TaskStats(strong_id='', json_metadata={}, decoder='').with_edits(
+        decoder='pymatching',
+        json_metadata={'a': 2},
+        strong_id='abcdefDIFFERENT',
+        shots=270,
+        errors=34,
+        discards=43,
+        seconds=52,
+        custom_counts=collections.Counter({'a': 11, 'b': 20, 'c': 3}),
+    )
+
+
+def test_is_equal_json_values():
+    assert _is_equal_json_values([1, 2], (1, 2))
+    assert _is_equal_json_values([1, [3, (5, 6)]], (1, (3, [5, 6])))
+    assert not _is_equal_json_values([1, [3, (5, 6)]], (1, (3, [5, 7])))
+    assert not _is_equal_json_values([1, [3, (5, 6)]], (1, (3, [5])))
+    assert not _is_equal_json_values([1, 2], (1, 3))
+    assert not _is_equal_json_values([1, 2], {1, 2})
+    assert _is_equal_json_values({'x': [1, 2]}, {'x': (1, 2)})
+    assert _is_equal_json_values({'x': (1, 2)}, {'x': (1, 2)})
+    assert not _is_equal_json_values({'x': (1, 2)}, {'y': (1, 2)})
+    assert not _is_equal_json_values({'x': (1, 2)}, {'x': (1, 2), 'y': []})
+    assert not _is_equal_json_values({'x': (1, 2), 'y': []}, {'x': (1, 2)})
+    assert not _is_equal_json_values({'x': (1, 2)}, {'x': (1, 3)})
+    assert not _is_equal_json_values(1, 2)
+    assert _is_equal_json_values(1, 1)
diff --git a/glue/sample/src/sinter/_task_test.py b/glue/sample/src/sinter/_data/_task_test.py
similarity index 100%
rename from glue/sample/src/sinter/_task_test.py
rename to glue/sample/src/sinter/_data/_task_test.py
diff --git a/glue/sample/src/sinter/_decoding/__init__.py b/glue/sample/src/sinter/_decoding/__init__.py
new file mode 100644
index 00000000..c0aaebfd
--- /dev/null
+++ b/glue/sample/src/sinter/_decoding/__init__.py
@@ -0,0 +1,16 @@
+from sinter._decoding._decoding import (
+    streaming_post_select,
+    sample_decode,
+)
+from sinter._decoding._decoding_decoder_class import (
+    CompiledDecoder,
+    Decoder,
+)
+from sinter._decoding._decoding_all_built_in_decoders import (
+    BUILT_IN_DECODERS,
+    BUILT_IN_SAMPLERS,
+)
+from sinter._decoding._sampler import (
+    Sampler,
+    CompiledSampler,
+)
diff --git a/glue/sample/src/sinter/_decoding.py b/glue/sample/src/sinter/_decoding/_decoding.py
similarity index 98%
rename from glue/sample/src/sinter/_decoding.py
rename to glue/sample/src/sinter/_decoding/_decoding.py
index 7170d443..1e54f87e 100644
--- a/glue/sample/src/sinter/_decoding.py
+++ b/glue/sample/src/sinter/_decoding/_decoding.py
@@ -11,9 +11,9 @@
 import numpy as np
 import stim
 
-from sinter._anon_task_stats import AnonTaskStats
-from sinter._decoding_all_built_in_decoders import BUILT_IN_DECODERS
-from sinter._decoding_decoder_class import CompiledDecoder, Decoder
+from sinter._data import AnonTaskStats
+from sinter._decoding._decoding_all_built_in_decoders import BUILT_IN_DECODERS
+from sinter._decoding._decoding_decoder_class import CompiledDecoder, Decoder
 
 if TYPE_CHECKING:
     import sinter
diff --git a/glue/sample/src/sinter/_decoding/_decoding_all_built_in_decoders.py b/glue/sample/src/sinter/_decoding/_decoding_all_built_in_decoders.py
new file mode 100644
index 00000000..b495d23f
--- /dev/null
+++ b/glue/sample/src/sinter/_decoding/_decoding_all_built_in_decoders.py
@@ -0,0 +1,20 @@
+from typing import Dict
+from typing import Union
+
+from sinter._decoding._decoding_decoder_class import Decoder
+from sinter._decoding._decoding_fusion_blossom import FusionBlossomDecoder
+from sinter._decoding._decoding_pymatching import PyMatchingDecoder
+from sinter._decoding._decoding_vacuous import VacuousDecoder
+from sinter._decoding._perfectionist_sampler import PerfectionistSampler
+from sinter._decoding._sampler import Sampler
+
+BUILT_IN_DECODERS: Dict[str, Decoder] = {
+    'vacuous': VacuousDecoder(),
+    'pymatching': PyMatchingDecoder(),
+    'fusion_blossom': FusionBlossomDecoder(),
+}
+
+BUILT_IN_SAMPLERS: Dict[str, Union[Decoder, Sampler]] = {
+    **BUILT_IN_DECODERS,
+    'perfectionist': PerfectionistSampler(),
+}
diff --git a/glue/sample/src/sinter/_decoding_decoder_class.py b/glue/sample/src/sinter/_decoding/_decoding_decoder_class.py
similarity index 100%
rename from glue/sample/src/sinter/_decoding_decoder_class.py
rename to glue/sample/src/sinter/_decoding/_decoding_decoder_class.py
diff --git a/glue/sample/src/sinter/_decoding_fusion_blossom.py b/glue/sample/src/sinter/_decoding/_decoding_fusion_blossom.py
similarity index 99%
rename from glue/sample/src/sinter/_decoding_fusion_blossom.py
rename to glue/sample/src/sinter/_decoding/_decoding_fusion_blossom.py
index 966b69a4..ac3e5bfb 100644
--- a/glue/sample/src/sinter/_decoding_fusion_blossom.py
+++ b/glue/sample/src/sinter/_decoding/_decoding_fusion_blossom.py
@@ -5,7 +5,7 @@
 import numpy as np
 import stim
 
-from sinter._decoding_decoder_class import Decoder, CompiledDecoder
+from sinter._decoding._decoding_decoder_class import Decoder, CompiledDecoder
 
 if TYPE_CHECKING:
     import fusion_blossom
diff --git a/glue/sample/src/sinter/_decoding_pymatching.py b/glue/sample/src/sinter/_decoding/_decoding_pymatching.py
similarity index 97%
rename from glue/sample/src/sinter/_decoding_pymatching.py
rename to glue/sample/src/sinter/_decoding/_decoding_pymatching.py
index c8fb7464..b57bb32b 100644
--- a/glue/sample/src/sinter/_decoding_pymatching.py
+++ b/glue/sample/src/sinter/_decoding/_decoding_pymatching.py
@@ -1,4 +1,4 @@
-from sinter._decoding_decoder_class import Decoder, CompiledDecoder
+from sinter._decoding._decoding_decoder_class import Decoder, CompiledDecoder
 
 
 class PyMatchingCompiledDecoder(CompiledDecoder):
diff --git a/glue/sample/src/sinter/_decoding_test.py b/glue/sample/src/sinter/_decoding/_decoding_test.py
similarity index 98%
rename from glue/sample/src/sinter/_decoding_test.py
rename to glue/sample/src/sinter/_decoding/_decoding_test.py
index 2ca9fbbc..50b31b26 100644
--- a/glue/sample/src/sinter/_decoding_test.py
+++ b/glue/sample/src/sinter/_decoding/_decoding_test.py
@@ -11,9 +11,9 @@
 import stim
 
 from sinter._collection import post_selection_mask_from_4th_coord
-from sinter._decoding import sample_decode
-from sinter._decoding_all_built_in_decoders import BUILT_IN_DECODERS
-from sinter._decoding_vacuous import VacuousDecoder
+from sinter._decoding._decoding_all_built_in_decoders import BUILT_IN_DECODERS
+from sinter._decoding._decoding import sample_decode
+from sinter._decoding._decoding_vacuous import VacuousDecoder
 
 
 def get_test_decoders() -> Tuple[List[str], Dict[str, sinter.Decoder]]:
diff --git a/glue/sample/src/sinter/_decoding_vacuous.py b/glue/sample/src/sinter/_decoding/_decoding_vacuous.py
similarity index 94%
rename from glue/sample/src/sinter/_decoding_vacuous.py
rename to glue/sample/src/sinter/_decoding/_decoding_vacuous.py
index d3d3113b..8e24d551 100644
--- a/glue/sample/src/sinter/_decoding_vacuous.py
+++ b/glue/sample/src/sinter/_decoding/_decoding_vacuous.py
@@ -1,6 +1,6 @@
 import numpy as np
 
-from sinter._decoding_decoder_class import Decoder, CompiledDecoder
+from sinter._decoding._decoding_decoder_class import Decoder, CompiledDecoder
 
 
 class VacuousDecoder(Decoder):
diff --git a/glue/sample/src/sinter/_decoding/_perfectionist_sampler.py b/glue/sample/src/sinter/_decoding/_perfectionist_sampler.py
new file mode 100755
index 00000000..7478164f
--- /dev/null
+++ b/glue/sample/src/sinter/_decoding/_perfectionist_sampler.py
@@ -0,0 +1,38 @@
+import time
+
+import numpy as np
+
+from sinter._data import Task, AnonTaskStats
+from sinter._decoding._sampler import Sampler, CompiledSampler
+
+
+class PerfectionistSampler(Sampler):
+    """Predicts obs aren't flipped. Discards shots with any detection events."""
+    def compiled_sampler_for_task(self, task: Task) -> CompiledSampler:
+        return CompiledPerfectionistSampler(task)
+
+
+class CompiledPerfectionistSampler(CompiledSampler):
+    def __init__(self, task: Task):
+        self.stim_sampler = task.circuit.compile_detector_sampler()
+
+    def sample(self, max_shots: int) -> AnonTaskStats:
+        t0 = time.monotonic()
+        dets, obs = self.stim_sampler.sample(
+            shots=max_shots,
+            bit_packed=True,
+            separate_observables=True,
+        )
+        num_shots = dets.shape[0]
+        discards = np.any(dets, axis=1)
+        errors = np.any(obs, axis=1)
+        num_discards = np.count_nonzero(discards)
+        num_errors = np.count_nonzero(errors & ~discards)
+        t1 = time.monotonic()
+
+        return AnonTaskStats(
+            shots=num_shots,
+            errors=num_errors,
+            discards=num_discards,
+            seconds=t1 - t0,
+        )
diff --git a/glue/sample/src/sinter/_decoding/_sampler.py b/glue/sample/src/sinter/_decoding/_sampler.py
new file mode 100644
index 00000000..0fe40ca1
--- /dev/null
+++ b/glue/sample/src/sinter/_decoding/_sampler.py
@@ -0,0 +1,72 @@
+import abc
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import sinter
+
+
+class CompiledSampler(metaclass=abc.ABCMeta):
+    """A sampler that has been configured for efficiently sampling some task."""
+
+    @abc.abstractmethod
+    def sample(self, suggested_shots: int) -> 'sinter.AnonTaskStats':
+        """Samples shots and returns statistics.
+
+        Args:
+            suggested_shots: The number of shots being requested. The sampler
+                may perform more shots or fewer shots than this, so technically
+                this argument can just be ignored. If a sampler is optimized for
+                a specific batch size, it can simply return one batch per call
+                regardless of this parameter.
+
+                However, this parameter is a useful hint about the amount of
+                work being done. The sampler can use this to optimize its
+                behavior. For example, it could adjust its batch size downward
+                if the suggested shots is very small. Whereas if the suggested
+                shots is very high, the sampler should focus entirely on
+                achieving the best possible throughput.
+
+                Note that, in typical workloads, the sampler will be called
+                repeatedly with the same value of suggested_shots. Therefore it
+                is reasonable to allocate buffers sized to accomodate the
+                current suggested_shots, expecting them to be useful again for
+                the next shot.
+
+        Returns:
+            A sinter.AnonTaskStats saying how many shots were actually taken,
+            how many errors were seen, etc.
+
+            The returned stats must have at least one shot.
+        """
+        pass
+
+    def handles_throttling(self) -> bool:
+        """Return True to disable sinter wrapping samplers with throttling.
+
+        By default, sinter will wrap samplers so that they initially only do
+        a small number of shots then slowly ramp up. Sometimes this behavior
+        is not desired (e.g. in unit tests). Override this method to return True
+        to disable it.
+        """
+        return False
+
+
+class Sampler(metaclass=abc.ABCMeta):
+    """A strategy for producing stats from tasks.
+
+    Call `sampler.compiled_sampler_for_task(task)` to get a compiled sampler for
+    a task, then call `compiled_sampler.sample(shots)` to collect statistics.
+
+    A sampler differs from a `sinter.Decoder` because the sampler is responsible
+    for the full sampling process (e.g. simulating the circuit), whereas a
+    decoder can do nothing except predict observable flips from detection event
+    data. This prevents the decoders from cheating, but makes them less flexible
+    overall. A sampler can do things like use simulators other than stim, or
+    really anything at all as long as it ends with returning statistics about
+    shot counts, error counts, and etc.
+    """
+
+    @abc.abstractmethod
+    def compiled_sampler_for_task(self, task: 'sinter.Task') -> 'sinter.CompiledSampler':
+        """Creates, configures, and returns an object for sampling the task."""
+        pass
diff --git a/glue/sample/src/sinter/_decoding/_stim_then_decode_sampler.py b/glue/sample/src/sinter/_decoding/_stim_then_decode_sampler.py
new file mode 100755
index 00000000..ee28c53e
--- /dev/null
+++ b/glue/sample/src/sinter/_decoding/_stim_then_decode_sampler.py
@@ -0,0 +1,222 @@
+import collections
+import pathlib
+import random
+import time
+from typing import Optional
+from typing import Union
+
+import numpy as np
+
+from sinter._data import Task, AnonTaskStats
+from sinter._decoding._sampler import Sampler, CompiledSampler
+from sinter._decoding._decoding_decoder_class import Decoder, CompiledDecoder
+
+
+class StimThenDecodeSampler(Sampler):
+    """Samples shots using stim, then decodes using the given decoder.
+
+    This is the default sampler; the one used to wrap decoders with no
+    specified sampler.
+
+    The decoder's goal is to predict the observable flips given the detection
+    event data. Errors are when the prediction is wrong. Discards are when the
+    decoder returns an extra byte of prediction data for each shot, and the
+    extra byte is not zero.
+    """
+    def __init__(
+        self,
+        *,
+        decoder: Decoder,
+        count_observable_error_combos: bool,
+        count_detection_events: bool,
+        tmp_dir: Optional[pathlib.Path],
+    ):
+        self.decoder = decoder
+        self.count_observable_error_combos = count_observable_error_combos
+        self.count_detection_events = count_detection_events
+        self.tmp_dir = tmp_dir
+
+    def compiled_sampler_for_task(self, task: Task) -> CompiledSampler:
+        return _CompiledStimThenDecodeSampler(
+            decoder=self.decoder,
+            task=task,
+            count_detection_events=self.count_detection_events,
+            count_observable_error_combos=self.count_observable_error_combos,
+            tmp_dir=self.tmp_dir,
+        )
+
+
+def classify_discards_and_errors(
+    *,
+    actual_obs: np.ndarray,
+    predictions: np.ndarray,
+    postselected_observables_mask: Union[np.ndarray, None],
+    out_count_observable_error_combos: Union[None, collections.Counter[str]],
+    num_obs: int,
+) -> tuple[int, int]:
+    num_discards = 0
+
+    # Added bytes are used for signalling discards.
+    if predictions.shape[1] == actual_obs.shape[1] + 1:
+        discard_mask = predictions[:, -1] != 0
+        predictions = predictions[:, :-1]
+        num_discards += np.count_nonzero(discard_mask)
+        discard_mask ^= True
+        actual_obs = actual_obs[discard_mask]
+        predictions = predictions[discard_mask]
+
+    # Mispredicted observables can be used for signalling discards.
+    if postselected_observables_mask is not None:
+        discard_mask = np.any((actual_obs ^ predictions) & postselected_observables_mask, axis=1)
+        num_discards += np.count_nonzero(discard_mask)
+        discard_mask ^= True
+        actual_obs = actual_obs[discard_mask]
+        predictions = predictions[discard_mask]
+
+    fail_mask = np.any(actual_obs != predictions, axis=1)
+    if out_count_observable_error_combos is not None:
+        for k in np.flatnonzero(fail_mask):
+            mistakes = np.unpackbits(actual_obs[k] ^ predictions[k], count=num_obs, bitorder='little')
+            err_key = "obs_mistake_mask=" + ''.join('_E'[b] for b in mistakes)
+            out_count_observable_error_combos[err_key] += 1
+
+    num_errors = np.count_nonzero(fail_mask)
+    return num_discards, num_errors
+
+
+class DiskDecoder(CompiledDecoder):
+    def __init__(self, decoder: Decoder, task: Task, tmp_dir: pathlib.Path):
+        self.decoder = decoder
+        self.task = task
+        self.top_tmp_dir: pathlib.Path = tmp_dir
+
+        while True:
+            k = random.randint(0, 2**64)
+            self.top_tmp_dir = tmp_dir / f'disk_decoder_{k}'
+            try:
+                self.top_tmp_dir.mkdir()
+                break
+            except FileExistsError:
+                pass
+        self.decoder_tmp_dir: pathlib.Path = self.top_tmp_dir / 'dec'
+        self.decoder_tmp_dir.mkdir()
+        self.num_obs = task.detector_error_model.num_observables
+        self.num_dets = task.detector_error_model.num_detectors
+        self.dem_path = self.top_tmp_dir / 'dem.dem'
+        self.dets_b8_in_path = self.top_tmp_dir / 'dets.b8'
+        self.obs_predictions_b8_out_path = self.top_tmp_dir / 'obs.b8'
+        self.task.detector_error_model.to_file(self.dem_path)
+
+    def decode_shots_bit_packed(
+            self,
+            *,
+            bit_packed_detection_event_data: np.ndarray,
+    ) -> np.ndarray:
+        num_shots = bit_packed_detection_event_data.shape[0]
+        with open(self.dets_b8_in_path, 'wb') as f:
+            bit_packed_detection_event_data.tofile(f)
+        self.decoder.decode_via_files(
+            num_shots=num_shots,
+            num_obs=self.num_obs,
+            num_dets=self.num_dets,
+            dem_path=self.dem_path,
+            dets_b8_in_path=self.dets_b8_in_path,
+            obs_predictions_b8_out_path=self.obs_predictions_b8_out_path,
+            tmp_dir=self.decoder_tmp_dir,
+        )
+        num_obs_bytes = (self.num_obs + 7) // 8
+        with open(self.obs_predictions_b8_out_path, 'rb') as f:
+            prediction = np.fromfile(f, dtype=np.uint8, count=num_obs_bytes * num_shots)
+            assert prediction.shape == (num_obs_bytes * num_shots,)
+        self.obs_predictions_b8_out_path.unlink()
+        self.dets_b8_in_path.unlink()
+        return prediction.reshape((num_shots, num_obs_bytes))
+
+
+def _compile_decoder_with_disk_fallback(
+    decoder: Decoder,
+    task: Task,
+    tmp_dir: Optional[pathlib.Path],
+) -> CompiledDecoder:
+    try:
+        return decoder.compile_decoder_for_dem(dem=task.detector_error_model)
+    except (NotImplementedError, ValueError):
+        pass
+    if tmp_dir is None:
+        raise ValueError(f"Decoder {task.decoder=} didn't implement `compile_decoder_for_dem`, but no temporary directory was provided for falling back to `decode_via_files`.")
+    return DiskDecoder(decoder, task, tmp_dir)
+
+
+class _CompiledStimThenDecodeSampler(CompiledSampler):
+    def __init__(
+        self,
+        *,
+        decoder: Decoder,
+        task: Task,
+        count_observable_error_combos: bool,
+        count_detection_events: bool,
+        tmp_dir: Optional[pathlib.Path],
+    ):
+        self.task = task
+        self.compiled_decoder = _compile_decoder_with_disk_fallback(decoder, task, tmp_dir)
+        self.stim_sampler = task.circuit.compile_detector_sampler()
+        self.count_observable_error_combos = count_observable_error_combos
+        self.count_detection_events = count_detection_events
+        self.num_det = self.task.circuit.num_detectors
+        self.num_obs = self.task.circuit.num_observables
+
+    def sample(self, max_shots: int) -> AnonTaskStats:
+        t0 = time.monotonic()
+        dets, actual_obs = self.stim_sampler.sample(
+            shots=max_shots,
+            bit_packed=True,
+            separate_observables=True,
+        )
+        num_shots = dets.shape[0]
+
+        custom_counts = collections.Counter()
+        if self.count_detection_events:
+            custom_counts['detectors_checked'] += self.num_det * num_shots
+            for b in range(8):
+                custom_counts['detection_events'] += np.count_nonzero(dets & (1 << b))
+
+        # Discard any shots that contain a postselected detection events.
+        if self.task.postselection_mask is not None:
+            discarded_flags = np.any(dets & self.task.postselection_mask, axis=1)
+            num_discards_1 = np.count_nonzero(discarded_flags)
+            if num_discards_1:
+                dets = dets[~discarded_flags, :]
+                actual_obs = actual_obs[~discarded_flags, :]
+        else:
+            num_discards_1 = 0
+
+        predictions = self.compiled_decoder.decode_shots_bit_packed(bit_packed_detection_event_data=dets)
+        if not isinstance(predictions, np.ndarray):
+            raise ValueError("not isinstance(predictions, np.ndarray)")
+        if predictions.dtype != np.uint8:
+            raise ValueError("predictions.dtype != np.uint8")
+        if len(predictions.shape) != 2:
+            raise ValueError("len(predictions.shape) != 2")
+        if predictions.shape[0] != num_shots:
+            raise ValueError("predictions.shape[0] != num_shots")
+        if predictions.shape[1] < actual_obs.shape[1]:
+            raise ValueError("predictions.shape[1] < actual_obs.shape[1]")
+        if predictions.shape[1] > actual_obs.shape[1] + 1:
+            raise ValueError("predictions.shape[1] > actual_obs.shape[1] + 1")
+
+        num_discards_2, num_errors = classify_discards_and_errors(
+            actual_obs=actual_obs,
+            predictions=predictions,
+            postselected_observables_mask=self.task.postselected_observables_mask,
+            out_count_observable_error_combos=custom_counts if self.count_observable_error_combos else None,
+            num_obs=self.num_obs,
+        )
+        t1 = time.monotonic()
+
+        return AnonTaskStats(
+            shots=num_shots,
+            errors=num_errors,
+            discards=num_discards_1 + num_discards_2,
+            seconds=t1 - t0,
+            custom_counts=custom_counts,
+        )
diff --git a/glue/sample/src/sinter/_decoding/_stim_then_decode_sampler_test.py b/glue/sample/src/sinter/_decoding/_stim_then_decode_sampler_test.py
new file mode 100755
index 00000000..413015f0
--- /dev/null
+++ b/glue/sample/src/sinter/_decoding/_stim_then_decode_sampler_test.py
@@ -0,0 +1,192 @@
+import collections
+
+import numpy as np
+
+from sinter._decoding._stim_then_decode_sampler import \
+    classify_discards_and_errors
+
+
+def test_classify_discards_and_errors():
+    assert classify_discards_and_errors(
+        actual_obs=np.array([
+            [1, 2],
+            [2, 2],
+            [3, 2],
+            [4, 3],
+            [1, 3],
+            [0, 3],
+            [0, 3],
+        ], dtype=np.uint8),
+        predictions=np.array([
+            [1, 2],
+            [2, 2],
+            [3, 2],
+            [4, 3],
+            [1, 3],
+            [0, 3],
+            [0, 3],
+        ], dtype=np.uint8),
+        postselected_observables_mask=None,
+        out_count_observable_error_combos=None,
+        num_obs=16,
+    ) == (0, 0)
+
+    assert classify_discards_and_errors(
+        actual_obs=np.array([
+            [1, 2],
+            [2, 2],
+            [3, 2],
+            [4, 3],
+            [1, 3],
+            [0, 3],
+            [0, 3],
+        ], dtype=np.uint8),
+        predictions=np.array([
+            [0, 0],
+            [2, 2],
+            [3, 2],
+            [4, 1],
+            [1, 3],
+            [0, 3],
+            [0, 3],
+        ], dtype=np.uint8),
+        postselected_observables_mask=None,
+        out_count_observable_error_combos=None,
+        num_obs=16,
+    ) == (0, 2)
+
+    assert classify_discards_and_errors(
+        actual_obs=np.array([
+            [1, 2],
+            [2, 2],
+            [3, 2],
+            [4, 3],
+            [1, 3],
+            [0, 3],
+            [0, 3],
+        ], dtype=np.uint8),
+        predictions=np.array([
+            [0, 0, 0],
+            [2, 2, 0],
+            [3, 2, 0],
+            [4, 1, 0],
+            [1, 3, 0],
+            [0, 3, 0],
+            [0, 3, 0],
+        ], dtype=np.uint8),
+        postselected_observables_mask=None,
+        out_count_observable_error_combos=None,
+        num_obs=16,
+    ) == (0, 2)
+
+    assert classify_discards_and_errors(
+        actual_obs=np.array([
+            [1, 2],
+            [2, 2],
+            [3, 2],
+            [4, 3],
+            [1, 3],
+            [0, 3],
+            [0, 3],
+        ], dtype=np.uint8),
+        predictions=np.array([
+            [0, 0, 0],
+            [2, 2, 1],
+            [3, 2, 0],
+            [4, 1, 0],
+            [1, 3, 0],
+            [0, 3, 0],
+            [0, 3, 0],
+        ], dtype=np.uint8),
+        postselected_observables_mask=None,
+        out_count_observable_error_combos=None,
+        num_obs=16,
+    ) == (1, 2)
+
+    assert classify_discards_and_errors(
+        actual_obs=np.array([
+            [1, 2],
+            [2, 2],
+            [3, 2],
+            [4, 3],
+            [1, 3],
+            [0, 3],
+            [0, 3],
+        ], dtype=np.uint8),
+        predictions=np.array([
+            [0, 0, 1],
+            [2, 2, 0],
+            [3, 2, 0],
+            [4, 1, 0],
+            [1, 3, 0],
+            [0, 3, 0],
+            [0, 3, 0],
+        ], dtype=np.uint8),
+        postselected_observables_mask=None,
+        out_count_observable_error_combos=None,
+        num_obs=16,
+    ) == (1, 1)
+
+    assert classify_discards_and_errors(
+        actual_obs=np.array([
+            [1, 2],
+            [2, 2],
+            [3, 2],
+            [4, 3],
+            [1, 3],
+            [0, 3],
+            [0, 3],
+        ], dtype=np.uint8),
+        predictions=np.array([
+            [0, 0, 1],
+            [2, 2, 1],
+            [3, 2, 0],
+            [4, 1, 0],
+            [1, 3, 0],
+            [0, 3, 0],
+            [0, 3, 0],
+        ], dtype=np.uint8),
+        postselected_observables_mask=None,
+        out_count_observable_error_combos=None,
+        num_obs=16,
+    ) == (2, 1)
+
+    assert classify_discards_and_errors(
+        actual_obs=np.array([
+            [1, 2],
+            [2, 2],
+            [3, 2],
+            [4, 3],
+            [1, 3],
+            [2, 3],
+            [1, 3],
+        ], dtype=np.uint8),
+        predictions=np.array([
+            [0, 0, 1],
+            [2, 2, 1],
+            [3, 2, 0],
+            [4, 1, 0],
+            [1, 3, 0],
+            [0, 3, 0],
+            [0, 3, 0],
+        ], dtype=np.uint8),
+        postselected_observables_mask=np.array([1, 0]),
+        out_count_observable_error_combos=None,
+        num_obs=16,
+    ) == (3, 2)
+
+    counter = collections.Counter()
+    assert classify_discards_and_errors(
+        actual_obs=np.array([
+            [1, 2],
+            [1, 2],
+        ], dtype=np.uint8),
+        predictions=np.array([
+            [1, 0],
+            [1, 2],
+        ], dtype=np.uint8),
+        postselected_observables_mask=np.array([1, 0]),
+        out_count_observable_error_combos=counter,
+        num_obs=13,
+    ) == (0, 1)
+    assert counter == collections.Counter(["obs_mistake_mask=_________E___"])
diff --git a/glue/sample/src/sinter/_decoding_all_built_in_decoders.py b/glue/sample/src/sinter/_decoding_all_built_in_decoders.py
deleted file mode 100644
index a9fc5e76..00000000
--- a/glue/sample/src/sinter/_decoding_all_built_in_decoders.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from typing import Dict
-
-from sinter._decoding_decoder_class import Decoder
-from sinter._decoding_fusion_blossom import FusionBlossomDecoder
-from sinter._decoding_pymatching import PyMatchingDecoder
-from sinter._decoding_vacuous import VacuousDecoder
-
-BUILT_IN_DECODERS: Dict[str, Decoder] = {
-    'vacuous': VacuousDecoder(),
-    'pymatching': PyMatchingDecoder(),
-    'fusion_blossom': FusionBlossomDecoder(),
-}
diff --git a/glue/sample/src/sinter/_plotting.py b/glue/sample/src/sinter/_plotting.py
index 24acfa20..a8e36cba 100644
--- a/glue/sample/src/sinter/_plotting.py
+++ b/glue/sample/src/sinter/_plotting.py
@@ -1,6 +1,6 @@
 import math
-import sys
 from typing import Callable, TypeVar, List, Any, Iterable, Optional, TYPE_CHECKING, Dict, Union, Literal, Tuple
+from typing import Sequence
 from typing import cast
 
 import numpy as np
@@ -13,6 +13,25 @@
 
 
 MARKERS: str = "ov*sp^<>8PhH+xXDd|" * 100
+LINESTYLES: tuple[str, ...] = (
+    'solid',
+    'dotted',
+    'dashed',
+    'dashdot',
+    'loosely dotted',
+    'dotted',
+    'densely dotted',
+    'long dash with offset',
+    'loosely dashed',
+    'dashed',
+    'densely dashed',
+    'loosely dashdotted',
+    'dashdotted',
+    'densely dashdotted',
+    'dashdotdotted',
+    'loosely dashdotdotted',
+    'densely dashdotdotted',
+)
 T = TypeVar('T')
 TVal = TypeVar('TVal')
 TKey = TypeVar('TKey')
@@ -37,21 +56,30 @@ def split_by(vs: Iterable[T], key_func: Callable[[T], Any]) -> List[List[T]]:
 
 class LooseCompare:
     def __init__(self, val: Any):
-        self.val = val
+        self.val: Any = None
 
-    def __lt__(self, other):
-        if isinstance(other, LooseCompare):
-            other_val = other.val
-        else:
-            other_val = other
+        self.val = val.val if isinstance(val, LooseCompare) else val
+
+    def __lt__(self, other: Any) -> bool:
+        other_val = other.val if isinstance(other, LooseCompare) else other
         if isinstance(self.val, (int, float)) and isinstance(other_val, (int, float)):
             return self.val < other_val
+        if isinstance(self.val, (tuple, list)) and isinstance(other_val, (tuple, list)):
+            return tuple(LooseCompare(e) for e in self.val) < tuple(LooseCompare(e) for e in other_val)
         return str(self.val) < str(other_val)
 
-    def __str__(self):
+    def __gt__(self, other: Any) -> bool:
+        other_val = other.val if isinstance(other, LooseCompare) else other
+        if isinstance(self.val, (int, float)) and isinstance(other_val, (int, float)):
+            return self.val > other_val
+        if isinstance(self.val, (tuple, list)) and isinstance(other_val, (tuple, list)):
+            return tuple(LooseCompare(e) for e in self.val) > tuple(LooseCompare(e) for e in other_val)
+        return str(self.val) > str(other_val)
+
+    def __str__(self) -> str:
         return str(self.val)
 
-    def __eq__(self, other):
+    def __eq__(self, other: Any) -> bool:
         if isinstance(other, LooseCompare):
             other_val = other.val
         else:
@@ -94,11 +122,12 @@ def better_sorted_str_terms(val: Any) -> Any:
         distance=199999, rounds=3
         distance=199999, rounds=199999
     """
-
+    if val is None:
+        return 'None'
     if isinstance(val, tuple):
         return tuple(better_sorted_str_terms(e) for e in val)
     if not isinstance(val, str):
-        return val
+        return LooseCompare(val)
     terms = split_by(val, lambda c: c in '.0123456789')
     result = []
     for term in terms:
@@ -117,6 +146,8 @@ def better_sorted_str_terms(val: Any) -> Any:
             except ValueError:
                 pass
         result.append(term)
+    if len(result) == 1 and isinstance(result[0], (int, float)):
+        return LooseCompare(result[0])
     return tuple(LooseCompare(e) for e in result)
 
 
@@ -155,6 +186,45 @@ def group_by(items: Iterable[TVal],
 TCurveId = TypeVar('TCurveId')
 
 
+class _FrozenDict:
+    def __init__(self, v: dict):
+        self._v = dict(v)
+        self._eq = frozenset(v.items())
+        self._hash = hash(self._eq)
+
+        terms = []
+        for k in sorted(self._v.keys(), key=lambda e: (e != 'sort', e)):
+            terms.append(k)
+            terms.append(better_sorted_str_terms(self._v[k])
+        )
+        self._order = tuple(terms)
+
+    def __eq__(self, other):
+        if isinstance(other, _FrozenDict):
+            return self._eq == other._eq
+        return NotImplemented
+
+    def __lt__(self, other):
+        if isinstance(other, _FrozenDict):
+            return self._order < other._order
+        return NotImplemented
+
+    def __ne__(self, other):
+        return not (self == other)
+
+    def __hash__(self):
+        return self._hash
+
+    def __getitem__(self, item):
+        return self._v[item]
+
+    def get(self, item, alternate = None):
+        return self._v.get(item, alternate)
+
+    def __str__(self):
+        return " ".join(str(v) for _, v in sorted(self._v.items()))
+
+
 def plot_discard_rate(
         *,
         ax: 'plt.Axes',
@@ -165,6 +235,7 @@ def plot_discard_rate(
         filter_func: Callable[['sinter.TaskStats'], Any] = lambda _: True,
         plot_args_func: Callable[[int, TCurveId, List['sinter.TaskStats']], Dict[str, Any]] = lambda index, group_key, group_stats: dict(),
         highlight_max_likelihood_factor: Optional[float] = 1e3,
+        point_label_func: Callable[['sinter.TaskStats'], Any] = lambda _: None,
 ) -> None:
     """Plots discard rates in curves with uncertainty highlights.
 
@@ -181,11 +252,21 @@ def plot_discard_rate(
         group_func: Optional. When specified, multiple curves will be plotted instead of one curve.
             The statistics are grouped into curves based on whether or not they get the same result
             out of this function. For example, this could be `group_func=lambda stat: stat.decoder`.
+            If the result of the function is a dictionary, then optional keys in the dictionary will
+            also control the plotting of each curve. Available keys are:
+                'label': the label added to the legend for the curve
+                'color': the color used for plotting the curve
+                'marker': the marker used for the curve
+                'linestyle': the linestyle used for the curve
+                'sort': the order in which the curves will be plotted and added to the legend
+            e.g. if two curves (with different resulting dictionaries from group_func) share the same
+            value for key 'marker', they will be plotted with the same marker.
+            Colors, markers and linestyles are assigned in order, sorted by the values for those keys.
         filter_func: Optional. When specified, some curves will not be plotted.
             The statistics are filtered and only plotted if filter_func(stat) returns True.
             For example, `filter_func=lambda s: s.json_metadata['basis'] == 'x'` would plot only stats
             where the saved metadata indicates the basis was 'x'.
-        plot_args_func: Optional. Specifies additional arguments to give the the underlying calls to
+        plot_args_func: Optional. Specifies additional arguments to give the underlying calls to
             `plot` and `fill_between` used to do the actual plotting. For example, this can be used
             to specify markers and colors. Takes the index of the curve in sorted order and also a
             curve_id (these will be 0 and None respectively if group_func is not specified). For example,
@@ -198,6 +279,7 @@ def plot_discard_rate(
         highlight_max_likelihood_factor: Controls how wide the uncertainty highlight region around curves is.
             Must be 1 or larger. Hypothesis probabilities at most that many times as unlikely as the max likelihood
             hypothesis will be highlighted.
+        point_label_func: Optional. Specifies text to draw next to data points.
     """
     if highlight_max_likelihood_factor is None:
         highlight_max_likelihood_factor = 1
@@ -228,6 +310,7 @@ def y_func(stat: 'sinter.TaskStats') -> Union[float, 'sinter.Fit']:
         group_func=group_func,
         filter_func=filter_func,
         plot_args_func=plot_args_func,
+        point_label_func=point_label_func,
     )
 
 
@@ -243,6 +326,7 @@ def plot_error_rate(
         plot_args_func: Callable[[int, TCurveId, List['sinter.TaskStats']], Dict[str, Any]] = lambda index, group_key, group_stats: dict(),
         highlight_max_likelihood_factor: Optional[float] = 1e3,
         line_fits: Optional[Tuple[Literal['linear', 'log', 'sqrt'], Literal['linear', 'log', 'sqrt']]] = None,
+        point_label_func: Callable[['sinter.TaskStats'], Any] = lambda _: None,
 ) -> None:
     """Plots error rates in curves with uncertainty highlights.
 
@@ -263,11 +347,21 @@ def plot_error_rate(
         group_func: Optional. When specified, multiple curves will be plotted instead of one curve.
             The statistics are grouped into curves based on whether or not they get the same result
             out of this function. For example, this could be `group_func=lambda stat: stat.decoder`.
+            If the result of the function is a dictionary, then optional keys in the dictionary will
+            also control the plotting of each curve. Available keys are:
+                'label': the label added to the legend for the curve
+                'color': the color used for plotting the curve
+                'marker': the marker used for the curve
+                'linestyle': the linestyle used for the curve
+                'sort': the order in which the curves will be plotted and added to the legend
+            e.g. if two curves (with different resulting dictionaries from group_func) share the same
+            value for key 'marker', they will be plotted with the same marker.
+            Colors, markers and linestyles are assigned in order, sorted by the values for those keys.
         filter_func: Optional. When specified, some curves will not be plotted.
             The statistics are filtered and only plotted if filter_func(stat) returns True.
             For example, `filter_func=lambda s: s.json_metadata['basis'] == 'x'` would plot only stats
             where the saved metadata indicates the basis was 'x'.
-        plot_args_func: Optional. Specifies additional arguments to give the the underlying calls to
+        plot_args_func: Optional. Specifies additional arguments to give the underlying calls to
             `plot` and `fill_between` used to do the actual plotting. For example, this can be used
             to specify markers and colors. Takes the index of the curve in sorted order and also a
             curve_id (these will be 0 and None respectively if group_func is not specified). For example,
@@ -283,6 +377,7 @@ def plot_error_rate(
         line_fits: Defaults to None. Set this to a tuple (x_scale, y_scale) to include a dashed line
             fit to every curve. The scales determine how to transform the coordinates before
             performing the fit, and can be set to 'linear', 'sqrt', or 'log'.
+        point_label_func: Optional. Specifies text to draw next to data points.
     """
     if highlight_max_likelihood_factor is None:
         highlight_max_likelihood_factor = 1
@@ -320,16 +415,17 @@ def y_func(stat: 'sinter.TaskStats') -> Union[float, 'sinter.Fit']:
         filter_func=filter_func,
         plot_args_func=plot_args_func,
         line_fits=line_fits,
+        point_label_func=point_label_func,
     )
 
 
-def _rescale(v: np.ndarray, scale: str, invert: bool) -> np.ndarray:
+def _rescale(v: Sequence[float], scale: str, invert: bool) -> np.ndarray:
     if scale == 'linear':
-        return v
+        return np.array(v)
     elif scale == 'log':
         return np.exp(v) if invert else np.log(v)
     elif scale == 'sqrt':
-        return v**2 if invert else np.sqrt(v)
+        return np.array(v)**2 if invert else np.sqrt(v)
     else:
         raise NotImplementedError(f'{scale=}')
 
@@ -341,6 +437,7 @@ def plot_custom(
         x_func: Callable[['sinter.TaskStats'], Any],
         y_func: Callable[['sinter.TaskStats'], Union['sinter.Fit', float, int]],
         group_func: Callable[['sinter.TaskStats'], TCurveId] = lambda _: None,
+        point_label_func: Callable[['sinter.TaskStats'], Any] = lambda _: None,
         filter_func: Callable[['sinter.TaskStats'], Any] = lambda _: True,
         plot_args_func: Callable[[int, TCurveId, List['sinter.TaskStats']], Dict[str, Any]] = lambda index, group_key, group_stats: dict(),
         line_fits: Optional[Tuple[Literal['linear', 'log', 'sqrt'], Literal['linear', 'log', 'sqrt']]] = None,
@@ -358,11 +455,22 @@ def plot_custom(
         group_func: Optional. When specified, multiple curves will be plotted instead of one curve.
             The statistics are grouped into curves based on whether or not they get the same result
             out of this function. For example, this could be `group_func=lambda stat: stat.decoder`.
+            If the result of the function is a dictionary, then optional keys in the dictionary will
+            also control the plotting of each curve. Available keys are:
+                'label': the label added to the legend for the curve
+                'color': the color used for plotting the curve
+                'marker': the marker used for the curve
+                'linestyle': the linestyle used for the curve
+                'sort': the order in which the curves will be plotted and added to the legend
+            e.g. if two curves (with different resulting dictionaries from group_func) share the same
+            value for key 'marker', they will be plotted with the same marker.
+            Colors, markers and linestyles are assigned in order, sorted by the values for those keys.
+        point_label_func: Optional. Specifies text to draw next to data points.
         filter_func: Optional. When specified, some curves will not be plotted.
             The statistics are filtered and only plotted if filter_func(stat) returns True.
             For example, `filter_func=lambda s: s.json_metadata['basis'] == 'x'` would plot only stats
             where the saved metadata indicates the basis was 'x'.
-        plot_args_func: Optional. Specifies additional arguments to give the the underlying calls to
+        plot_args_func: Optional. Specifies additional arguments to give the underlying calls to
             `plot` and `fill_between` used to do the actual plotting. For example, this can be used
             to specify markers and colors. Takes the index of the curve in sorted order and also a
             curve_id (these will be 0 and None respectively if group_func is not specified). For example,
@@ -380,6 +488,10 @@ def plot_custom(
             performing the fit, and can be set to 'linear', 'sqrt', or 'log'.
     """
 
+    def group_dict_func(item: 'sinter.TaskStats') -> _FrozenDict:
+        e = group_func(item)
+        return _FrozenDict(e if isinstance(e, dict) else {'label': str(e)})
+
     # Backwards compatibility to when the group stats argument wasn't present.
     import inspect
     if len(inspect.signature(plot_args_func).parameters) == 2:
@@ -392,44 +504,95 @@ def plot_custom(
         if filter_func(stat)
     ]
 
-    curve_groups = group_by(filtered_stats, key=group_func)
-    for k, curve_id in enumerate(sorted(curve_groups.keys(), key=better_sorted_str_terms)):
-        this_group_stats = sorted(curve_groups[curve_id], key=x_func)
-
-        xs = []
-        ys = []
-        xs_range = []
-        ys_low = []
-        ys_high = []
-        saw_fit = False
-        for stat in this_group_stats:
-            num_kept = stat.shots - stat.discards
-            if num_kept == 0:
-                continue
-            x = float(x_func(stat))
-            y = y_func(stat)
+    curve_groups = group_by(filtered_stats, key=group_dict_func)
+    colors = {
+        k: f'C{i}'
+        for i, k in enumerate(sorted({g.get('color', g) for g in curve_groups.keys()}, key=better_sorted_str_terms))
+    }
+    markers = {
+        k: MARKERS[i % len(MARKERS)]
+        for i, k in enumerate(sorted({g.get('marker', g) for g in curve_groups.keys()}, key=better_sorted_str_terms))
+    }
+    linestyles = {
+        k: LINESTYLES[i % len(LINESTYLES)]
+        for i, k in enumerate(sorted({g.get('linestyle', None) for g in curve_groups.keys()}, key=better_sorted_str_terms))
+    }
+
+    def sort_key(a: Any) -> Any:
+        if isinstance(a, _FrozenDict):
+            return a.get('sort', better_sorted_str_terms(a))
+        return better_sorted_str_terms(a)
+
+    for k, group_key in enumerate(sorted(curve_groups.keys(), key=sort_key)):
+        group = curve_groups[group_key]
+        group = sorted(group, key=x_func)
+        color = colors[group_key.get('color', group_key)]
+        marker = markers[group_key.get('marker', group_key)]
+        linestyle = linestyles[group_key.get('linestyle', None)]
+        label = str(group_key.get('label', group_key))
+        xs_label: list[float] = []
+        ys_label: list[float] = []
+        vs_label: list[float] = []
+        xs_best: list[float] = []
+        ys_best: list[float] = []
+        xs_low_high: list[float] = []
+        ys_low: list[float] = []
+        ys_high: list[float] = []
+        for item in group:
+            x = x_func(item)
+            y = y_func(item)
+            point_label = point_label_func(item)
             if isinstance(y, Fit):
-                xs_range.append(x)
-                ys_low.append(y.low)
-                ys_high.append(y.high)
-                saw_fit = True
-                y = y.best
-            if not math.isnan(y):
-                xs.append(x)
-                ys.append(y)
-
-        kwargs: Dict[str, Any] = dict(plot_args_func(k, curve_id, this_group_stats))
-        kwargs.setdefault('marker', MARKERS[k])
-        if curve_id is not None:
-            kwargs.setdefault('label', str(curve_id))
-            kwargs.setdefault('color', f'C{k}')
-        kwargs.setdefault('color', 'black')
-        ax.plot(xs, ys, **kwargs)
-
-        if line_fits is not None and len(set(xs)) >= 2:
+                if y.low is not None and y.high is not None and not math.isnan(y.low) and not math.isnan(y.high):
+                    xs_low_high.append(x)
+                    ys_low.append(y.low)
+                    ys_high.append(y.high)
+                if y.best is not None and not math.isnan(y.best):
+                    ys_best.append(y.best)
+                    xs_best.append(x)
+
+                if point_label:
+                    cy = None
+                    for e in [y.best, y.high, y.low]:
+                        if e is not None and not math.isnan(e):
+                            cy = e
+                            break
+                    if cy is not None:
+                        xs_label.append(x)
+                        ys_label.append(cy)
+                        vs_label.append(point_label)
+            elif not math.isnan(y):
+                xs_best.append(x)
+                ys_best.append(y)
+                if point_label:
+                    xs_label.append(x)
+                    ys_label.append(y)
+                    vs_label.append(point_label)
+        args = dict(plot_args_func(k, group_func(group[0]), group))
+        if 'linestyle' not in args:
+            args['linestyle'] = linestyle
+        if 'marker' not in args:
+            args['marker'] = marker
+        if 'color' not in args:
+            args['color'] = color
+        if 'label' not in args:
+            args['label'] = label
+        ax.plot(xs_best, ys_best, **args)
+        for x, y, lbl in zip(xs_label, ys_label, vs_label):
+            if lbl:
+                ax.annotate(lbl, (x, y))
+        if len(xs_low_high) > 1:
+            ax.fill_between(xs_low_high, ys_low, ys_high, color=args['color'], alpha=0.2, zorder=-100)
+        elif len(xs_low_high) == 1:
+            l, = ys_low
+            h, = ys_high
+            m = (l + h) / 2
+            ax.errorbar(xs_low_high, [m], yerr=([m - l], [h - m]), marker='', elinewidth=1, ecolor=color, capsize=5)
+
+        if line_fits is not None and len(set(xs_best)) >= 2:
             x_scale, y_scale = line_fits
-            fit_xs = _rescale(xs, x_scale, False)
-            fit_ys = _rescale(ys, y_scale, False)
+            fit_xs = _rescale(xs_best, x_scale, False)
+            fit_ys = _rescale(ys_best, y_scale, False)
 
             from scipy.stats import linregress
             line_fit = linregress(fit_xs, fit_ys)
@@ -447,21 +610,10 @@ def plot_custom(
             out_xs = _rescale(out_xs, x_scale, True)
             out_ys = _rescale(out_ys, y_scale, True)
 
-            line_kwargs = kwargs.copy()
-            line_kwargs.pop('marker', None)
-            line_kwargs.pop('label', None)
-            line_kwargs['linestyle'] = '--'
-            line_kwargs.setdefault('linewidth', 1)
-            line_kwargs['linewidth'] /= 2
-            ax.plot(out_xs, out_ys, **line_kwargs)
-
-        if saw_fit:
-            fit_kwargs = kwargs.copy()
-            fit_kwargs.setdefault('zorder', 0)
-            fit_kwargs.setdefault('alpha', 1)
-            fit_kwargs['zorder'] -= 100
-            fit_kwargs['alpha'] *= 0.25
-            fit_kwargs.pop('marker', None)
-            fit_kwargs.pop('linestyle', None)
-            fit_kwargs.pop('label', None)
-            ax.fill_between(xs_range, ys_low, ys_high, **fit_kwargs)
+            line_fit_kwargs = args.copy()
+            line_fit_kwargs.pop('marker', None)
+            line_fit_kwargs.pop('label', None)
+            line_fit_kwargs['linestyle'] = '--'
+            line_fit_kwargs.setdefault('linewidth', 1)
+            line_fit_kwargs['linewidth'] /= 2
+            ax.plot(out_xs, out_ys, **line_fit_kwargs)
diff --git a/glue/sample/src/sinter/_plotting_test.py b/glue/sample/src/sinter/_plotting_test.py
index 088a165f..acf69102 100644
--- a/glue/sample/src/sinter/_plotting_test.py
+++ b/glue/sample/src/sinter/_plotting_test.py
@@ -13,6 +13,9 @@ def test_better_sorted_str_terms():
     assert f('a1b2') == ('a', 1, 'b', 2)
     assert f('a1.5b2') == ('a', 1.5, 'b', 2)
     assert f('a1.5.3b2') == ('a', (1, 5, 3), 'b', 2)
+    assert f(1) < f(None)
+    assert f(1) < f('2')
+    assert f('2') > f(1)
     assert sorted([
         "planar d=10 r=30",
         "planar d=16 r=36",
diff --git a/glue/sample/src/sinter/_predict.py b/glue/sample/src/sinter/_predict.py
index eeefba04..f3a6336c 100644
--- a/glue/sample/src/sinter/_predict.py
+++ b/glue/sample/src/sinter/_predict.py
@@ -8,9 +8,7 @@
 from typing import Optional, Union, Dict, TYPE_CHECKING
 
 from sinter._collection import post_selection_mask_from_4th_coord
-from sinter._decoding_decoder_class import Decoder
-from sinter._decoding_all_built_in_decoders import BUILT_IN_DECODERS
-from sinter._decoding import streaming_post_select
+from sinter._decoding import Decoder, BUILT_IN_DECODERS, streaming_post_select
 
 if TYPE_CHECKING:
     import sinter
diff --git a/glue/sample/src/sinter/_probability_util.py b/glue/sample/src/sinter/_probability_util.py
index 62cf0ed7..76849dcd 100644
--- a/glue/sample/src/sinter/_probability_util.py
+++ b/glue/sample/src/sinter/_probability_util.py
@@ -2,6 +2,7 @@
 import math
 import pathlib
 from typing import Any, Dict, Union, Callable, Sequence, TYPE_CHECKING, overload
+from typing import Optional
 
 import numpy as np
 
@@ -208,9 +209,9 @@ class Fit:
             of the best fit's square error, or whose likelihood was within some
             maximum Bayes factor of the max likelihood hypothesis.
     """
-    low: float
-    best: float
-    high: float
+    low: Optional[float]
+    best: Optional[float]
+    high: Optional[float]
 
     def __repr__(self) -> str:
         return f'sinter.Fit(low={self.low!r}, best={self.best!r}, high={self.high!r})'
diff --git a/glue/sample/src/sinter/_worker.py b/glue/sample/src/sinter/_worker.py
deleted file mode 100644
index c01cd5ff..00000000
--- a/glue/sample/src/sinter/_worker.py
+++ /dev/null
@@ -1,212 +0,0 @@
-import os
-
-from typing import Any, Optional, Tuple, TYPE_CHECKING, Dict
-import tempfile
-
-if TYPE_CHECKING:
-    import multiprocessing
-    import numpy as np
-    import pathlib
-    import sinter
-    import stim
-
-
-class WorkIn:
-    def __init__(
-            self,
-            *,
-            work_key: Any,
-            circuit_path: str,
-            dem_path: str,
-            decoder: str,
-            strong_id: Optional[str],
-            postselection_mask: 'Optional[np.ndarray]',
-            postselected_observables_mask: 'Optional[np.ndarray]',
-            json_metadata: Any,
-            count_observable_error_combos: bool,
-            count_detection_events: bool,
-            num_shots: int):
-        self.work_key = work_key
-        self.circuit_path = circuit_path
-        self.dem_path = dem_path
-        self.decoder = decoder
-        self.strong_id = strong_id
-        self.postselection_mask = postselection_mask
-        self.postselected_observables_mask = postselected_observables_mask
-        self.json_metadata = json_metadata
-        self.count_observable_error_combos = count_observable_error_combos
-        self.count_detection_events = count_detection_events
-        self.num_shots = num_shots
-
-    def with_work_key(self, work_key: Any) -> 'WorkIn':
-        return WorkIn(
-            work_key=work_key,
-            circuit_path=self.circuit_path,
-            dem_path=self.dem_path,
-            decoder=self.decoder,
-            postselection_mask=self.postselection_mask,
-            postselected_observables_mask=self.postselected_observables_mask,
-            json_metadata=self.json_metadata,
-            strong_id=self.strong_id,
-            num_shots=self.num_shots,
-            count_observable_error_combos=self.count_observable_error_combos,
-            count_detection_events=self.count_detection_events,
-        )
-
-
-def auto_dem(circuit: 'stim.Circuit') -> 'stim.DetectorErrorModel':
-    """Converts a circuit into a detector error model, with some fallbacks.
-
-    First attempts to do it with folding and decomposition, then tries
-    giving up on the folding, then tries giving up on the decomposition.
-    """
-    try:
-        return circuit.detector_error_model(
-            allow_gauge_detectors=False,
-            approximate_disjoint_errors=True,
-            block_decomposition_from_introducing_remnant_edges=False,
-            decompose_errors=True,
-            flatten_loops=False,
-            ignore_decomposition_failures=False,
-        )
-    except ValueError:
-        pass
-
-    # This might be https://github.com/quantumlib/Stim/issues/393
-    # Try turning off loop flattening.
-    try:
-        return circuit.detector_error_model(
-            allow_gauge_detectors=False,
-            approximate_disjoint_errors=True,
-            block_decomposition_from_introducing_remnant_edges=False,
-            decompose_errors=True,
-            flatten_loops=False,
-            ignore_decomposition_failures=False,
-        )
-    except ValueError:
-        pass
-
-    # Maybe decomposition is impossible, but the decoder might not need it.
-    # Try turning off error decomposition.
-    try:
-        return circuit.detector_error_model(
-            allow_gauge_detectors=False,
-            approximate_disjoint_errors=True,
-            block_decomposition_from_introducing_remnant_edges=False,
-            decompose_errors=False,
-            flatten_loops=True,
-            ignore_decomposition_failures=False,
-        )
-    except ValueError:
-        pass
-
-    # Okay turn them both off...
-    return circuit.detector_error_model(
-        allow_gauge_detectors=False,
-        approximate_disjoint_errors=True,
-        block_decomposition_from_introducing_remnant_edges=False,
-        decompose_errors=False,
-        flatten_loops=True,
-        ignore_decomposition_failures=False,
-    )
-
-
-class WorkOut:
-    def __init__(
-            self,
-            *,
-            work_key: Any,
-            stats: Optional['sinter.AnonTaskStats'],
-            strong_id: str,
-            msg_error: Optional[Tuple[str, BaseException]]):
-        self.work_key = work_key
-        self.stats = stats
-        self.strong_id = strong_id
-        self.msg_error = msg_error
-
-
-def worker_loop(tmp_dir: 'pathlib.Path',
-                inp: 'multiprocessing.Queue',
-                out: 'multiprocessing.Queue',
-                custom_decoders: Optional[Dict[str, 'sinter.Decoder']],
-                core_affinity: Optional[int]) -> None:
-    try:
-        if core_affinity is not None and hasattr(os, 'sched_setaffinity'):
-            os.sched_setaffinity(0, {core_affinity})
-    except:
-        # If setting the core affinity fails, we keep going regardless.
-        pass
-
-    try:
-        with tempfile.TemporaryDirectory(dir=tmp_dir) as child_dir:
-            while True:
-                work: Optional[WorkIn] = inp.get()
-                if work is None:
-                    return
-                out.put(do_work_safely(work, child_dir, custom_decoders))
-    except KeyboardInterrupt:
-        pass
-
-
-def do_work_safely(work: WorkIn, child_dir: str, custom_decoders: Dict[str, 'sinter.Decoder']) -> WorkOut:
-    try:
-        return do_work(work, child_dir, custom_decoders)
-    except BaseException as ex:
-        import traceback
-        return WorkOut(
-            work_key=work.work_key,
-            stats=None,
-            strong_id=work.strong_id,
-            msg_error=(traceback.format_exc(), ex),
-        )
-
-
-def do_work(work: WorkIn, child_dir: str, custom_decoders: Dict[str, 'sinter.Decoder']) -> WorkOut:
-    import stim
-    from sinter._task import Task
-    from sinter._decoding import sample_decode
-
-    if work.strong_id is None:
-        # The work is to compute the DEM, as opposed to taking shots.
-
-        circuit = stim.Circuit.from_file(work.circuit_path)
-        dem = auto_dem(circuit)
-        dem.to_file(work.dem_path)
-
-        task = Task(
-            circuit=circuit,
-            decoder=work.decoder,
-            detector_error_model=dem,
-            postselection_mask=work.postselection_mask,
-            postselected_observables_mask=work.postselected_observables_mask,
-            json_metadata=work.json_metadata,
-        )
-
-        return WorkOut(
-            work_key=work.work_key,
-            stats=None,
-            strong_id=task.strong_id(),
-            msg_error=None,
-        )
-
-    stats: 'sinter.AnonTaskStats' = sample_decode(
-        num_shots=work.num_shots,
-        circuit_path=work.circuit_path,
-        circuit_obj=None,
-        dem_path=work.dem_path,
-        dem_obj=None,
-        post_mask=work.postselection_mask,
-        postselected_observable_mask=work.postselected_observables_mask,
-        decoder=work.decoder,
-        count_observable_error_combos=work.count_observable_error_combos,
-        count_detection_events=work.count_detection_events,
-        tmp_dir=child_dir,
-        custom_decoders=custom_decoders,
-    )
-
-    return WorkOut(
-        stats=stats,
-        work_key=work.work_key,
-        strong_id=work.strong_id,
-        msg_error=None,
-    )
diff --git a/glue/sample/src/sinter/_worker_test.py b/glue/sample/src/sinter/_worker_test.py
deleted file mode 100644
index d8049ee1..00000000
--- a/glue/sample/src/sinter/_worker_test.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import multiprocessing
-import pathlib
-import tempfile
-
-import stim
-
-from sinter._worker import WorkIn, WorkOut, worker_loop
-from sinter._worker import auto_dem
-
-
-def test_worker_loop_infers_dem():
-    with tempfile.TemporaryDirectory() as tmp_dir:
-        tmp_dir = pathlib.Path(tmp_dir)
-        circuit =  stim.Circuit("""
-            M(0.2) 0 1
-            DETECTOR rec[-1]
-            OBSERVABLE_INCLUDE(0) rec[-1] rec[-2]
-        """)
-        circuit_path = str(tmp_dir / 'input_circuit.stim')
-        dem_path = str(tmp_dir / 'input_dem.dem')
-        circuit.to_file(circuit_path)
-        inp = multiprocessing.Queue()
-        out = multiprocessing.Queue()
-        inp.put(WorkIn(
-            work_key='test1',
-            circuit_path=circuit_path,
-            dem_path=dem_path,
-            decoder='pymatching',
-            json_metadata=5,
-            strong_id=None,
-            num_shots=-1,
-            postselected_observables_mask=None,
-            postselection_mask=None,
-            count_detection_events=False,
-            count_observable_error_combos=False,
-        ))
-        inp.put(None)
-        worker_loop(tmp_dir, inp, out, None, 0)
-        result: WorkOut = out.get(timeout=1)
-        assert out.empty()
-
-        assert result.stats is None
-        assert result.work_key == 'test1'
-        assert result.msg_error is None
-        assert stim.DetectorErrorModel.from_file(dem_path) == circuit.detector_error_model()
-        assert result.strong_id is not None
-
-
-def test_worker_loop_does_not_recompute_dem():
-    with tempfile.TemporaryDirectory() as tmp_dir:
-        tmp_dir = pathlib.Path(tmp_dir)
-        circuit_path = str(tmp_dir / 'input_circuit.stim')
-        dem_path = str(tmp_dir / 'input_dem.dem')
-        stim.Circuit("""
-            M(0.2) 0 1
-            DETECTOR rec[-1]
-            OBSERVABLE_INCLUDE(0) rec[-1] rec[-2]
-        """).to_file(circuit_path)
-        stim.DetectorErrorModel("""
-            error(0.234567) D0 L0
-        """).to_file(dem_path)
-
-        inp = multiprocessing.Queue()
-        out = multiprocessing.Queue()
-        inp.put(WorkIn(
-            work_key='test1',
-            circuit_path=circuit_path,
-            dem_path=dem_path,
-            decoder='pymatching',
-            json_metadata=5,
-            strong_id="fake",
-            num_shots=1000,
-            postselected_observables_mask=None,
-            postselection_mask=None,
-            count_detection_events=False,
-            count_observable_error_combos=False,
-        ))
-        inp.put(None)
-        worker_loop(tmp_dir, inp, out, None, 0)
-        result: WorkOut = out.get(timeout=1)
-        assert out.empty()
-
-        assert result.stats.shots == 1000
-        assert result.stats.discards == 0
-        assert 0 < result.stats.errors < 1000
-        assert result.work_key == 'test1'
-        assert result.msg_error is None
-        assert result.strong_id == 'fake'
-
-
-def test_auto_dem():
-    assert auto_dem(stim.Circuit("""
-        REPEAT 100 {
-            CORRELATED_ERROR(0.125) X0 X1
-            CORRELATED_ERROR(0.125) X0 X1 X2 X3
-            MR 0 1 2 3
-            DETECTOR rec[-4]
-            DETECTOR rec[-3]
-            DETECTOR rec[-2]
-            DETECTOR rec[-1]
-        }
-    """)) == stim.DetectorErrorModel("""
-        REPEAT 99 {
-            error(0.125) D0 D1
-            error(0.125) D0 D1 ^ D2 D3
-            shift_detectors 4
-        }
-        error(0.125) D0 D1
-        error(0.125) D0 D1 ^ D2 D3
-    """)
-
-    assert auto_dem(stim.Circuit("""
-        CORRELATED_ERROR(0.125) X0 X1
-        CORRELATED_ERROR(0.125) X0 X1 X2 X3
-        M 0 1 2 3
-        DETECTOR rec[-4]
-        DETECTOR rec[-3]
-        DETECTOR rec[-2]
-        DETECTOR rec[-1]
-    """)) == stim.DetectorErrorModel("""
-        error(0.125) D0 D1
-        error(0.125) D0 D1 ^ D2 D3
-    """)
-
-    assert auto_dem(stim.Circuit("""
-        CORRELATED_ERROR(0.125) X0 X1 X2 X3
-        M 0 1 2 3
-        DETECTOR rec[-4]
-        DETECTOR rec[-3]
-        DETECTOR rec[-2]
-        DETECTOR rec[-1]
-    """)) == stim.DetectorErrorModel("""
-        error(0.125) D0 D1 D2 D3
-    """)
diff --git a/src/stim/circuit/circuit.pybind.cc b/src/stim/circuit/circuit.pybind.cc
index 8c801e5c..7798587b 100644
--- a/src/stim/circuit/circuit.pybind.cc
+++ b/src/stim/circuit/circuit.pybind.cc
@@ -3270,7 +3270,7 @@ void stim_pybind::pybind_circuit_methods(pybind11::module &, pybind11::class_<Ci
         pybind11::arg("rows") = pybind11::none(),
         pybind11::arg("filter_coords") = pybind11::none(),
         clean_doc_string(R"DOC(
-            @signature def diagram(self, type: str = 'timeline-text', *, tick: Union[None, int, range] = None, filter_coords: Iterable[Union[Iterable[float], stim.DemTarget]] = ((),)) -> 'stim._DiagramHelper':
+            @signature def diagram(self, type: str = 'timeline-text', *, tick: Union[None, int, range] = None, filter_coords: Iterable[Union[Iterable[float], stim.DemTarget]] = ((),), rows: int | None = None) -> 'stim._DiagramHelper':
             Returns a diagram of the circuit, from a variety of options.
 
             Args:
diff --git a/src/stim/util_bot/probability_util.h b/src/stim/util_bot/probability_util.h
index 0c28d2a3..b338ba50 100644
--- a/src/stim/util_bot/probability_util.h
+++ b/src/stim/util_bot/probability_util.h
@@ -70,9 +70,19 @@ struct RareErrorIterator {
 
 std::vector<size_t> sample_hit_indices(float probability, size_t attempts, std::mt19937_64 &rng);
 
+/// Create a fresh random number generator seeded by entropy from the operating system.
 std::mt19937_64 externally_seeded_rng();
+
+/// Create a random number generator either seeded by a --seed argument, or else by entropy from the operating system.
 std::mt19937_64 optionally_seeded_rng(int argc, const char **argv);
 
+/// Overwrite the given span with random data where bits are set with the given probability.
+///
+/// Args:
+///     probability: The chance that each bit will be on.
+///     start: Inclusive start of the memory span to overwrite.
+///     end: Exclusive end of the memory span to overwrite.
+///     rng: The random number generator to use to generate entropy.
 void biased_randomize_bits(float probability, uint64_t *start, uint64_t *end, std::mt19937_64 &rng);
 
 }  // namespace stim