diff --git a/CMakeLists.txt b/CMakeLists.txt
index 1ad94b5..f14eaca 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -63,6 +63,7 @@ option(AARE_FETCH_CATCH "Use FetchContent to download catch2" ON)
 option(AARE_FETCH_JSON "Use FetchContent to download nlohmann::json" ON)
 option(AARE_FETCH_ZMQ "Use FetchContent to download libzmq" ON)
 option(AARE_FETCH_LMFIT "Use FetchContent to download lmfit" ON)
+option(AARE_FETCH_MINUIT2 "Use FetchContent to download Minuit2" ON)
 
 
 #Convenience option to use system libraries only (no FetchContent)
@@ -74,8 +75,8 @@ if(AARE_SYSTEM_LIBRARIES)
     set(AARE_FETCH_CATCH OFF CACHE BOOL "Disabled FetchContent for catch2" FORCE)
     set(AARE_FETCH_JSON OFF CACHE BOOL "Disabled FetchContent for nlohmann::json" FORCE)
     set(AARE_FETCH_ZMQ OFF CACHE BOOL "Disabled FetchContent for libzmq" FORCE)
-    # Still fetch lmfit when setting AARE_SYSTEM_LIBRARIES since this is not available
-    # on conda-forge
+    # Still fetch lmfit and Minuit2 when setting AARE_SYSTEM_LIBRARIES
+    # since these are not available on conda-forge
 endif()
 
 if(AARE_BENCHMARKS)
@@ -134,6 +135,26 @@ else()
 endif()
 
 
+if(AARE_FETCH_MINUIT2)
+    FetchContent_Declare(
+        Minuit2
+        GIT_REPOSITORY https://github.com/GooFit/Minuit2.git
+        GIT_TAG        master
+    )
+    # Disable Minuit2 extras we don't need
+    set(minuit2_mpi OFF CACHE BOOL "")
+    set(minuit2_omp OFF CACHE BOOL "")
+    set(BUILD_TESTING OFF CACHE BOOL "")
+    set(CMAKE_POLICY_VERSION_MINIMUM 3.5 CACHE STRING "Allow older CMake compat for Minuit2")
+    FetchContent_MakeAvailable(Minuit2)
+    unset(CMAKE_POLICY_VERSION_MINIMUM CACHE)
+    set_property(TARGET Minuit2Math PROPERTY POSITION_INDEPENDENT_CODE ON)
+    set_property(TARGET Minuit2 PROPERTY POSITION_INDEPENDENT_CODE ON)
+else()
+    find_package(Minuit2 REQUIRED)
+endif()
+
+
 if(AARE_FETCH_ZMQ)
     # Fetchcontent_Declare is deprecated need to find a way to update this
     # for now setting the policy to old is enough
@@ -348,6 +369,9 @@ set(PUBLICHEADERS
     include/aare/Dtype.hpp
     include/aare/File.hpp
     include/aare/Fit.hpp
+    include/aare/Chi2.hpp
+    include/aare/FitModel.hpp
+    include/aare/Models.hpp
     include/aare/FileInterface.hpp
     include/aare/FilePtr.hpp
     include/aare/Frame.hpp
@@ -410,11 +434,15 @@ target_link_libraries(
     fmt::fmt 
     nlohmann_json::nlohmann_json 
     ${STD_FS_LIB} # from helpers.cmake
+    Minuit2::Minuit2
     PRIVATE 
     aare_compiler_flags 
     Threads::Threads
     $<BUILD_INTERFACE:lmfit>
+)
 
+target_include_directories(aare_core SYSTEM PRIVATE
+    $<TARGET_PROPERTY:Minuit2::Minuit2,INTERFACE_INCLUDE_DIRECTORIES>
 )
 
 set_property(TARGET aare_core PROPERTY POSITION_INDEPENDENT_CODE ON)
diff --git a/RELEASE.md b/RELEASE.md
index a48c221..3057de7 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -2,10 +2,18 @@
 
 ## HEAD 
 
-### New Features: 
 
+### New Features:
+
+- Added a new Minuit2-based fitting framework for ``Gaussian``, ``RisingScurve``, ``FallingScurve``, ``Pol1`` and ``Pol2`` models.
 - setter and getter for nSigma for ClusterFinder ``aare.ClusterFinder().nSigma = 2``, ``aare.ClusterFinderMT().set_nSigma(2)`` 
 
+### Bugfixes:
+
+- Fixed ``split_task(first, last, n_threads)`` so task ranges now correctly respect the ``first`` offset. Previously, non-zero starting indices could generate incorrect subranges.
+
+
+
 ## 2026.3.17
 
 ### New Features:
diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt
index 57ca213..74fb586 100644
--- a/benchmarks/CMakeLists.txt
+++ b/benchmarks/CMakeLists.txt
@@ -25,4 +25,11 @@ target_link_libraries(benchmarks PRIVATE benchmark::benchmark aare_core aare_com
 set_target_properties(benchmarks PROPERTIES
     RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}
     OUTPUT_NAME run_benchmarks
-)
\ No newline at end of file
+)
+
+add_executable(fit_benchmark fit_benchmark.cpp)
+target_link_libraries(fit_benchmark PRIVATE benchmark::benchmark aare_core aare_compiler_flags)
+target_include_directories(fit_benchmark SYSTEM PRIVATE
+    $<TARGET_PROPERTY:Minuit2::Minuit2,INTERFACE_INCLUDE_DIRECTORIES>
+)
+set_target_properties(fit_benchmark PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR})
\ No newline at end of file
diff --git a/benchmarks/fit_benchmark.cpp b/benchmarks/fit_benchmark.cpp
new file mode 100644
index 0000000..61370a2
--- /dev/null
+++ b/benchmarks/fit_benchmark.cpp
@@ -0,0 +1,162 @@
+// SPDX-License-Identifier: MPL-2.0
+#include "aare/Fit.hpp"
+#include "aare/Chi2.hpp"
+#include "aare/Models.hpp"
+#include "aare/FitModel.hpp"
+
+#include <benchmark/benchmark.h>
+#include <cmath>
+#include <random>
+#include <string>
+#include <vector>
+
+
+struct TestCase {
+    std::string name;
+    double true_A;
+    double true_mu;
+    double true_sig;
+    double noise_frac;
+};
+
+static const std::vector<TestCase> &get_test_cases() {
+    static const std::vector<TestCase> cases = {
+        {"Clean_signal",    1000.0,   50.0,  5.0, 0.02},
+        {"Moderate_noise",  1000.0,   50.0,  5.0, 0.10},
+        {"High_noise",      1000.0,   50.0,  5.0, 0.30},
+        {"Narrow_peak",      500.0,   25.0,  1.0, 0.05},
+        {"Wide_peak",        200.0,  100.0, 20.0, 0.05},
+        {"Off_center_peak",  800.0,  -15.0,  3.0, 0.05},
+    };
+    return cases;
+}
+
+// ----------------------------------------------------------------
+// Synthetic data generation (deterministic per test case)
+// ----------------------------------------------------------------
+static constexpr ssize_t N_POINTS = 100;
+static constexpr unsigned SEED = 42;
+
+struct GeneratedData {
+    aare::NDArray<double, 1> x;
+    aare::NDArray<double, 1> y;
+    aare::NDArray<double, 1> y_err;
+
+    GeneratedData() : x({N_POINTS}), y({N_POINTS}), y_err({N_POINTS}) {}
+};
+
+static GeneratedData generate_gaussian_data(const TestCase &tc) {
+    GeneratedData d;
+
+    double x_min = tc.true_mu - 5.0 * tc.true_sig;
+    double x_max = tc.true_mu + 5.0 * tc.true_sig;
+    double dx = (x_max - x_min) / (N_POINTS - 1);
+
+    std::mt19937 rng(SEED);
+    double noise_sigma = tc.noise_frac * tc.true_A;
+    std::normal_distribution<double> noise(0.0, noise_sigma);
+
+    for (ssize_t i = 0; i < N_POINTS; ++i) {
+        d.x[i] = x_min + i * dx;
+        double clean = tc.true_A *
+                       std::exp(-std::pow(d.x[i] - tc.true_mu, 2) /
+                                (2.0 * std::pow(tc.true_sig, 2)));
+        d.y[i] = clean + noise(rng);
+        d.y_err[i] = noise_sigma;
+    }
+    return d;
+}
+
+
+static void report_accuracy(benchmark::State &state,
+                            const TestCase &tc,
+                            const aare::NDArray<double, 1> &result) {
+    state.counters["dA"]   = result(0) - tc.true_A;
+    state.counters["dMu"]  = result(1) - tc.true_mu;
+    state.counters["dSig"] = result(2) - tc.true_sig;
+}
+
+// ----------
+// Benchmarks
+// ----------
+
+// 1. lmcurve
+static void BM_FitGausLm(benchmark::State &state) {
+    const auto &tc = get_test_cases()[state.range(0)];
+    auto data = generate_gaussian_data(tc);
+    auto xv = data.x.view();
+    auto yv = data.y.view();
+
+    aare::NDArray<double, 1> result;
+    for (auto _ : state) {
+        result = aare::fit_gaus(xv, yv);
+        benchmark::DoNotOptimize(result.data());
+    }
+
+    report_accuracy(state, tc, result);
+    state.SetLabel(tc.name);
+}
+
+// 2. Minuit2, analytic gradient (no Hesse)
+static void BM_FitGausMinuitGrad(benchmark::State &state) {
+    const auto &tc = get_test_cases()[state.range(0)];
+    auto data = generate_gaussian_data(tc);
+    auto xv = data.x.view();
+    auto yv = data.y.view();
+
+    const auto model = aare::FitModel<aare::model::Gaussian>(/*strategy = */0,
+                                                            /*max_calls = */500,             // increase for noisy signals
+                                                            /*tolerance = */0.5, 
+                                                            /*compute_errors = */false);
+
+    aare::NDArray<double, 1> result;
+    for (auto _ : state) {
+        result = aare::fit_pixel<aare::model::Gaussian, aare::func::Chi2Gaussian>(model, xv, yv);
+        benchmark::DoNotOptimize(result.data());
+    }
+
+    report_accuracy(state, tc, result);
+    state.SetLabel(tc.name);
+}
+
+// 3. Minuit2, analytic gradient + Hesse
+static void BM_FitGausMinuitGradHesse(benchmark::State &state) {
+    const auto &tc = get_test_cases()[state.range(0)];
+    auto data = generate_gaussian_data(tc);
+    auto xv = data.x.view();
+    auto yv = data.y.view();
+    auto ev = data.y_err.view();
+
+    const auto model = aare::FitModel<aare::model::Gaussian>(0, 500, 0.5, true); // compute_errors = true -> Runs Hesse and provides errors on fitted params
+
+    aare::NDArray<double, 1> result;
+    for (auto _ : state) {
+        result = aare::fit_pixel<aare::model::Gaussian, aare::func::Chi2Gaussian>(model, xv, yv, ev);
+        benchmark::DoNotOptimize(result.data());
+    }
+
+    // result has 6 elements: [A, mu, sig, err_A, err_mu, err_sig]
+    report_accuracy(state, tc, result);
+
+    // Also report Hesse uncertainties
+    if (result.size() >= 6) {
+        state.counters["errA"]   = result(3);
+        state.counters["errMu"]  = result(4);
+        state.counters["errSig"] = result(5);
+    }
+    state.SetLabel(tc.name);
+}
+
+BENCHMARK(BM_FitGausLm)
+    ->DenseRange(0, 5)
+    ->Unit(benchmark::kMicrosecond);
+
+BENCHMARK(BM_FitGausMinuitGrad)
+    ->DenseRange(0, 5)
+    ->Unit(benchmark::kMicrosecond);
+
+BENCHMARK(BM_FitGausMinuitGradHesse)
+    ->DenseRange(0, 5)
+    ->Unit(benchmark::kMicrosecond);
+
+BENCHMARK_MAIN();
\ No newline at end of file
diff --git a/include/aare/Chi2.hpp b/include/aare/Chi2.hpp
new file mode 100644
index 0000000..853a556
--- /dev/null
+++ b/include/aare/Chi2.hpp
@@ -0,0 +1,142 @@
+// SPDX-License-Identifier: MPL-2.0
+#pragma once
+#include <stdexcept>
+#include <algorithm>
+#include <cmath>
+#include <vector>
+#include <Minuit2/FCNGradientBase.h>
+#include "aare/NDView.hpp"
+#include "aare/Models.hpp"
+
+namespace aare {
+
+namespace func {
+
+/**
+ * @brief Generic chi-squared FCN with analytic gradient for a 1D model.
+ *
+ * @tparam Model  A model struct that satisfies:
+ *   - static constexpr std::size_t npar;
+ *   - static double eval(double x, const std::vector<double>& par);
+ *   - static void eval_and_grad(double x, const std::vector<double>& par,
+ *                                double& f, std::array<double, npar>& g);
+ *   - static bool is_valid(const std::vector<double>& par);
+ *
+ * Gradient:
+ *   d(chi2)/dp_k = -2 * sum_i  w_i * (y_i - f_i) * df_i/dp_k
+ *
+ *   where w_i = 1/sigma_i^2 (weighted) or 1 (unweighted).
+ *
+ * By providing analytic gradients we avoid 2*npar extra function evaluations
+ * per Minuit step that would otherwise be spent on finite differences.
+ * 
+ * @throws std::invalid_argument if par.size() != Model::npar.
+ *
+ * Invalid model parameters do not throw; they return a large penalty
+ * (and a zero gradient fallback) so the minimizer can remain in control.
+ */
+template <class Model>
+class Chi2Model1DGrad : public ROOT::Minuit2::FCNGradientBase {
+public:
+    Chi2Model1DGrad(NDView<double, 1> x,
+                    NDView<double, 1> y)
+        : x_(x), y_(y), s_(), weighted_(false) {}
+
+    Chi2Model1DGrad(NDView<double, 1> x,
+                    NDView<double, 1> y,
+                    NDView<double, 1> y_err)
+        : x_(x), y_(y), s_(y_err), weighted_(true) {}
+
+    ~Chi2Model1DGrad() override = default;
+
+    double operator()(const std::vector<double>& par) const override {
+        if (par.size() != Model::npar) {
+            throw std::invalid_argument("Chi2Model1DGrad: wrong parameter vector size.");
+        }
+        
+        if (!Model::is_valid(par)) return 1e20;
+
+        double chi2 = 0.0;
+
+        if (weighted_) {
+            for (ssize_t i = 0; i < x_.size(); ++i) {
+                const double si = s_[i];
+                if (si == 0.0) continue;
+
+                const double f_i = Model::eval(x_[i], par);
+                const double r_i = y_[i] - f_i;
+                chi2 += (r_i * r_i) / (si * si);
+            }
+        } else {
+            for (ssize_t i = 0; i < x_.size(); ++i) {
+                const double f_i = Model::eval(x_[i], par);
+                const double r_i = y_[i] - f_i;
+                chi2 += r_i * r_i;
+            }
+        }
+
+        return chi2;
+    }
+
+    std::vector<double> Gradient(const std::vector<double>& par) const override {
+        if (par.size() != Model::npar) {
+            throw std::invalid_argument("Chi2Model1DGrad: wrong parameter vector size.");
+        }
+        
+        std::vector<double> grad(Model::npar, 0.0);
+        if (!Model::is_valid(par)) return grad;
+
+        std::array<double, Model::npar> df{};
+        double f_i = 0.0;
+
+        if (weighted_) {
+            for (ssize_t i = 0; i < x_.size(); ++i) {
+                const double si = s_[i];
+                if (si == 0.0) continue;
+
+                Model::eval_and_grad(x_[i], par, f_i, df);
+
+                const double r_i = y_[i] - f_i;
+                const double c   = -2.0 * r_i / (si * si);
+
+                for (std::size_t k = 0; k < Model::npar; ++k) {
+                    grad[k] += c * df[k];
+                }
+            }
+        } else {
+            for (ssize_t i = 0; i < x_.size(); ++i) {
+                Model::eval_and_grad(x_[i], par, f_i, df);
+
+                const double r_i = y_[i] - f_i;
+                const double c   = -2.0 * r_i;
+
+                for (std::size_t k = 0; k < Model::npar; ++k) {
+                    grad[k] += c * df[k];
+                }
+            }
+        }
+
+        return grad;
+    }
+
+    /** @brief Error definition: 1.0 for chi-squared (delta_chi2 = 1 -> 1-sigma). */
+    double Up() const override { return 1.0; }
+
+private:
+    NDView<double, 1> x_;
+    NDView<double, 1> y_;
+    NDView<double, 1> s_;
+    bool weighted_;
+};
+
+// ── Convenient aliases ──────────────────────────────────────────────
+
+using Chi2Gaussian      = Chi2Model1DGrad<aare::model::Gaussian>;
+using Chi2RisingScurve  = Chi2Model1DGrad<aare::model::RisingScurve>;
+using Chi2FallingScurve = Chi2Model1DGrad<aare::model::FallingScurve>;
+using Chi2Pol1    = Chi2Model1DGrad<aare::model::Pol1>;
+using Chi2Pol2    = Chi2Model1DGrad<aare::model::Pol2>;
+
+} // namespace aare::func
+
+} // aare
\ No newline at end of file
diff --git a/include/aare/Fit.hpp b/include/aare/Fit.hpp
index c6091ad..f424416 100644
--- a/include/aare/Fit.hpp
+++ b/include/aare/Fit.hpp
@@ -5,7 +5,17 @@
 #include <fmt/core.h>
 #include <vector>
 
+#include "aare/utils/par.hpp"
+#include "aare/utils/task.hpp"
 #include "aare/NDArray.hpp"
+#include "aare/Chi2.hpp"
+#include "aare/FitModel.hpp"
+
+#include "Minuit2/FunctionMinimum.h"
+#include "Minuit2/MnMigrad.h"
+#include "Minuit2/MnHesse.h"
+#include "Minuit2/MnUserParameters.h"
+#include "Minuit2/MnPrint.h"
 
 namespace aare {
 
@@ -24,19 +34,6 @@ NDArray<double, 1> scurve2(NDView<double, 1> x, NDView<double, 1> par);
 
 } // namespace func
 
-/**
- * @brief Estimate the initial parameters for a Gaussian fit
- */
-std::array<double, 3> gaus_init_par(const NDView<double, 1> x,
-                                    const NDView<double, 1> y);
-
-std::array<double, 2> pol1_init_par(const NDView<double, 1> x,
-                                    const NDView<double, 1> y);
-
-std::array<double, 6> scurve_init_par(const NDView<double, 1> x,
-                                      const NDView<double, 1> y);
-std::array<double, 6> scurve2_init_par(const NDView<double, 1> x,
-                                       const NDView<double, 1> y);
 
 static constexpr int DEFAULT_NUM_THREADS = 4;
 
@@ -83,6 +80,7 @@ void fit_gaus(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_err,
               NDView<double, 3> par_out, NDView<double, 3> par_err_out,
               NDView<double, 2> chi2_out, int n_threads = DEFAULT_NUM_THREADS);
 
+
 NDArray<double, 1> fit_pol1(NDView<double, 1> x, NDView<double, 1> y);
 
 NDArray<double, 3> fit_pol1(NDView<double, 1> x, NDView<double, 3> y,
@@ -118,4 +116,221 @@ void fit_scurve2(NDView<double, 1> x, NDView<double, 3> y,
                  NDView<double, 3> y_err, NDView<double, 3> par_out,
                  NDView<double, 3> par_err_out, NDView<double, 2> chi2_out,
                  int n_threads);
+
+// Minuit2 fit_pixel / fit_3d object based API
+
+// _____________________________________________________________________
+//
+// fit_pixel — single-pixel minimisation
+// _____________________________________________________________________
+
+/**
+ * @brief Fit a single pixel's data using Minuit2.
+ *
+ * The caller provides a thread-local clone of MnUserParameters so that
+ * no heap allocation happens here (only SetValue/SetError stores).
+ *
+ * User-precedence rules:
+ *   - Fixed parameters: untouched (value and fixed flag preserved from clone).
+ *   - User-set start:   value preserved, step size auto-filled.
+ *   - Neither:          both value and step size auto-filled from data.
+ *
+ * @tparam Model  Model struct (Gaussian, RisingScurve, …).
+ * @tparam FCN    Chi2 functor type (Chi2Model1D or Chi2Model1DGrad instantiation).
+ *
+ * @param model       The FitModel configuration (read-only).
+ * @param upar_local  Thread-local clone of model.upar().  Modified in place.
+ * @param x           Scan points (shared across all pixels).
+ * @param y           Measured values for this pixel.
+ * @param y_err       Per-point uncertainties (empty view -> unweighted fit).
+ *
+ * @return NDArray<double,1> of size:
+ *   - compute_errors: [p0..pN, err0..errN, chi2]  -> 2*npar + 1
+ *   - otherwise:      [p0..pN, chi2]              -> npar + 1
+ */
+template<typename Model, typename FCN>
+NDArray<double, 1> fit_pixel(const FitModel<Model>& model,
+                            ROOT::Minuit2::MnUserParameters& upar_local,
+                            NDView<double, 1> x,
+                            NDView<double, 1> y,
+                            NDView<double, 1> y_err) {
+
+    constexpr std::size_t npar = Model::npar; 
+    const bool want_errors = model.compute_errors();
+    const ssize_t result_size = want_errors ? (2 * npar + 1) : (npar + 1);
+
+    // ──── automatic parameter estimation ─────────────
+    auto start = Model::estimate_par(x,y);
+              
+    // dead / degenerate pixel guard
+    if (!Model::is_valid(std::vector<double>(start.begin(), start.end()))) {
+        return NDArray<double, 1>({result_size}, 0.0);
+    }
+
+    // ──── data-range statistics for step sizes ─────────────
+    double x_range, y_range, slope_scale;
+    model::compute_ranges(x, y, x_range, y_range, slope_scale);
+ 
+    std::array<double, npar> steps{};
+    Model::compute_steps(start, x_range, y_range, slope_scale, steps);
+
+    // ── apply auto-estimates respecting user precedence ─────────────
+    for(std::size_t i = 0; i < npar; ++i){
+        // fixed: do not touch at all
+        if(model.is_user_fixed(i)){
+            continue;
+        }
+
+        if(!model.is_user_start(i)){
+            upar_local.SetValue(i, start[i]);
+        }
+
+        upar_local.SetError(i, steps[i]);
+    }
+
+    // ──── build functor ────────
+    auto chi2 = (y_err.size() > 0) ? FCN(x, y, y_err) : FCN(x, y);
+
+    // ──── run minimizer ────────
+    ROOT::Minuit2::MnMigrad migrad(chi2, upar_local, model.strategy());
+    ROOT::Minuit2::FunctionMinimum min = migrad(model.max_calls(), model.tolerance());
+
+    if (!min.IsValid())
+        return NDArray<double, 1>({result_size}, 0.0);
+
+    // ──── pack results ────────
+    if (want_errors) {
+        ROOT::Minuit2::MnHesse hesse;
+        hesse(chi2, min);
+ 
+        const auto& values = min.UserState().Params();
+        const auto& errors = min.UserState().Errors();
+ 
+        NDArray<double, 1> result({result_size});
+        for (std::size_t k = 0; k < npar; ++k) {
+            result[k]        = values[k];
+            result[npar + k] = errors[k];
+        }
+        result[2 * npar] = min.Fval();
+        return result;
+    }
+ 
+    const auto& values = min.UserState().Params();
+    NDArray<double, 1> result({result_size});
+    for (std::size_t k = 0; k < npar; ++k)
+        result[k] = values[k];
+    result[npar] = min.Fval();
+    return result;
+}
+
+// ── self-contained for 1D / standalone use ─────────
+template <typename Model, typename FCN>
+NDArray<double, 1> fit_pixel(const FitModel<Model>& model,
+                             NDView<double, 1> x,
+                             NDView<double, 1> y,
+                             NDView<double, 1> y_err)
+{
+    auto upar_local = model.upar();
+    return fit_pixel<Model, FCN>(model, upar_local, x, y, y_err);
+}
+
+// Overload: uncertainties not provided
+template <typename Model, typename FCN>
+NDArray<double, 1> fit_pixel(const FitModel<Model>& model,
+                             NDView<double, 1> x,
+                             NDView<double, 1> y)
+{
+    auto upar_local = model.upar();
+    return fit_pixel<Model, FCN>(model, upar_local, x, y, NDView<double, 1>{});
+}
+
+// _____________________________________________________________________
+//
+// fit_3d — row-parallel fitting over (rows, cols) pixel grid
+// _____________________________________________________________________
+/**
+ * @brief Fit all pixels in a 3D data cube (rows x cols x n_scan).
+ *
+ * @tparam Model  Model struct.
+ * @tparam FCN    Chi2 functor type.
+ *
+ * @param model      Fit configuration shared by all pixels.
+ * @param x          Scan points, shape `(n_scan)`.
+ * @param y          Measured values, shape `(rows, cols, n_scan)`.
+ * @param y_err      Uncertainties, same shape as y, or empty for unweighted fits.
+ * @param par_out    Output parameters, shape `(rows, cols, npar)`.
+ * @param err_out    Output parameter errors, shape `(rows, cols, npar)`, if used.
+ * @param chi2_out   Output chi-squared / objective values, shape `(rows, cols)`.
+ * @param n_threads  Number of threads used to split rows.
+ *
+ */
+template <typename Model, typename FCN>
+void fit_3d(const FitModel<Model>& model,
+            NDView<double, 1> x,     // (n_scan)
+            NDView<double, 3> y,     // (rows, cols, n_scan)
+            NDView<double, 3> y_err, // (rows, cols, n_scan) or empty for unweighted fit
+            NDView<double, 3> par_out,
+            NDView<double, 3> err_out,
+            NDView<double, 2> chi2_out,
+            int n_threads)
+{
+    const std::size_t npar = Model::npar;
+
+    // ──── checks ───────
+    if (x.size() != y.shape(2))
+        throw std::runtime_error("fit_3d: x.size() must match y.shape(2).");
+
+    if (par_out.shape(0) != y.shape(0) || par_out.shape(1) != y.shape(1) || par_out.shape(2) != npar)
+        throw std::runtime_error("par_out must have shape [rows, cols, npar].");
+
+    if (chi2_out.shape(0) != y.shape(0) || chi2_out.shape(1) != y.shape(1))
+        throw std::runtime_error("chi2_out must have shape [rows, cols].");
+    
+    const bool has_errors = (y_err.size() > 0);
+    const bool want_par_errors = (err_out.size() > 0) && model.compute_errors();
+
+    if (has_errors) {
+        if (y.shape(0) != y_err.shape(0) || y.shape(1) != y_err.shape(1) || y.shape(2) != y_err.shape(2))
+            throw std::runtime_error("fit_3d: y and y_err must have identical shape.");
+
+        if (err_out.shape(0) != y.shape(0) || err_out.shape(1) != y.shape(1) || err_out.shape(2) != npar)
+            throw std::runtime_error("err_out must have shape [rows, cols, npar].");
+    }
+
+    // ──── parallel dispatch ───────
+    auto process = [&](ssize_t first_row, ssize_t last_row) {
+
+        // one clone per thread 
+        auto upar_local = model.upar(); 
+
+        for (ssize_t row = first_row; row < last_row; row++) {
+            for (ssize_t col = 0; col < y.shape(1); col++) {
+
+                NDView<double, 1> values(&y(row, col, 0), {y.shape(2)});
+                NDView<double, 1> errors = has_errors 
+                        ? NDView<double, 1>(&y_err(row, col, 0), {y_err.shape(2)})
+                        : NDView<double, 1>{};
+
+                auto res = fit_pixel<Model, FCN>(model, upar_local, x, values, errors);
+                
+                for(std::size_t k = 0; k < npar; ++k) {
+                    par_out(row, col, k) = res(k);
+                }
+
+                if (want_par_errors) {
+                    for(std::size_t k = 0; k < npar; ++k){
+                        err_out(row, col, k) = res(npar + k);
+                    }
+                    chi2_out(row, col) = res(2 * npar);
+                } else {
+                    chi2_out(row, col) = res(npar);
+                }
+            }
+        }
+    };
+
+    auto tasks = split_task(0, static_cast<int>(y.shape(0)), n_threads);
+    RunInParallel(process, tasks);
+}
+
 } // namespace aare
\ No newline at end of file
diff --git a/include/aare/FitModel.hpp b/include/aare/FitModel.hpp
new file mode 100644
index 0000000..5bfce78
--- /dev/null
+++ b/include/aare/FitModel.hpp
@@ -0,0 +1,104 @@
+// SPDX-License-Identifier: MPL-2.0
+#pragma once
+
+#include <type_traits>
+#include "aare/Models.hpp"
+
+#include "Minuit2/MnUserParameters.h"
+#include "Minuit2/MnStrategy.h"
+
+namespace aare {
+
+
+template <typename Model>
+class FitModel {
+    ROOT::Minuit2::MnUserParameters upar_;
+    ROOT::Minuit2::MnStrategy strategy_;
+    unsigned int max_calls_;
+    double tolerance_;
+    bool compute_errors_;
+
+    std::array<bool, Model::npar> user_fixed_{};
+    std::array<bool, Model::npar> user_start_{};
+
+public:
+    static constexpr std::size_t npar = Model::npar;
+
+    /**
+     * @brief Construct a fit model with sensible defaults.
+     *
+     * @param strategy        Minuit2 strategy level (0 = fast/gradient, 1 = default).
+     * @param max_calls       Maximum FCN calls per pixel minimisation.
+     * @param tolerance       Minuit2 EDM tolerance.
+     * @param compute_errors  If true, run MnHesse after minimisation.
+     */
+    FitModel(unsigned int strategy = 0,
+             unsigned int max_calls = 100,
+             double tolerance = 0.5,
+             bool compute_errors = false)
+        : strategy_(strategy),
+          max_calls_(max_calls),
+          tolerance_(tolerance),
+          compute_errors_(compute_errors)
+    {
+        for(std::size_t i = 0; i < npar; ++i){
+            const auto pi = Model::param_info[i];
+            const bool has_lo = std::isfinite(pi.default_lo);
+            const bool has_hi = std::isfinite(pi.default_hi);
+
+            // Add parameters and valid bounds
+            if (has_lo && has_hi){
+                upar_.Add(pi.name, 0.0, 1.0, pi.default_lo, pi.default_hi);
+            } else if (has_lo) {
+                upar_.Add(pi.name, 0.0, 1.0, pi.default_lo, 1e6);
+            } else {
+                upar_.Add(pi.name, 0.0, 1.0);
+            }
+        } 
+    }
+
+    /** @brief Set lower and upper bounds for parameter idx.*/
+    void SetParLimits(unsigned int idx, double lo, double hi) { 
+        upar_.SetLimits(idx, lo, hi); 
+    }
+
+    /**
+     * @brief Fix parameter idx at value val.
+     *
+     * Excluded from minimisation.  Automatic estimates will not touch it.
+     */
+    void FixParameter(unsigned int idx, double val) { 
+        upar_.SetValue(idx, val); 
+        upar_.Fix(idx);
+        user_start_[idx] = true;
+        user_fixed_[idx] = true;
+    }
+    
+    /** @brief Release a previously fixed parameter, re-enabling auto estimates. */
+    void ReleaseParameter(unsigned int idx) {
+        upar_.Release(idx);
+        user_fixed_[idx] = false;
+    }
+
+    /** @brief Set an explicit starting value for parameter idx.*/
+    void SetParameter(unsigned int idx, double val) {
+        upar_.SetValue(idx, val);
+        user_start_[idx] = true;
+    }
+    
+    void SetMaxCalls(unsigned int n)  { max_calls_ = n; }
+    void SetTolerance(double t)       { tolerance_ = t; }  
+    void SetComputeErrors(bool b)     { compute_errors_ = b; }
+
+    // accessors
+    const ROOT::Minuit2::MnUserParameters& upar()    const { return upar_; }
+    const ROOT::Minuit2::MnStrategy&       strategy() const { return strategy_; }
+    unsigned int max_calls()      const { return max_calls_; }
+    double       tolerance()      const { return tolerance_; }
+    bool         compute_errors() const { return compute_errors_; }
+    bool         is_user_fixed(unsigned int idx) const { return user_fixed_[idx]; }
+    bool         is_user_start(unsigned int idx) const { return user_start_[idx]; }
+};
+
+
+} // namespace aare
\ No newline at end of file
diff --git a/include/aare/Models.hpp b/include/aare/Models.hpp
new file mode 100644
index 0000000..cec4c69
--- /dev/null
+++ b/include/aare/Models.hpp
@@ -0,0 +1,641 @@
+// SPDX-License-Identifier: MPL-2.0
+#pragma once
+
+#include <array>
+#include <cmath>
+#include <vector>
+#include <limits>
+#include <algorithm>
+#include "aare/NDView.hpp"
+
+namespace aare::model {
+
+inline constexpr double inv_sqrt2    = 0.70710678118654752440;
+inline constexpr double inv_sqrt_2pi = 0.39894228040143267794;
+
+
+/**
+ * @brief Per-parameter metadata: name and optional default bounds.
+ *
+ * Used by FitModel to generically initialise MnUserParameters.
+ * Unbounded directions use ±no_bound as sentinels.
+ */
+struct ParamInfo {
+    const char* name;   // name of parameter
+    double default_lo;  // lower bound value
+    double default_hi;  // upper bound value
+};
+ 
+inline constexpr double no_bound = std::numeric_limits<double>::infinity();
+
+/**
+ * @brief Compute data-range statistics used by step-size estimators.
+ *
+ * Model-independent, called once per pixel.
+ */
+inline void compute_ranges(NDView<double, 1> x,
+                           NDView<double, 1> y,
+                           double& x_range,
+                           double& y_range,
+                           double& slope_scale)
+{
+    const auto [x_min, x_max] = std::minmax_element(x.begin(), x.end());
+    const auto [y_min, y_max] = std::minmax_element(y.begin(), y.end());
+
+    x_range = std::max(*x_max - *x_min, 1e-9);
+    y_range = std::max(*y_max - *y_min, 1e-9);
+    
+    slope_scale = std::max(y_range / x_range, 1e-9);
+}
+
+// _____________________________________________________________________
+//
+// Pol1
+// _____________________________________________________________________
+
+/**
+ * @brief Affine function (polynomial of degree 1).
+ *
+ * f(x) = p0 + p1 *x
+ *
+ * Parameters:
+ *   par[0] = p0    (intercept)
+ *   par[1] = p1    (slope)
+ *
+ * Analytic partial derivatives:
+ *   df/dp0    = 1
+ *   df/dp1    = x
+ */
+struct Pol1 {
+    static constexpr std::size_t npar = 2;
+
+    static constexpr std::array<ParamInfo, npar> param_info = {{
+        {"p0",  -no_bound, no_bound},
+        {"p1",  -no_bound, no_bound},
+    }};
+
+    static double eval(double x, const std::vector<double>& par) {
+        return par[0] + par[1] * x;
+    }
+
+    static void eval_and_grad(double x,
+                              const std::vector<double>& par,
+                              double& f,
+                              std::array<double, npar>& g)
+    {
+        f = par[0] + par[1] * x;
+
+        g[0] = 1.0 ; // df/dp0
+        g[1] = x;    // df/dp1
+    }
+
+    static bool is_valid([[maybe_unused]] const std::vector<double>& par) {
+        return true; // always valid 
+    }
+
+    /** @brief Estimate from endpoints: slope = dy/dx, intercept from first point. */
+    static std::array<double, npar> estimate_par(NDView<double, 1> x,
+                                                NDView<double, 1> y)
+    {
+        const double dx = x[x.size()-1] - x[0];
+        const double dy = y[y.size()-1] - y[0];
+        const double slope = (std::abs(dx) > 1e-12) ? dy/dx : 0.0;
+        const double intercept = y[0] - slope * x[0];
+
+        return {intercept, slope};
+    }
+
+
+    static void compute_steps(const std::array<double, npar>& start,
+                              [[maybe_unused]] double x_range, double y_range, double slope_scale,
+                              std::array<double, npar>& steps)
+    {
+        steps[0] = std::max(0.1 * std::abs(start[0]), 0.1 * y_range);
+        steps[1] = 0.1 * slope_scale;
+    }
+    
+};
+
+// _____________________________________________________________________
+//
+// Pol2
+// _____________________________________________________________________
+
+/**
+ * @brief Polynomial fuction of degree 2
+ *
+ * f(x) = p0 + p1 * x + p2 * x * x  
+ *
+ * Parameters:
+ *   par[0] = p0    (constant term)
+ *   par[1] = p1    (linear coef)
+ *   par[2] = p2    (quadratic coef)
+ *
+ * Analytic partial derivatives:
+ *   df/dp0 = 1
+ *   df/dp1 = x
+ *   df/dp2 = x * x
+ */
+struct Pol2 {
+    static constexpr std::size_t npar = 3;
+
+    static constexpr std::array<ParamInfo, npar> param_info = {{
+        {"p0",  -no_bound, no_bound}, 
+        {"p1",  -no_bound, no_bound},
+        {"p2",  -no_bound, no_bound},
+    }};
+
+    static double eval(double x, const std::vector<double>& par) {
+        return par[0] + par[1] * x + par[2] * x * x;
+    }
+
+    static void eval_and_grad(double x,
+                              const std::vector<double>& par,
+                              double& f,
+                              std::array<double, npar>& g)
+    {
+        f = par[0] + par[1] * x + par[2] * x * x;
+
+        g[0] = 1.0 ; // df/dp0
+        g[1] = x;    // df/dp1
+        g[2] = x * x;    // df/dp2
+    }
+
+    static bool is_valid([[maybe_unused]]const std::vector<double>& par) {
+        return true; // always valid 
+    }
+
+    /**
+     * @brief Data-driven initial estimates for a degree-2 polynomial.
+     *
+     * For f(x) = p0 + p1*x + p2*x², the derivative is f'(x) = p1 + 2*p2*x.
+     * Measuring the slope at two positions and subtracting eliminates p1:
+     *
+     *   f'(x_e) = p1 + 2*p2*x_e
+     *   f'(x_s) = p1 + 2*p2*x_s
+     *   ─────────────────────────
+     *   f'(x_e) - f'(x_s) = 2*p2*(x_e - x_s)
+     *
+     *   => p2 = (slope_e - slope_s) / (2 * (x_e - x_s))
+     *
+     * Slopes are estimated over the first and last 10% of points to
+     * average out noise.  p1 is back-solved from the start slope, and
+     * p0 from the first data point.  Degrades to a linear estimate
+     * when curvature is negligible i.e. slope_e ≈ slope_s -> p2 ≈ 0.
+     */
+    static std::array<double, npar> estimate_par(NDView<double, 1> x,
+                                                NDView<double, 1> y)
+    {
+        const ssize_t n = y.size();
+        const ssize_t tail = std::max<ssize_t>(n / 10, 2);
+
+        // start: slope from first 10%
+        const double x_s = (x[0] + x[tail-1]) * 0.5;
+        const double slope_s = (y[tail-1] - y[0]) / (x[tail-1] - x[0]);
+
+        // end: slope from last 10%
+        const double x_e = (x[n-tail] + x[n-1]) * 0.5;
+        const double slope_e = (y[n-1] - y[n-tail]) / (x[n-1] - x[n-tail]);
+
+        const double dx = x_e - x_s;
+        const double p2 = (std::abs(dx) > 1e-12)
+                        ? (slope_e - slope_s) / (2.0 * dx)
+                        : 0.0;
+
+        // p1 from slope_s = p1 + 2*p2*x_s
+        const double p1 = slope_s - 2.0 * p2 * x_s;
+
+        // p0 from first point: y[0] = p0 + p1*x[0] + p2*x[0]^2
+        const double p0 = y[0] - p1 * x[0] - p2 * x[0] * x[0];
+
+        return {p0, p1, p2};
+    }
+
+
+    static void compute_steps(const std::array<double, npar>& start,
+                              double x_range, double y_range, double slope_scale,
+                              std::array<double, npar>& steps)
+    {
+        steps[0] = std::max(0.1 * std::abs(start[0]), 0.1 * y_range);
+        steps[1] = 0.1 * slope_scale;
+        steps[2] = 0.1 * slope_scale / std::max(x_range, 1e-12);
+    }
+};
+
+// _____________________________________________________________________
+//
+// Gaussian
+// _____________________________________________________________________
+
+/**
+ * @brief Unnormalized Gaussian model.
+ *
+ * f(x) = A * exp( -(x - mu)^2 / (2 * sigma^2) )
+ *
+ * Parameters:
+ *   par[0] = A     (amplitude)
+ *   par[1] = mu    (mean / center)
+ *   par[2] = sigma (standard deviation)
+ *
+ * Analytic partial derivatives:
+ *   df/dA     = exp( -(x-mu)^2 / (2*sigma^2) )
+ *   df/dmu    = A * exp(...) * (x - mu) / sigma^2
+ *   df/dsigma = A * exp(...) * (x - mu)^2 / sigma^3
+ */
+struct Gaussian {
+    static constexpr std::size_t npar = 3;
+
+    static constexpr std::array<ParamInfo, npar> param_info = {{
+        {"A",   -no_bound, no_bound},
+        {"mu",  -no_bound, no_bound},
+        {"sig",  1e-12,    no_bound},
+    }};
+
+    static double eval(double x, const std::vector<double>& par) {
+        const double A   = par[0];
+        const double mu  = par[1];
+        const double sig = par[2];
+
+        const double dx        = x - mu;
+        const double inv_2sig2 = 1.0 / (2.0 * sig * sig);
+        return A * std::exp(-dx * dx * inv_2sig2);
+    }
+
+    static void eval_and_grad(double x,
+                              const std::vector<double>& par,
+                              double& f,
+                              std::array<double, npar>& g)
+    {
+        const double A   = par[0];
+        const double mu  = par[1];
+        const double sig = par[2];
+
+        const double dx        = x - mu;
+        const double inv_2sig2 = 1.0 / (2.0 * sig * sig);
+        const double e         = std::exp(-dx * dx * inv_2sig2);
+
+        f = A * e;
+
+        g[0] = e;                                          // df/dA
+        g[1] = 2.0 * A * e * dx * inv_2sig2;              // df/dmu    = A*e*(x-mu)/sig^2
+        g[2] = 2.0 * A * e * dx * dx * inv_2sig2 / sig;   // df/dsigma = A*e*(x-mu)^2/sig^3
+    }
+
+    /** @brief Reject degenerate sigma (zero width). */
+    static bool is_valid(const std::vector<double>& par) {
+        return par[2] != 0.0;
+    }
+
+    /**
+     * @brief Quick data-driven initial parameter estimates.
+     *
+     * A  = max(y)
+     * mu = x at max(y)   (centroid of top 10% as refinement)
+     * sigma = FWHM / 2.35
+     */
+    static std::array<double, npar> estimate_par(NDView<double, 1> x,
+                                                  NDView<double, 1> y)
+    {
+        // find peak
+        const auto max_it = std::max_element(y.begin(), y.end());
+        const ssize_t i_max = std::distance(y.begin(), max_it);
+
+        const double A  = *max_it;
+        const double mu = x[i_max];
+ 
+        // FWHM estimate
+        const double half = A * 0.5;
+        double x_lo = mu, x_hi = mu;
+        for (ssize_t i = i_max; i >= 0; --i)
+            if (y[i] < half) { x_lo = x[i]; break; }
+        for (ssize_t i = i_max; i < y.size(); ++i)
+            if (y[i] < half) { x_hi = x[i]; break; }
+        const double sig = std::max((x_hi - x_lo) / 2.35, 1e-6);
+ 
+        return {A, mu, sig};
+    }
+
+    /**
+     * @brief Data-driven Minuit step sizes.
+     */
+    static void compute_steps(const std::array<double, npar>& start,
+                              double x_range, double y_range, double /*slope_scale*/,
+                              std::array<double, npar>& steps)
+    {
+        steps[0] = std::max(0.1 * std::abs(start[0]), 0.1 * y_range);
+        steps[1] = 0.05 * x_range;
+        steps[2] = 0.05 * x_range;
+    }
+};
+
+
+// _____________________________________________________________________
+//
+// RisingScurve
+// _____________________________________________________________________
+
+/**
+ * @brief Rising S-curve (error-function step) with linear baseline and
+ *        post-step slope.
+ *
+ * f(x) = (p0 + p1*x) + 0.5*(1 + erf((x - p2) / (sqrt(2)*p3))) * (p4 + p5*(x - p2))
+ *
+ * Parameters:
+ *   par[0] = p0  (baseline offset)
+ *   par[1] = p1  (baseline slope)
+ *   par[2] = p2  (threshold / inflection point)
+ *   par[3] = p3  (transition width, must be > 0)
+ *   par[4] = p4  (step amplitude)
+ *   par[5] = p5  (post-step slope)
+ */
+struct RisingScurve {
+    static constexpr std::size_t npar = 6;
+
+    static constexpr std::array<ParamInfo, npar> param_info = {{
+        {"p0", -no_bound, no_bound},
+        {"p1", -no_bound, no_bound},
+        {"p2", -no_bound, no_bound},
+        {"p3",  1e-12,    no_bound},
+        {"p4", -no_bound, no_bound},
+        {"p5", -no_bound, no_bound},
+    }};
+
+    static double eval(double x, const std::vector<double>& par) {
+        const double p0 = par[0];
+        const double p1 = par[1];
+        const double p2 = par[2];
+        const double p3 = par[3];
+        const double p4 = par[4];
+        const double p5 = par[5];
+
+        const double dx   = x - p2;
+        const double step = 0.5 * (1.0 + std::erf(dx * inv_sqrt2 / p3));
+        return (p0 + p1 * x) + step * (p4 + p5 * dx);
+    }
+
+    /**
+     * @brief Evaluate function value and partial derivatives in a single pass.
+     *
+     * Uses:
+     *   S(x)  = 0.5*(1 + erf(z)),  z = (x-p2) / (sqrt(2)*p3)
+     *   dS/dp2 = -(1/sqrt(2*pi)) * exp(-z^2) / p3
+     *   dS/dp3 = -(1/sqrt(2*pi)) * exp(-z^2) * (x-p2) / p3^2
+     */
+    static void eval_and_grad(double x,
+                              const std::vector<double>& par,
+                              double& f,
+                              std::array<double, npar>& g)
+    {
+        const double p0 = par[0];
+        const double p1 = par[1];
+        const double p2 = par[2];
+        const double p3 = par[3];
+        const double p4 = par[4];
+        const double p5 = par[5];
+
+        const double dx   = x - p2;
+        const double z    = dx * inv_sqrt2 / p3;
+        const double step = 0.5 * (1.0 + std::erf(z));
+        const double amp  = p4 + p5 * dx;
+
+        f = (p0 + p1 * x) + step * amp;
+
+        const double e     = std::exp(-z * z);
+        const double dSdp2 = -inv_sqrt_2pi * e / p3;
+        const double dSdp3 = -inv_sqrt_2pi * e * dx / (p3 * p3);
+
+        g[0] = 1.0;                         // df/dp0
+        g[1] = x;                            // df/dp1
+        g[2] = dSdp2 * amp - step * p5;     // df/dp2
+        g[3] = dSdp3 * amp;                 // df/dp3
+        g[4] = step;                         // df/dp4
+        g[5] = step * dx;                   // df/dp5
+    }
+
+    /** @brief Reject degenerate width (zero transition width). */
+    static bool is_valid(const std::vector<double>& par) {
+        return par[3] != 0.0;
+    }
+
+        
+    /** @brief Data-driven initial parameter estimates for a rising S-curve. */
+    static std::array<double, npar> estimate_par(NDView<double, 1> x,
+                                                  NDView<double, 1> y)
+    {
+        const ssize_t n = y.size();
+
+        // baseline: average of first ~10% of points (before turn-on)
+        ssize_t n_base = std::max<ssize_t>(n / 10, 2);
+        double sum_y = 0, sum_xy = 0, sum_x = 0, sum_x2 = 0;
+        for (ssize_t i = 0; i < n_base; ++i) {
+            sum_y  += y[i];
+            sum_x  += x[i];
+            sum_xy += x[i] * y[i];
+            sum_x2 += x[i] * x[i];
+        }
+        double denom = n_base * sum_x2 - sum_x * sum_x;
+        double p1 = (std::abs(denom) > 1e-30)
+                ? (n_base * sum_xy - sum_x * sum_y) / denom
+                : 0.0;
+        double p0 = (sum_y - p1 * sum_x) / n_base;
+
+        // plateau: average of last ~10%
+        double plateau = 0;
+        ssize_t n_plat = std::max<ssize_t>(n / 10, 2);
+        for (ssize_t i = n - n_plat; i < n; ++i)
+            plateau += y[i];
+        plateau /= n_plat;
+
+        // amplitude: plateau minus baseline at midpoint
+        double x_mid = 0.5 * (x[0] + x[n - 1]);
+        double baseline_at_mid = p0 + p1 * x_mid;
+        double p4 = plateau - baseline_at_mid;
+
+        // threshold: x where y first crosses 50% between baseline and plateau
+        double y_half = baseline_at_mid + 0.5 * p4;
+        double p2 = x_mid; // fallback
+        for (ssize_t i = 0; i < n; ++i) {
+            if (y[i] >= y_half) {
+                p2 = x[i];
+                break;
+            }
+        }
+
+        // sigma: estimate from transition width (10%-90% rise)
+        double y_10 = baseline_at_mid + 0.1 * p4;
+        double y_90 = baseline_at_mid + 0.9 * p4;
+        double x_10 = x[0], x_90 = x[n - 1];
+        for (ssize_t i = 0; i < n; ++i) {
+            if (y[i] >= y_10) { x_10 = x[i]; break; }
+        }
+        for (ssize_t i = 0; i < n; ++i) {
+            if (y[i] >= y_90) { x_90 = x[i]; break; }
+        }
+        // for a Gaussian CDF: 10%-90% width = 2 * 1.2816 * sigma
+        double p3 = std::max((x_90 - x_10) / 2.5631, 1.0);
+
+        double p5 = 0.0; // assume flat gain, let optimizer find the slope
+
+        return {p0, p1, p2, p3, p4, p5};
+    }
+ 
+    static void compute_steps(const std::array<double, npar>& start,
+                              double x_range, double y_range, double slope_scale,
+                              std::array<double, npar>& steps)
+    {
+        steps[0] = std::max(0.1 * std::abs(start[0]), 0.1 * y_range);
+        steps[1] = 0.1 * slope_scale;
+        steps[2] = 0.05 * x_range;
+        steps[3] = 0.05 * x_range;
+        steps[4] = std::max(0.1 * std::abs(start[4]), 0.1 * y_range);
+        steps[5] = 0.1 * slope_scale;
+    }
+};
+
+
+// _____________________________________________________________________
+//
+// FallingScurve
+// _____________________________________________________________________
+
+/**
+ * @brief Falling S-curve (complementary error-function step) with linear
+ *        baseline and post-step slope.
+ *
+ * f(x) = (p0 + p1*x) + 0.5*(1 - erf((x - p2) / (sqrt(2)*p3))) * (p4 + p5*(x - p2))
+ *
+ * Parameters are identical to RisingScurve.  The only difference is the
+ * sign of the erf term, which flips the step direction (and the signs of
+ * dS/dp2 and dS/dp3 in the gradient).
+ */
+struct FallingScurve {
+    static constexpr std::size_t npar = 6;
+
+    static constexpr std::array<ParamInfo, npar> param_info = {{
+        {"p0", -no_bound, no_bound},
+        {"p1", -no_bound, no_bound},
+        {"p2", -no_bound, no_bound},
+        {"p3",  1e-12,    no_bound},
+        {"p4", -no_bound, no_bound},
+        {"p5", -no_bound, no_bound},
+    }};
+
+    static double eval(double x, const std::vector<double>& par) {
+        const double p0 = par[0];
+        const double p1 = par[1];
+        const double p2 = par[2];
+        const double p3 = par[3];
+        const double p4 = par[4];
+        const double p5 = par[5];
+
+        const double dx   = x - p2;
+        const double step = 0.5 * (1.0 - std::erf(dx * inv_sqrt2 / p3));
+        return (p0 + p1 * x) + step * (p4 + p5 * dx);
+    }
+
+    static void eval_and_grad(double x,
+                              const std::vector<double>& par,
+                              double& f,
+                              std::array<double, npar>& g)
+    {
+        const double p0 = par[0];
+        const double p1 = par[1];
+        const double p2 = par[2];
+        const double p3 = par[3];
+        const double p4 = par[4];
+        const double p5 = par[5];
+
+        const double dx   = x - p2;
+        const double z    = dx * inv_sqrt2 / p3;
+        const double step = 0.5 * (1.0 - std::erf(z));
+        const double amp  = p4 + p5 * dx;
+
+        f = (p0 + p1 * x) + step * amp;
+
+        const double e     = std::exp(-z * z);
+        const double dSdp2 = +inv_sqrt_2pi * e / p3;          // sign flipped vs rising
+        const double dSdp3 = +inv_sqrt_2pi * e * dx / (p3 * p3);
+
+        g[0] = 1.0;
+        g[1] = x;
+        g[2] = dSdp2 * amp - step * p5;
+        g[3] = dSdp3 * amp;
+        g[4] = step;
+        g[5] = step * dx;
+    }
+
+    /** @brief Reject degenerate width (zero transition width). */
+    static bool is_valid(const std::vector<double>& par) {
+        return par[3] != 0.0;
+    }
+
+    /** @brief Data-driven initial parameter estimates for a falling S-curve. */
+    static std::array<double, npar> estimate_par(NDView<double, 1> x,
+                                                NDView<double, 1> y)
+    {
+        const ssize_t n = y.size();
+
+        // baseline: last ~10% of points (after turn-off)
+        ssize_t n_base = std::max<ssize_t>(n / 10, 2);
+        double sum_y = 0, sum_xy = 0, sum_x = 0, sum_x2 = 0;
+        for (ssize_t i = n - n_base; i < n; ++i) {
+            sum_y  += y[i];
+            sum_x  += x[i];
+            sum_xy += x[i] * y[i];
+            sum_x2 += x[i] * x[i];
+        }
+        double denom = n_base * sum_x2 - sum_x * sum_x;
+        double p1 = (std::abs(denom) > 1e-30)
+                ? (n_base * sum_xy - sum_x * sum_y) / denom
+                : 0.0;
+        double p0 = (sum_y - p1 * sum_x) / n_base;
+
+        // plateau: average of first ~10%
+        double plateau = 0;
+        ssize_t n_plat = std::max<ssize_t>(n / 10, 2);
+        for (ssize_t i = 0; i < n_plat; ++i)
+            plateau += y[i];
+        plateau /= n_plat;
+
+        // amplitude: plateau minus baseline at midpoint
+        double x_mid = 0.5 * (x[0] + x[n - 1]);
+        double baseline_at_mid = p0 + p1 * x_mid;
+        double p4 = plateau - baseline_at_mid;
+
+        // threshold: x where y first drops below 50%
+        double y_half = baseline_at_mid + 0.5 * p4;
+        double p2 = x_mid; // fallback
+        for (ssize_t i = 0; i < n; ++i) {
+            if (y[i] <= y_half) {
+                p2 = x[i];
+                break;
+            }
+        }
+
+        // sigma: estimate from transition width (90%-10% fall)
+        double y_90 = baseline_at_mid + 0.9 * p4;
+        double y_10 = baseline_at_mid + 0.1 * p4;
+        double x_90 = x[0], x_10 = x[n - 1];
+        for (ssize_t i = 0; i < n; ++i) {
+            if (y[i] <= y_90) { x_90 = x[i]; break; }
+        }
+        for (ssize_t i = 0; i < n; ++i) {
+            if (y[i] <= y_10) { x_10 = x[i]; break; }
+        }
+        // same CDF relationship: 10%-90% width = 2 * 1.2816 * sigma
+        double p3 = std::max((x_10 - x_90) / 2.5631, 1.0);
+
+        double p5 = 0.0;
+
+        return {p0, p1, p2, p3, p4, p5};
+    }
+ 
+    static void compute_steps(const std::array<double, npar>& start,
+                              double x_range, double y_range, double slope_scale,
+                              std::array<double, npar>& steps)
+    {
+        RisingScurve::compute_steps(start, x_range, y_range, slope_scale, steps);
+    }
+};
+
+} // namespace aare::model
diff --git a/include/aare/utils/par.hpp b/include/aare/utils/par.hpp
index 04ff6a2..db3c7f2 100644
--- a/include/aare/utils/par.hpp
+++ b/include/aare/utils/par.hpp
@@ -3,7 +3,7 @@
 #include <thread>
 #include <utility>
 #include <vector>
-
+#include "aare/NDView.hpp"
 #include "aare/utils/task.hpp"
 
 namespace aare {
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index ec2962c..aefbd95 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -26,6 +26,10 @@ set_target_properties(_aare PROPERTIES
 )
 target_link_libraries(_aare PRIVATE aare_core aare_compiler_flags)
 
+target_include_directories(_aare SYSTEM PRIVATE
+    $<TARGET_PROPERTY:Minuit2::Minuit2,INTERFACE_INCLUDE_DIRECTORIES>
+)
+
 # List of python files to be copied to the build directory
 set( PYTHON_FILES
     aare/__init__.py
diff --git a/python/aare/__init__.py b/python/aare/__init__.py
index 92d88db..7684df8 100644
--- a/python/aare/__init__.py
+++ b/python/aare/__init__.py
@@ -17,7 +17,8 @@ from .ClusterFinder import ClusterFinder, ClusterCollector, ClusterFinderMT, Clu
 from .ClusterVector import ClusterVector
 from .Cluster import Cluster
 
-
+from ._aare import Gaussian, RisingScurve, FallingScurve, Pol1, Pol2
+from ._aare import fit
 from ._aare import fit_gaus, fit_pol1, fit_scurve, fit_scurve2
 from ._aare import Interpolator
 from ._aare import calculate_eta2, calculate_eta3, calculate_cross_eta3, calculate_full_eta2
diff --git a/python/examples/fits.py b/python/examples/fits.py
index f3f6fc9..40172b2 100644
--- a/python/examples/fits.py
+++ b/python/examples/fits.py
@@ -1,8 +1,11 @@
 # SPDX-License-Identifier: MPL-2.0
 import matplotlib.pyplot as plt
 import numpy as np
+import sys
+sys.path.insert(0, '/home/kferjaoui/sw/aare/build')
 from aare import fit_gaus, fit_pol1
-from aare import gaus, pol1
+from aare import Gaussian, fit
+from aare import pol1
 
 textpm = f"±"  #
 textmu = f"μ"  #
@@ -15,35 +18,57 @@ textsigma = f"σ"  #
 mu = np.random.uniform(1, 100)  # Mean of Gaussian
 sigma = np.random.uniform(4, 20)  # Standard deviation
 num_points = 10000  # Number of points for smooth distribution
-noise_sigma = 100
+# noise_sigma = 10
 
 # Generate Gaussian distribution
 data = np.random.normal(mu, sigma, num_points)
+counts, edges = np.histogram(data, bins=100)
 
-# Generate errors for each point
-errors = np.abs(np.random.normal(0, sigma, num_points))  # Errors with mean 0, std 0.5
+x = 0.5 * (edges[:-1] + edges[1:])       # proper bin centers
+y = counts.astype(np.float64)
+
+# Poisson noise
+yerr = np.sqrt(np.maximum(y, 1))
+# yerr = np.abs(np.random.normal(0, noise_sigma, len(x)))
 
 # Create subplot
 fig0, ax0 = plt.subplots(1, 1, num=0, figsize=(12, 8))
 
-x = np.histogram(data, bins=30)[1][:-1] + 0.05
-y = np.histogram(data, bins=30)[0]
-yerr = errors[:30]
-
-
 # Add the errors as error bars in the step plot
 ax0.errorbar(x, y, yerr=yerr, fmt=". ", capsize=5)
 ax0.grid()
 
-par, err = fit_gaus(x, y, yerr)
-print(par, err)
+# Fit with lmfit
+result_lm = fit_gaus(x, y, yerr)
+par_lm = result_lm["par"]
+err_lm = result_lm["par_err"]
+chi2_lm = result_lm["chi2"] 
+print("[lmfit] fit_gaus:            ", par_lm, err_lm, chi2_lm)
+
+# Fit with Minuit2 + analytic gradient + Hesse errors
+gaussian = Gaussian()
+gaussian.compute_errors = True
+result_m2 = gaussian.fit(x, y, yerr)
+par_m2 = result_m2['par']
+err_m2 = result_m2['par_err']
+chi2_m2 = result_m2['chi2']
+print(f"[minuit2] gaussian.fit: par={par_m2}, err={err_m2}, chi2={chi2_m2}")
 
 x = np.linspace(x[0], x[-1], 1000)
-ax0.plot(x, gaus(x, par), marker="")
-ax0.set(xlabel="x", ylabel="Counts", title=f"A0 = {par[0]:0.2f}{textpm}{err[0]:0.2f}\n"
-                                           f"{textmu} = {par[1]:0.2f}{textpm}{err[1]:0.2f}\n"
-                                           f"{textsigma} = {par[2]:0.2f}{textpm}{err[2]:0.2f}\n"
-                                           f"(init: {textmu}: {mu:0.2f}, {textsigma}: {sigma:0.2f})")
+ax0.plot(x, gaussian(x, par_lm), marker="", label="fit_gaus")
+ax0.plot(x, gaussian(x, par_m2), marker="", linestyle=":", label="fit_gaus_minuit_grad")
+ax0.legend()
+ax0.set(xlabel="x", ylabel="Counts", 
+    title=(
+        f"fit_gaus:       A={par_lm[0]:0.2f}{textpm}{err_lm[0]:0.2f}  "
+        f"{textmu}={par_lm[1]:0.2f}{textpm}{err_lm[1]:0.2f}  "
+        f"{textsigma}={par_lm[2]:0.2f}{textpm}{err_lm[2]:0.2f}\n"
+        f"minuit_grad: A={par_m2[0]:0.2f}{textpm}{err_m2[0]:0.2f}  "
+        f"{textmu}={par_m2[1]:0.2f}{textpm}{err_m2[1]:0.2f}  "
+        f"{textsigma}={par_m2[2]:0.2f}{textpm}{err_m2[2]:0.2f}\n"
+        f"(truth: {textmu}={mu:0.2f}, {textsigma}={sigma:0.2f})"
+    ),
+)
 fig0.tight_layout()
 
 
@@ -66,8 +91,9 @@ y_values = slope * x_values + intercept + var_points
 
 fig1, ax1 = plt.subplots(1, 1, num=1, figsize=(12, 8))
 ax1.errorbar(x_values, y_values, yerr=errors, fmt=". ", capsize=5)
-par, err = fit_pol1(x_values, y_values, errors)
-
+result_pol = fit_pol1(x_values, y_values, errors)
+par = result_pol["par"]
+err = result_pol["par_err"]
 
 x = np.linspace(np.min(x_values), np.max(x_values), 1000)
 ax1.plot(x, pol1(x, par), marker="")
diff --git a/python/src/fit.hpp b/python/src/fit.hpp
index 57f49ac..9938ba0 100644
--- a/python/src/fit.hpp
+++ b/python/src/fit.hpp
@@ -6,10 +6,204 @@
 #include <pybind11/stl_bind.h>
 
 #include "aare/Fit.hpp"
+#include "aare/FitModel.hpp"
+#include "aare/Models.hpp"
+#include "aare/Chi2.hpp"
 
 namespace py = pybind11;
 using namespace pybind11::literals;
 
+template <typename Model, typename FCN>
+py::object fit_dispatch(
+    const aare::FitModel<Model>& model,
+    py::array_t<double, py::array::c_style | py::array::forcecast> x,
+    py::array_t<double, py::array::c_style | py::array::forcecast> y,
+    py::object y_err_obj,
+    int n_threads);
+
+template <typename Model>
+void bind_fit_model(py::module& m, const char* name) {
+    using FM = aare::FitModel<Model>;
+    using FCN = aare::func::Chi2Model1DGrad<Model>;
+    py::class_<FM>(m, name)
+        .def(py::init<unsigned int, unsigned int, double, bool>(),
+             py::arg("strategy")       = 0,
+             py::arg("max_calls")      = 100,
+             py::arg("tolerance")      = 0.5,
+             py::arg("compute_errors") = false)
+        .def("SetParLimits", &FM::SetParLimits, py::arg("idx"), py::arg("lo"), py::arg("hi"))
+        .def("FixParameter", &FM::FixParameter, py::arg("idx"), py::arg("val"))
+        .def("ReleaseParameter", &FM::ReleaseParameter, py::arg("idx"))
+        .def("SetParameter", &FM::SetParameter, py::arg("idx"), py::arg("val"))
+        .def_property("max_calls", &FM::max_calls, &FM::SetMaxCalls)
+        .def_property("tolerance", &FM::tolerance, &FM::SetTolerance)
+        .def_property("compute_errors", &FM::compute_errors, &FM::SetComputeErrors)
+        .def("__call__",
+            [](const FM& /*self*/,
+                py::array_t<double, py::array::c_style | py::array::forcecast> x,
+                py::array_t<double, py::array::c_style | py::array::forcecast> par)
+            {
+                auto x_view = make_view_1d(x);
+                auto p_view = make_view_1d(par);
+
+                std::vector<double> pvec(p_view.begin(), p_view.end());
+
+                auto* result = new aare::NDArray<double, 1>({x_view.size()});
+                for (ssize_t i = 0; i < x_view.size(); ++i)
+                    (*result)(i) = Model::eval(x_view[i], pvec);
+
+                return return_image_data(result);
+            },
+            py::arg("x"), py::arg("par"))
+        .def("fit",
+            [](const FM& self,
+            py::array_t<double, py::array::c_style | py::array::forcecast> x,
+            py::array_t<double, py::array::c_style | py::array::forcecast> y,
+            py::object y_err_obj,
+            int n_threads) -> py::object
+            {
+                return fit_dispatch<Model, FCN>(self, x, y, y_err_obj, n_threads);
+            },
+            R"doc(
+            Fit this model to 1D or 3D data using Minuit2.
+
+            Parameters
+            ----------
+            x : array_like, shape (n_scan,)
+                Scan points.
+            y : array_like, shape (n_scan,) or (rows, cols, n_scan)
+                Measured data.
+            y_err : array_like or None
+                Per-point uncertainties. None for unweighted fit.
+            n_threads : int
+                Number of threads for 3D parallel loop.
+            )doc",
+            py::arg("x"),
+            py::arg("y"),
+            py::arg("y_err")    = py::none(),
+            py::arg("n_threads") = 4);
+}
+
+template <typename Model>
+py::dict pack_1d_result_dict(const aare::NDArray<double, 1>& result,
+                             bool compute_errors)
+{
+    constexpr std::size_t npar = Model::npar;
+
+    auto res = result.view();
+
+    auto par_out  = new NDArray<double, 1>({npar}, 0.0);
+    auto chi2_out = new NDArray<double, 1>({1},    0.0);
+
+    auto par_view  = par_out->view();
+    auto chi2_view = chi2_out->view();
+
+    for (std::size_t i = 0; i < npar; ++i) {
+        par_view(i) = res(i);
+    }
+
+    if (compute_errors) {
+        auto err_out  = new NDArray<double, 1>({npar}, 0.0);
+        auto err_view = err_out->view();
+
+        for (std::size_t i = 0; i < npar; ++i) {
+            err_view(i) = res(npar + i);
+        }
+
+        chi2_view(0) = res(2 * npar);
+
+        return py::dict(
+            "par"_a     = return_image_data(par_out),
+            "par_err"_a = return_image_data(err_out),
+            "chi2"_a    = return_image_data(chi2_out));
+    } else {
+        chi2_view(0) = res(npar);
+
+        return py::dict(
+            "par"_a  = return_image_data(par_out),
+            "chi2"_a = return_image_data(chi2_out));
+    }
+}
+
+// Helper: typed dispatch for one Model, handles 1D/3D + y_err logic
+template <typename Model, typename FCN>
+py::object fit_dispatch(
+    const aare::FitModel<Model>& model,
+    py::array_t<double, py::array::c_style | py::array::forcecast> x,
+    py::array_t<double, py::array::c_style | py::array::forcecast> y,
+    py::object y_err_obj,
+    int n_threads)
+{
+    constexpr std::size_t npar = Model::npar;
+
+    if (y.ndim() == 3) {
+        auto par_out = new NDArray<double, 3>({y.shape(0), y.shape(1), npar}, 0.0);
+        auto chi2_out= new NDArray<double, 2>({y.shape(0), y.shape(1)}, 0.0);
+
+        auto x_view = make_view_1d(x);
+        auto y_view = make_view_3d(y);
+
+        if (!y_err_obj.is_none()) {
+            auto y_err = py::cast<py::array_t<double,
+                py::array::c_style | py::array::forcecast>>(y_err_obj);
+
+            if (y_err.ndim() != 3) {
+                throw std::runtime_error("For 3D input y, y_err must also be 3D.");
+            }
+            
+            auto err_out = new NDArray<double, 3>({y.shape(0), y.shape(1), npar}, 0.0);
+            auto y_view_err = make_view_3d(y_err);
+
+            aare::fit_3d<Model, FCN>(model, x_view, y_view, y_view_err, 
+                            par_out->view(), err_out->view(), chi2_out->view(), n_threads);
+            
+            if (model.compute_errors()) {
+                return py::dict("par"_a     = return_image_data(par_out),
+                                "par_err"_a = return_image_data(err_out),
+                                "chi2"_a    = return_image_data(chi2_out));
+            } else {
+                delete err_out;
+                return py::dict("par"_a  = return_image_data(par_out),
+                                "chi2"_a = return_image_data(chi2_out));
+            }
+        } else {
+
+            NDView<double, 3> dummy_err{};
+            NDView<double, 3> dummy_err_out{};
+
+            aare::fit_3d<Model, FCN>(model, x_view, y_view, dummy_err, 
+                            par_out->view(), dummy_err_out, chi2_out->view(), n_threads);
+                
+            return py::dict("par"_a = return_image_data(par_out),
+                            "chi2"_a = return_image_data(chi2_out));
+        }
+    } else if (y.ndim() == 1) {
+        NDArray<double, 1> result{};
+
+        auto x_view = make_view_1d(x);
+        auto y_view = make_view_1d(y);
+
+        if (!y_err_obj.is_none()) {
+            auto y_err = py::cast<py::array_t<double,
+                py::array::c_style | py::array::forcecast>>(y_err_obj);
+
+            if (y_err.ndim() != 1) {
+                throw std::runtime_error("For 1D input y, y_err must also be 1D.");
+            }
+
+            auto y_view_err = make_view_1d(y_err);
+            result = aare::fit_pixel<Model, FCN>(model, x_view, y_view, y_view_err);
+        } else {
+            result = aare::fit_pixel<Model, FCN>(model, x_view, y_view);
+        }
+
+        return pack_1d_result_dict<Model>(result, model.compute_errors());
+
+    } else {
+        throw std::runtime_error("Data must be 1D or 3D.");
+    }
+}
+
 void define_fit_bindings(py::module &m) {
 
     // TODO! Evaluate without converting to double
@@ -121,18 +315,17 @@ void define_fit_bindings(py::module &m) {
             }
         },
         R"(
+        Fit a 1D Gaussian to data.
 
-Fit a 1D Gaussian to data.
-
-Parameters
-----------
-x : array_like
-    The x values.
-y : array_like
-    The y values.
-n_threads : int, optional
-    The number of threads to use. Default is 4.
-)",
+        Parameters
+        ----------
+        x : array_like
+            The x values.
+        y : array_like
+            The y values.
+        n_threads : int, optional
+            The number of threads to use. Default is 4.
+        )",
         py::arg("x"), py::arg("y"), py::arg("n_threads") = 4);
 
     m.def(
@@ -187,22 +380,22 @@ n_threads : int, optional
             }
         },
         R"(
+        Fit a 1D Gaussian to data with error estimates.
 
-Fit a 1D Gaussian to data with error estimates.
-
-Parameters
-----------
-x : array_like
-    The x values.
-y : array_like
-    The y values.
-y_err : array_like
-    The error in the y values.
-n_threads : int, optional
-    The number of threads to use. Default is 4.
-)",
+        Parameters
+        ----------
+        x : array_like
+            The x values.
+        y : array_like
+            The y values.
+        y_err : array_like
+            The error in the y values.
+        n_threads : int, optional
+            The number of threads to use. Default is 4.
+        )",
         py::arg("x"), py::arg("y"), py::arg("y_err"), py::arg("n_threads") = 4);
 
+
     m.def(
         "fit_pol1",
         [](py::array_t<double, py::array::c_style | py::array::forcecast> x,
@@ -273,19 +466,19 @@ n_threads : int, optional
             }
         },
         R"(
-Fit a 1D polynomial to data with error estimates.
+        Fit a 1D polynomial to data with error estimates.
 
-Parameters
-----------
-x : array_like
-    The x values.
-y : array_like
-    The y values.
-y_err : array_like
-    The error in the y values.
-n_threads : int, optional
-    The number of threads to use. Default is 4.
-)",
+        Parameters
+        ----------
+        x : array_like
+            The x values.
+        y : array_like
+            The y values.
+        y_err : array_like
+            The error in the y values.
+        n_threads : int, optional
+            The number of threads to use. Default is 4.
+        )",
         py::arg("x"), py::arg("y"), py::arg("y_err"), py::arg("n_threads") = 4);
 
     //=========
@@ -359,21 +552,22 @@ n_threads : int, optional
             }
         },
         R"(
-Fit a 1D polynomial to data with error estimates.
+        Fit a 1D polynomial to data with error estimates.
 
-Parameters
-----------
-x : array_like
-    The x values.
-y : array_like
-    The y values.
-y_err : array_like
-    The error in the y values.
-n_threads : int, optional
-    The number of threads to use. Default is 4.
-)",
+        Parameters
+        ----------
+        x : array_like
+            The x values.
+        y : array_like
+            The y values.
+        y_err : array_like
+            The error in the y values.
+        n_threads : int, optional
+            The number of threads to use. Default is 4.
+        )",
         py::arg("x"), py::arg("y"), py::arg("y_err"), py::arg("n_threads") = 4);
 
+
     m.def(
         "fit_scurve2",
         [](py::array_t<double, py::array::c_style | py::array::forcecast> x,
@@ -444,18 +638,105 @@ n_threads : int, optional
             }
         },
         R"(
-Fit a 1D polynomial to data with error estimates.
+        Fit a 1D polynomial to data with error estimates.
 
-Parameters
-----------
-x : array_like
-    The x values.
-y : array_like
-    The y values.
-y_err : array_like
-    The error in the y values.
-n_threads : int, optional
-    The number of threads to use. Default is 4.
-)",
+        Parameters
+        ----------
+        x : array_like
+            The x values.
+        y : array_like
+            The y values.
+        y_err : array_like
+            The error in the y values.
+        n_threads : int, optional
+            The number of threads to use. Default is 4.
+        )",
         py::arg("x"), py::arg("y"), py::arg("y_err"), py::arg("n_threads") = 4);
+
+
+    // ── Bind model classes ──────────────────────────────────────────
+    bind_fit_model<aare::model::Gaussian>(m, "Gaussian");
+    bind_fit_model<aare::model::RisingScurve>(m, "RisingScurve");
+    bind_fit_model<aare::model::FallingScurve>(m, "FallingScurve");
+    bind_fit_model<aare::model::Pol1>(m, "Pol1");
+    bind_fit_model<aare::model::Pol2>(m, "Pol2");
+
+    m.def("fit",
+        [](py::object model_obj,
+           py::array_t<double, py::array::c_style | py::array::forcecast> x,
+           py::array_t<double, py::array::c_style | py::array::forcecast> y,
+           py::object y_err_obj,
+           int n_threads) -> py::object
+        {
+            using namespace aare::model;
+            using namespace aare::func;
+            
+            // ── Polynomial of degree 1 ───────
+            if(py::isinstance< aare::FitModel<Pol1> >(model_obj)) {
+                const auto& mdl = model_obj.cast< const aare::FitModel<Pol1>& >();
+                return fit_dispatch<Pol1, Chi2Pol1>(mdl, x, y, y_err_obj, n_threads); 
+            }
+
+            // ── Polynomial of degree 2 ───────
+            if(py::isinstance< aare::FitModel<Pol2> >(model_obj)) {
+                const auto& mdl = model_obj.cast< const aare::FitModel<Pol2>& >();
+                return fit_dispatch<Pol2, Chi2Pol2>(mdl, x, y, y_err_obj, n_threads); 
+            }
+            // ── Gaussian ───────
+            if(py::isinstance< aare::FitModel<Gaussian> >(model_obj)) {
+                const auto& mdl = model_obj.cast< const aare::FitModel<Gaussian>& >();
+                return fit_dispatch<Gaussian, Chi2Gaussian>(mdl, x, y, y_err_obj, n_threads); 
+            }
+
+            // ── Rising Scurve ───────
+            if(py::isinstance< aare::FitModel<RisingScurve> >(model_obj)) {
+                const auto& mdl = model_obj.cast< const aare::FitModel<RisingScurve>& >();
+                return fit_dispatch<RisingScurve, Chi2RisingScurve>(mdl, x, y, y_err_obj, n_threads); 
+            }
+
+            // ── Falling Scurve ───────
+            if(py::isinstance< aare::FitModel<FallingScurve> >(model_obj)) {
+                const auto& mdl = model_obj.cast< const aare::FitModel<FallingScurve>& >();
+                return fit_dispatch<FallingScurve, Chi2FallingScurve>(mdl, x, y, y_err_obj, n_threads); 
+            }
+
+            throw std::runtime_error(
+                "Unknown model type. Expected Pol1, Pol2, Gaussian, RisingScurve or FallingScurve."
+            );
+        },
+        R"(
+        Fit a model to 1D or 3D data using Minuit2.
+ 
+        Parameters
+        ----------
+        model : Pol1, Pol2, Gaussian, RisingScurve, or FallingScurve
+            Configured model object.  User-set limits, fixed parameters,
+            and start values take precedence over automatic estimates.
+        x : array_like, shape (n_scan,)
+            Scan points (e.g. energy or threshold values).
+        y : array_like, shape (n_scan,) or (rows, cols, n_scan)
+            Measured data.  1D for a single pixel, 3D for a detector image.
+        y_err : array_like or None
+            Per-point uncertainties.  Same shape as y.  None → unweighted fit.
+        n_threads : int
+            Number of threads for the 3D parallel loop.
+ 
+        Returns
+        -------
+        For 1D input:
+            numpy array of shape (2*npar+1,) if compute_errors else (npar+1,).
+            Layout: [params..., (errors...,) chi2].
+ 
+        For 3D input:
+            dict with keys:
+              "par"     : (rows, cols, npar) fitted parameters.
+              "par_err" : (rows, cols, npar) parameter errors (if compute_errors).
+              "chi2"    : (rows, cols)       chi-squared per pixel.
+        )",
+        py::arg("model"),
+        py::arg("x"),
+        py::arg("y"), 
+        py::arg("y_err") = py::none(), 
+        py::arg("n_threads") = 4
+    );
 }
\ No newline at end of file
diff --git a/python/tests/test_Minuit2_gauss.ipynb b/python/tests/test_Minuit2_gauss.ipynb
new file mode 100644
index 0000000..fc7bd0a
--- /dev/null
+++ b/python/tests/test_Minuit2_gauss.ipynb
@@ -0,0 +1,552 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "efef8e20-6571-4561-8f0b-6048b57907de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.gridspec import GridSpec\n",
+    "import sys\n",
+    "sys.path.insert(0, '/home/ferjao_k/sw/aare/build')\n",
+    "from aare import fit_gaus      # lmfit\n",
+    "from aare import Gaussian, fit # minuit2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "35772e2c-37c6-4986-a9c0-7dca4a034827",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROWS = 100\n",
+    "COLS = 100\n",
+    "N_SCAN = 100\n",
+    "NOISE_FRAC = 0.05\n",
+    "SEED = 42\n",
+    "N_THREADS = 4\n",
+    "\n",
+    "N_REPEATS = 7\n",
+    "N_WARMUP  = 3    # untimed iterations (icache + branch predictor warmup)\n",
+    "COOLDOWN  = 2.0  # seconds between (method, thread_count) pairs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be455445-7df8-47cc-9f39-d2a38856e0bd",
+   "metadata": {},
+   "source": [
+    "## Data generator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "06c8fddb-56d9-4f84-8b21-4ffd8b7bd26f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_3d_data(rows, cols, n_scan, noise_frac, seed):\n",
+    "    \"\"\"\n",
+    "    Generate a synthetic detector image stack where each pixel has a\n",
+    "    Gaussian response curve with per-pixel variation in A, mu, sigma.\n",
+    "\n",
+    "    Returns x (n_scan,), y (rows, cols, n_scan), y_err (rows, cols, n_scan),\n",
+    "    and the ground-truth parameter arrays.\n",
+    "    \"\"\"\n",
+    "    rng = np.random.default_rng(seed)\n",
+    "\n",
+    "    # Per-pixel true params each of shape: [rows, cols, 1]\n",
+    "    A_true = rng.uniform(200, 1000, size=(rows, cols))\n",
+    "    mu_true = rng.uniform(20, 80, size=(rows, cols))\n",
+    "    sig_true = rng.uniform(3, 12, size=(rows, cols))\n",
+    "    \n",
+    "    # One common binned energy array\n",
+    "    x = np.linspace(0, 100, n_scan) # shape [1, 1, nscan]\n",
+    "\n",
+    "    # Build ground truth signals per-pixel\n",
+    "    exponent = -0.5 * ((x[None, None, :] - mu_true[:, :, None]) / sig_true[:,:, None])**2 # shape [rows, cols, nscan]\n",
+    "    y_clean = A_true[:, :, None] * np.exp(exponent)\n",
+    "\n",
+    "    # Perturb with noise\n",
+    "    noise_sigma = noise_frac * A_true[:, :, None] * np.ones_like(y_clean) # shape [rows, cols, nscan]\n",
+    "    noise = rng.normal(0, noise_sigma)\n",
+    "    y = y_clean + noise\n",
+    "\n",
+    "    y_err = noise_sigma.copy()\n",
+    "\n",
+    "    return x, y, y_err, A_true, mu_true, sig_true    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30ed6bb8-4798-497f-9990-4c518f885855",
+   "metadata": {},
+   "source": [
+    "## Profiling function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dc0b52b6-9fc3-453d-b6eb-e325a2b8342e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bench(fn, n_warmup=N_WARMUP, n_repeats=N_REPEATS):\n",
+    "    \"\"\"\n",
+    "    Warmup then time `fn` over `n_repeats` calls.\n",
+    "    Returns (last_result, list_of_walltimes_in_seconds).\n",
+    "    \"\"\"\n",
+    "    # warmup: primes icache, branch predictor, and lets CPU ramp to boost clock\n",
+    "    for _ in range(n_warmup):\n",
+    "        res = fn()\n",
+    "\n",
+    "    times = []\n",
+    "    for _ in range(n_repeats):\n",
+    "        t0 = time.perf_counter()\n",
+    "        res = fn()\n",
+    "        t1 = time.perf_counter()\n",
+    "        times.append(t1 - t0)\n",
+    "    return res, times"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "377fc820-95b2-48aa-b104-a272c50e4103",
+   "metadata": {},
+   "source": [
+    "# Quick check on small (2x2) frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9cc999b9-e0d8-4deb-ae29-e8bd0141534b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "== True Gaussian params == \n",
+      "A_true = \n",
+      " [[819.16483884 551.1027518 ]\n",
+      " [886.87833593 757.89442325]]\n",
+      "mu_true = \n",
+      " [[25.65064087 78.5373411 ]\n",
+      " [65.66838212 67.16385832]]\n",
+      "sig_true = \n",
+      " [[ 4.15302269  7.05347344]\n",
+      " [ 6.33718222 11.3408849 ]]\n",
+      "\n",
+      "\n",
+      "== Fit results ==\n",
+      "A_fit = \n",
+      " [[812.09277132 559.04069721]\n",
+      " [899.09335849 759.24481682]]\n",
+      "mu_fit = \n",
+      " [[25.6598209  78.40461782]\n",
+      " [65.52261318 66.84540995]]\n",
+      "sig_fit = \n",
+      " [[ 4.2778026   7.041045  ]\n",
+      " [ 6.29190225 11.34233504]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate 2 x 2 dataset of Gaussian-like profiles for each pixel\n",
+    "x2, y2, yerr2, true_A2, true_mu2, true_sig2 = generate_3d_data(\n",
+    "    2, 2, N_SCAN, NOISE_FRAC, SEED\n",
+    ")\n",
+    "model_g = Gaussian()\n",
+    "model_g.compute_errors = True\n",
+    "result = model_g.fit(x2, y2, yerr2)\n",
+    "\n",
+    "from pprint import pprint\n",
+    "print(\"== True Gaussian params == \")\n",
+    "print(\"A_true = \\n\",  true_A2)\n",
+    "print(\"mu_true = \\n\", true_mu2)\n",
+    "print(\"sig_true = \\n\",true_sig2)\n",
+    "print(\"\\n\")\n",
+    "\n",
+    "print(\"== Fit results ==\")\n",
+    "par = result['par']\n",
+    "# print(par)\n",
+    "A_fit = par[:, :, 0]\n",
+    "mu_fit = par[:, :, 1]\n",
+    "sig_fit = par[:, :, 2]\n",
+    "print(\"A_fit = \\n\",  A_fit)\n",
+    "print(\"mu_fit = \\n\", mu_fit)\n",
+    "print(\"sig_fit = \\n\", sig_fit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a98a41f3-fd2e-4bfc-9ec6-0d23dd38e896",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 2, figsize=(12,8))\n",
+    "\n",
+    "# Gaussians in 2x2 frame: True vs Fit\n",
+    "for row in range(2):\n",
+    "    for col in range(2):\n",
+    "        ax[row, col].plot(x2, y2[row, col,:], label=\"data\")\n",
+    "        ax[row, col].plot(x2, model_g(x2, result['par'][row, col,:]), linewidth=1, color=\"green\", label=\"minuit\")\n",
+    "        ax[row, col].set_title(f\"Gaussian Fit to data in pixel [{row}, {col}]\")\n",
+    "        ax[row, col].legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcf52481-0278-4f95-8676-829a6d61eff8",
+   "metadata": {},
+   "source": [
+    "## Fit data with different backends"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1f6bc651-80c1-41dd-8a05-7f15aba006aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating synthetic data: 100x100 pixels, 100 scan points, noise_frac=0.05\n",
+      "\n",
+      "model.max_calls = 100\n",
+      "model.tolerance = 0.5\n",
+      "model.compute_errors = False\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ===============\n",
+    "# DATA GENERATION\n",
+    "# ===============\n",
+    "print(f\"Generating synthetic data: {ROWS}x{COLS} pixels, \"\n",
+    "      f\"{N_SCAN} scan points, noise_frac={NOISE_FRAC}\\n\")\n",
+    "\n",
+    "x, y, yerr, true_A, true_mu, true_sig = generate_3d_data(\n",
+    "    ROWS, COLS, N_SCAN, NOISE_FRAC, SEED\n",
+    ")\n",
+    "\n",
+    "model = Gaussian()\n",
+    "print(f\"model.max_calls = {model.max_calls}\")\n",
+    "print(f\"model.tolerance = {model.tolerance}\")\n",
+    "print(\"model.compute_errors =\", model.compute_errors)\n",
+    "METHOD_DEFS = [\n",
+    "    (\"lmfit (LM)\",\n",
+    "     lambda nt: lambda: fit_gaus(x, y, n_threads=nt),\n",
+    "     \"#2196F3\", {\"linewidth\": 3.0, \"linestyle\": \"-\"}),\n",
+    "\n",
+    "    (\"Minuit2 (obj API)\",\n",
+    "     lambda nt: lambda: model.fit(x, y, n_threads=nt),\n",
+    "     \"#FF9800\", {\"linewidth\": 2.5, \"linestyle\": \":\"}),\n",
+    "]\n",
+    "\n",
+    "colors = {label: c for label, _, c, _ in METHOD_DEFS}\n",
+    "styles = {label: s for label, _, _, s in METHOD_DEFS}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5a417145-ce42-4c3a-a7bf-05ff97ba0450",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Method                     time (ms)     med|dA|    med|dMu|   med|dSig|\n",
+      "--------------------------------------------------------------------------------\n",
+      "[lmfit (LM)            ]    189.58 ms       6.272      0.0940      0.0949\n",
+      "[Minuit2 (obj API)     ]    124.59 ms       6.272      0.0940      0.0949  chi2/ndf=880.9946\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ====================================\n",
+    "# SINGLE-CALL BENCHMARK (at N_THREADS)\n",
+    "# ====================================\n",
+    "def extract_result(label, res):\n",
+    "    \"\"\"Normalize return values across fitters into a common dict.\"\"\"\n",
+    "    if isinstance(res, dict):\n",
+    "        out = {\"par\": res[\"par\"]}\n",
+    "        if \"par_err\" in res:\n",
+    "            out[\"par_err\"] = res[\"par_err\"]\n",
+    "        if \"chi2\" in res:\n",
+    "            out[\"chi2\"] = res[\"chi2\"]\n",
+    "        return out\n",
+    "    # fit_gaus without y_err returns a raw array\n",
+    "    return {\"par\": res}\n",
+    "    \n",
+    "methods = {}\n",
+    "for label, factory, _, _ in METHOD_DEFS:\n",
+    "    time.sleep(COOLDOWN)\n",
+    "    res, times = bench(factory(N_THREADS))\n",
+    "    entry = extract_result(label, res)\n",
+    "    entry[\"times\"] = times\n",
+    "    methods[label] = entry\n",
+    "\n",
+    "# ---- Print summary ----\n",
+    "ndf = N_SCAN - 3\n",
+    "print(f\"{'Method':24s}  {'time (ms)':>10s}  {'med|dA|':>10s}  {'med|dMu|':>10s}  {'med|dSig|':>10s}\")\n",
+    "print(\"-\" * 80)\n",
+    "for name, m in methods.items():\n",
+    "    par   = m[\"par\"]\n",
+    "    med_t = np.median(m[\"times\"]) * 1e3\n",
+    "    dA    = np.median(np.abs(par[:,:,0] - true_A))\n",
+    "    dMu   = np.median(np.abs(par[:,:,1] - true_mu))\n",
+    "    dSig  = np.median(np.abs(par[:,:,2] - true_sig))\n",
+    "\n",
+    "    chi2_str = \"\"\n",
+    "    if \"chi2\" in m:\n",
+    "        chi2_str = f\"  chi2/ndf={np.median(m['chi2'] / ndf):.4f}\"\n",
+    "\n",
+    "    print(f\"[{name:22s}]  {med_t:8.2f} ms  \"\n",
+    "          f\"{dA:10.3f}  {dMu:10.4f}  {dSig:10.4f}{chi2_str}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "55ecc77c-0823-408d-a811-8e4f4900f332",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Minuit2 (obj API)       n_threads= 1    456.26 ±  10.76 ms  (45.6262 ± 1.0762 μs/pixel)\n",
+      "  lmfit (LM)              n_threads= 1    752.77 ± 108.16 ms  (75.2771 ± 10.8165 μs/pixel)\n",
+      "\n",
+      "\n",
+      "  Minuit2 (obj API)       n_threads= 2    238.34 ±  26.57 ms  (23.8345 ± 2.6566 μs/pixel)\n",
+      "  lmfit (LM)              n_threads= 2    410.95 ±  84.96 ms  (41.0945 ± 8.4959 μs/pixel)\n",
+      "\n",
+      "\n",
+      "  lmfit (LM)              n_threads= 4    205.44 ±  35.26 ms  (20.5445 ± 3.5259 μs/pixel)\n",
+      "  Minuit2 (obj API)       n_threads= 4    139.22 ±  14.06 ms  (13.9224 ± 1.4060 μs/pixel)\n",
+      "\n",
+      "\n",
+      "  Minuit2 (obj API)       n_threads= 8    130.12 ±   3.27 ms  (13.0118 ± 0.3269 μs/pixel)\n",
+      "  lmfit (LM)              n_threads= 8    199.97 ±   6.88 ms  (19.9968 ± 0.6877 μs/pixel)\n",
+      "\n",
+      "\n",
+      "  Minuit2 (obj API)       n_threads=16    134.81 ±  10.13 ms  (13.4807 ± 1.0130 μs/pixel)\n",
+      "  lmfit (LM)              n_threads=16    188.32 ±   9.86 ms  (18.8322 ± 0.9860 μs/pixel)\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ===============\n",
+    "# THREAD SCALING\n",
+    "# ===============\n",
+    "thread_counts = [1, 2, 4, 8, 16]\n",
+    "\n",
+    "thread_times  = {label: [] for label, _, _, _ in METHOD_DEFS}\n",
+    "ttimes_stddev = {label: [] for label, _, _, _ in METHOD_DEFS}\n",
+    "\n",
+    "for nt in thread_counts:\n",
+    "    # shuffle method order per thread count to decorrelate thermal bias\n",
+    "    run_order = list(METHOD_DEFS)\n",
+    "    random.shuffle(run_order)\n",
+    "\n",
+    "    for label, factory, _, _ in run_order:\n",
+    "        time.sleep(COOLDOWN)\n",
+    "        _, times = bench(factory(nt))\n",
+    "\n",
+    "        med = np.median(times) * 1e3\n",
+    "        std = np.std(times) * 1e3\n",
+    "        thread_times[label].append(med)\n",
+    "        ttimes_stddev[label].append(std)\n",
+    "\n",
+    "        per_px     = med / (ROWS * COLS) * 1e3\n",
+    "        per_px_std = std / (ROWS * COLS) * 1e3\n",
+    "        print(f\"  {label:22s}  n_threads={nt:2d}  \"\n",
+    "              f\"{med:8.2f} ± {std:6.2f} ms  \"\n",
+    "              f\"({per_px:.4f} ± {per_px_std:.4f} μs/pixel)\")\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d5f3152d-be84-420e-8045-9c42ac5c24cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# =============================\n",
+    "# FIGURE 1: Residual histograms\n",
+    "# =============================\n",
+    "param_names  = [\"A\", \"μ\", \"σ\"]\n",
+    "param_truths = [true_A, true_mu, true_sig]\n",
+    "\n",
+    "fig1, axes1 = plt.subplots(1, 3, figsize=(15, 5))\n",
+    "fig1.suptitle(f\"Parameter Residuals — {ROWS}×{COLS} pixels, {N_SCAN} scan points\",\n",
+    "              fontsize=14, fontweight=\"bold\")\n",
+    "\n",
+    "for col, (pname, truth) in enumerate(zip(param_names, param_truths)):\n",
+    "    ax = axes1[col]\n",
+    "\n",
+    "    # collect residuals across all methods for shared bin edges\n",
+    "    res_by_method = {}\n",
+    "    all_res = []\n",
+    "    for mname, m in methods.items():\n",
+    "        residual = (m[\"par\"][:, :, col] - truth).ravel()\n",
+    "        res_by_method[mname] = residual\n",
+    "        all_res.append(residual)\n",
+    "    all_res = np.concatenate(all_res)\n",
+    "\n",
+    "    lo, hi = np.percentile(all_res, [0.5, 99.5])\n",
+    "    edges  = np.linspace(lo, hi, 101)\n",
+    "\n",
+    "    for mname, r in res_by_method.items():\n",
+    "        ax.hist(r, bins=edges, histtype=\"step\", label=mname,\n",
+    "                color=colors[mname],\n",
+    "                linewidth=styles[mname][\"linewidth\"],\n",
+    "                linestyle=styles[mname][\"linestyle\"])\n",
+    "\n",
+    "    ax.axvline(0, color=\"k\", linestyle=\"--\", linewidth=1, alpha=0.7)\n",
+    "    ax.set_xlabel(f\"Fitted {pname} − True {pname}\")\n",
+    "    ax.set_ylabel(\"Pixel count\")\n",
+    "    ax.set_title(f\"Δ{pname}\")\n",
+    "    ax.legend(fontsize=8)\n",
+    "    ax.grid(alpha=0.3)\n",
+    "\n",
+    "fig1.tight_layout()\n",
+    "# fig1.savefig(\"fig1_residual_histograms.png\", dpi=150, bbox_inches=\"tight\")\n",
+    "# print(\"\\nSaved fig1_residual_histograms.png\")\n",
+    "\n",
+    "# ====================================================\n",
+    "# FIGURE 2: Performance — bar chart + thread scaling\n",
+    "# ====================================================\n",
+    "fig2 = plt.figure(figsize=(14, 5))\n",
+    "gs   = GridSpec(1, 2, figure=fig2, width_ratios=[1, 1.3])\n",
+    "\n",
+    "# -- Left: bar chart at N_THREADS --\n",
+    "ax2a    = fig2.add_subplot(gs[0])\n",
+    "names   = list(methods.keys())\n",
+    "medians = [np.median(methods[n][\"times\"]) * 1e3 for n in names]\n",
+    "bars    = ax2a.barh(names, medians,\n",
+    "                    color=[colors[n] for n in names],\n",
+    "                    edgecolor=\"white\", height=0.5)\n",
+    "ax2a.set_xlabel(\"Median wall time (ms)\")\n",
+    "ax2a.set_title(f\"Single call — {ROWS}×{COLS} px, {N_THREADS} threads\")\n",
+    "for bar, val in zip(bars, medians):\n",
+    "    ax2a.text(bar.get_width() + max(medians) * 0.02,\n",
+    "              bar.get_y() + bar.get_height() / 2,\n",
+    "              f\"{val:.1f} ms\", va=\"center\", fontsize=10)\n",
+    "ax2a.grid(axis=\"x\", alpha=0.3)\n",
+    "ax2a.set_xlim(0, max(medians) * 1.25)\n",
+    "\n",
+    "# -- Right: thread scaling with error bars --\n",
+    "ax2b = fig2.add_subplot(gs[1])\n",
+    "for label, _, _, _ in METHOD_DEFS:\n",
+    "    tt = thread_times[label]\n",
+    "    sd = ttimes_stddev[label]\n",
+    "    speedup     = [tt[0] / t for t in tt]\n",
+    "    # propagate uncertainty: S = t0/t  →  δS/S = sqrt((δt0/t0)² + (δt/t)²)\n",
+    "    speedup_err = [\n",
+    "        s * np.sqrt((sd[0] / tt[0])**2 + (sd[i] / tt[i])**2)\n",
+    "        for i, s in enumerate(speedup)\n",
+    "    ]\n",
+    "    ax2b.errorbar(thread_counts, speedup, yerr=speedup_err,\n",
+    "                  fmt=\"o-\", label=label, color=colors[label],\n",
+    "                  linewidth=2, markersize=7, capsize=4)\n",
+    "\n",
+    "ax2b.plot(thread_counts, thread_counts, \"k--\", alpha=0.4, label=\"Ideal linear\")\n",
+    "ax2b.set_xlabel(\"Number of threads\")\n",
+    "ax2b.set_ylabel(\"Speedup vs 1 thread\")\n",
+    "ax2b.set_title(\"Thread scaling\")\n",
+    "ax2b.set_xticks(thread_counts)\n",
+    "ax2b.legend(fontsize=9)\n",
+    "ax2b.grid(alpha=0.3)\n",
+    "\n",
+    "fig2.tight_layout()\n",
+    "# fig2.savefig(\"fig2_performance.png\", dpi=150, bbox_inches=\"tight\")\n",
+    "# print(\"Saved fig2_performance.png\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92a8c724-e139-45d4-a354-1b8408637ead",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/python/tests/test_Minuit2_polynomials_1d.ipynb b/python/tests/test_Minuit2_polynomials_1d.ipynb
new file mode 100644
index 0000000..a1e5dc8
--- /dev/null
+++ b/python/tests/test_Minuit2_polynomials_1d.ipynb
@@ -0,0 +1,174 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "efef8e20-6571-4561-8f0b-6048b57907de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import sys\n",
+    "sys.path.insert(0, '/home/ferjao_k/sw/aare/build')\n",
+    "from aare import Pol1, Pol2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8449f9e9-59ee-4194-b477-d014b8efc93b",
+   "metadata": {},
+   "source": [
+    "## Ground truth\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "35772e2c-37c6-4986-a9c0-7dca4a034827",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.linspace(0, 10, 50)\n",
+    " \n",
+    "# Pol1: y = 3.0 + 1.5*x\n",
+    "true_pol1 = [3.0, 1.5]\n",
+    "y_pol1 = true_pol1[0] + true_pol1[1] * x\n",
+    "y_pol1_noisy = y_pol1 + np.random.default_rng(42).normal(0, 0.5, x.size)\n",
+    " \n",
+    "# Pol2: y = 2.0 - 0.5*x + 0.3*x^2\n",
+    "true_pol2 = [2.0, -0.5, 0.3]\n",
+    "y_pol2 = true_pol2[0] + true_pol2[1] * x + true_pol2[2] * x**2\n",
+    "y_pol2_noisy = y_pol2 + np.random.default_rng(7).normal(0, 1.0, x.size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a318ab93-fe18-4798-9ddb-6ffc2c2e3875",
+   "metadata": {},
+   "source": [
+    "## Fit with error estimation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "06c8fddb-56d9-4f84-8b21-4ffd8b7bd26f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== Pol1 ===\n",
+      "  True:   p0=3.0000  p1=1.5000\n",
+      "  Fitted: p0=2.9325  p1=1.5226\n",
+      "  Chi2:   [7.00870822]\n",
+      "\n",
+      "=== Pol2 ===\n",
+      "  True:   p0=2.0000  p1=-0.5000  p2=0.3000\n",
+      "  Fitted: p0=2.1391  p1=-0.8442  p2=0.3383\n",
+      "  Chi2:   [34.11224393]\n"
+     ]
+    }
+   ],
+   "source": [
+    "m1 = Pol1()\n",
+    "m2 = Pol2()\n",
+    " \n",
+    "res1 = m1.fit(x, y_pol1_noisy)\n",
+    "res2 = m2.fit(x, y_pol2_noisy)\n",
+    "\n",
+    "p1 = res1['par']\n",
+    "print(\"=== Pol1 ===\")\n",
+    "print(f\"  True:   p0={true_pol1[0]:.4f}  p1={true_pol1[1]:.4f}\")\n",
+    "print(f\"  Fitted: p0={p1[0]:.4f}  p1={p1[1]:.4f}\")\n",
+    "print(f\"  Chi2:   {res1['chi2']}\")\n",
+    "print()\n",
+    "\n",
+    "p2 = res2['par']\n",
+    "print(\"=== Pol2 ===\")\n",
+    "print(f\"  True:   p0={true_pol2[0]:.4f}  p1={true_pol2[1]:.4f}  p2={true_pol2[2]:.4f}\")\n",
+    "print(f\"  Fitted: p0={p2[0]:.4f}  p1={p2[1]:.4f}  p2={p2[2]:.4f}\")\n",
+    "print(f\"  Chi2:   {res2['chi2']}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e185144-3b72-41f4-80d0-3c3504e48bba",
+   "metadata": {},
+   "source": [
+    "## Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9a0b3292-7c57-4483-aecc-7ef683ff0b62",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n",
+    " \n",
+    "# Pol1\n",
+    "ax1.plot(x, y_pol1_noisy, 'o', ms=4, alpha=0.6, label='data')\n",
+    "ax1.plot(x, y_pol1, 'k--', label='truth')\n",
+    "ax1.plot(x, m1(x, res1['par']), 'r-', lw=2, label='fit')\n",
+    "ax1.set_title('Pol1')\n",
+    "ax1.legend()\n",
+    "ax1.grid(alpha=0.3)\n",
+    " \n",
+    "# Pol2\n",
+    "ax2.plot(x, y_pol2_noisy, 'o', ms=4, alpha=0.6, label='data')\n",
+    "ax2.plot(x, y_pol2, 'k--', label='truth')\n",
+    "ax2.plot(x, m2(x, res2['par']), 'r-', lw=2, label='fit')\n",
+    "ax2.set_title('Pol2')\n",
+    "ax2.legend()\n",
+    "ax2.grid(alpha=0.3)\n",
+    " \n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e694c18a-a203-4132-a472-c93c4d3ce853",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/python/tests/test_Minuit2_scurve_1d.ipynb b/python/tests/test_Minuit2_scurve_1d.ipynb
new file mode 100644
index 0000000..13113b7
--- /dev/null
+++ b/python/tests/test_Minuit2_scurve_1d.ipynb
@@ -0,0 +1,386 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "efef8e20-6571-4561-8f0b-6048b57907de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import random\n",
+    "import numpy as np\n",
+    "np.set_printoptions(suppress=True, precision=6)\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.special import erf\n",
+    "\n",
+    "import sys\n",
+    "# sys.path.insert(0, '/home/ferjao_k/sw/aare/build')\n",
+    "sys.path.insert(0, '/home/kferjaoui/sw/aare/build')\n",
+    "\n",
+    "from aare import fit_scurve, fit_scurve2          # lmfit\n",
+    "from aare import RisingScurve, FallingScurve, fit # minuit2 (object based API)\n",
+    "\n",
+    "from pprint import pprint"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8449f9e9-59ee-4194-b477-d014b8efc93b",
+   "metadata": {},
+   "source": [
+    "## Model curves"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "35772e2c-37c6-4986-a9c0-7dca4a034827",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def scurve(x, p): # rising Scurve\n",
+    "    p0, p1, p2, p3, p4, p5 = p\n",
+    "    return (p0 + p1*x) + 0.5 *(1 + erf((x-p2) / (np.sqrt(2)*p3))) * (p4 + p5*(x-p2))\n",
+    "\n",
+    "def scurve2(x, p): #falling Scurve\n",
+    "    p0, p1, p2, p3, p4, p5 = p\n",
+    "    return (p0 + p1*x) + 0.5 *(1 - erf((x-p2) / (np.sqrt(2)*p3))) * (p4 + p5*(x-p2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a318ab93-fe18-4798-9ddb-6ffc2c2e3875",
+   "metadata": {},
+   "source": [
+    "## Generate data (1D)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "06c8fddb-56d9-4f84-8b21-4ffd8b7bd26f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.linspace(0,120, 121)\n",
+    "\n",
+    "rng = np.random.default_rng(42)\n",
+    "0\n",
+    "p_true_rising = np.array([100.0, 0.25, 60.0, 6.0, 120.0,  1.0])\n",
+    "p_true_falling = np.array([100.0, 0.25, 60.0, 6.0, 120.0, -1.0])\n",
+    "\n",
+    "y_true_rising = scurve(x, p_true_rising)\n",
+    "y_true_falling = scurve2(x, p_true_falling)\n",
+    "\n",
+    "noise_sigma = 4\n",
+    "\n",
+    "y_rising = y_true_rising + rng.normal(0, noise_sigma, size=x.shape)\n",
+    "# y_err_r = np.full_like(x, noise_sigma)\n",
+    "\n",
+    "y_falling = y_true_falling + rng.normal(0, noise_sigma, size=x.shape)\n",
+    "# y_err_f = np.full_like(x, noise_sigma)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e185144-3b72-41f4-80d0-3c3504e48bba",
+   "metadata": {},
+   "source": [
+    "## Plot synthetic data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9a0b3292-7c57-4483-aecc-7ef683ff0b62",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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A5lOmamL58uV46KGHMGrUKLz00kvQaDT44IMP4O7ujry8vCqfW1hYiPbt2+Oxxx7DiBEjIJfLUVxcjIMHD+KTTz5BRESE1V5hvSFDhuCpp57C22+/jezsbIwbNw4ymQxnz56Fq6sr5s2bB0A3be7111/HG2+8gaFDhyIhIQGffvopvLy8arW/3t7eeOSRR7B+/XoUFBRg0aJFZuc1n3/+OcaMGYNRo0ZhxowZaNu2LfLy8pCYmIgzZ87gu+++q9VrEtkaA22iOpg3bx4+/fRTLF++HE888QSkUqnZOkOGDMH69evx7bffIj8/H/7+/rjvvvvw1VdfGYZd14ebmxv279+P+fPn4+WXX4YgCBg5ciQ+++wzjB07ttphbfPmzcM333yD//3vf7h16xZKSkrQpk0bDBw4EF999RWio6OrbcOoUaNw+PBhLF++HH/961+hVCoRGhqKCRMmGNYJDw/H9u3b8eqrr2Ly5MkIDg7GggULcPv2bbz55pu12mf9CUJaWhqWLFlidtB1c3PDkSNH8O677+KLL75AUlISXFxc0K5dO4wYMYI92kREVGOffPIJHB0dsXLlShQXF6NPnz748ccf8dprrzV6W0aPHo0ffvgBb7zxBqZOnYqgoCC8+OKLyMjIwMaNG6t8rqenJ958803s27cPr776KrKzsyEIAsLDwzF//ny88sorcHV1rbYN+vJaa9euxfr16+Hi4oLIyEi8+uqrhnX+/ve/Q6FQYP369fjnP/+Je++9F99++y0efvjhWu/zs88+i6+//hoALJbqGjZsGE6ePIkVK1Zg/vz5yM/Ph5+fHyIjIzFlypRavx6RrQmiPs0gEbUI77zzDl577TWkpqba5ao7ERERNTy1Wo1evXqhbdu2+O233+zdHCKqhD3aRM3Yp59+CgDo2rUr1Go19u/fj3//+9+YPn06g2wiIqIWZObMmXjwwQcN09H++9//IjExEZ988om9m0ZEFjDQJmrGXF1d8dFHHyE5ORkqlQrt2rXDK6+8YpdhbURERNRwioqKsGjRIty+fRuOjo7o06cPdu7caZavhIiaBg4dJyIiIiIiIrIhlvciIiIiIiIisiEG2kREREREREQ2xECbiIiIiIiIyIaaZTI0rVaLjIwMeHh4QBAEezeHiIgIoiiiqKgIISEhZjXeqW54vCcioqakNsf6ZhloZ2RkQC6X27sZREREZtLS0lhez0Z4vCcioqaoJsf6Zhloe3h4ANDtoKenp51bQ0REBCgUCsjlcsMxiuqPx3siImpKanOsb5aBtn74mKenJw+8RETUpHCIs+3weE9ERE1RTY71nERGREREREREZEMMtImIiIiIiIhsiIE2ERERERERkQ01yznaNaXRaKBWq+3djBbB0dERUqnU3s0gIiIiIqJKGPfYjpOTk03KdLbIQFsURWRlZaGgoMDeTWlRvL29ERQUxEQ/RERERERNAOMe25NIJAgPD4eTk1O9ttMiA239hy0gIACurq4MDOtJFEWUlpYiJycHABAcHGznFhEREREREeMe29JqtcjIyEBmZibatWtXr/ezxQXaGo3G8GHz8/Ozd3NaDBcXFwBATk4OAgICOIyciIiIiMiOGPc0jDZt2iAjIwMVFRVwdHSs83ZaXDI0/dwEV1dXO7ek5dG/p5z/QURERERkX4x7GoZ+yLhGo6nXdlpcj7Yeh03YHt9TImpulGoNYo+nIC2vFHJfV0yPDoOzI0fkkA4/H0TUEvAc3bZs9X622ECbiIhaN6Vag0mrjyEhUwGpIEAjith69hZ+eGEQgyni54OIiBpUixs6TkREBACxx1OQkKmAKAIVWhGiCCRkKhB7PMXeTaMmgJ8PIiJqSAy0m5CYmBjMnz/f3s0gImoR0vJKIa00/EsqCEjLK7VTi6gpqc3nQ6nWYM2Rm1i6PR5rjtyEUl2/eXtERK1da4h7OHS8GRFFERqNBg4O/GcjIqqO3NcVGlE0WaYRRch9mTSGav754BBzIqLG1xLiHvZoNxEzZszAoUOH8Mknn0AQBAiCgPXr10MQBOzevRv9+vWDTCbDkSNHMGPGDEycONHk+fPnz0dMTIzhviiKeP/999GhQwe4uLigZ8+e+P777xt3p4iI7Gh6dBgigz0hCICDRIAgAJHBnpgeHWbvplETUPnzAQABHjIk3Skx6bXmEHMiIttqLXFP871EUAuiKKLMTsO8XBylNcpc98knn+Dq1auIiorC8uXLAQCXLl0CALz88sv45z//iQ4dOsDb27tGr/vaa6/hxx9/xOrVq9GpUyccPnwY06dPR5s2bTB06NA67w8RUXPh7CjFDy8MqjartFKtYc9kC2ecXTzIyxmCICCzoAzjegRjfM8QpOWWYN/lHGQXqfDNqTSTXmv9EPMKo95vTkEgoqaoOcQ8QOuJe1pFoF2m1iDyjd12ee2E5aPg6lT92+zl5QUnJye4uroiKCgIAHD58mUAwPLly/Hggw/W+DVLSkrw4YcfYv/+/Rg4cCAAoEOHDjh69Cg+//xzBtpE1Go4O0oxa0gHs+WiKOKPG7n4eN81BHjI8OmTfezQOmoMxkO/JQA0d+NlB4luGHhksCfG9wxBdpFK12t9N6DW91pzCgIRNRfNIeYBWk/c0yoC7eauX79+tVo/ISEBSqXS7ENaXl6O3r1727JpRETNSll5Bd76JQF7E3OQU6QCAMgcJMgtVsHPXWbn1lFDiD2egksZCgCAcT9PhfbPgNrFUWq113rx2AhsPXvLZI42pyAQETWMlhT3tIpA28VRioTlo+z22vXl5uZmcl8ikUCsdHVdrVYb/tZqtQCAHTt2oG3btibryWQ8kSSi1kcURexNzMZfvz5nMqzO180RP74wmEF2C6Yb+v1nT3Zl+szjlXutK7QiMgrKsHJnomGIeWZBmdUpCERE9tbcYx6gZcU9rSLQFgShxkMZ7MnJyQkaTfXzKtq0aYP4+HiTZefOnYOjoyMAIDIyEjKZDKmpqRwmTkStgrU5uKE+Lgjzc8NnB2/gXFqB2fPyStRY9N15jI4KYvDUQumGflt/vEIr4oGubVCm1hh6rSu0IpwdJdh7OcekF7spZxo3/g7wYgBR69RcYh6gdcQ9zeNfopVo3749Tpw4geTkZLi7uxuu0FT2wAMP4IMPPsBXX32FgQMHIjY2FvHx8YbhER4eHli0aBH+9re/QavV4r777oNCocCxY8fg7u6OZ555pjF3i4ioQVmbgysRAK1RgCWVCNBqRVSOuc6k5iMuNZ8lm1qo6dFhhqHfgghYOrJ+cyodj/Rpi+ERASgsVSOjoAx7L+dYnLNtac6/vbEEGRE1N60h7mF5ryZk0aJFkEqliIyMRJs2bZCammpxvVGjRuH111/Hyy+/jP79+6OoqAhPP/20yTpvvfUW3njjDaxcuRIREREYNWoUfv75Z4SHhzfGrhARNRrj8kvGPZfGQXZ0uC/mPXAPYCEhqlYESza1YPrs80vGRuCpgWF4ZXQXvDK6C8ZEBaKX3AuuTlKk5JXi473X8On+60jJK0W5Rmt2gtSUM42zBBkRNTetIe4RxMqD3psBhUIBLy8vFBYWwtPT0+QxpVKJpKQkhIeHw9nZ2U4tbJn43hJRU7R0ezxij6dUMQdX16u5eGyEodcPIsx6th0kAqYNaIc3H46qUzuqOjY1JytXrsSPP/6Iy5cvw8XFBYMGDcJ7772HLl26GNaxVsLl/fffx9///ncAQExMDA4dOmTy+NSpU7Fly5Yat6Ux3tMSVQV+jc/Ct6fScDI5z+p6ggAsGRvRJHu0l26Px6YTqYYEb0D9P89E1PTx3LxhVPW+1ua4xKHjRETUbFSeh/rkve1QrKqocg6uFrp5usZ1tXfFZyEuJd8k2GbJJp1Dhw5hzpw56N+/PyoqKrBkyRKMHDkSCQkJhiQ1mZmZJs/59ddfMXPmTEyaNMlk+ezZsw01UgHAxcWl4XegltxkDpjcNxST+4Yi6U4Jvo9Lw3en0w1Z6fWCPJ0xvmeInVppmf77cClDAY3W9EvAzzMRkX0x0CYiombB0lzsD3Zfgariz3ldAv7sqTauk6wvxaSvqz09OsxsTitLNuns2rXL5P66desQEBCAuLg43H///QBgqHuqt337dgwbNgwdOpj29hrXSG0Owv3d8PdRXfG3EZ2x/3IOPtpzFVeyi6AVgcxCJYa8fwCjuwVhSj85BnX0g0RiuWe/MVT+PlT1uSciosZXq0B79erVWL16NZKTkwEA3bp1wxtvvIExY8YA0JVPefPNN/HFF18gPz8fAwYMwH/+8x9069bNsA2VSoVFixbh66+/RllZGYYPH47PPvsMoaGhttsrIiJqcSzVQ1ZVaCEVgAEd/NC/vS8KSstNso5by75s3LvNLM1VKywsBAD4+vpafDw7Oxs7duzAhg0bzB7btGkTYmNjERgYiDFjxmDp0qXw8PBo0PbagoNUgpHdgjCyWxDySsqx7ewtfHs6DZezivDT+Qz8dD4Dbb1dDD3htek5tlV2cJPcBEbLe8m9mUWfiKgJqFWgHRoainfffRf33HMPAGDDhg14+OGHcfbsWXTr1g3vv/8+PvzwQ6xfvx6dO3fG22+/jQcffBBXrlwxHFjnz5+Pn3/+GVu2bIGfnx8WLlyIcePGIS4uDlIpDwhERGROoxVx6EqOxce0IvDHzVwUlqlrlWVZ37tN1omiiAULFuC+++5DVJTlub4bNmyAh4cHHn30UZPl06ZNQ3h4OIKCghAfH4/Fixfj/Pnz2LNnj9XXU6lUUKn+HLKtUChssyP14OvmhOfuC8ezg9sj/pYC355Ow7Zzt3CroAyf7LuGT/Zdw6COfpjST47RUUFVfv5smR1cVx9cMGRFB3S92d1CPPm5JiJqAmoVaI8fP97k/ooVK7B69WocP34ckZGR+Pjjj7FkyRLDwXbDhg0IDAzE5s2b8fzzz6OwsBBr167Fxo0bMWLECABAbGws5HI59u7di1Gj7FNgnYiI7MtaL1+FRoufzmfg0/3XcfNOicXninf/05TLLzVXc+fOxYULF3D06FGr63z55ZeYNm2aWcKY2bNnG/6OiopCp06d0K9fP5w5cwZ9+vSxuK2VK1fizTfftE3jbUwQBHQP9UL3UC8seSgCuy9l4bvT6Th6/Q6O3cjFsRu58NjugAk9QzClnxw9Qr3MksaZZAevZ9kwXX1wzssmImqq6jxHW6PR4LvvvkNJSQkGDhyIpKQkZGVlYeTIkYZ1ZDIZhg4dimPHjuH5559HXFwc1Gq1yTohISGIiorCsWPHGGgTEbUi+uA66XYx9l3OQXaRytDL9+OZdEyPDsMXh28iOVdXUsnLxREyBwlyilQmc7H1mnL5peZo3rx5+Omnn3D48GGr07uOHDmCK1eu4Jtvvql2e3369IGjoyOuXbtmNdBevHgxFixYYLivUCggl8vrtgMNyNlRiod7tcWobkH4dP81HLl2B8m5pSgsU2PTiVRsOpGKLoEeeKxfKB7p3RZ+7jIAlnuh6/q5Na4PzjwDRERNT60D7YsXL2LgwIFQKpVwd3fH1q1bERkZiWPHjgEAAgMDTdYPDAxESoqujmNWVhacnJzg4+Njtk5WVpbV12yKQ8mIiKh61nqqjYfQCviz5vWfvXxFeHVrPADAx9URs4Z0wDOD2sNBIjBreAMTRRHz5s3D1q1bcfDgwSrrkK5duxZ9+/ZFz549q93upUuXoFarERwcbHUdmUwGmUxWp3Y3tsrDwCu0IsL8XNG9rRf2JGTjSnYR3t6RiBU7E9El0APzR3RCW28Xm/VCM88AEVHTVutAu0uXLjh37hwKCgrwww8/4JlnnjGpk1l5mJQoilbrbdZ0naY8lIyIiCyraj6q8RBaa5W5pBIBQzu3wQeTexh6BAEwa3gDmzNnDjZv3ozt27fDw8PDcCHcy8vLpDyXQqHAd999h3/9619m27hx4wY2bdqEsWPHwt/fHwkJCVi4cCF69+6NwYMHN9q+NCRLw8BT80rxVHQY3hgXiYf/8zsyC5UQReByVhH+L/YM2rg7wc/NCXeKy+ucHdxWydSIiKhh1TrQdnJyMiRD69evH06dOoVPPvkEr7zyCgBdr7Xx1eqcnBxDL3dQUBDKy8uRn59v0qudk5ODQYMGWX3N5jKUjIiI/lTVfFRLQ2gr02pFHLiSg6e/PGmWLIq9eQ1n9erVAICYmBiT5evWrcOMGTMM97ds2QJRFPHEE0+YbcPJyQn79u3DJ598guLiYsjlcjz00ENYunRps098qg90v49LN5vCoB8G/tP5DGQplGbPvV1cbvjb180J94b74s0J3Wr8ubVlMjUiImpY9a6jLYoiVCqVIbPonj170Lt3bwBAeXk5Dh06hPfeew8A0LdvXzg6OmLPnj2YMmUKACAzMxPx8fF4//33rb5GcxpKZk/Lli3Dtm3bcO7cOXs3hYioyvmowV7OqNBaD7KB6pOcMWt4wxCruPhh7C9/+Qv+8pe/WHxMLpebjHZrKaxNedCr0Iq4lKHApQyFhc8+MKRTG0gkAg5eyUFOkQq/XMjE7ktZCPN1xeB7/PGPMV3h4mR+aqYP7nfFZyEhQwER9U+mRkTUnDWHuKdWgfarr76KMWPGQC6Xo6ioCFu2bMHBgwexa9cuCIKA+fPn45133kGnTp3QqVMnvPPOO3B1dcWTTz4JQDfsbObMmVi4cCH8/Pzg6+uLRYsWoXv37oYs5FR3ixYtwrx58+zdDCIiAJazIldoRWQplNh1yTwvR4CHE7xdnXA9p9gkgBEAfB+XDgDstSa7sjblwbhn+1xagcWLSFoA93Xyx6whHZCtUOKbU2n4dP91lGu0uH67BNdvl+Drk2mY+8A9eLy/HAGeuizuxsE9LEy1YBJAImqNmkPcU6tAOzs7G0899RQyMzPh5eWFHj16YNeuXXjwwQcBAC+//DLKysrw4osvIj8/HwMGDMBvv/1mqKENAB999BEcHBwwZcoUlJWVYfjw4Vi/fn2zH0rWFLi7u8Pd3d3ezSAiAmCaFVkCQCPq6vzuvpQNAAj0lKGX3Btt3GVo7++G6dFhiD2eghU7E022oxWBq9lFWLEzkcNkya4sjdKQCIC/uwy3i1S6nmajIFuALtdA5bnYgZ7OcHWSolyjNdl+uUaLD/dcxSf7riGmcxs81k+OlNwSQ3BvCZMAElFr1BziHkltVl67di2Sk5OhUqmQk5ODvXv3GoJsQJcIbdmyZcjMzIRSqcShQ4cQFRVlsg1nZ2esWrUKubm5KC0txc8//8z51nfFxMTgr3/9K15++WX4+voiKCgIy5YtMzyempqKhx9+GO7u7vD09MSUKVOQnZ1teHzZsmXo1auX4f7Bgwdx7733ws3NDd7e3hg8eDBSUlKQnJwMiUSC06dPm7z+qlWrEBYWVuNhg0REVXF2lCJ25gCM6BoAJwddYFyhFRHi5Yy3Jkbh8MvD8PlT/fD2I90xa0gHODtKMT06DJHBnhAEXQCjpxUB0WgYOZE9WBqlIQJo5+sKqcQ0qatUAPqG+WDagHZYMjbC7AJRWl4pHCo9RyIAbTxk0GhF7Lucg/+LjcNHe65azBgoEQBBAJMAElGz1BrinnrP0W7J7JHZc8OGDViwYAFOnDiBP/74AzNmzMDgwYMxYsQITJw4EW5ubjh06BAqKirw4osvYurUqTh48KDZdioqKjBx4kTMnj0bX3/9NcrLy3Hy5EkIgoCwsDCMGDEC69atQ79+/QzP0Se6qS5LPBFRdUrLK7DpeCo+P3wTd4p15RnbertgzrB7MKlvW8gcLP+WGic5+z4uHVezi0yGkXOYLNmTtdrVIyIDEZeab7KuFsDoqCCrc6etBe3P398Bw7oG4LvT6fjhTDpuF6ksPr9POx+Mjgqyem7C7OREVFP2+r1o6XEPA20r7JXZs0ePHli6dCkAoFOnTvj000+xb98+AMCFCxeQlJRkGAGwceNGdOvWDadOnUL//v1NtqNQKFBYWIhx48ahY8eOAICIiAjD47NmzcL//d//4cMPP4RMJsP58+dx7tw5/Pjjjw22b0TU/NT24FuiqsC635Pw2cEbKC3XANAF2PMeuAeP9gmFk0P1A6mMk5xVHkbOYbJkT9ay3QPAz+czrJabs/Q9sha0679j/xjTFYtGdsaexGy8/P0FFCkrDO0QAAR7OSMy2BNOUvPvFLOTE1FN2fP3oqXHPQy0raiqLE1DZvbs0aOHyf3g4GDk5OQgMTERcrncZJh9ZGQkvL29kZiYaPaB8/X1xYwZMzBq1Cg8+OCDGDFiBKZMmWIovTZx4kTMnTsXW7duxeOPP44vv/wSw4YNQ/v27Rts34io6bIUCACo8cG3WFWBDceS8b8jN1FQqjZ5zMvFARN7t61RkG2sqkCEyF6sZbu3Vm6uqpPY6krUOUglGBMVjGFdAvD5oRs4cu0OUnJLcbtYhZ8vZOLnC5kI9XHBY33lmNwvFG29dXXO7XUOQ0TNjz1/L1p63MNA24qqytI0JEdHR5P7giBAq9VCFEWLQxusLQd0QyL++te/YteuXfjmm2/w2muvYc+ePYiOjoaTkxOeeuoprFu3Do8++ig2b96Mjz/+uCF2iYiaOGuBwPieIVUefJVqDdYeuYm9iTm4nFWEMrXG4vYTs4rqdMBmrWxqTqwF4NWdxNbke+HsKMVLIzrjpRGdIYoizqcX4rvTafjpXAbS88vw0d6r+HjfVdx3jz8e6ydH8p0Su5zDEFHzY6+YB2j5cQ8DbSsszZ2y55DFyMhIpKamIi0tzXB1JyEhAYWFhSZDIyrr3bs3evfujcWLF2PgwIHYvHkzoqOjAeiGUURFReGzzz6DWq3Go48+2ij7QkRNi7VAwMVRavXgm6NQ4qFVR03mjzpJJegb5oOTSbnQWJlXXduh6KyVTc1dXU9irX1XBEFAL7k3esm98dpDkdh1KRPfnU7HsRu5OHLtDo5cuwNnB4lZiTFOuyAiS5pazAO0nLiHgbYVTW3I4ogRI9CjRw9MmzYNH3/8sSEpwNChQ00m9uslJSXhiy++wIQJExASEoIrV67g6tWrePrppw3rREREIDo6Gq+88gqee+45uLi4NOYuEVETYS0QAGCxDvbNOyUY8v4BqCrMSxOl5Zeicglh/QGb80apNarpSaxxYB3k5Yyfz2cgMauoyu+Ki5MUj/QOxSO9Q5GWV4rv4tLx/ek0ZBQqzdrROdDd7ByGCdOIqKnFPEDLiXsYaFvR1IYsCoKAbdu2Yd68ebj//vshkUgwevRorFq1yuL6rq6uuHz5MjZs2IDc3FwEBwdj7ty5eP75503WmzlzJo4dO4bnnnuuMXaDiJoga4HA8IgAlKk1JnWwJQJw5Nodq9u6lV9mqETkUKl+MOeNUmtUk5PYyhehjHuja/pdkfu6YsGDnfHS8E74/fodbDmVit2XsqG5u62bt0uw4NtzeKyfHPd3agO1RlvvC18M1Imav6YW8wAtJ+4RxGZYNFmhUMDLywuFhYXw9PQ0eUypVCIpKQnh4eFwdna2UwubjxUrVmDLli24ePFitevyvSVqmSz1NEcGe+KHFwYhv7Qci749j5PJeVDfHQ/eJdAD3UI88ePZW1Vut1+YafmhpdvjselEqkkQ4SARMG1AO7z5cFSD7mNjqOrYRHXTUt7T6gLSNUduYsXORFR1RlaX70pBaTl+Op+Bb0+nIf6WwrA8yNMZHdu44fcbuSbrCwKwZGxEjS58VfW7wWCbqHHw3Lx2ahr3VPW+1ua4xB7tVqq4uBiJiYlYtWoV3nrrLXs3h4jsyNLV7LHdg/Hx3mvY+EcySu6W6eoa5IEXYjoiR6FE8p0SBHnKkF2kAkSgcnzgIBHQLcTT5IS9Kc4DI2oM1eUasDR9o7K6fFe8XZ3w9MD2eHpgeyRkKPBdXBq2nr2FLIUSWQrz4eW1SYDEESpE1FzYK+5hoN1KzZ07F19//TUmTpzIYeNEZAgE7hSrsPrgDQz94IChBzsy2BMvjeiEIff447HP/zAZ3hroKUNbbxecTS0wCbYtBQVNcR4YUVNg6SIUoKuXLZXY5rsSGeKJpSHd8I8xXbE3IQcf7rmCG7dLTNap0IoQhKoz++rZM1MxEVFt2CvuYaDdSq1fvx7r16+3dzOIqInIKVLif4dvIvZ4qoUyXSKGdm5jsQcrp0iFGYPaQ1WhrTaAborzwIiaAksXoboGeeDhXm2RWVBm0++KzEGKh3oEY3hEACZ8ehRXs4tNHl9/LAVHrt3BlH5yPNKnLQI8LA9HbegRKpz/TUS2Yq+4h4E2EVELVt3Jao5Cif8euolNJ1LMsojr6etgW+vByipU1jiAZrkuInMNfRHK2u/AT3PvQ+zxFKTmlkCEgCKVGrvjs3HjdglW/noZ7+++gmFdAjClXyiGdQ2Ao1Ri2GZDjlBhhQIiagkYaBMRtVBVnawWlKrxnwPX8fXJP5OT9Qz1gr+7DAev5Fisg11VDxYDaKL6aajvUHVBa+XXLHpYjR0XMrHlVBrOpRVgb2I29iZmw8/NCZP6hmJCzxAcv5mLtLxSjOsRjPE9Q2ze687530TUErTYQFurtdwzQ3XH95SoebF0snopQ4Hpa07gwq1ClFfqwVZrtOgf7ov9V3JMluuDac6xJmp+ahq0Vq7jXV6hm0IiQJfsMLekHF8cvokvDt8EoCv1pxWBbiF/ZhpXqjVYc+RmvXvlOf+bqHZ4jm5btirK1eICbScnJ0gkEmRkZKBNmzZwcnKqNqEHVU0URZSXl+P27duQSCRwcnKyd5OIqAasZTI+nZJvcf3ErCKMvxs8WwqmOceaqPmpSdBaVR1v418PZ0cJlGrdCb1+lUsZCry9IwFLxkZg8n//sMlwb1YoIKoZxj22J4oibt++DUEQ4OjoWK9ttbhAWyKRIDw8HJmZmcjIyLB3c1oUV1dXtGvXDhKJpPqVicju5L6uJifMeu18XdE1yAP7ErPNhohXN9+aQ8SJmpeaBK2Wer0t0QfZlcUeT8WOC5nIL1UDqP9wb46eIaoZxj0NQxAEhIaGQiqtX0dCiwu0Ad3VnXbt2qGiogIaTeXsuVQXUqkUDg4OvEpG1Eyk5ZXiSlaR2fJ2vi54ckA77E3IRuUYnPOtiVqemgStNanjXRUnB4khyDYhAt/HpRvaUdOe7YYcPcNs5tTSMO6xPUdHx3oH2UALDbQBGLr769vlT0TUnKTkluDT/dfx49lb0NyNpMP93dAl0B095d74+XwG3tt1GRL8OSTUwUZ1eomo6alJ0FpVHW/AdPi4nkTQLY8M9sTGmfdi2U+X8NP5TJN1RACXs4rw9o7EWg8jt3bBrz6BMrOZU0vFuKdparGBNhFRa5J0Rxdgbzv3Z4A9pJM/5o/ohL5hvgCANUduIjGrCKIIGF/z7iX3xuioIPbsELVQ1Y1SqaqO996EbMSl5JsF233a+Zj8brw/uSdu3C7BpQyFxde4lKHAS1vO4r1JPeDtWrdcL/UNlJnNnIgaEwNtIqJm7MbtYny6/zq2n7tlGAoe06UN/jq8E/q08zFZ19LwUAeJgG4hnjzJJGolrPUIW+v1njGovVlwGxnsidhZA0yCW+NtfB+XjitZRWbB+e5L2ThweR9GdgvElH5yDL7HH1JJzaek1TdQZjZzImpMDLSJiJqh6zlFWLX/On4+n2EIsB/oGoC/Du+ErkEeiD2egu1nb5mcMDOTL1HrVtua2kDt5ksbb2PFzkSzMeeBnjJkK1T45UImfrmQiRAvZ0zuG4rJfeVo5+da7bDw+gbK/A0kosbEQJuIqAmrfOI5INwXnx++iR0XM6E/XxwRoQuwe4R6V3kizUy+RK1bXXuEa5sg0dpvzQ8vDML1nGJ8ezoN289lIKNQiX/vv45/77+OAeG+SM8vxa0CpSFvROVh4fUNlPkbSESNiYE2EZGdVNd7Yxw0SwCTUlwAMDIyEH8d3glRbb0My6o7kWYdbKLWq7GGTlfVCx7V1gtRbb3w6tgI7LyYiVX7ryPpTglOJOUZnq8vS3gpQ/fbNT06DLHHU5B0uxiBHjJkF6nqFCg3ZDZzIqLKGGgTEdlBTZL6xB5PMSQWMk5e1jXIA/+a0hPdQrxMtqefGynAdMSm8Yk0S3cRtV6NOXTa+LfG0kVFAFh7NAnJuSWQCuYXEvV2XsjEd6fTcDWnWHeRQCsi0FOGERGBCPd3q3WgzN9AImosDLSJiOygup7n+FuF2HAs2ex5UgEYEO5rFmTrg3YBsFofm4haN3sMnbZ2UXF8zxDDb2BVlX/PpBUY/tb/VuYUqRDu78aAmYiaNAbaRESNqKqeZwG6AHzLyVRcv11i8flawBA067e1Kz4LCRkKiJW2Z1znlnMQicgeQ6etXVR0cZSaDWPXc5Doeq6DvZxRXqFFbkm56QoiEH+rsMHaTERkCwy0iYgaSXU9z1oRSM79c66kcSCuTw6kD5qNtwXRLLkvJALQOdADk/uGcg4iERk09tBpa/PCAZgNYweAByMCEOLtYrgIEHs8BW/vSDRZRwSw7VwGbhWUYUo/OcZ2D4abjKe0RNS0SOzdACKilkyp1mDNkZtYuj0e8zafMfTsVA6yLTFepZfcG0vGRhjmcBv3ElnalAhgct9QzBrSgUE21crKlSvRv39/eHh4ICAgABMnTsSVK1dM1pkxYwYEQTC5RUdHm6yjUqkwb948+Pv7w83NDRMmTEB6enpj7go1AdbmhQ+PCEBksCcEQXchURCAbiGeWPVkH7z5cJTht2t6dBi6hejWk94tue0uc4BEAE4l5+Pv31/AvSv24h8/XMCZ1HyIFoL3mjL+vV5z5CaU6qoGtRMRVY2X/4iIGkjluYkVNYmuLXCQCOgW4olZQzoYTgQtDT0HOFyc6u/QoUOYM2cO+vfvj4qKCixZsgQjR45EQkIC3NzcDOuNHj0a69atM9x3cnIy2c78+fPx888/Y8uWLfDz88PChQsxbtw4xMXFQSrlxZ/Wwtq88GcHh+PZweHVDmO3Nty9oFSNLadSseFYMvJL1dhyKg1bTqWhc6A7pvST49E+ofB1c7LSKnM1SVBJRFQbDLSJiBqIpbmJloR4OSOjUGn1cX0ys+qGngNAn3Y+GB0VxOHiVGe7du0yub9u3ToEBAQgLi4O999/v2G5TCZDUFCQxW0UFhZi7dq12LhxI0aMGAEAiI2NhVwux969ezFq1KiG2wFqUioHykFezhAEASt3JtZ4jril4e7ersCehGzkl6oNWcsFAbiaXYy3dyTi/V1X8GC3QDzRvx0GdfSDRCJU+Rp1rTFORGQNA20iogZiaW5iZZ0C3PHd/w3EtDUnzOplV56XbW24uHEvduysAQywyaYKC3VJp3x9fU2WHzx4EAEBAfD29sbQoUOxYsUKBAQEAADi4uKgVqsxcuRIw/ohISGIiorCsWPHGGi3MvpA2Za9xvrfQ+DP30xRBMZEBSE9vwwXbxVix4VM7LiQiXa+rpjaX47H+oUiwMPZ4vaqqjFuqTwZf2eJqDoMtImI6sjayZd+efytQovDxdt6OyM63A8RIZ6G51jq8cksKDPZrqUTQSY9o4YkiiIWLFiA++67D1FRUYblY8aMwWOPPYawsDAkJSXh9ddfxwMPPIC4uDjIZDJkZWXByckJPj4+JtsLDAxEVlaW1ddTqVRQqVSG+wqFwvY7RXZjy15jS7+HDhIBAR4yrJ7eF/G3CvHNqTRsO3sLqXml+GD3FXy05ypGdgvEtAFhGNjBtJfb2lzyIC9nDiknojphoE1EVAfWemY2zRqAJ/93HAmZRRaf1ynAHT/Pu8/iPMTqTjQtnQgaJz0jsrW5c+fiwoULOHr0qMnyqVOnGv6OiopCv379EBYWhh07duDRRx+1uj1RFCEI1ofwrly5Em+++Wb9G05NUlW9xrVlLTDWlz+MauuFqLZeeHVsBHZczMTmEyk4k1qAnRezsPNiFsL93fBUdBgm9wuFp7Oj1bnkgiBwSDkR1QmzjhMR1YFJz4xWhCgClzIUeG7dSYtBdo+2XnjtoQiLQXZNTY8OM8vSy6Rn1FDmzZuHn376CQcOHEBoaGiV6wYHByMsLAzXrl0DAAQFBaG8vBz5+fkm6+Xk5CAwMNDqdhYvXozCwkLDLS0trf47Qk1GdcFxbdT099DFSYrJfUPx44uD8etLQ/BUdBjcZQ5IulOC5b8kIPqdfXht20XcKijDDy8MwpKxEZg2oJ2hykNmQZmhHJleXS8OEFHrwh5tIqI6sDb/+kxaodm6DhIBvdt517v3w1r2XQ5fJFsSRRHz5s3D1q1bcfDgQYSHh1f7nNzcXKSlpSE4OBgA0LdvXzg6OmLPnj2YMmUKACAzMxPx8fF4//33rW5HJpNBJpPZZkeoybHWa1yXi4WWfg8n9w21OAXH+O8wP1cc+nsMfo3PwoZjybiWU4zY46mIPZ6KEREBmDWkA2beF24YeWHLiwNE1Low0CYiqgO5r2uNy3XZ8qSsJkPMiepjzpw52Lx5M7Zv3w4PDw/DnGovLy+4uLiguLgYy5Ytw6RJkxAcHIzk5GS8+uqr8Pf3xyOPPGJYd+bMmVi4cCH8/Pzg6+uLRYsWoXv37oYs5NT62PpiofHvofF0HuOkkvqM5MCfCSb1c6ynDWiHP27k4svfk7Hvcjb2JuZgb2IOerfzxrwH7sGwLgE2vThARK2LIIpVpMNtohQKBby8vFBYWAhPT097N4eIWhlRFPFrfBYWfHsOSrXW5DEJAP0S46zhTJzT8rWUY5O1OdTr1q3DjBkzUFZWhokTJ+Ls2bMoKChAcHAwhg0bhrfeegtyudywvlKpxN///nds3rwZZWVlGD58OD777DOTdarTUt5TanhrjtzEip2JqMlZrSAAS8ZGmFy0vHm7GGuPJuH7uHSoKnS/4lFtPTF/eGcMvscPm06kVpmskohah9oclxhoExHVkCiK2JOQjX/vv4b4W7psyI5SAb5uTshWqMzW7xfGmtatCY9Ntsf3lGpq6fZ4bDqRWqORRg4SAdMGtMPisRFmvesKpRprjiQh9ngKSss1AIB72/ti8diuiAj2NEuCyQupRK1LbY5LHDpORFQNURTxW0I2/r3vGi5l6AJsVycpnh7YHrOHhOPf+66ZneA5SAR0C/HkMG8iokZgaS61NdWV7Xp1bAT+b2hHfHH4Jtb9noSTyXl45LNjiAz2NNTuZgZyIqoOA20iarWs1cHW02pF7LiYibd3JBh6rF2dpHhmUHvMHtIBvm5OAJgsh4jI3oznUlc3R7smZbt83ZzwjzFd8fTAMHy05yq+P5NuCLKNMQM5EVnDQJuIWiVrdbB/eGEQtFoRb/wUj32JOcgvVZs8T+7rgpeGdzIJyJksh4jIvionWrOWdVx/UXXlzsQqa3obX4jtEuSBH18YhDmbzyCjQGnyuryoSkTWMNAmolbJpA723ROtSxkKLP7xAn69mAVlhdbi865kFWPe5jMI8XYx6QVn2S0iIvuqTVWGqkYiWboQGxnsiZ/n3oeHVh1FVuGfwXZbbxdeVCUiixhoE1GLZm14uLU62FvPZlS7zT2JOWZlYlh2i4io+ahqJJKlC7EJmQpsPXsLBxfF4D8HruObU2nIKVIhPb8ME1YdxaN92mLG4HBeYCUiAwbaRNRiVTU83FodbAFATdLp6J/LRDhERM1PVSORLF2I1Q8rd3aUYuHILnj+/g6I+edB3Ckux9WcYry76wq2ncvAtjmDGWwTEQAG2kTUglnrldj4RzI6tHGHi6MEZWrLQ8QrkwiApaoxTIRDRNQ8WRuJVJMEl1tOpSG3pNxknctZRZi14RQ6tnHnFCIiYqBNRC2XpV4JQQQ+2XcdxaoKALostH7uf9bBNj610gfXgZ4yjIgIRI5Cib2Xc2B8/sVEOERELUtNElxam3509Hou/riRCy1gMrWIiFofBtpE1OLo52VfylBAU6kbWgsYgmwA6NjGDf3a++KbU2kmQ8klAtA50AOT+4YaeiWsJchhIhwiopajJgkuq6rbrS8nxqlFRK0bA20iarYsJToDYAiGJah+vvXVnGK0s3DCJAKY3DfU5ASJ2cWJiFqH6hJcVu71tpTzQwJwahFRK8ZAm4iaJWuJzsb3DDHMy9ZUeo6vqyMKy9SG3gZAN8c6wEOGyGDPGvVUM7s4ERFVvvCaUVBmYWoR4O8hs1r9gohaNgbaRNQsWUt0JnOQmGUOFwA81CMYveTeWLEz0WQ7GlFEeBt3vD6+G0+EiIioxowvvBpf/JXgz+Hjh67cxq8XM5GYVWRW/YLHGKKWjYE2ETVLFhOdQRdsm43gE4Becu8qE9ywp5qIiOqqcg+3zEGC2BOpOJ2Sb1jH+KIw524TtXwMtImoWbKUiEYrAkq1FoIAiCIgFXTJz4yDac6xJiKihlC5h/t2cTm2nr1lth7LQhK1Dgy0iahZ0c91S7pdjAB3J2QXmdYxFaALsvUlucL93UyCafZcExFRQzIeRm4Jy0IStQ6S2qy8cuVK9O/fHx4eHggICMDEiRNx5coVk3VmzJgBQRBMbtHR0SbrqFQqzJs3D/7+/nBzc8OECROQnp5e/70hohZHqdZgzZGbWLo9HqsPXsejn/2OFTsTseVUmlmQDfw5NzunSIVwfzfMGtKBPdZERNRojHOIWMKykEStQ616tA8dOoQ5c+agf//+qKiowJIlSzBy5EgkJCTAzc3NsN7o0aOxbt06w30nJyeT7cyfPx8///wztmzZAj8/PyxcuBDjxo1DXFwcpFKeEBORTuXM4sblU4wzirfzccGtgjKzbOIcmkdERI3NUg4RvdHdAvHx4715AZioFahVoL1r1y6T++vWrUNAQADi4uJw//33G5bLZDIEBQVZ3EZhYSHWrl2LjRs3YsSIEQCA2NhYyOVy7N27F6NGjartPhBRC2Ups3hlUgEI8HRGWkGZyXIOzSMiInuwlENE7/fruVCUqRloE7UCtRo6XllhYSEAwNfX12T5wYMHERAQgM6dO2P27NnIyckxPBYXFwe1Wo2RI0caloWEhCAqKgrHjh2z+DoqlQoKhcLkRkQtn75XoCpaAMMjAhAZ7AlBABwkAgSBQ/OIiMg+pkeHWTgmeSCqrSeKVBVY/kuCvZtIRI2gzsnQRFHEggULcN999yEqKsqwfMyYMXjssccQFhaGpKQkvP7663jggQcQFxcHmUyGrKwsODk5wcfHx2R7gYGByMrKsvhaK1euxJtvvlnXphJRM1VVr4CD5M/yXM8ODsezg8OZTZyIiOzOWoWL6znFmPDpUfxyIROP9buNoZ3b2LupRNSA6hxoz507FxcuXMDRo0dNlk+dOtXwd1RUFPr164ewsDDs2LEDjz76qNXtiaIIwUrP1eLFi7FgwQLDfYVCAblcXtemE1EzMT06DP87chPZCpVhWacAN0zqK0dmQZlZQM1s4kRE1BRYqnAR1dYLzw4Ox9qjSXht20Xs+dtQXhAmasHqFGjPmzcPP/30Ew4fPozQ0NAq1w0ODkZYWBiuXbsGAAgKCkJ5eTny8/NNerVzcnIwaNAgi9uQyWSQyWR1aSoRNVNarYgPdl8xBNldgzzwaJ+2eHpge56YEBFRs/RiTEd8ezoNaXllmLf5DFY92YfHNKIWqlZztEVRxNy5c/Hjjz9i//79CA8Pr/Y5ubm5SEtLQ3BwMACgb9++cHR0xJ49ewzrZGZmIj4+3mqgTUStS4VGi799ew5rjyYBABaP6YpfXxqCv9zfkSckRETULCnVGjz95UkUKSsAAHsSczDxP79DqdZU80wiao5qFWjPmTMHsbGx2Lx5Mzw8PJCVlYWsrCyUlemy/RYXF2PRokX4448/kJycjIMHD2L8+PHw9/fHI488AgDw8vLCzJkzsXDhQuzbtw9nz57F9OnT0b17d0MWciJqvbRaES//cAHbz2XAUSrgk8d74fmhHa1OLSEiImoO9JU0jF3OKkLs8RQ7tYiIGlKtho6vXr0aABATE2OyfN26dZgxYwakUikuXryIr776CgUFBQgODsawYcPwzTffwMPDw7D+Rx99BAcHB0yZMgVlZWUYPnw41q9fzxraRK2cKIp4bXs8fjxzC1KJgFVP9MHoKMulAomIiJoTa/W1b+QU26lFRNSQahVoi1ay/+q5uLhg9+7d1W7H2dkZq1atwqpVq2rz8kTUgomiiLd+ScTmE6kQBGBCz2D8ceMO0vNLmUGciIiaPWuVNNILyuzQGiJqaHXOOk5EZEtf/ZGCL3/XzckO9nLGtnMZkAq6El5bz97CDy8MYrBNRETN1vToMGw9ewsJmQpdz7ZWF3SfSc1Hfkk5fNyc7NxCIrKlWs3RJiJqCMeu38HyXxIAAMO7BiCzUAlRBCq0IkQRSMhUcA4bERE1a/r62kvGRmDagHZYMrYrIoI8UKLSYMMfyfZuHhHZGHu0iciuUnNL8eLmM9BoRTzSuy08ZFKzOWxSQUBaXqkdW0lERFR/letrB3q54K9fn0Xs8VS8ENMRMgeO3CJqKdijTUR2U6KqwKyvTqGgVI3ubb3QOdAdCZlF0GhN57BpRBFyX1c7tZKIiKhhjIkKQpCnM+4Uq7DjQqa9m0NENsRAm4js5u0dCbiaXYw2HjKoKjR4f/cVnE3Nhz7MdpAIEAQgMtgT06PD7NpWotZi5cqV6N+/Pzw8PBAQEICJEyfiypUrhsfVajVeeeUVdO/eHW5ubggJCcHTTz+NjIwMk+3ExMRAEAST2+OPP97Yu0PUpDlKJXhqoO74tu735GoTDxNR88FAm4jsYl9iNr4+mQYAGBUZiGs5xRBFQGN0jtFL7o0lYyOYCI2oER06dAhz5szB8ePHsWfPHlRUVGDkyJEoKSkBAJSWluLMmTN4/fXXcebMGfz444+4evUqJkyYYLat2bNnIzMz03D7/PPPG3t3iJq8J+5tBycHCS7eKkRcSr69m0NENsI52kTU6HKLVXjlh4sAgJn3haNCozWbl+0gEdAtxNNkLhsRNbxdu3aZ3F+3bh0CAgIQFxeH+++/H15eXtizZ4/JOqtWrcK9996L1NRUtGvXzrDc1dUVQUFBjdJuoubK180JE3uF4NvT6Vj3ezL6tfe1d5OIyAbYo01EDUqp1mDNkZtYuj0ea47cRH6JCtPWnMCdYhX83Z3w1wfusVhblPOyiZqGwsJCAICvr/WT/8LCQgiCAG9vb5PlmzZtgr+/P7p164ZFixahqKioIZtK1KwYHx+9XBwBALsuZSGDdbWJWgT2aBNRg1GqNZi0+phJzdD3dl2G+u748DvF5Rj58WHEdGmDQA8ZsotUhtrZnJdNZH+iKGLBggW47777EBUVZXEdpVKJf/zjH3jyySfh6elpWD5t2jSEh4cjKCgI8fHxWLx4Mc6fP2/WG25MpVJBpVIZ7isUCtvtDFETUvn4qBFFuDpJUVquQezxFLw8uqu9m0hE9cRAm4gaTOzxFCRkKnQ1se/2WKs1pj3X2QoVvjudDq0IBHrKMCIiEOH+bpgeHcZ52UR2NnfuXFy4cAFHjx61+Lharcbjjz8OrVaLzz77zOSx2bNnG/6OiopCp06d0K9fP5w5cwZ9+vSxuL2VK1fizTfftN0OEDVRlo6PpeUaAMAPZ9KxcGQXSCWCPZtIRPXEoeNE1GDS8kohFao/UdBX88opUiHc3w2zhnRgkE1kZ/PmzcNPP/2EAwcOIDQ01OxxtVqNKVOmICkpCXv27DHpzbakT58+cHR0xLVr16yus3jxYhQWFhpuaWlp9d4PoqbI0vFRKgBOUgmyFSr8fv2OnVpGRLbCQJuIGoyluddVkQoC0vJKG7BFRFQdURQxd+5c/Pjjj9i/fz/Cw8PN1tEH2deuXcPevXvh5+dX7XYvXboEtVqN4OBgq+vIZDJ4enqa3IhaIkvHRy2A7m11n/kfzqTboVVEZEsMtImowUyPDkNksCcEAdBft9ePhLM0Io4J0Ijsb86cOYiNjcXmzZvh4eGBrKwsZGVloaxMl6CpoqICkydPxunTp7Fp0yZoNBrDOuXl5QCAGzduYPny5Th9+jSSk5Oxc+dOPPbYY+jduzcGDx5sz90jahKMj48OEgGCAEQGexrmZu++lIUipdrOrSSi+hBEsRbdTU2EQqGAl5cXCgsLebWbqIlTqjVYuj0e35xOh0QAfnxhEE6n5CPpdjH2Xc4xS4DGmtnUXLWUY5NgZbrHunXrMGPGDCQnJ1vs5QaAAwcOICYmBmlpaZg+fTri4+NRXFwMuVyOhx56CEuXLq0ye3llLeU9JbJEqdYlPkvLK4Xc1xXTo8Mgc5Bg+IeHcPN2Ccb1CIafm5PhMR4bieyvNsclJkMjogblJJXgbFoBAGD2kA7o1c4Hvdr5AABet3CSwRMJIvuq7vp7+/btq11HLpfj0KFDtmwWUYvj7CjFrCEdDPeVag3WHk2Ch0x3ev7LhUw4SHQVO/535CaThRI1Mwy0iahB7biYiavZxfBwdsCLw+4xeazySQYREVFrZFzuC0bXsSruZgvNVqjw9clUiAC2nr3F0V9EzQDnaBNRg9FoRfx7ny7D8Kz7OsDLxdHOLSIiImp6jMt9WRsvohUBUQQSMhWIPZ7SqO0jotpjoE1EDWbHxUxcyymGp7MDnr2vvb2bQ0RE1CTVtBwmwAodRM0FA20iahAmvdlDOsDTmb3ZREREltSmHCYrdBA1Dwy0iahB/HIhA9fv9mbPGNze3s0hIiJqsiqX+wIAJwfdabqbk9SsDNj06DB7NpeIaoDJ0IjI5kpVFXjrlwQAQO92PnCS8poeERGRNc6OUvzwwiCTShxuMgcs/vEiQrxdMLW/nBU6iJoZBtpEZFNKtQajPzmCO8XlAIBDV29j0upjzJBKRERUhcqVOApL1Xh9Wzyu5RTjga4B6NDG3Y6tI6LaYjcTEdlU7PEUpFZK0sIMqURERLXj5eqIgR39AAC/xmfZuTVEVFsMtInIpi6mF5otY4ZUIiKi2hsTFQwA2MVAm6jZYaBNRDaVlm8eUDNDKhERUe2N7BYIiQBcvFXIC9ZEzQwDbSKymYLSciRmFhnuM0MqERFR3fm7y9C/vS8AYPcl9moTNSdMhkZENrPlVBrK1Bp0DfLApD5tkZ5fxgypRERE9TAmKggnkvLwa3yWSbI0ImraGGgTkU0UK9X47MB1AMA9Ae54amB7BtdERET1NDoqGMt+TkBcSj5uF6nQxkNm7yYRUQ1w6DgR1ZtSrcHYfx+BQlkBAPjlQiYmrT4GpVpj55YRERE1b0FezugW4gkAOHz1tp1bQ0Q1xUCbiOpNV9KrzGQZS3oRERHZxgNdAwAAB67k2LklRFRTDLSJqM6Uag3WHLmJLSdTzR5jSS8iIiLbiOmiC7QPX72NCo3Wzq0hoprgHG0iqhOlWoNJq48hIVMBiOaPs6QXERGRbfSSe8Pb1REFpWqcTSswZCInoqaLPdpEVCexx1OQkKmAKJrG2RIBLOlFRERkQ1KJgKGd2wAADlzm8HGi5oCBNhHVSVpeKaSCYLJMANA50ANLxkbghxcGMes4ERGRjQzrop+nzYRoRM0BA20iqhO5rys0YqUx4wIwuW8oZg3pwCCbiIjIhu7v3AaCACRmKrDo23NYc+Qmq3sQNWEMtImoTqZHh6FLoIfJMg4XJyIiahiuTlI4O+guYv949hZW7ExkKU2iJoyBNhHVibOjFDMGtQcAeDg7YMnYrhwuTkRE1EBij6eg7G5QrRUBUWQpTaKmjFnHiajOfo3PAgDMuq8DZt/f0c6tISIiarl0uVEAjdGsLZbSJGq62KNNRHWSW6zC0et3AAATeoXYuTVEREQtmy43iukyltIkaroYaBNRrSjVGqw5chMvbjoDjVZEtxBPhPu72btZRERELdr06DB0C/E0WcbcKERNFwNtIqoxpVqDSauPYcXORJxIygMA3ClWMRELERFRA3N2lOKHFwZhfM9gAECItzNzoxA1YQy0iajGYo+nICFTAeOqXtkKFROxEBERNQJnRyleHRsBAMgqVEKl1tq5RURkDQNtIqoxXSIWwWSZg4SJWIiIiBpLsJcLOrZxg1YE/riZa+/mEJEVDLSJqMZ0iVhMM7EwEQsREVHjuu8efwDA73eTkhJR08NAm4hqbHp0GDq2MU18xkQsREREjWsQA22iJo+BNhHVmLOjFGOidElY2no747WHIpiIhaiFWblyJfr37w8PDw8EBARg4sSJuHLlisk6oihi2bJlCAkJgYuLC2JiYnDp0iWTdVQqFebNmwd/f3+4ublhwoQJSE9Pb8xdIWqxojv4QSIAN++UIKOgzN7NISILGGgTUa3sTcwBALw0ojNmDenAIJuohTl06BDmzJmD48ePY8+ePaioqMDIkSNRUlJiWOf999/Hhx9+iE8//RSnTp1CUFAQHnzwQRQVFRnWmT9/PrZu3YotW7bg6NGjKC4uxrhx46DRsEoBUX15uTiiR6g3APZqEzVVDLSJqMZSckuQmKmAVCLgwYhAezeHiBrArl27MGPGDHTr1g09e/bEunXrkJqairi4OAC63uyPP/4YS5YswaOPPoqoqChs2LABpaWl2Lx5MwCgsLAQa9euxb/+9S+MGDECvXv3RmxsLC5evIi9e/fac/eIWgzO0yZq2hhoE1GN7b6UBQCI7uALHzcnO7eGiBpDYWEhAMDX1xcAkJSUhKysLIwcOdKwjkwmw9ChQ3Hs2DEAQFxcHNRqtck6ISEhiIqKMqxjiUqlgkKhMLkRkWX9w30AALsuZeF/h29AqeZoEaKmhIE2EdXYrnhdoD26W5CdW0JEjUEURSxYsAD33XcfoqKiAABZWbrfgcBA01EtgYGBhseysrLg5OQEHx8fq+tYsnLlSnh5eRlucrnclrtD1GIo1Rq8++vlu39rsWLnZUxafYzBNlETwkCbiKqkVGuw5shN/P27cziTWgAAGMlAm6hVmDt3Li5cuICvv/7a7DFBEEzui6Jotqyy6tZZvHgxCgsLDbe0tLS6NZyohYs9noLLWUUmyxIyFYg9nmKnFhFRZQ72bgARNV1KtQaTVh9DQqYC+lNjF0cpvFwc7douImp48+bNw08//YTDhw8jNDTUsDwoSHehLSsrC8HBwYblOTk5hl7uoKAglJeXIz8/36RXOycnB4MGDbL6mjKZDDKZzNa7QtTipOWVQioIqBBFwzKpICAtr9SOrSIiY7Xq0WbJD6LWJfZ4ChIyFRBFQHv3WF6m1vCKOVELJooi5s6dix9//BH79+9HeHi4yePh4eEICgrCnj17DMvKy8tx6NAhQxDdt29fODo6mqyTmZmJ+Pj4KgNtIqoZua8rNEZBNgBUaEXIfV3t1CIiqqxWgTZLfhC1Lvor5sakAnjFnKgFmzNnDmJjY7F582Z4eHggKysLWVlZKCvT1eoVBAHz58/HO++8g61btyI+Ph4zZsyAq6srnnzySQCAl5cXZs6ciYULF2Lfvn04e/Yspk+fju7du2PEiBH23D2iFmF6dBgigz1NlnVs44bp0WF2ahERVVaroeO7du0yub9u3ToEBAQgLi4O999/v1nJDwDYsGEDAgMDsXnzZjz//POGkh8bN240HGxjY2Mhl8uxd+9ejBo1yka7RkT1ZemKufbuciJqmVavXg0AiImJMVm+bt06zJgxAwDw8ssvo6ysDC+++CLy8/MxYMAA/Pbbb/Dw8DCs/9FHH8HBwQFTpkxBWVkZhg8fjvXr10MqlTbWrhC1WM6OUvzwwiDEHk/Bl0eTkFGoxNT+cjg78vtF1FTUKxlaY5b8IKLGZ+mKeWSwJ6+YE7VgoihavOmDbEDXq71s2TJkZmZCqVTi0KFDhqzkes7Ozli1ahVyc3NRWlqKn3/+mVnEiWzI2VGKWUM6YPpA3TE5LiXfzi0iImN1DrQbs+QH62oS2YezoxSbZg2Ag0Q3fHzWfeH44YVBvGJORETURER38AMAnEjKg1YrVrM2ETWWOgfajVnyg3U1ieznTGo+KrQiQrycseShCAbZRERETUj3tl5wdZKioFSNK9lF1T+BiBpFnQJtfcmPAwcOWC35YcxayQ9r61TGuppE9rMnIQcAMDwisNoLZkRERNS4HKUS9A3TjRQ9cTPXzq0hIr1aBdr2Kvkhk8ng6elpciOihieKIvZfzgYADI8IsHNriIiIyBL98PEtp9KwdHs81hy5CaWa1XyI7KlWWcfnzJmDzZs3Y/v27YaSH4CujIeLi4tJyY9OnTqhU6dOeOedd6yW/PDz84Ovry8WLVrEkh9ETVD8LQWyFSq4OUkxsKOfvZtDREREFvRp5w0AuJxVhGvZRdAC2Hr2FvOqENlRrQJtlvwgal32Jup6s4d0agOZA7+fRERETdGF9ELD35q7+dASMhWIPZ6CWUM62KlVRK1brQJtUaw+k6G+5MeyZcusrqMv+bFq1aravDwRNbJ9HDZORETU5GUUlEEAYHymLhUEpOWV2qtJRK1erQJtImodlGoNPjtwHfG3dKX0BnHYOBERUZMl93VF5e4wjShC7utql/YQUT3KexFRy6RUazBp9TH8e/91w7K/bIxjUhUiIqImanp0GNr7mQbVkcGemB4dZqcWEREDbSIyEXs8BQmZCpNl+nleRERE1PQ4O0rx09z7IJXoynDOHhKO8T1DsHJnIjOQE9kJh44TkYm0vFJIABgfkjnPi4iIqGnzdHFE3zAfnEzKw44LmchUKCEVBGhEkRnIieyAPdpEZELu62rIWKrHeV5ERERN373tfQEAGYVKiCJQoRUhihyZRmQPDLSJyMT06DD4uDoCAAQAgsB5XkRERM1B/3Bfi8s5Mo2o8XHoOBGZkDlI4OIoRT7UeKBrGwzs6I/p0WEcbkZERNTE9Q3zMSvzBXBkGpE9MNAmIhPXcoqRUaiEzEGCT5/sCxcnBthERETNgbvMAZEhnriUoUtq6iDRzdHmyDSixsdAm4hM7L+cAwAY2NGPQTYREVEzE93BD5cyFOjbzhtRbb0g93XlyDQiO2CgTUQmDtwNtId1CbBzS4iIiKi2+rf3xdqjSShSVeDNh6Ps3RyiVovJ0IjIoLBMjdMp+QAYaBMRETVH/dv7AACuZhcjv6Tczq0har0YaBORwdFrd6DRiujYxg3t/Jg0hYiIqLnxc5fhngB3AMCp5Dw7t4ao9WKgTUQG+vnZD3RlbzYREVFzde/dMl8MtInsh4E2EQEASlUV2BWfqfu7XAOlWmPnFhEREVFd3NteF2ifTGKgTWQvDLSJCEq1BuNWHUVJuS643nQiFZNWH2OwTURE1Az1v9ujfSlDgdLyCju3hqh1YqBNRIg9noKbd0pMliVkKhB7PMVOLSIiIqK6auvtgrbeLqjQijibWmDv5hC1Sgy0iQhpeaVmy6SCYHE5ERERNX397mYf5/BxIvtgoE1E8HVzMlumEUXIfZl5nIiIqDnq354J0YjsiYE2ESHYy8Xwt4NEgCAAkcGemB4dZsdWERERUV3pM4+fTS2AWqO1c2uIWh8HezeAiOzv9xt3AACDOvqhU4A75L6umB4dBmdHqZ1bRkRERHVxTxt3eLs6oqBUjUsZCvSSe9u7SUStCgNtolZOoxVx6OptAMDfHuxsGGpGREREzZdEIqBfmA/2JubgVFIeA22iRsah40St3Lm0AhSUquHp7IDePAgTERG1GPqL5yc5T5uo0THQJmrlDl7JAQDc37kNHKT8SSAiImop9PW0TyfnQRRFO7eGqHXhWTVRK3fgbqAd0yXAzi0hoqbi8OHDGD9+PEJCQiAIArZt22byuCAIFm8ffPCBYZ2YmBizxx9//PFG3hOi1i0qxAvOjhLkl6oxf8s5rDlyE0q1xt7NImoVGGgTtWI5RUrE31IAAIZ2bmPn1hBRU1FSUoKePXvi008/tfh4Zmamye3LL7+EIAiYNGmSyXqzZ882We/zzz9vjOYT0V1aUYREEAAAP1/IwIqdiZi0+hiDbaJGwGRoRK3YwSu6JGjd23qhjYfMzq0hoqZizJgxGDNmjNXHg4KCTO5v374dw4YNQ4cOHUyWu7q6mq1LRI0n9ngKSst1QbX27sjxhEwFYo+nYNaQDlU8k4jqiz3aRK3Ygcu6YePDunLYOBHVTXZ2Nnbs2IGZM2eaPbZp0yb4+/ujW7duWLRoEYqKiqrclkqlgkKhMLkRUd2l5ZVCIpgukwoC0vJK7dMgolaEPdpErVR5hRZHrunqZz/AQJuI6mjDhg3w8PDAo48+arJ82rRpCA8PR1BQEOLj47F48WKcP38ee/bssbqtlStX4s0332zoJhO1GnJfV0NPtp5GFCH3dbVPg4haEQbaRK3U6eQ8FKsq4O/uhB5tvezdHCJqpr788ktMmzYNzs7OJstnz55t+DsqKgqdOnVCv379cObMGfTp08fithYvXowFCxYY7isUCsjl8oZpOFErMD06DFvP3sKljD9Hh0QGe2J6dJgdW0XUOnDoOFEro1RrsObITby9IxEAcF8nf0gqjysjIqqBI0eO4MqVK5g1a1a16/bp0weOjo64du2a1XVkMhk8PT1NbkRUd86OUvzwwiAMuFvmq087b/zwwiA4O0rt3DKilo892kStiFKtwaTVx5CQqYC+nObp5Hwo1RoedImo1tauXYu+ffuiZ8+e1a576dIlqNVqBAcHN0LLiEjP2VGKmfeF40RSHhTKCh7viRoJe7SJWpHY4ykmQTYApOeXYfqaE6ytSUQGxcXFOHfuHM6dOwcASEpKwrlz55CammpYR6FQ4LvvvrPYm33jxg0sX74cp0+fRnJyMnbu3InHHnsMvXv3xuDBgxtrN4jorv7tdT3a13OKcadYZefWELUODLSJWpG0vFJIBfNh4mdS81lbk4gMTp8+jd69e6N3794AgAULFqB379544403DOts2bIFoijiiSeeMHu+k5MT9u3bh1GjRqFLly7461//ipEjR2Lv3r2QStmbRtTYfNyc0DnQHYAuRwsRNTwOHSdqReS+rtCIotly1tYkImMxMTEQLfxWGPvLX/6Cv/zlLxYfk8vlOHToUEM0jYjq6N5wX1zNLsbJpHyMjuIUDqKGxh5tolZkenQYugZ6WH2ctTWJiIhapnvD/QAAJ5Nz7dwSotaBgTZRK+LsKMW8BzoBAJyk5l9/1tYkIiJqme69O087IUMBhVJt59YQtXwMtIlamSPX7wAAJvcLRbcQTwgC4CARIAisrUlERNRSBXk5I8zPFVoRiEvJt3dziFo8ztEmakW0WhH7ErMBAKO7BeGNcZGIPZ6CtLxSyH1dMT06jGU/iIiIWqh72/siJbcUp5LyMKxLgL2bQ9SiMdAmakUu3ipETpEK7jIHDOjgC5mDlInPiIiIWon+4b74Li4dJ5OYeZyooXHoOFErsidB15s9tHMbyBzYc01ERNSaDAjXzdM+n17Acp5EDYyBNlErsvfusPERkRwuRkRE1NoEeMjgIXOAWiPi7V8SGGwTNSAG2kStRFpeKS5nFUEqETgvi4iIqJVRqjWY/N8/UKSqAADEnkjFpNXHGGwTNRAG2kSthL43u1+YD7xdnezcGiIiImpMscdTkJCpMFmWkKlA7PEUO7WIqGVjMjSiVkIfaD8YGWjnlhAREVFjS8srhVQQUCGKfy4Uge/j0gGAlUeIbIw92kStQGGZGidu6jKMDo9goE1ERNTayH1doTEOsgGIAK5mF2HFzkQOIyeyMQbaRK3Aoau3UaEVcU+AO8L93ezdHCIiImpk06PDEBnsCUEwXa4VAVHkMHIiW+PQcaIWSKnWIPZ4CtLySiH3dcWZ1HwAwAj2ZhMREbVKzo5S/PDCIMQeT8GaIzeRpVCZPC4VBKTlldqpdUQtDwNtohZGqdZg0upjSMhU6OZiaUVI7l695vxsIiKi1svZUYpZQzogr6Qcnx28YfKYRhQh93W1U8uIWh4OHSdqYfRZRUURqNDq5mJpRcBRKuBMSh7nXxEREbVy8x64B45S3VV4iQAIAhAZ7Inp0WF2bhlRy8FAm6iF0WcVrUytEfHOr5eZ7ISIiKiVc3FywNioYABAtxBPLBkbgR9eGMSs40Q2xECbqIWxlFVUj8lOiIiICAAG3+MPAHBy0A0nZ5BNZFsMtIlaGGtZRfWY7ISIiIgGdvQDAJxPK0BpeYWdW0PU8jDQJmph9FlFl4yNgL+7k9njTHZCREREoT4uaOvtggqtiNPJ+fZuDlGLU+tA+/Dhwxg/fjxCQkIgCAK2bdtm8viMGTMgCILJLTo62mQdlUqFefPmwd/fH25ubpgwYQLS09PrtSNE9CdnRylmDGoPjfbPIeQOEoHJToiIiAgAdOfoHXS92r/fuGPn1hC1PLUOtEtKStCzZ098+umnVtcZPXo0MjMzDbedO3eaPD5//nxs3boVW7ZswdGjR1FcXIxx48ZBo2GCJiJbOZmch/xSNbxcHLF4TFdMG9COyU6IiIjIYEgn3Tzto9cYaBPZWq3raI8ZMwZjxoypch2ZTIagoCCLjxUWFmLt2rXYuHEjRowYAQCIjY2FXC7H3r17MWrUqNo2iYgs+PViFgBgVLdAPD+0o51bQ0RERE2NPiHapQwFcotV8HOX2blFRC1Hg8zRPnjwIAICAtC5c2fMnj0bOTk5hsfi4uKgVqsxcuRIw7KQkBBERUXh2LFjFrenUqmgUChMbkRknUYrYtclXaA95m75DiIiIiJjbTxkiAj2BAAcvc5ebSJbsnmgPWbMGGzatAn79+/Hv/71L5w6dQoPPPAAVCoVACArKwtOTk7w8fExeV5gYCCysrIsbnPlypXw8vIy3ORyua2bTdSi/HEjF7eLVPB2dTRcrSYiIiKqjMPHiRqGzQPtqVOn4qGHHkJUVBTGjx+PX3/9FVevXsWOHTuqfJ4oihCs1CNavHgxCgsLDbe0tDRbN5uoRdl69hYA4KHuwXByYHEBIiIisuy+uxfkj16/A1EUq1mbiGqq1nO0ays4OBhhYWG4du0aACAoKAjl5eXIz8836dXOycnBoEGDLG5DJpNBJuOcEaKaKCvXYPfdYeMTe7e1c2uIiIioKbs33BdODhJkFirx3q+XUabWQO7riunRYUyeSlQPDd7VlZubi7S0NAQH6+aJ9u3bF46OjtizZ49hnczMTMTHx1sNtImo5vYmZqNYVYFQHxf0bedT/ROIiIio1XJ2lKJfmO584b+Hb2LTiVSs2JmISauPQalmRSCiuqp1j3ZxcTGuX79uuJ+UlIRz587B19cXvr6+WLZsGSZNmoTg4GAkJyfj1Vdfhb+/Px555BEAgJeXF2bOnImFCxfCz88Pvr6+WLRoEbp3727IQk5Edbf9nG7Y+MO9QiCRWJ6OQURERKRn3HNdodUNH0/IVCD2eApmDelgr2YRNWu1DrRPnz6NYcOGGe4vWLAAAPDMM89g9erVuHjxIr766isUFBQgODgYw4YNwzfffAMPDw/Dcz766CM4ODhgypQpKCsrw/Dhw7F+/XpIpRyeQlQfeSXlOHjlNgBgYi8OGyciIqLquTqZn4NLBQFpeaV2aA1Ry1DrQDsmJqbKRAm7d++udhvOzs5YtWoVVq1aVduXJ6Iq7LiQgQqtiG4hnugU6FH9E4iIiKjV6xnqhV8uZJosq9CKuJShwJojNzlfm6gOmI6YqIVQqjX4/PBNAECwlzPnVRFRnR0+fBjjx49HSEgIBEHAtm3bTB6fMWMGBEEwuUVHR5uso1KpMG/ePPj7+8PNzQ0TJkxAenp6I+4FEdXUUwPbw8tF1/9mPOnsXFoB52sT1REDbaIWQKnWYNyqo0jPLwMA7E3M4UGRiOqspKQEPXv2xKeffmp1ndGjRyMzM9Nw27lzp8nj8+fPx9atW7FlyxYcPXoUxcXFGDduHDQa/i4RNTXOjlL8Y0wEAMDFSWoItiu0IkTxz/naRFRzDV7ei4gaXuzxFFzPKTZZxiQmRFRXY8aMwZgxY6pcRyaTISgoyOJjhYWFWLt2LTZu3GhIdBobGwu5XI69e/di1KhRNm8zEdXPg5GBeHXrRZSWayAVAI3RTFHO1yaqPfZoE7UAyXdKzJbxoEhEDengwYMICAhA586dMXv2bOTk5Bgei4uLg1qtxsiRIw3LQkJCEBUVhWPHjlndpkqlgkKhMLkRUePwd5ehl9wbgGmQrbsvQu7r2viNImrGGGgTtQAl5RVmy3hQJKKGMmbMGGzatAn79+/Hv/71L5w6dQoPPPAAVCoVACArKwtOTk7w8fExeV5gYCCysrKsbnflypXw8vIy3ORyeYPuBxGZGhERCABwlzlAEAAHiQBBACKDPTE9OszOrSNqXjh0nKgFuFWgNPztIBGgEUUeFImowUydOtXwd1RUFPr164ewsDDs2LEDjz76qNXniaIIQRCsPr548WJD2VAAUCgUDLaJGtEDXQPwwe4rUGu0eHlUF2QVKiH3dWXWcaI6YKBN1MzduF2Mk0l5kAjAnGH3QFGm5kGRiBpVcHAwwsLCcO3aNQBAUFAQysvLkZ+fb9KrnZOTg0GDBlndjkwmg0wma/D2EpFlXYM80NbbBbcKytA50AMvxNxj7yYRNVscOk7UzH19IhUAENMlAAtHdsGbD0dh1pAODLKJqNHk5uYiLS0NwcHBAIC+ffvC0dERe/bsMayTmZmJ+Pj4KgNtIrIvQRDwQNcAAMC+yznVrE1EVWGgTdSMKdUafH9GV5f2yXvb2bk1RNRSFBcX49y5czh37hwAICkpCefOnUNqaiqKi4uxaNEi/PHHH0hOTsbBgwcxfvx4+Pv745FHHgEAeHl5YebMmVi4cCH27duHs2fPYvr06ejevbshCzkRNU3DI3SB9v7EHIiiWM3aRGQNh44TNWO/XMhEQakawV7OiOnSxt7NIaIW4vTp0xg2bJjhvn7e9DPPPIPVq1fj4sWL+Oqrr1BQUIDg4GAMGzYM33zzDTw8PAzP+eijj+Dg4IApU6agrKwMw4cPx/r16yGVcrQNUVMW3cEPrk5SZCmUuJShQFRbL3s3iahZYqBN1ExptSI+P3QDABDs5Yz1x5I5L5uIbCImJqbKnqzdu3dXuw1nZ2esWrUKq1atsmXTiKiBOTtKcd89/vgtIRv7EnMYaBPVEYeOEzVTuxOycC2nGABwPq0AK3YmYtLqY1CqNXZuGRERETVn+uHjexOz7dwSouaLgTZRM/XOjkTD3xoREEUgIVOB2OMpdmwVERERNXfDIwIhEYCLtwqRklti7+YQNUsMtImaobiUPKTll5ktlwoC0vJK7dAiIiIiain83WUY1NEfgC4fDBHVHgNtomZo9cGbFpdrRBFyX9dGbg0RERG1NON66Mr17WCgTVQnDLSJmplr2UXYm5gNQQDuCXCDIAAOEgGCAEQGe2J6dJi9m0hERETN3OioIDhIBCRkKnDzdrG9m0PU7DDrOFEzolRr8NKWcwCAzgEe+Ob5aHwfl460vFLIfV2ZdZyIiIhswtvVCfd18sfBK7fxy4VM/HV4J3s3iahZYaBN1Ewo1RqM+eQIku7okpJcyS7CtDUn8MMLgxhcExERkc2N6xGCg1duY+MfKcgtVvGiPlEtcOg4UTOx8Y9kQ5CtxyzjRERE1FCGdvaHAOB2sQqxx1NYSpSoFhhoEzUTx27kmi1jlnEiIiJqKNvPZUC8+zdLiRLVDgNtomZArdHiQnqh2XJmGSciIqKGkpZXColguowX+YlqhoE2UROnVGvw16/PIrekHMLdgx2zjBMREVFDk/u6QiuaLuNFfqKaYTI0oiZMqdZgwqdHcTVbV1ZDFIFATxlGRAQi3N+NCUmIiIiowUyPDsPWs7dwKUNhWMaL/EQ1wx5toiZs4x/JhiBbL6dIhXB/N8wa0oFBNhERETUYZ0cpfnhhEGYM0gXWDhIB62b05/lHK6JUa7DmyE0s3R6PNUduMhFeLbBHm6gJO3T1ttkyzo0iIiKixuLsKMXS8d1w7EYurmYXY/elLDw1sL29m0WNQKnWYNLqY0jIVEAqCNCIIraevcXSsjXEHm2iJqLyFcOU3BKcTs43W49zo4iIiKgxCYKAJ+5tBwDYdCIVoihW8wxqCWKPpyAhUwFRBCq0IrPO1xJ7tImagMpXDCu0Ij7eew3KCi2cHSVQqrVwkOiuJHJuFBERETW2R3q3xbu/XsblrCKcTy9EL7m3vZtEDSwtr1R3Xmp0YYUjK2uOgTZRE2ByxfDuj1mxqgJSiYDvnh+IE0l5SMsrhdzXlQnQiIiIqNF5uzphbPdgbD17C1+fSGWg3QrIfV2hqTR6oUIr4lKGAmuO3OQ5aTUYaBM1AZauGALAwA6+6B7qje6h3vZpGBEREdFdT9zbDlvP3sLPFzLw2rgIeDg7Gh5TqjWIPZ7CjoEWRJ91PiFTAQkAzd3T1HNpBYhLzed87Wow0CZqAixdMQSAmC4BdmgNERERkbn+7X1wT4A7rucUY/OJVDw/tCMAJs1qqfRZ52OPp2BXfBbiUvIhQterDfw5X3vWkA512n5LvzjDZGhEdqRPgJZ0uxh+ro4mj0UGe3AuNhERETUZgiDgL/frgqr/HUkylHpi0qyWy9lRillDOqBbiCekEsHksfrM19ZfnFmxMxGbTqRixc5ETFp9rEWVD2OgTWQnxj8wW06l4U6JGgDg5eKIv4/sjB9fHNyiruoRERFR8/dI77Zo6+2CO8UqfHc6DcCfU+CMCQC+j0tn7eUWwtLoy/pUwmkNF2cYaBM1sMpluyxd/dUY/W49N7g9Zg7pgNjjKWbPISIiIrInR6kEzw/V9Wr/99BNqDVai0GYVgSuZhe1yJ7K1mh6dBgigz0hCICDRIAgoF6VcCxdnGlpGc05R5vsqiXNzbC0LwDMynb978hNjIgIxJWsIggAjA9LUgHIUSg5z4mIiIiarCn95Pj3vuu4VVCGbWdvmSTNEqALsmH0//rO5SX7M56vbYvzdlv3kDdFDLTJblpS4gxr+zK+Z4hZ2a5shQqbT6TCPPUZoAWQU6Qyew4PUERERGRvxp0KPUK9sP9yDlYfvIFH+4QagrDv49JxNbvIEGQDLa+nsrXSz9e2BeOLM/pz5/r0kFtjz049BtpkN5ZqRzfXgNLavrg4Si2W7aocZEsFXZAdGeyJAE9ns+fwAEVERET2VLlToUIrQioAN++U4JcLGXi4V1vD+duKnYkmz21pPZX20txHglZu/6ZZA/B9XHqD7U9ZeQUe+ewYLmcVQSLoRlg0ZqceA22yG0u1o5trQGlpXwQAqXml0Ggt9V3rSAD0CfNBtxBPww9M7PGUFj+UhoiIiJoXS50K+hwzK3dexoiIQLjJHBqtp7K1ae4jQW3RfksXGjRaEWn5pUjPK0NafinS8sqQnl+KtPwy3LxdDFWFFoB9pjEw0Ca7scXcjPpe2bPV8y9lKMwCaq0I3C5SWRwiricKwOioIJMvOw9QRERE1NRY7iABXJwckKVQYtX+6/jHmK42n8tLOs19JGhd269Ua5CeX4akO8V4Y/slZBYqDTmOVv56ucoOLUsas1OPgTbZTX0DyrpcGTMOrIO8nPHz+QwkZhWZJSoL93er9qBg/PoS/DkcXD80BTAfIu4g0b2Og8T6/vIARURERE2NxcziAMZ2D8K3p9Ox9uhNPNYvFB3buNt0Lm9rZKkjqLmPBLXW/pQ7JUjJLUF6fhnS8kp1vdN3/07PL0NOkcpsW/ot6INsb1dHhHi7QKsVIREEtPN1wSN9QnEmJR+fH75p8tzGHCXKQJvspr4BZW2vjFmaW6RnnKjs65O6RGX6oF3/WvrgXBAEZBaUIaOg7M/yXEavI3OQosxCCYv7O/njv9P7YvPJ1Gr3lwcoIiIiakqsdZAsfzgKd4rLsf9yDpb9dAlfPXcvhEplm6jmqkqw21hTC205F1yjFZGtUEJ7t162sQqtiNgTqdh4IrXKbbg5SeHkIEFBqdqsWs+UfnIsndANk1Yfw5VsXedZYpYCafll2DRrAI5ev2O3UaIMtMmixkq2UNuA0rhdlzIUtbqyZykwt8R4Dse635Pwy4VMQ6+1fi6SvmfaEktBtiAA93duA1eZAwNoIiIianaq6iBZOj4SR6/fwZFrd/BrfBbGdg+udnvNPbFXVeqzb5bOVy9lKCCTShDoIUN2kapWQWPl0Zz6DqPK7dKvl3S7GPsu55i8TlUjRkVRxO1ilWFudHr+3TnSd+dMZxSUQa2xft4tApA5SBDq44JQH1fIfV0g93E1+dvb1RFrjybpkuwZbUoLoGOAu9X3bNaG0xjXIxjje4ZY3OeGxkCbzDTVZAtV9UjrVXVlz9KQlapIBQF7E7JxKUOh27bRY9aCbACICvGEQqlGal5ZlUPEiYiaqsOHD+ODDz5AXFwcMjMzsXXrVkycOBEAoFar8dprr2Hnzp24efMmvLy8MGLECLz77rsICQkxbCMmJgaHDh0y2e7UqVOxZcuWxtwVIrIhax0kYX5u+L/7O+Df+69j8Y8X0b2tV5U9rY15rtnYAX19983a+eq59AJoRSDQU1bnaY7GHUbG7QJgWM+4Drpx0PrB7svoG+ZrGNKtSzym+1ufcMwaB4mAEG8XtPV2QZlaAwFAmJ8rpvaXo2Mbd/i7y1Cu0Rr+nbSiLsHZyaRcw79ZVVNOV+5MtPienUnNR1xqPiKDPe0SxzDQJjMNmWzB1lf4AF12b2kNAlpLc4uMt1H5kQqtiLNpBTVqm35edmSwB76vNNy8pV2lJaKWr6SkBD179sSzzz6LSZMmmTxWWlqKM2fO4PXXX0fPnj2Rn5+P+fPnY8KECTh9+rTJurNnz8by5csN911cXBql/UTU8Cqf080a0gGHrt3B+bQCzNl8Bt/930DIHCyf+zRWYi97dB7Vd9+sna/qg9+cIhXC/d1qtC3jtljqMNK3S1WhMXQsWetKWns0GWuPJlt8TCLozsXVGtFwTt3W2xkrH+2BUB8X7E3Mxq186z3KNb0gUHlExeS+oVaTEhu/Z/ZKGsdAuxWobXBb12QL1oam6P9Oyy2p1VCUmrUL6N1OVx5L/zordyYi1McFY7oHo6BUjZwiJbIVKhSUquHt4oj8UrXZtq39qNQkkeGDEQEI8XYxe285RJyImqsxY8ZgzJgxFh/z8vLCnj17TJatWrUK9957L1JTU9GuXTvDcldXVwQFBTVoW4mo8VkLYP/9RG9MWn0MF9ILsfznBKx4pLvF5zdWYi97ZOqu774Z99xCND9Hrc22qhvNKYrAB7uvVNsjDQDuMik6B3qYDOmW+7oi1McFu+Kz8O6uy7pt3l0/o1CJSxmFeG/X5WovdNT0gsCsIR0M/27WkhJb6jyzV9I4BtrNQH16getyJa8uZbesXYmSCn/+bZyNu65X+CoP2daIgKtMClWFFp8dvIEiZYXhsRU7L1e7TQBwcpAg2MsZgZ7OKCvXABDR1tsFXq5OKFOpcSIpDznF5RavsEUGe2LVk33YW01ErVphYSEEQYC3t7fJ8k2bNiE2NhaBgYEYM2YMli5dCg8PD6vbUalUUKn+zDCrUCgaqslEVA/WAtgDl3Pw8dReeHb9KWw6kYq+YT54tE+o2fNtUeK1JuyRqbu++2Y8F35XfBbiUvJNAkf9tozjgxBvF8R0aYPbReVIyy9F0p0SHL1+Byl3Sqqc7gigyiBbH7RGBHtg64uDrZ7vZhSUWXyf9yXm1OhCR3UXBCz9m1kLztv6uOBWfpnF96yxMdBu4uo75KUuV/LqUnbL2ofdOPeBpe+5/otTodHiTnE5coqUyFGokH33/zlFKty+2yOdrVBafO3DV+9YbZebTIpwfzcEejgjwFOGQE/nuzcZgjxdEOTlDB9XR5PsmPr3fHdCtmEuuH4+TKiPi9UkEkRErZFSqcQ//vEPPPnkk/D09DQsnzZtGsLDwxEUFIT4+HgsXrwY58+fN+sNN7Zy5Uq8+eabjdFsIqqHqgLYWUM6YN6we/Dv/dfx8vcXIHOQ4qEepsnR6lvitaaqCnobau52VftW09fUz4WfHh2GSauP3U0ArDuvDvZyRk6RCgPe2YfCsj9Haa78tWYdTMCfAbTc1wWfPtEHbb1d8My6kyZ5kGoyF1y/P5aGbuvf95pc6Khqeqd+W5UDZUufQQeJgKGd/HEuvdBumcaNMdC2s+oyAdZ3yEtdruTVpexWbRON6VVoRXx/Jh1fHU9BTZ8q3G2jl4sDugR5IsjTGYmZCsRnFJoE8wIAuY8rHuoebHhf3WUOeKR32yr3xdJ7Xpv5MERErYVarcbjjz8OrVaLzz77zOSx2bNnG/6OiopCp06d0K9fP5w5cwZ9+vSxuL3FixdjwYIFhvsKhQJyubxhGk9EdVZdr+1LIzojNa8U285lYN7XZ1Ch7YWHe7U1rFvfEq81ZS3ondw3tMHmblvbNwBWX9NJKsGdYtXdBGOmWbsLy9SQGI0QzShQ4otKtaH1/N2d4O3iiOu3S8we69vOGyMiA612GNX238Pa0G3jUZ8jIgMRl5pv8jxLQbPxv5O1EaSVA2Vrn8HwNu54fXy3JpEriYG2HdVk4n8vubdZACsA+D4uHQAspuU3/lDVdfiKcVZJS9sVBJj0OGcplNUOTbGmRGVeDst4X0UA3i4OiO7ojy6B7pg9pAPcnR1N1ltz5CYuZhSaLBMBXMkqwnu7rgAwT6hg7Qtnj2FGRETNjVqtxpQpU5CUlIT9+/eb9GZb0qdPHzg6OuLatWtWA22ZTAaZTNYQzSUiG6quR1oqEfCvKb3gIJXg+7h0vLTlHL45lYYHugYYzl1rWuK1Pj3P1oLehp67XXnfRFHEqv3XDAnHjLN5D353P4pUFSivZp60o1RAW29dCaxshRI3bhebdDBJALT3c0OxqsJkuiagOweOauuFF2LuqXGbq2NtNGsvuTdGRwUZPgs/n8+otne58r9TVWXI9Kr6DNZ2XxoKA+0aaKihJTWZ+B/i5WwWKGtF4Gp2EVbsTLSYlt/4KtmmWQNMPoQVWhEBHjIk3SnBmiM3MblvKL6PSzfJ3qcoq9AN4S5SIT2/FF8cvok7xeWGoHfFzsQa9z5b4uIoQadAD8h9XDGhVwjaertgb0I2Pt53zWxd/csUlFXgt0tZ2H0J2JuYYxYoG3/ZjMsSGDfTUkIFSxpr3hARUXOlD7KvXbuGAwcOwM/Pr9rnXLp0CWq1GsHB1dfXJaKmrSY90lKJgOUTumFfYjbyS9U4diMXx27k4vu4dGybY32+r7G6TqGs7ty9ITpVFEo10vL+7JGuXE+6tNxyx1JuSbnF5W08ZFj4YGd0aOOOUB8XBHo6QyrRTXVcc+Smrqa0ES2AuJR8CIL5dE1L57H1jW+sDd3uFuJpuJiRllda4zrWtQ2OG2tURH0w0LairkXba6MmE/8DPGSIDPY0CyArp6vX/135ytz3cen44YVB+PJoEi5nFuLo9VxkK1T4+mQqtCLwzs5Eky/j2ztMv7TG9KtZaq4+CHeTSXW1E31c4e7sgBJVBdr7uUIikVT5BfvudBocJJZrY+tVlaLf+Mv2fVw6rmYXWc0YXt0PaWPNGyIiaqqKi4tx/fp1w/2kpCScO3cOvr6+CAkJweTJk3HmzBn88ssv0Gg0yMrKAgD4+vrCyckJN27cwKZNmzB27Fj4+/sjISEBCxcuRO/evTF48GB77RYR2VBNAqPNJ1PNqr1czirCOzsTsfzhqGpfoy49z1UF5/ptWptTrA9GLQWhWlHU1Y/W15GuVE9aYZSQtzYsZckGgDvFKhSrKnBvuK/ZY5XPVfXnzyJMz9Mlgm5Z5fNYW5Q9s9YxFeTlbLbthqpj3VR6rq1hoG2B8YfPUtF2Ww0tqW7if4VWxJXsYsOVoG1nb5kFkBIA8bcKUVauMf+iisC/913DJ3uvoUhl+uWvHLAbEwTAy8URHfzdkFtcjtS8Uqvlr4xeCgBQWq7BiIjAWr831b0XxqwFysZftspX+YxV1zvdHK6QERE1pNOnT2PYsGGG+/p508888wyWLVuGn376CQDQq1cvk+cdOHAAMTExcHJywr59+/DJJ5+guLgYcrkcDz30EJYuXQqplL+lRK1FWl6pxY6Ur/5IwR83cvHWxChEd7A+IqYuPc/WgvN1vyfhlwuZVucUdw3ywKCOftiTkIU3tl9CZqHScG698tfLFus0V8XP3Qnzht2D8Ls90v5uTnhyzYkqR18aq2o/K5+rXspQ4Fxagcn7LBGAzoEemNw31Ow81hZD5611TAmCYNNh+Q01srgxMNC2wPjDZ+nDb+2Db+2DYG25tYn/EuiGfwDAmdR8nE7JR6CnDHJfVwtDQYBt5zIs7ocImFxdq67H2PiJhWVqqCq0eGJAO7y367L1X4FK6jrsxup7UcPhLzXZVlUJFSpr6lfIiIgaUkxMDMQqLn5W9RgAyOVyHDp0yNbNIqJmpqqOlGs5xXj8i+Po315XAuyBrgEI9HSu9vnVZQ23FpzvvpRtmCNtPIhb5iCBIACJmUUY+++jJq+l34I+yPZ0djCpIx3qo5szfTI5F18cTjJ5bl5JOSq0IoZ2bmNYVtPRl8b7aY3xueqaIzfNko6JACb3DbV4PmuLofPWOqZW7ky02bB8W/S821OtA+3Dhw/jgw8+QFxcHDIzM7F161ZMnDjR8LgoinjzzTfxxRdfID8/HwMGDMB//vMfdOvWzbCOSqXCokWL8PXXX6OsrAzDhw/HZ599htBQ8zp7Dc3Sl7S6Id3W5jlYmyM97e7VK33Q9/XJVLw6NgL5pWqM6hYEVycpchQqVGhFlKgqoFCqDd9s/RdQV95KBUvcZFK0cZchp0iFUqOe7VBvF6x4NAqhPq4I9HTGlpOpNZpfLd79T0KmAuN6BFscum5NXecyW0uCkJZbYjZ0v7pAuS4JFYiIiIjItqzl0DF2Kjkfp5J1QWL3tl7oJfdG50B3dAr0QEyXNvg+Lh2Xs4pMOkwm9w3Fo5/9joTMIkPZq6/+SEbvdj44k5Jv1rFUoRVxLq3AYhtLjOZOuzlJ4eQgQUGp2qSPSSoAj/ULxbuTelrcxpFrt806tCwFl9WNvhSgm9te22mLtZ32aKt8RJY6pmyZ66ihk9Y1tFoH2iUlJejZsyeeffZZTJo0yezx999/Hx9++CHWr1+Pzp074+2338aDDz6IK1euwMPDAwAwf/58/Pzzz9iyZQv8/PywcOFCjBs3DnFxcY06pMxacDy+Z4jFq2/W5jlotSI+P3TDYibBgSv3oUyt65/Wf41v3C7BzA2na9VWAUBkiCcm9AzB5awilJZXoGMbdzwVHYYdFzPNAkr93/sTcwzBpaX5HM6OEl2hegu991JBQFah0hC0Vp6vbvx8W8xlttaL/HodhoywR5qIiIjIvqrLoSMVgO6hXgAEnE8vwMVbhbh4q9BsO8Ld/ztKJEi6U4J+b+81BLX6kYupeWVIzSurdRsn92mLGYPDEerjAi8XR6w9mqQLgo3aqQVwT4CHyfOMO+syCspqFVxaCoy7Bnng4V5t69QxVNtpjw2Zj8iW227ulYAEsbrxX1U9WRBMerRFUURISAjmz5+PV155BYCu9zowMBDvvfcenn/+eRQWFqJNmzbYuHEjpk6dCgDIyMiAXC7Hzp07MWrUqGpfV6FQwMvLC4WFhdWWE6mKPmOf8TsgCMDLo7rglwuZJsXhve/WbHZ2lCDE2wV3ist1pa0UStwuVkGtqd3b6O3iiO6hXmjjIUOAhzMCPXX/D/CU4eCVHPznwA2T9QUBWDI2wiR4tHShIDLY06QXvXISAgAmX0J91vFd8VmIS8k3Cbatvaal5zfHeRNERLZkq2MT/YnvKVHLYe28W3+uebtIhd+v38HlrCJczS7CtZwi3CkqR5naehnY6rT3c8Xge/wxPToMgZ4yPLX2ZLVJuiqfX1doRQR6yjAiIhDh/m4Wa2Jb63yqaoizveceN+Tr22rb1X1m7KE2xyWbBto3b95Ex44dcebMGfTu3duw3sMPPwxvb29s2LAB+/fvx/Dhw5GXlwcfHx/DOj179sTEiRPx5ptvVvu6tjrwLt0ej9jjKTCOkQUAvm5OcJAKyClS1auMVVX6hfkYasxV/uBZC6Arf1mtffhGdA3A3ss5tfpQ1vQ1iYjIMgaFtsf3lKjlqOu5plKtQWGZGooyNV7cdAbXcoqtZuo25iARMG1AO7xplN28pgFgVdWHIoM9Mb5nCN7bddksTngwIgAh3i41ytVE1WuK8Ultjks2TYamL+8RGBhosjwwMBApKSmGdZycnEyCbP06+udXplKpoFL9OT9ZoVDYpL26OQSmy0SY1rMTBMDPTXa3x/nPXucADxkCPJ0N//eQOeCJ/x1HQqbC4jBswDR9/7m0AsSl5luc0F/T4R9Wh1Pkl9V6mAUzbRMRERFRQ6nruaazoxTOjlL8fD4D128XAzA9z7aUOBewPHS7plML9eutOXIT2afSTOYIX8pQ4HaRyizYd5AICPF2MQvsm3MyL3tr7vFJg2QdFwTB5L4oimbLKqtqnZUrV9aop7u2pkeHYcOxZKTllxm+LMFeznh9XKShMLyfmxMcpJIabU//QbA0DBsA2vq44FZ+GUTAMK/E2oT+mvwQWE024OOCK9lF5surSULAec1ERERE1FBqeq5Z02TF+hJWD/cKwc/nM5CYVWTTOcfWEiTfLlKZnedbOtdu7sm8moLmHJ/YNNAOCgoCoOu1Dg4ONizPyckx9HIHBQWhvLwc+fn5Jr3aOTk5GDRokMXtLl682FDDE9D1aMvl8nq319lRij0LhtrsKon+gzA9OsziMIdecm98cyqt2oyENWUt2cAHj/W0OEfbFgkOiIiIiIhsrarh2taSFRuXsHp2cLjNez6tlSer3KNuKVky0PyTeVH92DTQDg8PR1BQEPbs2WOYo11eXo5Dhw7hvffeAwD07dsXjo6O2LNnD6ZMmQIAyMzMRHx8PN5//32L25XJZJDJZLZsqoHxVZK6zKGw9hxLwxx088Ftk+5e33Zrwyma8zALIiIiImo9jIdYG5cBM+4FNi45a6kjqSF6Po07tSxNDdX3qE/uG2rxXNuWpa6o+al1oF1cXIzr168b7iclJeHcuXPw9fVFu3btMH/+fLzzzjvo1KkTOnXqhHfeeQeurq548sknAQBeXl6YOXMmFi5cCD8/P/j6+mLRokXo3r07RowYYbs9q6W6zKGo7jmVv+wNkUrf2o9Kcx5mQURERESth/EQa0t5jiqXnG2sjiTjzitLU0ONe9QtacgyWtT01TrQPn36NIYNG2a4rx/S/cwzz2D9+vV4+eWXUVZWhhdffBH5+fkYMGAAfvvtN0MNbQD46KOP4ODggClTpqCsrAzDhw/H+vXrG7WGdmV1mUNR2+ewp5mIiIiIyJS1udB6+l5ge3QkVTc1tKqgmef+rVutA+2YmBhUVRFMEAQsW7YMy5Yts7qOs7MzVq1ahVWrVtX25RtMTedQGA8V19XZrn12b/Y0ExERERHpWJsLXdX8Z1urbgppfbKm89y/dWqQrOPNUU3mUFgqYF8Z510QEREREdVc5SHWFVoRgZ4yjIgIRLi/W4P3Atd0CimDZqoNBtp31WQOhaWh4oCuPrZUwnkXRERERES1Ze8h1izDRQ2BgfZdNfmCWx5eDvRu54NuIZ6cd0FEREREVAf27C1mGS5qCAy0jVT3Bbc0vFwLYHRUEK92ERERERE1QyzDRQ1BYu8GNCfTo8MQGewJQQAcJAIEoXGSMxARERERUcPgOT41BPZo14K9548QEREREZFt8RyfGgID7VpitkEiIiIiopaF5/hkaxw6TkRERERERGRDDLSJiIiIiIiIbIiBNhEREREREZENMdAmIiIiIiIisiEG2kREREREREQ2xECbiIiIiIiIyIYYaBMRERERERHZEANtIiIiIiIiIhtysHcD6kIURQCAQqGwc0uIiIh09Mck/TGK6o/HeyIiakpqc6xvloF2UVERAEAul9u5JURERKaKiorg5eVl72a0CDzeExFRU1STY70gNsNL71qtFhkZGfDw8IAgCPXenkKhgFwuR1paGjw9PW3QwuaF+8/95/5z/7n/9d9/URRRVFSEkJAQSCScmWULtjze87PO/ef+c/+5/9z/xjzWN8sebYlEgtDQUJtv19PTs1V++PS4/9x/7j/3v7Wy1f6zJ9u2GuJ4z88695/7z/1vrbj/jXus5yV3IiIiIiIiIhtioE1ERERERERkQwy0AchkMixduhQymczeTbEL7j/3n/vP/ef+t879b01a+78195/7z/3n/nP/G3f/m2UyNCIiIiIiIqKmij3aRERERERERDbEQJuIiIiIiIjIhhhoExEREREREdkQA20iIiIiIiIiG2r1gfZnn32G8PBwODs7o2/fvjhy5Ii9m9QgVq5cif79+8PDwwMBAQGYOHEirly5YrKOKIpYtmwZQkJC4OLigpiYGFy6dMlOLW44K1euhCAImD9/vmFZa9j3W7duYfr06fDz84Orqyt69eqFuLg4w+Mt+T2oqKjAa6+9hvDwcLi4uKBDhw5Yvnw5tFqtYZ2WtP+HDx/G+PHjERISAkEQsG3bNpPHa7KvKpUK8+bNg7+/P9zc3DBhwgSkp6c34l7UXVX7r1ar8corr6B79+5wc3NDSEgInn76aWRkZJhsoznvP5njsf5PLem3rjqt8XjPYz2P9Xo81jeBY73Yim3ZskV0dHQU//e//4kJCQniSy+9JLq5uYkpKSn2bprNjRo1Sly3bp0YHx8vnjt3TnzooYfEdu3aicXFxYZ13n33XdHDw0P84YcfxIsXL4pTp04Vg4ODRYVCYceW29bJkyfF/2/v/kKaev84gL/V6ewPrVRy6cgUAisrbFJQklTiRUYXQaH90ehqkTUTypEX3WR5FRGUUUQ3FkpkYBHRLBOkyFBXVoRGKyMUidIEM5f7/C6+7OBp1tfvj+XyOe8X7MLnPIzzftTz9pm6s2jRIlmxYoU4nU5tXPXsnz9/lpSUFNm7d688efJEvF6vNDY2yps3b7Q5Kq/BiRMnJD4+Xm7fvi1er1euX78us2fPljNnzmhzVMp/584dqaiokBs3bggAuXnzpu74ZLI6HA5JTk4Wt9st7e3tsmHDBlm5cqX8+PFjitP8d7/LPzAwILm5uVJXVyevX7+Wx48fy5o1a8Rut+ueYzrnJz12vfG6XsSYfc+uZ9ePx64Pf9cbeqO9evVqcTgcurH09HRxuVxhOqOp09/fLwCkublZRET8fr9YrVapqqrS5oyMjIjFYpELFy6E6zRDamhoSBYvXixut1tycnK04jVC9vLycsnOzv7lcdXXID8/X/bt26cb27Ztm+zevVtE1M7/c/lMJuvAwIBER0dLbW2tNufjx48SGRkpd+/enbJzD4WJfvj4WWtrqwDQNl4q5Sd2vdG6XsS4fc+uZ9cHsOuDhaPrDfun46Ojo2hra0NeXp5uPC8vD48ePQrTWU2dwcFBAEBcXBwAwOv1oq+vT7ceZrMZOTk5yqzHgQMHkJ+fj9zcXN24EbI3NDQgKysL27dvx/z585GZmYlLly5px1Vfg+zsbNy/fx9dXV0AgGfPnqGlpQWbN28GoH7+8SaTta2tDT6fTzcnKSkJGRkZyq0H8M/1MCIiAnPnzgVgvPwqY9cbr+sB4/Y9u55dH8CuDxaOrjeF5FmmoU+fPmFsbAyJiYm68cTERPT19YXprKaGiKCsrAzZ2dnIyMgAAC3zROvx/v37KT/HUKutrUV7ezuePn0adEz17ADw9u1bVFdXo6ysDMeOHUNraysOHToEs9mMoqIi5degvLwcg4ODSE9PR1RUFMbGxlBZWYnCwkIAxvgaCJhM1r6+PsTExGDevHlBc1S7Po6MjMDlcmHnzp2YM2cOAGPlVx273lhdDxi779n17PoAdr1euLresBvtgIiICN3HIhI0ppqSkhI8f/4cLS0tQcdUXI8PHz7A6XTi3r17iI2N/eU8FbMH+P1+ZGVl4eTJkwCAzMxMvHz5EtXV1SgqKtLmqboGdXV1qKmpwbVr17Bs2TJ4PB6UlpYiKSkJxcXF2jxV80/k/8mq2nr4fD4UFBTA7/fj/Pnz/zpftfxGYqTv7QCjdT3AvmfXs+t/xq4Pb9cb9k/HExISEBUVFfSKRX9/f9CrPyo5ePAgGhoa0NTUBJvNpo1brVYAUHI92tra0N/fD7vdDpPJBJPJhObmZpw9exYmk0nLp2L2gAULFmDp0qW6sSVLlqCnpweA2p9/ADhy5AhcLhcKCgqwfPly7NmzB4cPH8apU6cAqJ9/vMlktVqtGB0dxZcvX345Z7rz+XzYsWMHvF4v3G639go3YIz8RsGuN07XA+x7dj27PoBd/49wd71hN9oxMTGw2+1wu926cbfbjbVr14bprP4cEUFJSQnq6+vx4MEDpKam6o6npqbCarXq1mN0dBTNzc3Tfj02bdqEzs5OeDwe7ZGVlYVdu3bB4/EgLS1N2ewB69atC7rFS1dXF1JSUgCo/fkHgOHhYURG6i93UVFR2i0/VM8/3mSy2u12REdH6+b09vbixYsXSqxHoHi7u7vR2NiI+Ph43XHV8xsJu944XQ+w79n17PoAdv1f0vUheUu1aSpwy4/Lly/Lq1evpLS0VGbNmiXv3r0L96mF3P79+8ViscjDhw+lt7dXewwPD2tzqqqqxGKxSH19vXR2dkphYeG0veXBvxn/LqQi6mdvbW0Vk8kklZWV0t3dLVevXpWZM2dKTU2NNkflNSguLpbk5GTtlh/19fWSkJAgR48e1eaolH9oaEg6Ojqko6NDAMjp06elo6NDe6fNyWR1OBxis9mksbFR2tvbZePGjdPmlh+/y+/z+WTr1q1is9nE4/Horoffv3/XnmM65yc9dr1xu17EWH3PrmfXs+v/rq439EZbROTcuXOSkpIiMTExsmrVKu0WGKoBMOHjypUr2hy/3y/Hjx8Xq9UqZrNZ1q9fL52dneE76T/o5+I1QvZbt25JRkaGmM1mSU9Pl4sXL+qOq7wGX79+FafTKQsXLpTY2FhJS0uTiooK3cVWpfxNTU0Tfr8XFxeLyOSyfvv2TUpKSiQuLk5mzJghW7ZskZ6enjCk+e9+l9/r9f7yetjU1KQ9x3TOT8HY9Ve0OSpd6ybDaH3PrmfXs+v/nq6PEBEJze/GiYiIiIiIiMiw/6NNRERERERE9Cdwo01EREREREQUQtxoExEREREREYUQN9pEREREREREIcSNNhEREREREVEIcaNNREREREREFELcaBMRERERERGFEDfaRERERERERCHEjTYRERERERFRCHGjTURERERERBRC3GgTERERERERhRA32kREREREREQh9D9BSl3fJLcb/QAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(12,4), sharex=True)\n",
+    "\n",
+    "ax[0].plot(x, y_true_rising, label=\"true\")\n",
+    "ax[0].scatter(x, y_rising, s=12, label=\"noisy\")\n",
+    "ax[0].set_title(\"Rising S-curve\")\n",
+    "ax[0].legend()\n",
+    "\n",
+    "ax[1].plot(x, y_true_falling, label=\"true\")\n",
+    "ax[1].scatter(x, y_falling, s=12, label=\"noisy\")\n",
+    "ax[1].set_title(\"Falling S-curve\")\n",
+    "ax[1].legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95c826d1-9223-4fb2-9ada-d9fc1acad2fb",
+   "metadata": {},
+   "source": [
+    "## Fit the Rising S-curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "37d2fda4-40b8-4ea2-8f2c-6c3f2384d158",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "== Tuned fit settings ==\n",
+      "max_calls : 500\n",
+      "tolerance : 0.01\n",
+      "compute_erros: True\n",
+      "\n",
+      "\n",
+      "== Results ==\n",
+      "True rising params:          [100.     0.25  60.     6.   120.     1.  ]\n",
+      "lmfit rising result:         [ 99.467831   0.287017  60.078405   5.715461 117.257939   0.967234]\n",
+      "minuit_grad (obj api ) rising result:\n",
+      "{'par': array([ 99.288864,   0.291575,  60.091759,   5.675642, 117.08836 ,\n",
+      "         0.963865]),\n",
+      " 'par_err': array([2.853807, 0.104648, 0.764892, 0.930086, 8.309872, 0.191738]),\n",
+      " 'chi2': array([7.28966])}\n"
+     ]
+    }
+   ],
+   "source": [
+    "res_r_lmfit = fit_scurve(x, y_rising)\n",
+    "\n",
+    "model_r = RisingScurve()\n",
+    "# model_r.FixParameter(0, 100)  # Fixed p0 = 100 (optimizer does not touch the value)\n",
+    "model_r.SetParameter(4, 100) # Start value p4 = 100 \n",
+    "print(\"== Tuned fit settings ==\")\n",
+    "model_r.compute_errors = True\n",
+    "model_r.max_calls = 500\n",
+    "model_r.tolerance = 0.01\n",
+    "print(f\"max_calls : {model_r.max_calls}\")\n",
+    "print(f\"tolerance : {model_r.tolerance}\")\n",
+    "print(f\"compute_erros: {model_r.compute_errors}\")\n",
+    "print(\"\\n\")\n",
+    "print(\"== Results ==\")\n",
+    "res_r_munuit_obj = model_r.fit(x, y_rising, np.sqrt(y_rising))\n",
+    "\n",
+    "print(\"True rising params:         \", p_true_rising)\n",
+    "print(\"lmfit rising result:        \", res_r_lmfit)\n",
+    "print(\"minuit_grad (obj api ) rising result:\")\n",
+    "pprint(res_r_munuit_obj, sort_dicts=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ebf1328-954a-43ce-953b-c402fd7998f9",
+   "metadata": {},
+   "source": [
+    "## Fit the Falling S-curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5ce22d2d-1adb-47d9-bf36-6cb535fa8b77",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True falling params:   [100.     0.25  60.     6.   120.    -1.  ]\n",
+      "lmfit falling result:   [101.734479   0.225786  60.251971   6.092915 118.476735  -0.998677]\n",
+      "minuit_grad (obj api ) falling result\n",
+      "{'par': array([101.734403,   0.225778,  60.251667,   6.093245, 118.479457,\n",
+      "        -0.998603]),\n",
+      " 'chi2': array([1865.728851])}\n"
+     ]
+    }
+   ],
+   "source": [
+    "res_f_lmfit = fit_scurve2(x, y_falling)\n",
+    "\n",
+    "model_f = FallingScurve()\n",
+    "model_f.SetParameter(2, 20) # set start p2 = 20\n",
+    "model_f.SetParameter(4, 90) # set start p4 = 90\n",
+    "model_f.SetParLimits(4, 80, 130)\n",
+    "model_f.max_calls = 150 # this is limits the number of calls done by the minimizer (increasing the `max_calls` may help if the fit fails) \n",
+    "res_f_munuit_obj = model_f.fit(x, y_falling)\n",
+    "\n",
+    "print(\"True falling params:  \", p_true_falling)\n",
+    "print(\"lmfit falling result:  \", res_f_lmfit)\n",
+    "print(\"minuit_grad (obj api ) falling result\")\n",
+    "pprint(res_f_munuit_obj, sort_dicts=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fe72503a-fc77-4b17-9b17-b98055e2fc87",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(12,4))\n",
+    "\n",
+    "# rising\n",
+    "ax[0].plot(x, y_true_rising, label=\"true\")\n",
+    "ax[0].scatter(x, y_rising, s=12, label=\"data\")\n",
+    "ax[0].scatter(x, scurve(x, res_r_lmfit[:6]), s=20, marker=\"x\", label=\"lmfit\")\n",
+    "ax[0].plot(x, model_r(x, res_r_munuit_obj['par']), linewidth=1, color=\"green\", label=\"minuit\")\n",
+    "ax[0].set_title(\"Rising S-curve fits\")\n",
+    "ax[0].legend()\n",
+    "\n",
+    "# falling\n",
+    "ax[1].plot(x, y_true_falling, label=\"true\")\n",
+    "ax[1].scatter(x, y_falling, s=12, label=\"data\")\n",
+    "ax[1].scatter(x, scurve2(x, res_f_lmfit[:6]), s=20, marker=\"x\", label=\"lmfit\")\n",
+    "ax[1].plot(x, model_f(x, res_f_munuit_obj['par']), linewidth=1, color=\"green\", label=\"minuit\")\n",
+    "ax[1].set_title(\"Falling S-curve fits\")\n",
+    "ax[1].legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c42ad91-fd59-47ba-b90d-8300f97eb27a",
+   "metadata": {},
+   "source": [
+    "## Quick error check"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3ba40e9a-0c64-4ef6-ba90-b1116f99b862",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rising abs error lmfit :  [0.532169 0.037017 0.078405 0.284539 2.742061 0.032766]\n",
+      "Rising abs error minuit_grad:  [0.711136 0.041575 0.091759 0.324358 2.91164  0.036135]\n",
+      "\n",
+      "\n",
+      "Falling abs error lmfit :  [1.734479 0.024214 0.251971 0.092915 1.523265 0.001323]\n",
+      "Falling abs error minuit_grad:  [1.734403 0.024222 0.251667 0.093245 1.520543 0.001397]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Rising abs error lmfit : \", np.abs(res_r_lmfit[:6] - p_true_rising))\n",
+    "print(\"Rising abs error minuit_grad: \", np.abs(res_r_munuit_obj['par'] - p_true_rising))\n",
+    "print(\"\\n\")\n",
+    "print(\"Falling abs error lmfit : \", np.abs(res_f_lmfit[:6] - p_true_falling))\n",
+    "print(\"Falling abs error minuit_grad: \", np.abs(res_f_munuit_obj['par'] - p_true_falling))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9bbabfd-e24e-43ec-8622-983b3757d7a9",
+   "metadata": {},
+   "source": [
+    "## Benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "dc0b52b6-9fc3-453d-b6eb-e325a2b8342e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rising  lmfit       : 0.445 ms\n",
+      "Rising  minuit2     : 0.322 ms\n",
+      "\n",
+      "Falling lmfit       : 0.392 ms\n",
+      "Falling minuit2     : 0.309 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "def bench(fn, n_repeats=200):\n",
+    "    # warmup\n",
+    "    for _ in range(3):\n",
+    "        fn()\n",
+    "\n",
+    "    t0 = time.perf_counter()\n",
+    "    for _ in range(n_repeats):\n",
+    "        res = fn()\n",
+    "    t1 = time.perf_counter()\n",
+    "    return res, (t1 - t0) / n_repeats\n",
+    "\n",
+    "model_rising  = RisingScurve()\n",
+    "model_falling = FallingScurve()\n",
+    "\n",
+    "res_r_lmfit,  t_r_lmfit       = bench(lambda: fit_scurve(x, y_rising))\n",
+    "res_r_minuit, t_r_minuit_grad = bench(lambda: model_rising.fit(x, y_rising))\n",
+    "\n",
+    "res_f_lmfit,  t_f_lmfit       = bench(lambda: fit_scurve2(x, y_falling))\n",
+    "res_f_minuit, t_f_minuit_grad = bench(lambda: model_falling.fit(x, y_falling))\n",
+    "\n",
+    "print(f\"Rising  lmfit       : {1e3*t_r_lmfit:.3f} ms\")\n",
+    "print(f\"Rising  minuit2     : {1e3*t_r_minuit_grad:.3f} ms\")\n",
+    "print()\n",
+    "print(f\"Falling lmfit       : {1e3*t_f_lmfit:.3f} ms\")\n",
+    "print(f\"Falling minuit2     : {1e3*t_f_minuit_grad:.3f} ms\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e694c18a-a203-4132-a472-c93c4d3ce853",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/python/tests/test_Minuit2_scurve_3d.ipynb b/python/tests/test_Minuit2_scurve_3d.ipynb
new file mode 100644
index 0000000..3aab57a
--- /dev/null
+++ b/python/tests/test_Minuit2_scurve_3d.ipynb
@@ -0,0 +1,509 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "efef8e20-6571-4561-8f0b-6048b57907de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import random\n",
+    "import numpy as np\n",
+    "from scipy.special import erf\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.gridspec import GridSpec\n",
+    "import sys\n",
+    "# sys.path.insert(0, '/home/ferjao_k/sw/aare/build')\n",
+    "sys.path.insert(0, '/home/kferjaoui/sw/aare/build')\n",
+    "\n",
+    "from aare import fit_scurve2\n",
+    "from aare import FallingScurve, fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2536939d-8f34-438e-850a-6f3bdf48f658",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/kferjaoui/sw/aare/build/aare/__init__.py'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import aare\n",
+    "aare.__file__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "35772e2c-37c6-4986-a9c0-7dca4a034827",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROWS = 100\n",
+    "COLS = 100\n",
+    "N_SCAN = 100\n",
+    "NOISE_FRAC = 0.05 # fraction of step height (p4)\n",
+    "SEED = 42\n",
+    "N_THREADS = 4\n",
+    "\n",
+    "N_REPEATS = 10\n",
+    "N_WARMUP  = 3    # untimed iterations (icache + branch predictor warmup)\n",
+    "COOLDOWN  = 2.0  # seconds between (method, thread_count) pairs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d35abe9-8d28-4417-ad49-ff1e18e298c1",
+   "metadata": {},
+   "source": [
+    "## Synthetic data genrator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "06c8fddb-56d9-4f84-8b21-4ffd8b7bd26f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Models in use\n",
+    "def scurve(x, p0, p1, p2, p3, p4, p5): # rising Scurve\n",
+    "    z = (x - p2) / (np.sqrt(2) * p3)\n",
+    "    return (p0 + p1 * x) + 0.5 * (1 + erf(z)) * (p4 + p5 * (x - p2))\n",
+    "\n",
+    "def scurve2(x, p0, p1, p2, p3, p4, p5): # Falling Scurve\n",
+    "    z = (x - p2) / (np.sqrt(2) * p3)\n",
+    "    return (p0 + p1 * x) + 0.5 * (1 - erf(z)) * (p4 + p5 * (x - p2))\n",
+    "    \n",
+    "def generate_3d_scurve_data(fn, rows, cols, n_scan, noise_frac, seed):\n",
+    "    \"\"\"\n",
+    "    Synthetic detector image stack with per-pixel rising S-curves.\n",
+    " \n",
+    "    Returns\n",
+    "    -------\n",
+    "    x       : (n_scan,)\n",
+    "    y       : (rows, cols, n_scan)\n",
+    "    y_err   : (rows, cols, n_scan)\n",
+    "    truths  : dict  {name: (rows, cols) array}\n",
+    "    \"\"\"\n",
+    "    rng = np.random.default_rng(seed)\n",
+    " \n",
+    "    # Per-pixel ground truth - shapes (rows, cols)\n",
+    "    p0_true = rng.uniform(-5, 5, (rows, cols))          # baseline offset\n",
+    "    p1_true = rng.uniform(-0.02, 0.02, (rows, cols))    # baseline slope\n",
+    "    p2_true = rng.uniform(30, 70, (rows, cols))          # threshold\n",
+    "    p3_true = rng.uniform(2, 8, (rows, cols))            # width\n",
+    "    p4_true = rng.uniform(200, 800, (rows, cols))        # step height\n",
+    "    p5_true = rng.uniform(-0.5, 0.5, (rows, cols))      # post-step slope\n",
+    " \n",
+    "    x = np.linspace(0, 100, n_scan)\n",
+    " \n",
+    "    # broadcast: x -> (1, 1, n_scan),  params -> (rows, cols, 1)\n",
+    "    y_clean = fn(\n",
+    "        x[None, None, :],\n",
+    "        p0_true[:, :, None], p1_true[:, :, None],\n",
+    "        p2_true[:, :, None], p3_true[:, :, None],\n",
+    "        p4_true[:, :, None], p5_true[:, :, None],\n",
+    "    )\n",
+    " \n",
+    "    noise_sigma = noise_frac * p4_true[:, :, None] * np.ones_like(y_clean)\n",
+    "    y = y_clean + rng.normal(0, noise_sigma)\n",
+    "    y_err = noise_sigma.copy()\n",
+    " \n",
+    "    truths = dict(p0=p0_true, p1=p1_true, p2=p2_true,\n",
+    "                  p3=p3_true, p4=p4_true, p5=p5_true)\n",
+    "    return x, y, y_err, truths    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dc0b52b6-9fc3-453d-b6eb-e325a2b8342e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bench(fn, n_warmup=N_WARMUP, n_repeats=N_REPEATS):\n",
+    "    for _ in range(n_warmup):\n",
+    "        res = fn()\n",
+    "    times = []\n",
+    "    for _ in range(n_repeats):\n",
+    "        t0 = time.perf_counter()\n",
+    "        res = fn()\n",
+    "        t1 = time.perf_counter()\n",
+    "        times.append(t1 - t0)\n",
+    "    return res, times"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24ec20a3-8117-4718-9236-19d37897e04e",
+   "metadata": {},
+   "source": [
+    "## Data generation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "13bcd5a6-8f95-4300-a002-04023d0f9bab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating synthetic data: 100x100 pixels, 100 scan points, noise_frac=0.05\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Generating synthetic data: {ROWS}x{COLS} pixels, \"\n",
+    "      f\"{N_SCAN} scan points, noise_frac={NOISE_FRAC}\\n\")\n",
+    "\n",
+    "x, y, yerr, truths = generate_3d_scurve_data(\n",
+    "    scurve2, ROWS, COLS, N_SCAN, NOISE_FRAC, SEED\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e16e2559-059b-4058-88c9-a9632988fb74",
+   "metadata": {},
+   "source": [
+    "## Define the tested/benched methods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1f6bc651-80c1-41dd-8a05-7f15aba006aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = FallingScurve()\n",
+    "METHOD_DEFS = [\n",
+    "    (\"lmfit (LM)\",\n",
+    "     lambda nt: lambda: fit_scurve2(x, y, n_threads=nt),\n",
+    "     \"#FF9800\", {\"linewidth\": 3.0, \"linestyle\": \"-\"}),\n",
+    "\n",
+    "    (\"Minuit2 (analytic)\",\n",
+    "     lambda nt: lambda: model.fit(x, y, n_threads=nt),\n",
+    "     \"#4CAF50\", {\"linewidth\": 4.0, \"linestyle\": \":\"}),\n",
+    "]\n",
+    "\n",
+    "colors = {label: c for label, _, c, _ in METHOD_DEFS}\n",
+    "styles = {label: s for label, _, _, s in METHOD_DEFS}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc2f50a4-ea9d-4b0d-86f6-39b2f71e9592",
+   "metadata": {},
+   "source": [
+    "## Single-call benchmard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5a417145-ce42-4c3a-a7bf-05ff97ba0450",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Method                     time (ms)    med|dp0|    med|dp1|    med|dp2|    med|dp3|    med|dp4|    med|dp5|\n",
+      "--------------------------------------------------------------------------------------------------------------\n",
+      "[lmfit (LM)            ]   1030.91 ms     22.1470      0.2336      0.2184      0.3132     14.3955      0.3997\n",
+      "[Minuit2 (analytic)    ]    435.57 ms     17.1099      0.2147      0.2293      0.2664     11.9425      0.3518  chi2/ndf=596.8385\n"
+     ]
+    }
+   ],
+   "source": [
+    "PARAM_NAMES = [\"p0\", \"p1\", \"p2\", \"p3\", \"p4\", \"p5\"]\n",
+    "ndf = N_SCAN - len(PARAM_NAMES)\n",
+    "# NOTE: fit_scurve returns Ndf = n_scan - 2 in its dict, which looks wrong\n",
+    "# for a 6-parameter model.  We use our own ndf = n_scan - 6 everywhere.\n",
+    "\n",
+    "def extract_result(label, res):\n",
+    "    if isinstance(res, dict):\n",
+    "        out = {\"par\": res[\"par\"]}\n",
+    "        if \"par_err\" in res:\n",
+    "            out[\"par_err\"] = res[\"par_err\"]\n",
+    "        if \"chi2\" in res:\n",
+    "            out[\"chi2\"] = res[\"chi2\"]\n",
+    "        return out\n",
+    "    # fallback: raw array, assume shape (rows, cols, 6)\n",
+    "    return {\"par\": res}\n",
+    "\n",
+    "methods = {}\n",
+    "for label, factory, _, _ in METHOD_DEFS:\n",
+    "    time.sleep(COOLDOWN)\n",
+    "    res, times = bench(factory(N_THREADS))\n",
+    "    entry = extract_result(label, res)\n",
+    "    entry[\"times\"] = times\n",
+    "    methods[label] = entry\n",
+    "\n",
+    "# ---- summary table ----\n",
+    "header = f\"{'Method':24s}  {'time (ms)':>10s}\"\n",
+    "for pn in PARAM_NAMES:\n",
+    "    header += f\"  {'med|d' + pn + '|':>10s}\"\n",
+    "print(header)\n",
+    "print(\"-\" * (26 + 12 + 12 * len(PARAM_NAMES)))\n",
+    " \n",
+    "for name, m in methods.items():\n",
+    "    par   = m[\"par\"]\n",
+    "    med_t = np.median(m[\"times\"]) * 1e3\n",
+    " \n",
+    "    deltas = \"  \".join(\n",
+    "        f\"{np.median(np.abs(par[:, :, i] - truths[pn])):10.4f}\"\n",
+    "        for i, pn in enumerate(PARAM_NAMES)\n",
+    "    )\n",
+    "    chi2_str = \"\"\n",
+    "    if \"chi2\" in m:\n",
+    "        chi2_str = f\"  chi2/ndf={np.median(m['chi2'] / ndf):.4f}\"\n",
+    " \n",
+    "    print(f\"[{name:22s}]  {med_t:8.2f} ms  {deltas}{chi2_str}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b6a3419-52a7-4b1b-92b6-b160d4227f64",
+   "metadata": {},
+   "source": [
+    "## Thread scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "55ecc77c-0823-408d-a811-8e4f4900f332",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Minuit2 (analytic)      n_threads= 1   1616.61 ±  22.09 ms  (161.6606 ± 2.2089 µs/pixel)\n",
+      "  lmfit (LM)              n_threads= 1   2927.48 ±  36.18 ms  (292.7479 ± 3.6182 µs/pixel)\n",
+      "\n",
+      "\n",
+      "  lmfit (LM)              n_threads= 2   1471.15 ±  23.35 ms  (147.1150 ± 2.3353 µs/pixel)\n",
+      "  Minuit2 (analytic)      n_threads= 2    825.64 ±  68.98 ms  (82.5642 ± 6.8976 µs/pixel)\n",
+      "\n",
+      "\n",
+      "  Minuit2 (analytic)      n_threads= 4    507.67 ±  93.00 ms  (50.7666 ± 9.2997 µs/pixel)\n",
+      "  lmfit (LM)              n_threads= 4    965.44 ± 137.53 ms  (96.5436 ± 13.7534 µs/pixel)\n",
+      "\n",
+      "\n",
+      "  Minuit2 (analytic)      n_threads= 8    352.28 ±  41.26 ms  (35.2275 ± 4.1260 µs/pixel)\n",
+      "  lmfit (LM)              n_threads= 8    555.64 ±  25.59 ms  (55.5636 ± 2.5588 µs/pixel)\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "thread_counts = [1, 2, 4, 8]\n",
+    " \n",
+    "thread_times  = {label: [] for label, _, _, _ in METHOD_DEFS}\n",
+    "ttimes_stddev = {label: [] for label, _, _, _ in METHOD_DEFS}\n",
+    " \n",
+    "for nt in thread_counts:\n",
+    "    run_order = list(METHOD_DEFS)\n",
+    "    random.shuffle(run_order)\n",
+    " \n",
+    "    for label, factory, _, _ in run_order:\n",
+    "        time.sleep(COOLDOWN)\n",
+    "        _, times = bench(factory(nt))\n",
+    " \n",
+    "        med = np.median(times) * 1e3\n",
+    "        std = np.std(times)   * 1e3\n",
+    "        thread_times[label].append(med)\n",
+    "        ttimes_stddev[label].append(std)\n",
+    " \n",
+    "        per_px     = med / (ROWS * COLS) * 1e3\n",
+    "        per_px_std = std / (ROWS * COLS) * 1e3\n",
+    "        print(f\"  {label:22s}  n_threads={nt:2d}  \"\n",
+    "              f\"{med:8.2f} ± {std:6.2f} ms  \"\n",
+    "              f\"({per_px:.4f} ± {per_px_std:.4f} µs/pixel)\")\n",
+    "    \n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7aedaf85-0c70-449f-94c1-4acd6ff991eb",
+   "metadata": {},
+   "source": [
+    "## Visualization: Residuals & Performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d5f3152d-be84-420e-8045-9c42ac5c24cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x900 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# FIGURE 1: Residual histograms (6 panels)\n",
+    "truth_arrays = [truths[pn] for pn in PARAM_NAMES]\n",
+    " \n",
+    "fig1, axes1 = plt.subplots(2, 3, figsize=(16, 9))\n",
+    "fig1.suptitle(\n",
+    "    f\"Parameter Residuals — {ROWS}×{COLS} pixels, {N_SCAN} scan points\",\n",
+    "    fontsize=14, fontweight=\"bold\")\n",
+    " \n",
+    "for idx, (pname, truth) in enumerate(zip(PARAM_NAMES, truth_arrays)):\n",
+    "    ax = axes1.flat[idx]\n",
+    " \n",
+    "    res_by_method = {}\n",
+    "    all_res = []\n",
+    "    for mname, m in methods.items():\n",
+    "        residual = (m[\"par\"][:, :, idx] - truth).ravel()\n",
+    "        res_by_method[mname] = residual\n",
+    "        all_res.append(residual)\n",
+    "    all_res = np.concatenate(all_res)\n",
+    " \n",
+    "    lo, hi = np.percentile(all_res, [0.5, 99.5])\n",
+    "    edges  = np.linspace(lo, hi, 101)\n",
+    " \n",
+    "    for mname, r in res_by_method.items():\n",
+    "        ax.hist(r, bins=edges, histtype=\"step\", label=mname,\n",
+    "                color=colors[mname],\n",
+    "                linewidth=styles[mname][\"linewidth\"],\n",
+    "                linestyle=styles[mname][\"linestyle\"])\n",
+    " \n",
+    "    ax.axvline(0, color=\"k\", linestyle=\"--\", linewidth=1, alpha=0.7)\n",
+    "    ax.set_xlabel(f\"Fitted {pname} − True {pname}\")\n",
+    "    ax.set_ylabel(\"Pixel count\")\n",
+    "    ax.set_title(f\"Δ{pname}\")\n",
+    "    ax.legend(fontsize=8)\n",
+    "    ax.grid(alpha=0.3)\n",
+    " \n",
+    "fig1.tight_layout()\n",
+    "\n",
+    "# FIGURE 2: bar chart + thread scaling\n",
+    "fig2 = plt.figure(figsize=(14, 5))\n",
+    "gs   = GridSpec(1, 2, figure=fig2, width_ratios=[1, 1.3])\n",
+    "\n",
+    "# -- Left: bar chart at N_THREADS --\n",
+    "fig2 = plt.figure(figsize=(14, 5))\n",
+    "gs   = GridSpec(1, 2, figure=fig2, width_ratios=[1, 1.3])\n",
+    " \n",
+    "ax2a    = fig2.add_subplot(gs[0])\n",
+    "names   = list(methods.keys())\n",
+    "medians = [np.median(methods[n][\"times\"]) * 1e3 for n in names]\n",
+    "bars    = ax2a.barh(names, medians,\n",
+    "                    color=[colors[n] for n in names],\n",
+    "                    edgecolor=\"white\", height=0.5)\n",
+    "ax2a.set_xlabel(\"Median wall time (ms)\")\n",
+    "ax2a.set_title(f\"Single call — {ROWS}×{COLS} px, {N_THREADS} threads\")\n",
+    "for bar, val in zip(bars, medians):\n",
+    "    ax2a.text(bar.get_width() + max(medians) * 0.02,\n",
+    "              bar.get_y() + bar.get_height() / 2,\n",
+    "              f\"{val:.1f} ms\", va=\"center\", fontsize=10)\n",
+    "ax2a.grid(axis=\"x\", alpha=0.3)\n",
+    "ax2a.set_xlim(0, max(medians) * 1.25)\n",
+    " \n",
+    "ax2b = fig2.add_subplot(gs[1])\n",
+    "for label, _, _, _ in METHOD_DEFS:\n",
+    "    tt = thread_times[label]\n",
+    "    sd = ttimes_stddev[label]\n",
+    "    speedup     = [tt[0] / t for t in tt]\n",
+    "    speedup_err = [\n",
+    "        s * np.sqrt((sd[0] / tt[0])**2 + (sd[i] / tt[i])**2)\n",
+    "        for i, s in enumerate(speedup)\n",
+    "    ]\n",
+    "    ax2b.errorbar(thread_counts, speedup, yerr=speedup_err,\n",
+    "                  fmt=\"o-\", label=label, color=colors[label],\n",
+    "                  linewidth=2, markersize=7, capsize=4)\n",
+    " \n",
+    "ax2b.plot(thread_counts, thread_counts, \"k--\", alpha=0.4, label=\"Ideal linear\")\n",
+    "ax2b.set_xlabel(\"Number of threads\")\n",
+    "ax2b.set_ylabel(\"Speedup vs 1 thread\")\n",
+    "ax2b.set_title(\"Thread scaling\")\n",
+    "ax2b.set_xticks(thread_counts)\n",
+    "ax2b.legend(fontsize=9)\n",
+    "ax2b.grid(alpha=0.3)\n",
+    " \n",
+    "fig2.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cda3dc45-eec8-466a-a0e1-6d62fa37d5da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/Fit.cpp b/src/Fit.cpp
index d7f1c51..f7a175d 100644
--- a/src/Fit.cpp
+++ b/src/Fit.cpp
@@ -1,11 +1,13 @@
 // SPDX-License-Identifier: MPL-2.0
 #include "aare/Fit.hpp"
+#include "aare/Chi2.hpp"
+#include "aare/Models.hpp"
 #include "aare/utils/par.hpp"
 #include "aare/utils/task.hpp"
 #include <lmcurve2.h>
 #include <lmfit.hpp>
 #include <thread>
-
+#include <type_traits>
 #include <array>
 
 namespace aare {
@@ -65,7 +67,7 @@ NDArray<double, 1> scurve2(NDView<double, 1> x, NDView<double, 1> par) {
 } // namespace func
 
 NDArray<double, 1> fit_gaus(NDView<double, 1> x, NDView<double, 1> y) {
-    NDArray<double, 1> result = gaus_init_par(x, y);
+    NDArray<double, 1> result = model::Gaussian::estimate_par(x, y);
     lm_status_struct status;
 
     lmcurve(result.size(), result.data(), x.size(), x.data(), y.data(),
@@ -95,43 +97,7 @@ NDArray<double, 3> fit_gaus(NDView<double, 1> x, NDView<double, 3> y,
     return result;
 }
 
-std::array<double, 3> gaus_init_par(const NDView<double, 1> x,
-                                    const NDView<double, 1> y) {
-    std::array<double, 3> start_par{0, 0, 0};
-    auto e = std::max_element(y.begin(), y.end());
-    auto idx = std::distance(y.begin(), e);
 
-    start_par[0] = *e; // For amplitude we use the maximum value
-    start_par[1] =
-        x[idx]; // For the mean we use the x value of the maximum value
-
-    // For sigma we estimate the fwhm and divide by 2.35
-    // assuming equally spaced x values
-    auto delta = x[1] - x[0];
-    start_par[2] = std::count_if(y.begin(), y.end(),
-                                 [e](double val) { return val > *e / 2; }) *
-                   delta / 2.35;
-
-    return start_par;
-}
-
-std::array<double, 2> pol1_init_par(const NDView<double, 1> x,
-                                    const NDView<double, 1> y) {
-    // Estimate the initial parameters for the fit
-    std::array<double, 2> start_par{0, 0};
-
-    auto y2 = std::max_element(y.begin(), y.end());
-    auto x2 = x[std::distance(y.begin(), y2)];
-    auto y1 = std::min_element(y.begin(), y.end());
-    auto x1 = x[std::distance(y.begin(), y1)];
-
-    start_par[0] =
-        (*y2 - *y1) / (x2 - x1); // For amplitude we use the maximum value
-    start_par[1] =
-        *y1 - ((*y2 - *y1) / (x2 - x1)) *
-                  x1; // For the mean we use the x value of the maximum value
-    return start_par;
-}
 
 void fit_gaus(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
               NDView<double, 1> par_out, NDView<double, 1> par_err_out,
@@ -156,7 +122,7 @@ void fit_gaus(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
     // } lm_status_struct;
 
     lm_status_struct status;
-    par_out = gaus_init_par(x, y);
+    par_out = model::Gaussian::estimate_par(x, y);
     std::array<double, 9> cov{0, 0, 0, 0, 0, 0, 0, 0, 0};
 
     // void lmcurve2( const int n_par, double *par, double *parerr, double
@@ -233,7 +199,7 @@ void fit_pol1(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
     }
 
     lm_status_struct status;
-    par_out = pol1_init_par(x, y);
+    par_out = model::Pol1::estimate_par(x, y);
     std::array<double, 4> cov{0, 0, 0, 0};
 
     lmcurve2(par_out.size(), par_out.data(), par_err_out.data(), cov.data(),
@@ -280,7 +246,7 @@ NDArray<double, 1> fit_pol1(NDView<double, 1> x, NDView<double, 1> y) {
     // throw std::runtime_error("Data, x, data_err must have the same size "
     //                      "and par_out, par_err_out must have size 2");
     // }
-    NDArray<double, 1> par = pol1_init_par(x, y);
+    NDArray<double, 1> par = model::Pol1::estimate_par(x, y);
 
     lm_status_struct status;
     lmcurve(par.size(), par.data(), x.size(), x.data(), y.data(),
@@ -312,35 +278,10 @@ NDArray<double, 3> fit_pol1(NDView<double, 1> x, NDView<double, 3> y,
 
 // ~~ S-CURVES ~~
 
-// SCURVE --
-std::array<double, 6> scurve_init_par(const NDView<double, 1> x,
-                                      const NDView<double, 1> y) {
-    // Estimate the initial parameters for the fit
-    std::array<double, 6> start_par{0, 0, 0, 0, 0, 0};
-
-    auto ymax = std::max_element(y.begin(), y.end());
-    auto ymin = std::min_element(y.begin(), y.end());
-    start_par[4] = *ymin + (*ymax - *ymin) / 2;
-
-    // Find the first x where the corresponding y value is above the threshold
-    // (start_par[4])
-    for (ssize_t i = 0; i < y.size(); ++i) {
-        if (y[i] >= start_par[4]) {
-            start_par[2] = x[i];
-            break; // Exit the loop after finding the first valid x
-        }
-    }
-
-    start_par[3] = 2 * sqrt(start_par[2]);
-    start_par[0] = 100;
-    start_par[1] = 0.25;
-    start_par[5] = 1;
-    return start_par;
-}
 
 // - No error
 NDArray<double, 1> fit_scurve(NDView<double, 1> x, NDView<double, 1> y) {
-    NDArray<double, 1> result = scurve_init_par(x, y);
+    NDArray<double, 1> result = model::RisingScurve::estimate_par(x, y);
     lm_status_struct status;
 
     lmcurve(result.size(), result.data(), x.size(), x.data(), y.data(),
@@ -386,7 +327,7 @@ void fit_scurve(NDView<double, 1> x, NDView<double, 1> y,
     }
 
     lm_status_struct status;
-    par_out = scurve_init_par(x, y);
+    par_out = model::RisingScurve::estimate_par(x, y);
     std::array<double, 36> cov = {0}; // size 6x6
     // std::array<double, 4> cov{0, 0, 0, 0};
 
@@ -430,34 +371,11 @@ void fit_scurve(NDView<double, 1> x, NDView<double, 3> y,
 
 // SCURVE2 ---
 
-std::array<double, 6> scurve2_init_par(const NDView<double, 1> x,
-                                       const NDView<double, 1> y) {
-    // Estimate the initial parameters for the fit
-    std::array<double, 6> start_par{0, 0, 0, 0, 0, 0};
 
-    auto ymax = std::max_element(y.begin(), y.end());
-    auto ymin = std::min_element(y.begin(), y.end());
-    start_par[4] = *ymin + (*ymax - *ymin) / 2;
-
-    // Find the first x where the corresponding y value is above the threshold
-    // (start_par[4])
-    for (ssize_t i = 0; i < y.size(); ++i) {
-        if (y[i] <= start_par[4]) {
-            start_par[2] = x[i];
-            break; // Exit the loop after finding the first valid x
-        }
-    }
-
-    start_par[3] = 2 * sqrt(start_par[2]);
-    start_par[0] = 100;
-    start_par[1] = 0.25;
-    start_par[5] = -1;
-    return start_par;
-}
 
 // - No error
 NDArray<double, 1> fit_scurve2(NDView<double, 1> x, NDView<double, 1> y) {
-    NDArray<double, 1> result = scurve2_init_par(x, y);
+    NDArray<double, 1> result = model::FallingScurve::estimate_par(x, y);
     lm_status_struct status;
 
     lmcurve(result.size(), result.data(), x.size(), x.data(), y.data(),
@@ -503,7 +421,7 @@ void fit_scurve2(NDView<double, 1> x, NDView<double, 1> y,
     }
 
     lm_status_struct status;
-    par_out = scurve2_init_par(x, y);
+    par_out = model::FallingScurve::estimate_par(x, y);
     std::array<double, 36> cov = {0}; // size 6x6
     // std::array<double, 4> cov{0, 0, 0, 0};
 
@@ -545,4 +463,5 @@ void fit_scurve2(NDView<double, 1> x, NDView<double, 3> y,
     RunInParallel(process, tasks);
 }
 
+
 } // namespace aare
\ No newline at end of file
diff --git a/src/utils/task.cpp b/src/utils/task.cpp
index 6eb77b2..1296449 100644
--- a/src/utils/task.cpp
+++ b/src/utils/task.cpp
@@ -8,19 +8,19 @@ std::vector<std::pair<int, int>> split_task(int first, int last,
     std::vector<std::pair<int, int>> vec;
     vec.reserve(n_threads);
 
-    int n_frames = last - first;
+    int n_items = last - first;
 
-    if (n_threads >= n_frames) {
-        for (int i = 0; i != n_frames; ++i) {
-            vec.push_back({i, i + 1});
+    if (n_threads >= n_items) {
+        for (int i = 0; i != n_items; ++i) {
+            vec.push_back({first + i, first + i + 1});
         }
         return vec;
     }
 
-    int step = (n_frames) / n_threads;
+    int step = n_items / n_threads;
     for (int i = 0; i != n_threads; ++i) {
-        int start = step * i;
-        int stop = step * (i + 1);
+        int start = first + step * i;
+        int stop = first + step * (i + 1);
         if (i == n_threads - 1)
             stop = last;
         vec.push_back({start, stop});