diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml
index fede760..52ad7e1 100644
--- a/.github/workflows/testing.yml
+++ b/.github/workflows/testing.yml
@@ -11,23 +11,14 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: [3.7, 3.9, '3.10']
+        python-version: ["3.10", "3.11"]
 
     runs-on: ${{ matrix.os }}
     steps:
     - uses: actions/checkout@v2
-    - name: Cache conda
-      uses: actions/cache@v1
-      env:
-        # Increase this value to reset cache if etc/example-environment.yml has not changed
-        CACHE_NUMBER: 0
-      with:
-        path: ~/conda_pkgs_dir
-        key:
-          ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{
-          hashFiles('.ci_support/environment.yml') }}
     - uses: conda-incubator/setup-miniconda@v2
       with:
+        miniforge-variant: Mambaforge
         activate-environment: mamonca-test
         channel-priority: strict
         environment-file: .ci_support/environment.yml
diff --git a/README.md b/README.md
index f244ec5..2fdadb4 100644
--- a/README.md
+++ b/README.md
@@ -55,6 +55,11 @@ More complete list of examples can be found in `notebooks/first_steps.ipynb`
 
 As a rule of thumb, you can set all input parameters via functions starting with `set_`. Similarly, output values can be obtained via functions whose names start with `get_`. Most notably, you can get all basic output values via `get_output()` in a dictionary. Otherwise, take a look at the list of auto-complete and see their docstrings
 
+## Dependencies
+
+- Cython
+- numpy
+
 ## Notes
 
 - Currently only Linux installation is supported
diff --git a/mamonca/cMC.cpp b/mamonca/cMC.cpp
index 888c86f..4acc094 100644
--- a/mamonca/cMC.cpp
+++ b/mamonca/cMC.cpp
@@ -1,5 +1,6 @@
 #include "cMC.h"
 
+// Random number generator for the Monte Carlo moves
 double RandomNumberFactory::uniform(bool symmetric, double max_value){
     if (symmetric)
         return max_value*(1.0-2.0*((double)rand()/(double)RAND_MAX));
@@ -7,29 +8,37 @@ double RandomNumberFactory::uniform(bool symmetric, double max_value){
         return max_value*((double)rand()/(double)RAND_MAX);
 }
 
+// Vector of random numbers of length `size`
+// whose magnitude is given between -1 and 1
 valarray<double> RandomNumberFactory::on_sphere(int size){
     for(int i=0; i<size; i++)
-        m_new[i] = uniform();
+        m_new[i] = normal();
     m_new *= uniform()/sqrt((m_new*m_new).sum());
     return m_new;
 }
 
+// Random number according to normal distribution
 double RandomNumberFactory::normal(){
     return distribution(generator);
 }
 
+// Vector of random numbers of length `size`
+// with magnitude distributed by normal distribution
 valarray<double> RandomNumberFactory::n_on_sphere(int size){
     return normal()*on_sphere(size);
 }
 
+// Norm of a vector
 double m_norm(valarray<double> mm){
     return sqrt((mm*mm).sum());
 }
 
+// Cross product of two vectors
 valarray<double> m_cross(valarray<double>& m_one, valarray<double> m_two){
     return m_one.cshift(1)*m_two.cshift(2)-m_one.cshift(2)*m_two.cshift(1);
 }
 
+// Power function to accelerate calculations
 double power(double x, int exponent){
     switch(exponent){
         case 1:
@@ -49,6 +58,12 @@ double power(double x, int exponent){
     }
 }
 
+//
+// Depending on the usage, a magnitude dependent term can be defined and
+// implemented here, in which case the value itself and the gradient must
+// be defined
+//
+
 double Magnitude::value(double xxx){return 0;}
 double Square::value(double xxx){ return xxx*xxx; }
 double Quartic::value(double xxx){ return square.value(xxx)*square.value(xxx); }
@@ -80,6 +95,12 @@ valarray<double> Decic::gradient(valarray<double> &m){
     return 10.*m.apply([](double x){return x*x*x*x*x*x*x*x;}).sum()*m;
 }
 
+//
+// Just like for magnitude, pairwise interactions can be defined here
+// if some expert users wish to defined their own Hamiltonian, in which
+// case the value itself and the magnitude must be defined
+//
+
 double Product::value(Atom &neigh, Atom &me){
     return 0.;
 }
@@ -143,6 +164,7 @@ Atom::Atom() : mabs(1), mmax(100), acc(0), count(0), debug(false)
     update_flag(false);
 }
 
+// Check if the energy values are up to date
 void Atom::update_flag(bool ff){
     up_to_date.E.assign(2, ff);
     up_to_date.dE.assign(2, ff);
@@ -202,11 +224,13 @@ void Atom::set_m(valarray<double> m_new, bool diff){
     update_flag(false);
     mabs_tmp = mabs;
     m_tmp = m;
-    if(diff && abs(dm-1)+abs(dphi-1)==0)
-        m += m_new;
-    else if(diff){
-        m += dphi*m_new;
-        m *= m_norm(m_tmp+dm*m_new)/sqrt((m*m).sum());
+    if(diff){
+        if(abs(dm-1)+abs(dphi-1)==0)
+            m += m_new;
+        else{
+            m += dphi*m_new;
+            m *= m_norm(m_tmp+dm*m_new)/sqrt((m*m).sum());
+        }
     }
     else{
         m = m_new;
@@ -219,7 +243,8 @@ void Atom::set_m(valarray<double> m_new, bool diff){
 void Atom::check_consistency() {
     if ( abs(sqrt((m*m).sum())-abs(mabs))>1.0e-8 )
         throw invalid_argument(
-            "mabs: "+to_string(sqrt((m*m).sum()))+" vs. "+to_string(abs(mabs)));
+            "mabs: "+to_string(sqrt((m*m).sum()))+" vs. "+to_string(abs(mabs))
+        );
 }
 
 void Atom::revoke(){
@@ -408,6 +433,7 @@ double average_energy::E_var(int index=0){
 
 void average_energy::reset()
 {
+    // Clear statistics
     EE.assign(2, 0);
     E_sum.assign(2, 0);
     EE_sq.assign(2, 0);
@@ -500,15 +526,13 @@ bool cMC::thermodynamic_integration(){
     return false;
 }
 
-void cMC::run_spin_dynamics(double kBT, int threads){
-    double mu_s = sqrt(2*constants.damping_parameter*constants.hbar*kBT/constants.delta_t);
-    {
-        for (int i=0; i<n_tot; i++)
-            atom[i].calc_spin_dynamics(
-                constants.damping_parameter, constants.delta_t, mu_s, lambda);
-        for (int i=0; i<n_tot; i++)
-            atom[i].update_spin_dynamics();
-    }
+void cMC::run_spin_dynamics(double kBT){
+    double mu_s = sqrt(18*constants.damping_parameter*constants.hbar*kBT/constants.delta_t);
+    for (int i=0; i<n_tot; i++)
+        atom[i].calc_spin_dynamics(
+            constants.damping_parameter, constants.delta_t, mu_s, lambda);
+    for (int i=0; i<n_tot; i++)
+        atom[i].update_spin_dynamics();
     E_tot.add(get_energy(0), true);
     if(thermodynamic_integration())
         E_tot.add(get_energy(1), true);
@@ -585,6 +609,7 @@ double cMC::get_energy(int index=0){
     return EE;
 }
 
+// Gradient descent to minimize the energy
 double cMC::run_gradient_descent(int max_iter, double step_size, double decrement, double diff)
 {
     reset();
@@ -594,9 +619,12 @@ double cMC::run_gradient_descent(int max_iter, double step_size, double decremen
         dot_product = 0;
         for(int i_atom=0; i_atom<n_tot; i_atom++)
         {
+            // if dot_product is negative, it means the step size is too large,
+            // otherwise it is too small
             dot_product += atom[i_atom].run_gradient_descent(
                 step_size, lambda*thermodynamic_integration());
             residual = atom[i_atom].get_gradient_residual();
+            // maximum residual for the convergence
             if(i_atom==0 || residual_max<residual)
                 residual_max = residual;
         }
@@ -610,7 +638,8 @@ double cMC::run_gradient_descent(int max_iter, double step_size, double decremen
     return residual_max;
 }
 
-void cMC::run(double T_in, int number_of_iterations, int threads){
+// run for both MC and spin dynamics
+void cMC::run(double T_in, int number_of_iterations){
     double kBT = constants.kB*T_in;
     vector<double> dEE_tot;
     auto begin = std::chrono::high_resolution_clock::now();
@@ -622,8 +651,9 @@ void cMC::run(double T_in, int number_of_iterations, int threads){
     }
     for(int iter=0; iter<number_of_iterations; iter++)
     {
+        // In both cases the number of steps corresponds to the number of atoms
         if (spin_dynamics_flag)
-            run_spin_dynamics(kBT, threads);
+            run_spin_dynamics(kBT);
         else
             run_mc(kBT);
         magnetization_hist.push_back(m_norm(magnetization));
@@ -635,10 +665,12 @@ void cMC::run(double T_in, int number_of_iterations, int threads){
     steps_per_second = n_tot*number_of_iterations/double(duration.count())*1.0e6;
 }
 
+// Used only for external tools
 double cMC::get_steps_per_second(){
     return (double)steps_per_second;
 }
 
+// Used only for external tools
 vector<double> cMC::get_magnetic_moments(){
     vector<double> m(n_tot*3);
     for(int i_atom=0; i_atom<n_tot; i_atom++)
@@ -647,6 +679,7 @@ vector<double> cMC::get_magnetic_moments(){
     return m;
 }
 
+// Used only for external tools
 vector<double> cMC::get_magnetic_gradients(){
     vector<double> m(n_tot*3);
     valarray<double> grad(3);
@@ -659,8 +692,10 @@ vector<double> cMC::get_magnetic_gradients(){
     return m;
 }
 
+// Set the initial magnetic moments
 void cMC::set_magnetic_moments(vector<double> m_in)
 {
+    // Check whether there are 3 * n_atoms entries
     if(int(m_in.size())!=3*n_tot)
         throw invalid_argument("Length of magnetic moments not correct");
     for(int i_atom=0; i_atom<n_tot; i_atom++)
@@ -671,20 +706,24 @@ void cMC::set_magnetic_moments(vector<double> m_in)
     reset();
 }
 
+// Used only for external tools
 double cMC::get_mean_energy(int index){
     return E_tot.E_mean(index);
 }
 
+// Used only for external tools
 double cMC::get_energy_variance(int index){
     return E_tot.E_var(index);
 }
 
+// Used only for external tools
 double cMC::get_acceptance_ratio(){
     if(MC_count==0)
         return 0;
     return acc/(double)MC_count;
 } 
 
+// Used only for external tools
 vector<double> cMC::get_acceptance_ratios(){
     vector<double> v(n_tot);
     for(int i=0; i<n_tot; i++)
@@ -694,6 +733,7 @@ vector<double> cMC::get_acceptance_ratios(){
 
 void cMC::set_magnitude(vector<double> dm, vector<double> dphi, vector<int> flip)
 {
+    // Check whether magnitude is defined for all atoms
     if(int(dm.size())!=int(dphi.size()) || n_tot!=int(dm.size()))
         throw invalid_argument("Length of vectors not consistent");
     for(int i=0; i<n_tot; i++)
@@ -717,19 +757,30 @@ void cMC::switch_spin_dynamics(bool on, double damping_parameter, double delta_t
     constants.delta_t = delta_t;
 }
 
-void cMC::set_metadynamics(double aa, double bb, double cc, int dd, double ee, int ff)
-{
-    meta.set_metadynamics(aa, bb, cc, dd, ee ,ff);
+    
+void cMC::set_metadynamics(
+    double max_range_in,
+    double energy_increment_in,
+    double length_scale_in,
+    int bins,
+    double cutoff_in,
+    int derivative
+) {
+    meta.set_metadynamics(
+        max_range_in, energy_increment_in, length_scale_in, bins, cutoff_in, derivative
+    );
 }
 
 void cMC::update_magnetization(int mc_id, bool backward)
 {
+    // backward is used for not-accepted steps
     if (backward)
         magnetization -= atom[mc_id].delta_m()/(double)n_tot;
     else
         magnetization += atom[mc_id].delta_m()/(double)n_tot;
 }
 
+// Used only for external tools
 vector<double> cMC::get_magnetization(){
     return magnetization_hist;
 }
@@ -740,6 +791,7 @@ vector<double> cMC::get_histogram(int derivative){
 
 void cMC::reset()
 {
+    // Reset statistics
     acc = 0;
     MC_count = 0;
     E_tot.reset();
@@ -786,11 +838,15 @@ void Metadynamics::set_metadynamics(
     denominator = length_scale_in*length_scale_in*2;
     hist.assign(bins, 0);
     cutoff = cutoff_in*length_scale_in;
+    // Whether to use the derivative of the free energy surface to avoid discontinuity
+    // between bins. From the computational point of view, it makes little sense to
+    // not use derivative
     use_derivative = false;
     if (derivative != 0)
         use_derivative = true;
 }
 
+// Give the gradient at the given magnetic moment. Only used for external tools
 double Metadynamics::get_biased_gradient(double m){
     if (!initialized)
         throw invalid_argument("metadynamics not initialized yet");
@@ -802,6 +858,7 @@ double Metadynamics::get_biased_gradient(double m){
     return hist.at(int(m*0.5/mass));
 }
 
+// Give the energy value at the given magnetic moment. Only used for external tools
 double Metadynamics::get_biased_energy(double m_new, double m_tmp){
     if (!initialized)
         throw invalid_argument("metadynamics not initialized yet");
@@ -840,14 +897,19 @@ vector<double> Metadynamics::get_histogram(vector<double>& magnetization, int de
     if (derivative!=0 && !use_derivative)
         throw invalid_argument("derivative can be taken only if use_derivative is activated");
     vector<double> m_range(hist.size());
+    // First n values are the positions of the magnetic moments
     for (int i=0; i<int(m_range.size()); i++)
         m_range.at(i) = max_range*(0.5+i)/m_range.size();
     if (!use_derivative || derivative!=0)
     {
+        // Second n values are the hist values, which means the derivative
+        // of the free energy surface.
         m_range.insert( m_range.end(), hist.begin(), hist.end() );
         return m_range;
     }
     vector<double> h_tmp (hist.size(), 0);
+    // If the user wishes, they can also get the free energy values, which are not
+    // stored in mamonca, so they must be calculated here.
     for (auto m: magnetization)
         for (int i=i_min(m); i<i_max(m); i++)
             h_tmp.at(i) += gauss_exp(m, i);
diff --git a/mamonca/cMC.h b/mamonca/cMC.h
index 74a5e8a..94b7b2f 100644
--- a/mamonca/cMC.h
+++ b/mamonca/cMC.h
@@ -21,20 +21,20 @@ class RandomNumberFactory{
         normal_distribution<double> distribution;
         valarray<double> m_new;
     public:
-        valarray<double> on_sphere(int size=3); // size
+        valarray<double> on_sphere(int size=3);
         double uniform(bool symmetric=true, double max_value=1.0);
         double normal();
-        valarray<double> n_on_sphere(int size=3); //size
+        valarray<double> n_on_sphere(int size=3);
         RandomNumberFactory(){
             m_new.resize(3, 0);
         }
 } rand_generator;
 
 struct Constants{
-    const double hbar = 0.6582119569;
-    const double kB = 8.617333262145e-5;
+    const double hbar = 0.6582119569;  // eV * fs
+    const double kB = 8.617333262145e-5;  // eV / K
     double damping_parameter = 8.0e-3;
-    double delta_t = 1.0e-3;
+    double delta_t = 1.0e-3;  // fs
 } constants;
 
 struct Product;
@@ -134,7 +134,7 @@ class cMC{
         average_energy E_tot;
         bool thermodynamic_integration();
         void run_mc(double);
-        void run_spin_dynamics(double, int);
+        void run_spin_dynamics(double);
         bool metropolis(double, double);
         vector<int> selectable_id;
         valarray<double> magnetization;
@@ -147,7 +147,7 @@ class cMC{
         ~cMC();
         void create_atoms(int);
         void activate_debug();
-        void run(double, int number_of_iterations=1, int threads=1);
+        void run(double, int number_of_iterations=1);
         void set_lambda(double);
         vector<double> get_magnetic_moments();
         vector<double> get_magnetic_gradients();
@@ -178,6 +178,9 @@ class cMC{
         vector<double> get_histogram(int);
 };
 
+//
+// On-site longitudinal component. Value and gradient must be defined
+//
 struct Magnitude{
     virtual double value(double);
     virtual valarray<double> gradient(valarray<double>&);
@@ -208,6 +211,10 @@ struct Decic : Magnitude {
     valarray<double> gradient(valarray<double>&);
 } decic;
 
+//
+// Pairwise interactions. Just like for Magnitude,
+// value and gradient must be defined
+//
 struct Product{
     virtual double value(Atom&, Atom&);
     virtual double diff(Atom&, Atom&);
diff --git a/mamonca/cMC.pxd b/mamonca/cMC.pxd
index 780c0ea..c097bfe 100644
--- a/mamonca/cMC.pxd
+++ b/mamonca/cMC.pxd
@@ -11,7 +11,7 @@ cdef extern from "cMC.h":
         void set_heisenberg_coeff(vector[double], vector[int], vector[int], int, int) except +
         void clear_landau_coeff(int) except +
         void clear_heisenberg_coeff(int) except +
-        void run(double, int, int) except +
+        void run(double, int) except +
         void activate_debug()
         vector[double] get_magnetic_moments()
         vector[double] get_magnetic_gradients()
diff --git a/mamonca/mc.pyx b/mamonca/mc.pyx
index 23431eb..5c2a018 100644
--- a/mamonca/mc.pyx
+++ b/mamonca/mc.pyx
@@ -192,7 +192,7 @@ cdef class MC:
                 'steps_per_second': self.get_steps_per_second()}
 
 
-    def run(self, temperature, number_of_iterations=1, reset=True, threads=1):
+    def run(self, temperature, number_of_iterations=1, reset=True):
         """
             Args:
                 temperature (float): Temperature in K
@@ -202,7 +202,7 @@ cdef class MC:
         """
         if reset:
             self.c_mc.reset()
-        self.c_mc.run(temperature, number_of_iterations, threads)
+        self.c_mc.run(temperature, number_of_iterations)
 
     def get_acceptance_ratio(self, individual=False):
         """
diff --git a/notebooks/first_steps.ipynb b/notebooks/first_steps.ipynb
index c05d0b4..64e2100 100644
--- a/notebooks/first_steps.ipynb
+++ b/notebooks/first_steps.ipynb
@@ -25,6 +25,8 @@
    "source": [
     "from mamonca import MC\n",
     "import numpy as np\n",
+    "\n",
+    "from tqdm.auto import tqdm\n",
     "import matplotlib.pylab as plt"
    ]
   },
@@ -33,74 +35,235 @@
    "execution_count": 2,
    "id": "a175712e",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyiron_atomistics.atomistics.structure.factory import StructureFactory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8e508b81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create bcc structure (which is the ground state of iron)\n",
+    "bcc = StructureFactory().bulk(\"Fe\", cubic=True).repeat(10)\n",
+    "\n",
+    "neigh = bcc.get_neighbors(num_neighbors=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19d1f767-0aea-41d0-ab77-4388b8c9a575",
+   "metadata": {},
+   "source": [
+    "### Monte Carlo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9eb9e964-4b50-4a46-b8c9-b25f59f3c0be",
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e11d4810f88740d493d345138f54fad7",
+       "model_id": "4b0c343da34b404989f09cf8db1051e9",
        "version_major": 2,
        "version_minor": 0
       },
-      "text/plain": []
+      "text/plain": [
+       "  0%|          | 0/16 [00:00<?, ?it/s]"
+      ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "from pyiron_atomistics.atomistics.structure.factory import StructureFactory"
+    "J = 0.05 * neigh.get_shell_matrix()[0]\n",
+    "\n",
+    "mc = MC(len(bcc))\n",
+    "mc.set_heisenberg_coeff(J)\n",
+    "m_mc_lst = []\n",
+    "T_lst = np.arange(0, 1600, 100)\n",
+    "for T in tqdm(T_lst):\n",
+    "    mc.run(temperature=T, number_of_iterations=20000)\n",
+    "    m_mc_lst.append(mc.get_magnetization()[-1])"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "8e508b81",
+   "cell_type": "markdown",
+   "id": "a3d9c2d5-9725-4184-9a05-dcdfe1c360de",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Create bcc structure (which is the ground state of iron)\n",
-    "bcc = StructureFactory().bulk(\"Fe\", cubic=True).repeat(10)\n",
+    "### Spin Dynamics\n",
     "\n",
-    "neigh = bcc.get_neighbors(num_neighbors=8)"
+    "Spin dynamics can be activated by the command `switch_spin_dynamics`. It takes a default value of 0.008 (unitless) for the damping parameter of the Langevin dynamics, following [this article](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.86.054416), but in order to speed up the calculation, we use the value of 1, which strongly affects the dynamics. You can also see that even with such a high damping value the simulation takes quite some time."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "b2540f68",
+   "id": "a288c556-5bb3-43e5-961e-d5e7d5be36fa",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2bd4b4141b94893b7148dc8373e3754",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/16 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "J = 0.05 * neigh.get_shell_matrix()[0]\n",
     "\n",
     "mc = MC(len(bcc))\n",
     "mc.set_heisenberg_coeff(J)\n",
-    "m_lst = []\n",
+    "mc.switch_spin_dynamics()\n",
+    "m_sd_lst = []\n",
     "T_lst = np.arange(0, 1600, 100)\n",
-    "for T in T_lst:\n",
-    "    mc.run(temperature=T, number_of_iterations=10000)\n",
-    "    m_lst.append(mc.get_magnetization()[-1])"
+    "for T in tqdm(T_lst):\n",
+    "    mc.switch_spin_dynamics(damping_parameter=1)\n",
+    "    mc.run(temperature=T, number_of_iterations=100000)\n",
+    "    m_sd_lst.append(mc.get_magnetization()[-1])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "96e733b9",
+   "id": "e89e68b3-ec2d-4cba-b604-ebea7cbf0cbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.xlabel(\"Temperature K\")\n",
+    "plt.ylabel(\"Magnetization\")\n",
+    "plt.ylim([0, 1])\n",
+    "plt.grid()\n",
+    "plt.plot(T_lst, m_mc_lst, label=\"Monte Carlo\")\n",
+    "plt.plot(T_lst, m_sd_lst, label=\"Spin dynamics\")\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38c184fb",
+   "metadata": {},
+   "source": [
+    "Here, as we chose the Heisenberg parameter randomly, the Curie temperature is somewhat higher than the experimental value of 1,043 K. In addition, since the system is relatively small, the total magnetization after the transition does not completely vanish. The residual magnetization values above the Curie temperature can therefore vary each time you run the simulation. The unit if the magnetization is given by the Heisenberg parameter. You can see that Monte Carlo and spin dynamics are (not so surprisingly) comparable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68e1ce81-e718-456b-a1dd-2f9c3c709fa1",
+   "metadata": {},
+   "source": [
+    "## Reproduce literature values\n",
+    "\n",
+    "Here I do a small comparison between mamonca and the results given [in this paper](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.103.024421). The paper is about Fe-Mn, but for the sake of simplicity, I reproduce the results only for Fe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1a31f058-a750-4d7a-8d4d-ad3061bf6406",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create bcc structure (which is the ground state of iron)\n",
+    "bcc = StructureFactory().bulk(\"Fe\", cubic=True).repeat(20)\n",
+    "\n",
+    "neigh = bcc.get_neighbors(num_neighbors=80)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2ed26f5d-c6ee-48d3-b37d-0daba5e3912f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "J_lst = np.array([3.39, 2.26, 0.83, 0.42, 0.44]) / 1000\n",
+    "A = -0.259\n",
+    "B = 0.0276"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6d446af4-3b94-4749-80fc-070baf05eefd",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e70717fe59d454f8a01b357bbf8f3a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fb01e85abd0>]"
+       "  0%|          | 0/16 [00:00<?, ?it/s]"
       ]
      },
-     "execution_count": 6,
      "metadata": {},
-     "output_type": "execute_result"
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mc = MC(len(bcc))\n",
+    "for nn, JJ in zip(neigh.get_shell_matrix()[:len(J_lst)], J_lst):\n",
+    "    mc.set_heisenberg_coeff(nn * JJ)\n",
+    "mc.set_landau_coeff(A, 2)\n",
+    "mc.set_landau_coeff(B, 4)\n",
+    "m_lst = []\n",
+    "T_lst = np.arange(0, 1600, 100)\n",
+    "\n",
+    "for T in tqdm(T_lst):\n",
+    "    mc.run(temperature=T, number_of_iterations=20000)\n",
+    "    m_lst.append(mc.get_magnetization()[-100:].mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "76c25102-0caf-4907-8248-98358a2317c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"../references/magnetization.dat\", \"r\") as f:\n",
+    "    data = np.array(f.read().split(), dtype=float).reshape(-1, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0f20ea8e-18aa-43d0-b05e-730ab00ac09e",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -110,18 +273,20 @@
     }
    ],
    "source": [
-    "plt.xlabel(\"Temperature K\")\n",
+    "plt.plot(*data.T, label=\"reference\")\n",
     "plt.ylabel(\"Magnetization\")\n",
-    "plt.ylim([0, 1])\n",
-    "plt.plot(T_lst, m_lst)"
+    "plt.xlabel(\"Temperature K\")\n",
+    "plt.grid()\n",
+    "plt.plot(T_lst, m_lst / m_lst[0], label=\"mamonca\")\n",
+    "plt.legend();"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "38c184fb",
+   "id": "0568b0b5-4c0b-4840-84ea-22eb352835ce",
    "metadata": {},
    "source": [
-    "Here, as we chose the Heisenberg parameter randomly, the Curie temperature is somewhat higher than the experimental value of 1,043 K. In addition, since the system is relatively small, the total magnetization after the transition does not completely vanish. The residual magnetization values above the Curie temperature can therefore vary each time you run the simulation. The unit if the magnetization is given by the Heisenberg parameter."
+    "We can see that there's a small difference between mamonca and the reference values. The reason is probably because the values in the paper are truncated at 0.01 meV for all J values and , which can have quite some effects on the final results."
    ]
   },
   {
@@ -142,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 12,
    "id": "110b315b",
    "metadata": {},
    "outputs": [],
@@ -159,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 13,
    "id": "7f61d836",
    "metadata": {},
    "outputs": [],
@@ -170,7 +335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 14,
    "id": "a04adf31",
    "metadata": {},
    "outputs": [],
@@ -182,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 15,
    "id": "55a4c4a7",
    "metadata": {},
    "outputs": [],
@@ -193,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 16,
    "id": "12103d66",
    "metadata": {},
    "outputs": [],
@@ -212,13 +377,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 17,
    "id": "d8f7f333",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+oAsvvDBMVQIAmuvnTFRqokOVtW7tKavSiL5pVpcEtBBVI0PB+vDDD5Wbm9ui7ZxzztGmTZvU0NDQ5jF1dXWqqKho8QAAHD/DMJpNlVVaXA3QWpcOQyUlJcrIyGjRlpGRIbfbrbKysjaPycvLk9Pp9D8yMzMjUSoAdGnZTVNlOw8ShtD5dOkwJDX+i6Q50zTbbPdZsmSJXC6X/1FUVBT2GgGgq2NkCJ1ZVK0ZClbfvn1VUtJyX5zS0lI5HA717NmzzWMSEhKUkJAQifIAIGb4FlHvYGQInVCXHhmaMmWK8vPzW7S9+eabmjhxouLi4iyqCgBij29kqOjbw6qqc1tcDdBSVIWhqqoqbdmyRVu2bJHUeOn8li1bVFhYKKlximvevHn+/vPnz9e+ffu0ePFibd++XU899ZSefPJJ/frXv7aifACIWd1T4tUntXHUfRejQ+hkoioMbdq0SRMmTNCECRMkSYsXL9aECRN0yy23SJKKi4v9wUiSsrKytHbtWr377rsaP3687rzzTj344INcVg8AFhjOImp0UlG1Zuj000/3L4BuyzPPPNOq7bTTTtOnn34axqoAAIHIzkjVhl1l+pJF1OhkompkCAAQvRgZQmdFGAIARMT3l9ezYSs6F8IQACAihmV0k2FIZVV1OlRVZ3U5gB9hCAAQEcnxDg3qkSyJ+w2hcyEMAQAiJps7UaMTIgwBACJmBIuo0QkRhgAAEcPIEDojwhAAIGK+v7y+6qj3jQMiiTAEAIiYrF4pirMbqqpz6+vyw1aXA0giDAEAIijObtMJvbtJYqoMnQdhCAAQUf51QyyiRidBGAIARJR/3RAjQ+gkCEMAgIjybcvBhq3oLAhDAICI8o0M7fmmWg0er8XVAIQhAECEDUhPUkq8XfUer/Ydqra6HIAwBACILJvN0DCmytCJEIYAABE3gkXU6EQIQwCAiMtmZAidCGEIABBxw9mwFZ0IYQgAEHG+MLTv2xodrvdYXA1iHWEIABBxvbolqGdKvExT2lXK6BCsRRgCAFjCNzrEHmWwGmEIAGAJ/x5lhCFYjDAEALCEf2SIRdSwGGEIAGAJrihDZ0EYAgBYwjdNdrCiTuU19RZXg1hGGAIAWKJbgkMDuydJYt0QrEUYAgBYZngGU2WwHmEIAGCZ7L5sywHrEYYAAJYZwSJqdAKEIQCAZZpv2GqapsXVIFYRhgAAlhnaO0V2m6HKWrdKKmqtLgcxijAEALBMgsOuob1SJHFFGaxDGAIAWCqbPcpgMcIQAMBSIzLYlgPWIgwBACzFyBCsRhgCAFjKd+PFXaVV8ni5ogyRF3VhaPny5crKylJiYqJycnK0YcOGo/ZfsWKFxo0bp+TkZPXr109XXHGFDh06FKFqAQDHMqhHshLjbKp3e7XvULXV5SAGRVUYWrVqlRYtWqSlS5eqoKBA06ZN03nnnafCwsI2+7/33nuaN2+errzySn3xxRdavXq1PvnkE1111VURrhwA0B6bzfDfb4ipMlghqsLQsmXLdOWVV+qqq67SyJEj9cADDygzM1OPPvpom/03btyoIUOGaMGCBcrKytJ//Md/6JprrtGmTZva/R11dXWqqKho8QAAhFc2i6hhoagJQ/X19dq8ebNyc3NbtOfm5uqDDz5o85ipU6dq//79Wrt2rUzT1MGDB7VmzRqdf/757f6evLw8OZ1O/yMzMzOk7wMA0BrbcsBKUROGysrK5PF4lJGR0aI9IyNDJSUlbR4zdepUrVixQnPmzFF8fLz69u2r9PR0PfTQQ+3+niVLlsjlcvkfRUVFIX0fAIDWmm/LAURa1IQhH8MwWjw3TbNVm8+2bdu0YMEC3XLLLdq8ebNef/117d27V/Pnz2/39RMSEpSWltbiAQAIL9/I0Fdl1apt8FhcDWKNw+oCAtWrVy/Z7fZWo0ClpaWtRot88vLydOqpp+qGG26QJI0dO1YpKSmaNm2a7rrrLvXr1y/sdQMAjq13aoLSk+NUXtOgf5dWafQAp9UlIYZEzchQfHy8cnJylJ+f36I9Pz9fU6dObfOYmpoa2Wwt36LdbpckdkcGgE7EML6/oox1Q4i0qAlDkrR48WI98cQTeuqpp7R9+3Zdf/31Kiws9E97LVmyRPPmzfP3nzlzpl5++WU9+uij2rNnj95//30tWLBAkyZNUv/+/a16GwCANvimyriiDJEWNdNkkjRnzhwdOnRId9xxh4qLizV69GitXbtWgwcPliQVFxe3uOfQ5ZdfrsrKSj388MP61a9+pfT0dJ155pm69957rXoLAIB2cK8hWMUwmS86qoqKCjmdTrlcLhZTA0AYbfrqW1302Ifq70zUB0vOsrocRLlgPr+japoMANB1DWsaGTrgqpXrcIPF1SCWEIYAAJ2CMylO/ZyJkqRdrBtCBBGGAACdxnAWUcMChCEAQKcxnEXUsEDQYejpp5/W6tWrW7WvXr1azz77bEiKAgDEJq4ogxWCDkP33HOPevXq1aq9T58+uvvuu0NSFAAgNg1vtmErFzsjUoIOQ/v27VNWVlar9sGDB7e4xw8AAME6sU832Qzpu5oGfVNZZ3U5iBFBh6E+ffros88+a9W+detW9ezZMyRFAQBiU2KcXUN6pUhiETUiJ+gwdPHFF2vBggV655135PF45PF49Pbbb2vhwoW6+OKLw1EjACCGsIgakRb0dhx33XWX9u3bp7POOksOR+PhXq9X8+bNY80QAKDDsjNS9Y/PSwhDiJigw1B8fLxWrVqlu+66S1u2bFFSUpLGjBnj3x8MAICOGNGX3esRWce9UeuwYcM0bNiwUNYCAICy/WGoSl6vKZvNsLgidHVBrxm66KKLdM8997Rqv++++/TTn/40JEUBAGLX4B7JinfYdLjBo6LvaqwuBzEg6DC0bt06nX/++a3azz33XK1fvz4kRQEAYpfDbtOwPt0ksYgakRF0GKqqqlJ8fHyr9ri4OFVUVISkKABAbOOKMkRS0GFo9OjRWrVqVav2F154QaNGjQpJUQCA2MaGrYikoBdQ33zzzbrwwgu1e/dunXnmmZKkf/7zn1q5cmWbe5YBABAs3yJqRoYQCUGHoVmzZunVV1/V3XffrTVr1igpKUljx47VW2+9pdNOOy0cNQIAYoxvmmxvWbXq3B4lOOwWV4SuLKAw9OCDD+rqq69WYmKiCgsLNWPGjDYXUQMAEAr9nIlKTXSostatvWXVGtE3zeqS0IUFtGZo8eLF/sXRWVlZ+uabb8JaFAAgthmGwSJqRExAI0P9+/fXSy+9pBkzZsg0Te3fv1+1tbVt9h00aFBICwQAxKbhfVO1ad93hCGEXUBh6He/+51++ctf6rrrrpNhGPrBD37Qqo9pmjIMQx6PJ+RFAgBiz3AWUSNCAgpDV199tS655BLt27fPv1i6Z8+e4a4NABDDsjO4vB6REfDVZKmpqRo9erSefvppnXrqqUpISDhq/5UrV2rWrFlKSUnpcJEAgNjjWzO0/7vDqqpzq1vCcW+nCRxV0DddvOyyy44ZhCTpmmuu0cGDB4+rKAAAuqfEq09q4+cNO9gjnIIOQ4EyTTNcLw0AiBG+dUM7WTeEMApbGAIAoKOGs24IEUAYAgB0WmzLgUggDAEAOq0RvmkyRoYQRoQhAECndWKfbjIMqayqXmVVdVaXgy4qbGFo8ODBiouLC9fLAwBiQHK8Q4N6JEtiETXCJ+Aw9PHHH7e4u/SRV4vV1dXpxRdf9D///PPPlZmZGYISAQCxjEXUCLeAw9CUKVN06NAh/3On06k9e/b4n5eXl+uSSy4JbXUAgJjHthwIt4DD0JEjQW3dR4h7CwEAQo1tORBuIV0zZBhGKF8OAIDvrygrqZTXyz+6EXpcTQYA6NSG9EpRnN1Qdb1HX5cftrocdEFBhaFt27bps88+02effSbTNPXll1/6n3/xxRfhqrGF5cuXKysrS4mJicrJydGGDRuO2r+urk5Lly7V4MGDlZCQoBNOOEFPPfVURGoFAHRcnN2mE3p3k8T9hhAeQW0BfNZZZ7VYF/TDH/5QUuP0mGmaYZ8mW7VqlRYtWqTly5fr1FNP1eOPP67zzjtP27Zt06BBg9o8Zvbs2Tp48KCefPJJnXjiiSotLZXb7Q5rnQCA0BreN1VfllTqy5JKnTUyw+py0MUEHIb27t0bzjoCsmzZMl155ZW66qqrJEkPPPCA3njjDT366KPKy8tr1f/111/XunXrtGfPHvXo0UOSNGTIkEiWDAAIAd8iakaGEA4Bh6HBgweHs45jqq+v1+bNm3XTTTe1aM/NzdUHH3zQ5jGvvfaaJk6cqN///vf629/+ppSUFM2aNUt33nmnkpKS2jymrq5OdXXf3+W0oqIidG8CAHBcRnB5PcIoqGkyK5WVlcnj8Sgjo+XwaEZGhkpKSto8Zs+ePXrvvfeUmJioV155RWVlZfrFL36hb7/9tt11Q3l5ebr99ttDXj8A4Pj5RoZ2f1OlBo9XcXau/0HoRN2fpiPXJR1trZLX65VhGFqxYoUmTZqkGTNmaNmyZXrmmWd0+HDbVyQsWbJELpfL/ygqKgr5ewAABGdg9ySlxNvV4DH1VVm11eWgi4maMNSrVy/Z7fZWo0ClpaWtRot8+vXrpwEDBsjpdPrbRo4cKdM0tX///jaPSUhIUFpaWosHAMBahmEou2mq7EumyhBiUROG4uPjlZOTo/z8/Bbt+fn5mjp1apvHnHrqqTpw4ICqqqr8bTt37pTNZtPAgQPDWi8AILSGs4gaYdKhMHTPPfeovLw8RKUc2+LFi/XEE0/oqaee0vbt23X99dersLBQ8+fPl9Q4xTVv3jx//7lz56pnz5664oortG3bNq1fv1433HCD/uu//qvdBdQAgM6JPcoQLh1aQH333Xdr9uzZSk9PD1E5RzdnzhwdOnRId9xxh4qLizV69GitXbvWf6VbcXGxCgsL/f27deum/Px8/fKXv9TEiRPVs2dPzZ49W3fddVdE6gUAhA671yNcDLMDu6umpqZq69atGjp0aChr6lQqKirkdDrlcrlYPwQAFiqrqtPEu96SYUhf3H6OkuOj5oJoWCCYz++oWTMEAIhtvbolqFe3eJmmtOtg1bEPAALUoTC0bds2y2/GCACIHdlMlSEMOhSGMjMzZbfbQ1ULAABH5VtEvZNF1AghpskAAFGDRdQIB8IQACBqZHN5PcKAMAQAiBq+NUOllXX6rrre4mrQVRCGAABRo1uCQwO7N940l6kyhErQYeiiiy7SPffc06r9vvvu009/+tOQFAUAQHvYlgOhFnQYWrdunc4///xW7eeee67Wr18fkqIAAGjPcDZsRYgFHYaqqqoUHx/fqj0uLk4VFRUhKQoAgPZweT1CLegwNHr0aK1atapV+wsvvKBRo0aFpCgAANrj37D1YKU6sKMU4Bf0xi4333yzLrzwQu3evVtnnnmmJOmf//ynVq5cqdWrV4e8QAAAmhvaq5scNkOVtW4Vu2rVPz3J6pIQ5YIOQ7NmzdKrr76qu+++W2vWrFFSUpLGjh2rt956S6eddlo4agQAwC/eYVNWrxTtKq3SjoOVhCF02HFt+Xv++ee3uYgaAIBIGN43tTEMlVTqjOF9rC4HUS7oNUOffPKJPvroo1btH330kTZt2hSSogAAOBr/5fUsokYIBB2Grr32WhUVFbVq//rrr3XttdeGpCgAAI4muy97lCF0gg5D27Zt08knn9yqfcKECdq2bVtIigIA4GhGNIWhXaVVcnu8FleDaBd0GEpISNDBgwdbtRcXF8vhOK4lSAAABCWze7KS4uyqd3u179saq8tBlAs6DE2fPl1LliyRy+Xyt5WXl+u3v/2tpk+fHtLiAABoi81mKDujmyTWDaHjgg5D999/v4qKijR48GCdccYZOuOMM5SVlaWSkhLdf//94agRAIBWfDvYsy0HOiroea0BAwbos88+04oVK7R161YlJSXpiiuu0CWXXKK4uLhw1AgAQCv+bTlYRI0OOq5FPikpKbr66qtDXQsAAAHzb8vByBA66LjC0I4dO/TQQw9p+/btMgxDI0aM0HXXXacRI0aEuj4AANrku9fQV4eqVdvgUWKc3eKKEK2CXjO0Zs0ajR49Wps3b9a4ceM0duxYffrppxozZgx7kwEAIqZ3aoK6J8fJa0r/Lq2yuhxEsaBHhn7zm99oyZIluuOOO1q033rrrbrxxhv105/+NGTFAQDQHsMwlJ2Rqo/2fqsdJZUaPcBpdUmIUkGPDJWUlGjevHmt2n/2s5+ppKQkJEUBABAIFlEjFIIOQ6effro2bNjQqv29997TtGnTQlIUAACB8IUhLq9HRwQ0Tfbaa6/5v581a5ZuvPFGbd68WZMnT5Ykbdy4UatXr9btt98enioBAGiDf8NWRobQAYZpmuaxOtlsgQ0gGYYhj8fT4aI6k4qKCjmdTrlcLqWlpVldDgCgGdfhBo27/U1J0tZbc+VM4n53aBTM53dAKcfr9Qb06GpBCADQuTmT4tTfmSiJ0SEcv6DXDAVqzJgxKioqCtfLAwAgScrm5ovooLCFoa+++koNDQ3henkAACRxRRk6LmxhCACASBjOhq3oIMIQACCqZTe7oiyAa4KAVghDAICodmKfbrIZUnlNg0or66wuB1GIMAQAiGqJcXYN6ZUiiUXUOD5RF4aWL1+urKwsJSYmKicnp827Ybfl/fffl8Ph0Pjx48NbIAAg4rj5IjoibGHo8ccfV0ZGRkhfc9WqVVq0aJGWLl2qgoICTZs2Teedd54KCwuPepzL5dK8efN01llnhbQeAEDnwLYc6IiAw9CMGTPkcrn8z//nf/5H5eXl/ueHDh3SqFGj/M/nzp2rlJSU0FTZZNmyZbryyit11VVXaeTIkXrggQeUmZmpRx999KjHXXPNNZo7d66mTJkS0noAAJ0DI0PoiIDD0BtvvKG6uu8Xpt1777369ttv/c/dbrd27NgR2uqaqa+v1+bNm5Wbm9uiPTc3Vx988EG7xz399NPavXu3br311oB+T11dnSoqKlo8AACdW/N7DXm8XFGG4AQcho68XDHSly+WlZXJ4/G0mnrLyMhQSUlJm8fs2rVLN910k1asWCGHI6A9aZWXlyen0+l/ZGZmdrh2AEB4De6ZoniHTbUNXhV9W2N1OYgyUbeA2jCMFs9N02zVJkkej0dz587V7bffruzs7IBff8mSJXK5XP4HW4oAQOdntxka1qebJGkHU2UIUmDDJWoMIUeGjrZCSLj06tVLdru91ShQaWlpmwu1KysrtWnTJhUUFOi6666T1LjhrGmacjgcevPNN3XmmWe2Oi4hIUEJCQnheRMAgLAZ3jdVXxyo0I6SSp1zUl+ry0EUCTgMmaapyy+/3B8UamtrNX/+fP8i6ebricIhPj5eOTk5ys/P149//GN/e35+vn70ox+16p+WlqZ//etfLdqWL1+ut99+W2vWrFFWVlZY6wUARJZvETUjQwhWwGHosssua/H8Zz/7Was+8+bN63hFR7F48WJdeumlmjhxoqZMmaI///nPKiws1Pz58yU1TnF9/fXX+utf/yqbzabRo0e3OL5Pnz5KTExs1Q4AiH7+RdRcXo8gBRyGnn766XDWEZA5c+bo0KFDuuOOO1RcXKzRo0dr7dq1Gjx4sCSpuLj4mPccAgB0Tb4wtKesWnVujxIcdosrQrQwTHa1O6qKigo5nU65XC6lpaVZXQ4AoB2maWrs7W+qstatfyycppH9+H92LAvm8zvqriYDAKAthmFoRNPoEHuUIRiEIQBAl5HNImocB8IQAKDLYBE1jgdhCADQZfgur2fDVgSDMAQA6DJ8I0Nflx9WZW2DxdUgWhCGAABdRnpyvDLSGm8OvPNglcXVIFoQhgAAXYpvEfVOFlEjQIQhAECX4t+Wg3VDCBBhCADQpQznXkMIEmEIANCl+C+vZ5oMASIMAQC6lGF9UmUY0qHqepVV1VldDqIAYQgA0KUkxds1uEeyJKbKEBjCEACgy8lmETWCQBgCAHQ5bNiKYBCGAABdTnZfNmxF4AhDAIAux3evoV0HK+X1mhZXg86OMAQA6HKG9EpRvN2m6nqPvi4/bHU56OQIQwCALifObtPQ3imSWDeEYyMMAQC6pBGsG0KACEMAgC4pmyvKECDCEACgSxrO7vUIEGEIANAl+fYo2/1NlRo8XourQWdGGAIAdEkD0pOUEm9Xg8fU3rJqq8tBJ0YYAgB0SYZhsG4IASEMAQC6LLblQCAIQwCALsu/YSuLqHEUhCEAQJc1nJEhBIAwBADosnyX1xd+W6OaerfF1aCzIgwBALqsnt0S1KtbvCRp18Eqi6tBZ0UYAgB0aUyV4VgIQwCALo1F1DgWwhAAoEtjWw4cC2EIANCl+abJvmSaDO0gDAEAurRhTSND31TW6dvqeourQWdEGAIAdGndEhzK7JEkiUXUaBthCADQ5bFuCEdDGAIAdHlcUYajibowtHz5cmVlZSkxMVE5OTnasGFDu31ffvllTZ8+Xb1791ZaWpqmTJmiN954I4LVAgA6A+41hKOJqjC0atUqLVq0SEuXLlVBQYGmTZum8847T4WFhW32X79+vaZPn661a9dq8+bNOuOMMzRz5kwVFBREuHIAgJV8YWhnSaVM07S4GnQ2hhlFfypOOeUUnXzyyXr00Uf9bSNHjtQFF1ygvLy8gF7jpJNO0pw5c3TLLbcE1L+iokJOp1Mul0tpaWnHVTcAwFr1bq9G3fK63F5T7990pgakJ1ldEsIsmM/vqBkZqq+v1+bNm5Wbm9uiPTc3Vx988EFAr+H1elVZWakePXq026eurk4VFRUtHgCA6BbvsGlo7xRJjaNDQHNRE4bKysrk8XiUkZHRoj0jI0MlJSUBvcb999+v6upqzZ49u90+eXl5cjqd/kdmZmaH6gYAdA4sokZ7oiYM+RiG0eK5aZqt2tqycuVK3XbbbVq1apX69OnTbr8lS5bI5XL5H0VFRR2uGQBgvREsokY7HFYXEKhevXrJbre3GgUqLS1tNVp0pFWrVunKK6/U6tWrdfbZZx+1b0JCghISEjpcLwCgc/GPDBGGcISoGRmKj49XTk6O8vPzW7Tn5+dr6tSp7R63cuVKXX755Xr++ed1/vnnh7tMAEAn5bui7N/fVMnt8VpcDTqTqBkZkqTFixfr0ksv1cSJEzVlyhT9+c9/VmFhoebPny+pcYrr66+/1l//+ldJjUFo3rx5+tOf/qTJkyf7R5WSkpLkdDotex8AgMjL7J6spDi7Djd49NWhGp3Yp5vVJaGTiJqRIUmaM2eOHnjgAd1xxx0aP3681q9fr7Vr12rw4MGSpOLi4hb3HHr88cfldrt17bXXql+/fv7HwoULrXoLAACL2GyGsjMaAxDbcqC5qLrPkBW4zxAAdB2/WbNVL27arwVnDdPi6dlWl4Mw6pL3GQIAoKN8i6i51xCaIwwBAGKGf1sOpsnQDGEIABAzfGHoq0PVqm3wWFwNOgvCEAAgZvTulqDuyXHymtK/S6usLgedBGEIABAzDMPwrxv6knVDaEIYAgDElBGsG8IRCEMAgJiSzR5lOAJhCAAQU9iwFUciDAEAYsqwpjVDJRW1ctU0WFwNOgPCEAAgpqQlxqm/M1GStLOU0SEQhgAAMch3vyGuKINEGAIAxCDfImq25YBEGAIAxKDhGSyixvcIQwCAmOObJttxsFKmaVpcDaxGGAIAxJwTeneT3WbIdbhBpZV1VpcDixGGAAAxJzHOriE9kyWxiBqEIQBAjBrOImo0IQwBAGISG7bChzAEAIhJbNgKH8IQACAm+UaGdpVWyuPlirJYRhgCAMSkwT1TlOCwqbbBq8Jva6wuBxYiDAEAYpLdZmhYRjdJ3Hwx1hGGAAAxyzdVxrqh2EYYAgDELN8iakaGYhthCAAQs3wjQzsYGYpphCEAQMzy3Xhxb1m16twei6uBVQhDAICY1TctUWmJDnm8pnaXVltdDixCGAIAxCzDML7floOpsphFGAIAxDRfGGJbjthFGAIAxLThXF4f8whDAICY5r+ijJGhmEUYAgDENN802dflh1VZ22BxNbACYQgAENPSk+OVkZYgSdp5sMriamAFwhAAIOYxVRbbCEMAgJg3gsvrYxphCAAQ8xgZim1RF4aWL1+urKwsJSYmKicnRxs2bDhq/3Xr1iknJ0eJiYkaOnSoHnvssQhVCgCIFiP6pklq3KPMNE2Lq0GkRVUYWrVqlRYtWqSlS5eqoKBA06ZN03nnnafCwsI2++/du1czZszQtGnTVFBQoN/+9rdasGCBXnrppQhXDgDozE7s002GIX1bXa+yqnqry0GEGWYUReBTTjlFJ598sh599FF/28iRI3XBBRcoLy+vVf8bb7xRr732mrZv3+5vmz9/vrZu3aoPP/wwoN9ZUVEhp9Mpl8ultLS0jr8JAECndPp97+irQzV67spT9B/DelldDjoomM/vqBkZqq+v1+bNm5Wbm9uiPTc3Vx988EGbx3z44Yet+p9zzjnatGmTGhravpdEXV2dKioqWjwAAF2f735DO1hEHXOiJgyVlZXJ4/EoIyOjRXtGRoZKSkraPKakpKTN/m63W2VlZW0ek5eXJ6fT6X9kZmaG5g0AADo1/7YcLKKOOVEThnwMw2jx3DTNVm3H6t9Wu8+SJUvkcrn8j6Kiog5WDACIBsObFlF/ychQzHFYXUCgevXqJbvd3moUqLS0tNXoj0/fvn3b7O9wONSzZ882j0lISFBCQkJoigYARI3hfbtJknYdrJTXa8pma/8f2uhaomZkKD4+Xjk5OcrPz2/Rnp+fr6lTp7Z5zJQpU1r1f/PNNzVx4kTFxcWFrVYAQPQZ3DNF8Xabauo92v/dYavLQQRFTRiSpMWLF+uJJ57QU089pe3bt+v6669XYWGh5s+fL6lximvevHn+/vPnz9e+ffu0ePFibd++XU899ZSefPJJ/frXv7bqLQAAOqk4u00n9GkcHWIRdWyJmmkySZozZ44OHTqkO+64Q8XFxRo9erTWrl2rwYMHS5KKi4tb3HMoKytLa9eu1fXXX69HHnlE/fv314MPPqgLL7zQqrcAAOjEhmd00/biCu08WKnpo9pegoGuJ6ruM2QF7jMEALFj+bv/1u9f36GZ4/rroUsmWF0OOqBL3mcIAIBw82/YyuX1MYUwBABAE9+Grbu/qVK922txNYgUwhAAAE0GpCepW4JDbq+pvWXVVpeDCCEMAQDQxDAMZWdwRVmsIQwBANCMf4+yEvamjBWEIQAAmvHtUbajpMriShAphCEAAJrJ9l1RxjRZzCAMAQDQjG9kqPDbGlXXuS2uBpFAGAIAoJme3RLUq1vjht27SpkqiwVRtR0HAACRMLxvN5X9u043rN6q7L6p6u9MVD9nkvqnN37tl56oXikJ7GzfRRCGAAA4wtQTeun9fx/SrtKqdkeH4uyG+vpCkjNR/dKT/KGpX3qi+juTlJ4cJ8MgMHV27E12DOxNBgCxxzRNff51hYq+q9GB8sMqdtWq2HVYB8obv5ZW1imQT8/EOJv6N4Wj5qGpnzNR/Zu+pibGhf8NxaBgPr8ZGQIA4AiGYWjMQKfGDHS2+fMGj1cHK2pV7Kr9PiyVH9aBptBUXF6rQ9X1qm3wak9ZtfYc5W7WqQmO78OSbxquWVjq50xSUrw9XG8VIgwBABC0OLtNA7sna2D35Hb71DZ4VOKq1YGmcFTsagpLTeHpQPlhVdS6VVnnVuXBKu082P5i7e7Jca3WLPVvFpoy0hIV7+CaqONFGAIAIAwS4+wa0itFQ3qltNunus7dYvrN97X5iFNNvUff1TTou5oGbStu+67YhiH16pag/ulJOql/msZnpmtCZrpO6N2NRd4BYM3QMbBmCABgFdM0VXHY3Ti61DwslTeNOLkap+rq3d42j09NcGhsplPjBqZrfGa6xg9KV5/UxAi/C2sE8/lNGDoGwhAAoDMzTVOHqutVXF6rwm9r9Nn+chUUletf+1063OBp1X9AepLGZ6ZrXKZT4zO7a8wAZ5dck0QYCiHCEAAgGrk9Xu08WKUtReXaUvSdtha5tLO0stVVcHaboeEZqRo/KF3jBzaOHp3YBabXCEMhRBgCAHQVVXVufba/vDEgFTZ+La2sa9WvW4JDYwc6m0aQGtcf9UmLruk1wlAIEYYAAF1ZseuwPxgdbXqtvzOxcfQoM13jBqZrzECnkuM773VYhKEQIgwBAGKJ2+PVrtKqFqNH7U2vZWek+q9cGz+o8eo1eyeZXiMMhRBhCAAQ66rq3PrXfpd//dGWonIdrGh7em3MAKd/BGl8ZroyLJpeIwyFEGEIAIDW/NNr+xtHkP71tUs19a2n1/o5E/3BaHxm5KbXCEMhRBgCAODYmk+vbS1qml47WCnvESnDZkjZGama4B896q4T+4R+eo0wFEKEIQAAjk/z6TVfQCqpqG3Vb8rQnlp59eSQ/m42agUAAJbrluDQlBN6asoJPf1tJa5abSn6TgVF30+vjeiXamGVhCEAABBBfZ2JOtfZT+eO7idJ8njNNi/ljyS2uAUAAJax2wx1S7B2bIYwBAAAYhphCAAAxDTCEAAAiGmEIQAAENMIQwAAIKYRhgAAQEwjDAEAgJhGGAIAADGNMAQAAGJa1ISh7777TpdeeqmcTqecTqcuvfRSlZeXt9u/oaFBN954o8aMGaOUlBT1799f8+bN04EDByJXNAAA6PSiJgzNnTtXW7Zs0euvv67XX39dW7Zs0aWXXtpu/5qaGn366ae6+eab9emnn+rll1/Wzp07NWvWrAhWDQAAOjvDNE3T6iKOZfv27Ro1apQ2btyoU045RZK0ceNGTZkyRV9++aWGDx8e0Ot88sknmjRpkvbt26dBgwa12aeurk51dXX+5xUVFcrMzJTL5VJaWlrH3wwAAAi7iooKOZ3OgD6/o2Jk6MMPP5TT6fQHIUmaPHmynE6nPvjgg4Bfx+VyyTAMpaent9snLy/PPxXndDqVmZnZkdIBAEAnZ+02sQEqKSlRnz59WrX36dNHJSUlAb1GbW2tbrrpJs2dO/eoCXHJkiVavHix/7nL5dKgQYNUUVERfOEAAMASvs/tQCbALA1Dt912m26//faj9vnkk08kSYZhtPqZaZptth+poaFBF198sbxer5YvX37UvgkJCUpISPA/951MRogAAIg+lZWVcjqdR+1jaRi67rrrdPHFFx+1z5AhQ/TZZ5/p4MGDrX72zTffKCMj46jHNzQ0aPbs2dq7d6/efvvtoNf99O/fX0VFRUpNTQ0oeAXDtx6pqKiI9UhhxHmODM5zZHCeI4PzHDnhOtemaaqyslL9+/c/Zl9Lw1CvXr3Uq1evY/abMmWKXC6XPv74Y02aNEmS9NFHH8nlcmnq1KntHucLQrt27dI777yjnj17Bl2jzWbTwIEDgz4uGGlpafxliwDOc2RwniOD8xwZnOfICce5PtaIkE9ULKAeOXKkzj33XP385z/Xxo0btXHjRv385z/XD3/4wxZXko0YMUKvvPKKJMntduuiiy7Spk2btGLFCnk8HpWUlKikpET19fVWvRUAANDJREUYkqQVK1ZozJgxys3NVW5ursaOHau//e1vLfrs2LFDLpdLkrR//3699tpr2r9/v8aPH69+/fr5H8FcgQYAALq2qLiaTJJ69Oih55577qh9mq8YHzJkSEAryK2UkJCgW2+9tcWCbYQe5zkyOM+RwXmODM5z5HSGcx0VN10EAAAIl6iZJgMAAAgHwhAAAIhphCEAABDTCEMAACCmEYbCbPny5crKylJiYqJycnK0YcOGo/Zft26dcnJylJiYqKFDh+qxxx6LUKXRLZjz/PLLL2v69Onq3bu30tLSNGXKFL3xxhsRrDZ6Bfvn2ef999+Xw+HQ+PHjw1tgFxHsea6rq9PSpUs1ePBgJSQk6IQTTtBTTz0VoWqjV7DnecWKFRo3bpySk5PVr18/XXHFFTp06FCEqo1O69ev18yZM9W/f38ZhqFXX331mMdY8jloImxeeOEFMy4uzvzLX/5ibtu2zVy4cKGZkpJi7tu3r83+e/bsMZOTk82FCxea27ZtM//yl7+YcXFx5po1ayJceXQJ9jwvXLjQvPfee82PP/7Y3Llzp7lkyRIzLi7O/PTTTyNceXQJ9jz7lJeXm0OHDjVzc3PNcePGRabYKHY853nWrFnmKaecYubn55t79+41P/roI/P999+PYNXRJ9jzvGHDBtNms5l/+tOfzD179pgbNmwwTzrpJPOCCy6IcOXRZe3atebSpUvNl156yZRkvvLKK0ftb9XnIGEojCZNmmTOnz+/RduIESPMm266qc3+v/nNb8wRI0a0aLvmmmvMyZMnh63GriDY89yWUaNGmbfffnuoS+tSjvc8z5kzx/zd735n3nrrrYShAAR7nv/xj3+YTqfTPHToUCTK6zKCPc/33XefOXTo0BZtDz74oDlw4MCw1djVBBKGrPocZJosTOrr67V582bl5ua2aM/NzW33Dtgffvhhq/7nnHOONm3apIaGhrDVGs2O5zwfyev1qrKyUj169AhHiV3C8Z7np59+Wrt379att94a7hK7hOM5z6+99pomTpyo3//+9xowYICys7P161//WocPH45EyVHpeM7z1KlTtX//fq1du1amaergwYNas2aNzj///EiUHDOs+hyMmjtQR5uysjJ5PB5lZGS0aM/IyFBJSUmbx5SUlLTZ3+12q6ysTP369QtbvdHqeM7zke6//35VV1dr9uzZ4SixSzie87xr1y7ddNNN2rBhgxwO/lcTiOM5z3v27NF7772nxMREvfLKKyorK9MvfvELffvtt6wbasfxnOepU6dqxYoVmjNnjmpra+V2uzVr1iw99NBDkSg5Zlj1OcjIUJgZhtHiuWmardqO1b+tdrQU7Hn2WblypW677TatWrVKffr0CVd5XUag59nj8Wju3Lm6/fbblZ2dHanyuoxg/jx7vV4ZhqEVK1Zo0qRJmjFjhpYtW6ZnnnmG0aFjCOY8b9u2TQsWLNAtt9yizZs36/XXX9fevXs1f/78SJQaU6z4HOSfa2HSq1cv2e32Vv/KKC0tbZV6ffr27dtmf4fDoZ49e4at1mh2POfZZ9WqVbryyiu1evVqnX322eEsM+oFe54rKyu1adMmFRQU6LrrrpPU+KFtmqYcDofefPNNnXnmmRGpPZocz5/nfv36acCAAXI6nf62kSNHyjRN7d+/X8OGDQtrzdHoeM5zXl6eTj31VN1www2SpLFjxyolJUXTpk3TXXfdxch9iFj1OcjIUJjEx8crJydH+fn5Ldrz8/M1derUNo+ZMmVKq/5vvvmmJk6cqLi4uLDVGs2O5zxLjSNCl19+uZ5//nnm/AMQ7HlOS0vTv/71L23ZssX/mD9/voYPH64tW7bolFNOiVTpUeV4/jyfeuqpOnDggKqqqvxtO3fulM1m08CBA8Nab7Q6nvNcU1Mjm63lR6bdbpekTr8peDSx7HMwrMuzY5zv0s0nn3zS3LZtm7lo0SIzJSXF/Oqrr0zTNM2bbrrJvPTSS/39fZcUXn/99ea2bdvMJ598kkvrAxDseX7++edNh8NhPvLII2ZxcbH/UV5ebtVbiArBnucjcTVZYII9z5WVlebAgQPNiy66yPziiy/MdevWmcOGDTOvuuoqq95CVAj2PD/99NOmw+Ewly9fbu7evdt87733zIkTJ5qTJk2y6i1EhcrKSrOgoMAsKCgwJZnLli0zCwoK/Lcw6Cyfg4ShMHvkkUfMwYMHm/Hx8ebJJ59srlu3zv+zyy67zDzttNNa9H/33XfNCRMmmPHx8eaQIUPMRx99NMIVR6dgzvNpp51mSmr1uOyyyyJfeJQJ9s9zc4ShwAV7nrdv326effbZZlJSkjlw4EBz8eLFZk1NTYSrjj7BnucHH3zQHDVqlJmUlGT269fP/M///E9z//79Ea46urzzzjtH/f9tZ/kcNEyT8T0AABC7WDMEAABiGmEIAADENMIQAACIaYQhAAAQ0whDAAAgphGGAABATCMMAQCAmEYYAgAAMY0wBKDTOv3007Vo0aJO+TuGDBmiBx54IOT1AIg8whAAAIhphCEAABDTCEMAosJzzz2niRMnKjU1VX379tXcuXNVWlrq//m7774rwzD0xhtvaMKECUpKStKZZ56p0tJS/eMf/9DIkSOVlpamSy65RDU1NS1e2+1267rrrlN6erp69uyp3/3ud2q+bWNpaalmzpyppKQkZWVlacWKFa3qW7ZsmcaMGaOUlBRlZmbqF7/4haqqqsJ3QgCEDGEIQFSor6/XnXfeqa1bt+rVV1/V3r17dfnll7fqd9ttt+nhhx/WBx98oKKiIs2ePVsPPPCAnn/+ef39739Xfn6+HnrooRbHPPvss3I4HProo4/04IMP6o9//KOeeOIJ/88vv/xyffXVV3r77be1Zs0aLV++vEUQkySbzaYHH3xQn3/+uZ599lm9/fbb+s1vfhOWcwEgxDq+8T0AhMdpp51mLly4sM2fffzxx6Yks7Ky0jRN03znnXdMSeZbb73l75OXl2dKMnfv3u1vu+aaa8xzzjmnxe8YOXKk6fV6/W033nijOXLkSNM0TXPHjh2mJHPjxo3+n2/fvt2UZP7xj39st/YXX3zR7NmzZ1DvF4A1GBkCEBUKCgr0ox/9SIMHD1ZqaqpOP/10SVJhYWGLfmPHjvV/n5GRoeTkZA0dOrRF25GjOpMnT5ZhGP7nU6ZM0a5du+TxeLR9+3Y5HA5NnDjR//MRI0YoPT29xWu88847mj59ugYMGKDU1FTNmzdPhw4dUnV1dUffOoAwIwwB6PSqq6uVm5urbt266bnnntMnn3yiV155RVLj9FlzcXFx/u8Nw2jx3Nfm9XoD/t1m09qh5mHpSPv27dOMGTM0evRovfTSS9q8ebMeeeQRSVJDQ0PAvwuANRxWFwAAx/Lll1+qrKxM99xzjzIzMyVJmzZtCtnrb9y4sdXzYcOGyW63a+TIkXK73dq0aZMmTZokSdqxY4fKy8v9/Tdt2iS32637779fNlvjvzFffPHFkNUHILwYGQLQ6Q0aNEjx8fF66KGHtGfPHr322mu68847Q/b6RUVFWrx4sXbs2KGVK1fqoYce0sKFCyVJw4cP17nnnquf//zn+uijj7R582ZdddVVSkpK8h9/wgknyO12++v729/+psceeyxk9QEIL8IQgE6vd+/eeuaZZ7R69WqNGjVK99xzj/7whz+E7PXnzZunw4cPa9KkSbr22mv1y1/+UldffbX/508//bQyMzN12mmn6Sc/+Ymuvvpq9enTx//z8ePHa9myZbr33ns1evRorVixQnl5eSGrD0B4GabZ7GYaAAAAMYaRIQAAENMIQwAAIKYRhgAAQEwjDAEAgJhGGAIAADGNMAQAAGIaYQgAAMQ0whAAAIhphCEAABDTCEMAACCmEYYAAEBM+/8BZsjj1tTjfRYAAAAASUVORK5CYII=",
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aP3687r//fklSeXm5LxhJ0rBhw7R69Wr99a9/1bhx4/TII4/oqaee4rZ6AIgA7+KLO5k3hB4mquYMXXjhhSdcvfTll1/u1HbBBRfoiy++CGFVAAB/eLfl2MHIEHqYqBoZAgBEr7z+nstkhCH0NIQhAEBY5PVPkyTtPlSvFqc7wtUAxxCGAABhkWNPUIrNKqfb0DfVDZEuB/AhDAEAwsJkMmlkNneUoechDAEAwsZ7qYxtOdCTEIYAAGGT1zYyxIat6EkIQwCAsPFu2ModZehJCEMAgLDxbthaerhRjS3OCFcDeBCGAABhk5FiU2aKZzPsXQfrI1wN4EEYAgCEFYsvoqchDAEAwsq3LQeTqNFDEIYAAGHFhq3oaQhDAICw8o4MsfAiegrCEAAgrEa0haFDdc063NAS4WoAwhAAIMxSbFbl9k2UxKUy9AyEIQBA2HnXG+KOMvQEhCEAQNhxRxl6EsIQACDs8rx3lDEyhB6AMAQACDtvGNpxsE6GYUS4GsQ6whAAIOyGZ6bIajaprsmpckdTpMtBjCMMAQDCLt5q1vB+yZKYN4TIIwwBACJiJHeUoYcgDAEAIsJ7ez2TqBFphCEAQES0n0QNRBJhCAAQEd4wtKuyXk6XO8LVIJYRhgAAEZHbJ0mJcRa1ON3ad7gx0uUghhGGAAARYTabNDI7RRLzhhBZhCEAQMR47yjbThhCBBGGAAAR49uWg0nUiCDCEAAgYrijDD0BYQgAEDHetYa+qWpQU6srwtUgVhGGAAAR0y/VpvSkOLkN6R+V9ZEuBzGKMAQAiBiTyXRsJWoulSFCCEMAgIjyzRvijjJECGEIABBRvg1bGRlChBCGAAARNao/G7YisghDAICIGtE2MnTA0STH0dYIV4NYFHVhaMmSJRo2bJgSEhKUn5+vdevWnbD/8uXLde655yopKUkDBgzQjTfeqOrq6jBVCwA4GXtinAbYEyRJu7hUhgiIqjC0cuVK3Xnnnbr33ntVUlKiadOm6fLLL1dpaWmX/T/++GMVFhZq3rx5+vrrr7Vq1Sp9/vnnuummm8JcOQDgRFh8EZEUVWFo8eLFmjdvnm666SaNHj1aTz75pHJzc/Xcc8912X/Dhg0aOnSo7rjjDg0bNkz//M//rFtuuUWbNm3q9nc0Nzertra2wwMAEFre2+u5owyREDVhqKWlRZs3b1ZBQUGH9oKCAq1fv77LY6ZOnar9+/dr9erVMgxDBw8e1BtvvKErrrii299TVFQku93ue+Tm5gb1cwAAOhtJGEIERU0YqqqqksvlUnZ2dof27OxsVVRUdHnM1KlTtXz5cs2ePVvx8fHq37+/0tPT9fTTT3f7exYtWiSHw+F7lJWVBfVzAAA6a79hq2EYEa4GsSZqwpCXyWTq8NwwjE5tXlu3btUdd9yh+++/X5s3b9Z7772nvXv3av78+d2+v81mU1paWocHACC0zsxKkdkkHWls1aG65kiXgxhjjXQB/srMzJTFYuk0ClRZWdlptMirqKhI559/vn72s59Jks455xwlJydr2rRpevTRRzVgwICQ1w0AOLmEOIuGZiRrT1WDdhysU1ZaQqRLQgyJmpGh+Ph45efnq7i4uEN7cXGxpk6d2uUxjY2NMps7fkSLxSJJDMMCQA/DthyIlKgJQ5K0cOFCvfjii1q6dKm2bdumu+66S6Wlpb7LXosWLVJhYaGv/5VXXqm33npLzz33nPbs2aNPPvlEd9xxhyZOnKicnJxIfQwAQBeYRI1IiZrLZJI0e/ZsVVdX6+GHH1Z5ebnGjh2r1atXa8iQIZKk8vLyDmsOzZ07V3V1dXrmmWf07//+70pPT9dFF12kxx9/PFIfAQDQjfaTqIFwMhlcLzqh2tpa2e12ORwOJlMDQAjtPlSvi59Yo8Q4i75+6FKZzV3fHAP4I5Dv76i6TAYA6L2G9E1SvNWso60ulR1pjHQ5iCGEIQBAj2C1mHVmvxRJzBtCeBGGAAA9xijmDSECCEMAgB5jZFsY2s7IEMIo4DC0bNkyrVq1qlP7qlWr9Ic//CEoRQEAYpN3w1ZGhhBOAYehxx57TJmZmZ3as7Ky9Ktf/SooRQEAYpP39vo9hxrU4nRHuBrEioDD0L59+zRs2LBO7UOGDOmwxg8AAIEaYE9Qqs0qp9vQnqr6SJeDGBFwGMrKytJXX33Vqf3LL79URkZGUIoCAMQmk8nkmzfEHWUIl4DD0LXXXqs77rhDH330kVwul1wulz788EMtWLBA1157bShqBADEEFaiRrgFvB3Ho48+qn379uniiy+W1eo53O12q7CwkDlDAIDTlsceZQizgMNQfHy8Vq5cqUcffVRbtmxRYmKizj77bN/+YAAAnA7fhq2MDCFMTnmj1hEjRmjEiBHBrAUAAN9lsrLDR9XQ7FSyLar2FEcUCnjO0DXXXKPHHnusU/tvfvMb/eAHPwhKUQCA2NU3OV79Um2SmDeE8Ag4DK1Zs0ZXXHFFp/bLLrtMa9euDUpRAIDYxuKLCKeAw1B9fb3i4+M7tcfFxam2tjYoRQEAYlue7/Z61hpC6AUchsaOHauVK1d2av/jH/+oMWPGBKUoAEBs891RdpB/ZCP0Ap6V9stf/lJXX321du/erYsuukiS9Je//EUrVqzocs8yAAACNZKRIYRRwGFo5syZeuedd/SrX/1Kb7zxhhITE3XOOefoz3/+sy644IJQ1AgAiDEjs1MkSVX1zaqub1ZGii3CFaE38+sy2VNPPaWmpiZJUmlpqWbMmKFPPvlEDQ0Nqqqq0ocffkgQAgAETVK8VYP7JklivSGEnl9haOHChb7J0cOGDdOhQ4dCWhQAAN7FF3eyEjVCzK/LZDk5OXrzzTc1Y8YMGYah/fv3+0aKjjd48OCgFggAiE2j+qfqz9sOasdB5g0htPwKQ/fdd59+8pOf6Pbbb5fJZNI//dM/depjGIZMJpNcLlfQiwQAxJ5jk6i5owyh5VcYuvnmm3Xddddp3759vsnSGRkZoa4NABDDji28WO/7BzcQCn7fTZaamqqxY8dq2bJlOv/882WznXhm/4oVKzRz5kwlJyefdpEAgNgzLDNZcRaT6pudOuBo0sD0xEiXhF4q4EUXb7jhhpMGIUm65ZZbdPDgwVMqCgCAeKtZwzM9t9gziRqhFHAY8pdhGKF6awBAjPDOG9pOGEIIhSwMAQBwukb1Z8NWhB5hCADQY3nXGtrByBBCiDAEAOixvHeU/eNQvZwud4SrQW9FGAIA9FiD+iQqKd6iFqdb31Q3Rroc9FIhC0NDhgxRXFxcqN4eABADzGaTRmQzbwih5XcY+uyzzzqsLn383WLNzc16/fXXfc///ve/Kzc3NwglAgBiWV7bDvbcUYZQ8TsMTZkyRdXV1b7ndrtde/bs8T2vqanRddddF9zqAAAxjw1bEWp+h6HjR4K6WkeItYUAAME2qn+aJC6TIXSCOmeIfWMAAME2sr/nMtk31Q1qamUzcAQfd5MBAHq0fik29UmKk9uQ/lFZH+ly0AsFFIa2bt2qr776Sl999ZUMw9D27dt9z7/++utQ1djBkiVLNGzYMCUkJCg/P1/r1q07Yf/m5mbde++9GjJkiGw2m8444wwtXbo0LLUCAE6fyWRSXn8WX0To+L1rvSRdfPHFHeYFffe735Xk+YtqGEbIL5OtXLlSd955p5YsWaLzzz9fL7zwgi6//HJt3bpVgwcP7vKYWbNm6eDBg3rppZd05plnqrKyUk6nM6R1AgCCKy87VRv2HNYO5g0hBPwOQ3v37g1lHX5ZvHix5s2bp5tuukmS9OSTT+r999/Xc889p6Kiok7933vvPa1Zs0Z79uxR3759JUlDhw4NZ8kAgCAYycgQQsjvMDRkyJBQ1nFSLS0t2rx5s+65554O7QUFBVq/fn2Xx7z77ruaMGGCfv3rX+uVV15RcnKyZs6cqUceeUSJiYldHtPc3Kzm5mbf89ra2uB9CADAKWHDVoRSQJfJIqmqqkoul0vZ2dkd2rOzs1VRUdHlMXv27NHHH3+shIQEvf3226qqqtKtt96qw4cPdztvqKioSA899FDQ6wcAnDrvKtTljiY5GltlT2KHAwRP1N1Ndvy8pBPNVXK73TKZTFq+fLkmTpyoGTNmaPHixXr55Zd19OjRLo9ZtGiRHA6H71FWVhb0zwAACExaQpxy7AmSpJ2VjA4huKImDGVmZspisXQaBaqsrOw0WuQ1YMAADRw4UHa73dc2evRoGYah/fv3d3mMzWZTWlpahwcAIPK4owyhEjVhKD4+Xvn5+SouLu7QXlxcrKlTp3Z5zPnnn68DBw6ovv7YuhQ7d+6U2WzWoEGDQlovACC4mESNUDmtMPTYY4+ppqYmSKWc3MKFC/Xiiy9q6dKl2rZtm+666y6VlpZq/vz5kjyXuAoLC33958yZo4yMDN14443aunWr1q5dq5/97Gf6t3/7t24nUAMAeqa8tnlD3F6PYDutCdS/+tWvNGvWLKWnpwepnBObPXu2qqur9fDDD6u8vFxjx47V6tWrfXe6lZeXq7S01Nc/JSVFxcXF+slPfqIJEyYoIyNDs2bN0qOPPhqWegEAwZPX7o6ycKxth9hhMk5jd9XU1FR9+eWXGj58eDBr6lFqa2tlt9vlcDiYPwQAEdTU6tKY+9+T25A2/uJiZaclRLok9GCBfH9HzZwhAEBsS4izaGhmsiTmDSG4TisMbd26NeKLMQIAYgeLLyIUTisM5ebmymKxBKsWAABOaGTbJOrtjAwhiLhMBgCIGt47yhgZQjARhgAAUaP9HWVu9ynf/wN0QBgCAESNIRnJirea1dTqVunhxkiXg16CMAQAiBoWs0kjslIksfgigifgMHTNNdfoscce69T+m9/8Rj/4wQ+CUhQAAN3xXSpjEjWCJOAwtGbNGl1xxRWd2i+77DKtXbs2KEUBANAd7yTq7YwMIUgCDkP19fWKj4/v1B4XF6fa2tqgFAUAQHdGMjKEIAs4DI0dO1YrV67s1P7HP/5RY8aMCUpRAAB0x7vw4t6qBjU7XRGuBr1BwBu1/vKXv9TVV1+t3bt366KLLpIk/eUvf9GKFSu0atWqoBcIAEB7/dMSlJpgVV2TU3sONWj0APaNxOkJeGRo5syZeuedd/SPf/xDt956q/793/9d+/fv15///GddddVVISgRAIBjTCYTiy8iqAIeGZKkK664ostJ1AAAhENe/1Rt2neEDVsRFAGPDH3++efauHFjp/aNGzdq06ZNQSkKAIAT8d5eTxhCMAQchm677TaVlZV1av/222912223BaUoAABOxLthKwsvIhgCDkNbt27Veeed16l9/Pjx2rp1a1CKAgDgRLxzhvYfOar6ZmeEq0G0CzgM2Ww2HTx4sFN7eXm5rNZTmoIEAEBA+iTHKyvVJolJ1Dh9AYeh6dOna9GiRXI4HL62mpoa/eIXv9D06dODWhwAAN1hWw4ES8BDOU888YT+5V/+RUOGDNH48eMlSVu2bFF2drZeeeWVoBcIAEBX8rJTtW5XFfOGcNoCDkMDBw7UV199peXLl+vLL79UYmKibrzxRl133XWKi4sLRY0AAHQykjvKECSnNMknOTlZN998c7BrAQDAbyy8iGA5pTC0Y8cOPf3009q2bZtMJpNGjRql22+/XaNGjQp2fQAAdGlEdopMJqmqvkVV9c3KTLFFuiREqYAnUL/xxhsaO3asNm/erHPPPVfnnHOOvvjiC5199tnsTQYACJukeKsG902SxCRqnJ6AR4Z+/vOfa9GiRXr44Yc7tD/wwAO6++679YMf/CBoxQEAcCIjs1O1r7pROw7WaeqZmZEuB1Eq4JGhiooKFRYWdmr/0Y9+pIqKiqAUBQCAP0b1Z94QTl/AYejCCy/UunXrOrV//PHHmjZtWlCKAgDAH95tObZzmQynwa/LZO+++67v55kzZ+ruu+/W5s2bNXnyZEnShg0btGrVKj300EOhqRIAgC60X3jRMAyZTKYIV4RoZDIMwzhZJ7PZvwEkk8kkl8t12kX1JLW1tbLb7XI4HEpLS4t0OQCAdlpdbo25/z21ugx9fPd3NKhPUqRLQg8RyPe3XynH7Xb79ehtQQgA0LPFWcw6o1+KJBZfxKkLeM6Qv84++2yVlZWF6u0BAJB0bN4Q23LgVIUsDH3zzTdqbW0N1dsDACCJDVtx+kIWhgAACIc87ijDaSIMAQCimndkaM+hBrW63BGuBtGIMAQAiGoD0xOVHG9Ri8utfdUNkS4HUYgwBACIamazSSO4VIbTEHVhaMmSJRo2bJgSEhKUn5/f5WrYXfnkk09ktVo1bty40BYIAAg777whJlHjVIQsDL3wwgvKzs4O6nuuXLlSd955p+69916VlJRo2rRpuvzyy1VaWnrC4xwOhwoLC3XxxRcHtR4AQM/gnTfE7fU4FX6HoRkzZsjhcPie/8d//Idqamp8z6urqzVmzBjf8zlz5ig5OTk4VbZZvHix5s2bp5tuukmjR4/Wk08+qdzcXD333HMnPO6WW27RnDlzNGXKlKDWAwDoGXxhiJEhnAK/w9D777+v5uZm3/PHH39chw8f9j13Op3asWNHcKtrp6WlRZs3b1ZBQUGH9oKCAq1fv77b45YtW6bdu3frgQce8Ov3NDc3q7a2tsMDANCzeRde3He4UUdb2A0BgfE7DB2/hZkfW5oFVVVVlVwuV6dLb9nZ2aqoqOjymF27dumee+7R8uXLZbX6tSetioqKZLfbfY/c3NzTrh0AEFr9Um3KSI6XYUj/qKyPdDmIMlE3gfr4HYm726XY5XJpzpw5euihhzRy5Ei/33/RokVyOBy+B1uKAEB0GOm7o4wRfQTGv+ESeULI8aGjqxASKpmZmbJYLJ1GgSorK7ucqF1XV6dNmzappKREt99+uyTPhrOGYchqteqDDz7QRRdd1Ok4m80mm80Wmg8BAAiZvP6p+nRPtXYyiRoB8jsMGYahuXPn+oJCU1OT5s+f75sk3X4+USjEx8crPz9fxcXF+v73v+9rLy4u1ve+971O/dPS0vS3v/2tQ9uSJUv04Ycf6o033tCwYcNCWi8AILyO3VHGZTIExu8wdMMNN3R4/qMf/ahTn8LCwtOv6AQWLlyo66+/XhMmTNCUKVP0X//1XyotLdX8+fMleS5xffvtt/rv//5vmc1mjR07tsPxWVlZSkhI6NQOAIh+vt3ruUyGAPkdhpYtWxbKOvwye/ZsVVdX6+GHH1Z5ebnGjh2r1atXa8iQIZKk8vLyk645BADonUZmp0iSDtY2q6axRelJ8RGuCNHCZIT7trAoU1tbK7vdLofDobS0tEiXAwA4gfMf+1Df1hzV67dM0cRhfSNdDiIokO/vqLubDACA7hxbfJFLZfAfYQgA0Gv45g1xRxkCQBgCAPQaef0984Z2VnBHGfxHGAIA9Bp52Z65IdsrasO+UwKiF2EIANBrDO+XLIvZpNompw7Whnb9O/QehCEAQK+REGfR0IwkScwbgv8IQwCAXmVUf8+lMu4og78IQwCAXuXYStRMooZ/CEMAgF7Fd0cZl8ngJ8IQAKBXyWu7TLbzYJ1cbu4ow8kRhgAAvcrgvkmyWc1qdrpVergx0uUgChCGAAC9isVs0oi2TVt3VHCpDCdHGAIA9DrexRcJQ/AHYQgA0OswiRqBIAwBAHodNmxFIAhDAIBex7vw4t6qBjU7XRGuBj0dYQgA0Otkp9mUlmCVy21od2VDpMtBD0cYAgD0OiaTSXn9PZfKmDeEkyEMAQB6JW8Y2s4dZTgJwhAAoFfKy2ZkCP4hDAEAeqVjG7YShnBihCEAQK/kvUz2bc1R1TW1Rrga9GSEIQBAr5SeFK/sNJskaefB+ghXg56MMAQA6LVGMm8IfiAMAQB6rVH9mTeEkyMMAQB6LSZRwx+EIQBAr8XCi/AHYQgA0GuNyEqVySRVN7ToUF1zpMtBD0UYAgD0WonxFg3pmySJ0SF0jzAEAOjVmDeEkyEMAQB6Ne4ow8kQhgAAvdpIbxjiMhm6QRgCAPRq3g1bdx2sk9ttRLga9ESEIQBArzY0M1nxFrMaWlz6tuZopMtBD0QYAgD0anEWs4b3S5bEvCF0jTAEAOj18pg3hBMgDAEAer087ijDCURdGFqyZImGDRumhIQE5efna926dd32feuttzR9+nT169dPaWlpmjJlit5///0wVgsA6Any2L0eJxBVYWjlypW68847de+996qkpETTpk3T5ZdfrtLS0i77r127VtOnT9fq1au1efNmfec739GVV16pkpKSMFcOAIgk78KLuw/Vq9XljnA16GlMhmFEzX2GkyZN0nnnnafnnnvO1zZ69GhdddVVKioq8us9zjrrLM2ePVv3339/l683NzerufnY/jW1tbXKzc2Vw+FQWlra6X0AAEBEGIahsQ+8r4YWlz6461984Qi9V21trex2u1/f31EzMtTS0qLNmzeroKCgQ3tBQYHWr1/v13u43W7V1dWpb9++3fYpKiqS3W73PXJzc0+rbgBA5JlMpmOLLzJvCMeJmjBUVVUll8ul7OzsDu3Z2dmqqKjw6z2eeOIJNTQ0aNasWd32WbRokRwOh+9RVlZ2WnUDAHoG5g2hO9ZIFxAok8nU4blhGJ3aurJixQo9+OCD+p//+R9lZWV1289ms8lms512nQCAnsV7R9l2RoZwnKgJQ5mZmbJYLJ1GgSorKzuNFh1v5cqVmjdvnlatWqVLLrkklGUCAHooRobQnai5TBYfH6/8/HwVFxd3aC8uLtbUqVO7PW7FihWaO3euXnvtNV1xxRWhLhMA0EN55wyVHm5UY4szwtWgJ4maMCRJCxcu1IsvvqilS5dq27Ztuuuuu1RaWqr58+dL8sz3KSws9PVfsWKFCgsL9cQTT2jy5MmqqKhQRUWFHA5HpD4CACBCMlNsykyJl2FIuw7WR7oc9CBRFYZmz56tJ598Ug8//LDGjRuntWvXavXq1RoyZIgkqby8vMOaQy+88IKcTqduu+02DRgwwPdYsGBBpD4CACCCvLfUsy0H2ouqdYYiIZB1CgAAPduD736tl9d/o5v+eZju++6YSJeDEOqV6wwBAHC6RrFhK7pAGAIAxAwWXkRXCEMAgJjhnTNUWdesIw0tEa4GPQVhCAAQM1JsVg3qkyiJ9YZwDGEIABBT8rijDMchDAEAYgrzhnA8whAAIKZ47yjjMhm8CEMAgJjinUS9vaJOLLUHiTAEAIgxw/sly2I2qa7JqYrapkiXgx6AMAQAiCk2q0XDM5MlMW8IHoQhAEDMYRI12iMMAQBiDrfXoz3CEAAg5uRxRxnaIQwBAGKOd2Ro18F6udzcURbrCEMAgJiT2zdJCXFmNTvd2lfdEOlyEGGEIQBAzLGYTb71hrhUBsIQACAmtV98EbGNMAQAiEl5jAyhDWEIABCT8lhrCG0IQwCAmOQNQ99UN6qp1RXhahBJhCEAQEzKSrXJnhgnl9vQ7kP1kS4HEUQYAgDEJJPJxLwhSCIMAQBimPdSGXeUxTbCEAAgZnk3bN1JGIpphCEAQMw6dpmMOUOxjDAEAIhZ3jD0bc1R1Ta1RrgaRAphCAAQs+xJceqfliBJ2sUk6phFGAIAxLSRvsUXuVQWqwhDAICYNsoXhmojXAkihTAEAIhp3g1bd3CZLGYRhgAAMc07iXpHRZ0Mw4hwNYgEwhAAIKaNyE6RySQdaWzVofrmSJeDCCAMAQBiWkKcRUMzkiVJO5lEHZMIQwCAmDcyO0US84ZiFWEIABDz8vqnSeKOslhFGAIAxDzfJGq25YhJUReGlixZomHDhikhIUH5+flat27dCfuvWbNG+fn5SkhI0PDhw/X888+HqVIAQLTI6++5TLbrYJ3cbu4oizVRFYZWrlypO++8U/fee69KSko0bdo0XX755SotLe2y/969ezVjxgxNmzZNJSUl+sUvfqE77rhDb775ZpgrBwD0ZEMzkhVvMauxxaX9R45GuhyEmcmIokUVJk2apPPOO0/PPfecr2306NG66qqrVFRU1Kn/3XffrXfffVfbtm3ztc2fP19ffvmlPv30U79+Z21trex2uxwOh9LS0k7/QwAAeqTL/3OdtpXX6veFEzR9THaky8FpCuT7O2pGhlpaWrR582YVFBR0aC8oKND69eu7PObTTz/t1P/SSy/Vpk2b1Nra9e7Ezc3Nqq2t7fAAAPR+eW13lO3kjrKYEzVhqKqqSi6XS9nZHdN6dna2KioqujymoqKiy/5Op1NVVVVdHlNUVCS73e575ObmBucDAAB6NO8dZdsrCEOxJmrCkJfJZOrw3DCMTm0n699Vu9eiRYvkcDh8j7KystOsGAAQDbyTqHcShmKONdIF+CszM1MWi6XTKFBlZWWn0R+v/v37d9nfarUqIyOjy2NsNptsNltwigYARA3vhq27D9WrxelWvDXqxgtwiqLmf+n4+Hjl5+eruLi4Q3txcbGmTp3a5TFTpkzp1P+DDz7QhAkTFBcXF7JaAQDRZ2B6olJsVjndhvZWNUS6HIRR1IQhSVq4cKFefPFFLV26VNu2bdNdd92l0tJSzZ8/X5LnEldhYaGv//z587Vv3z4tXLhQ27Zt09KlS/XSSy/ppz/9aaQ+AgCghzKZTGzLEaOi5jKZJM2ePVvV1dV6+OGHVV5errFjx2r16tUaMmSIJKm8vLzDmkPDhg3T6tWrddddd+nZZ59VTk6OnnrqKV199dWR+ggAgB4sr3+qviit8cwbOjfS1SBcomqdoUhgnSEAiB0vf7JXD/7/W3XJ6Gy9eMOESJeD09Ar1xkCACDURvb3TKJmraHYQhgCAKCNd8PW0sONamxxRrgahAthCACANhkpNmWmeJZX2ckO9jGDMAQAQDssvhh7CEMAALTjXXyR2+tjB2EIAIB2RrVNot7ByFDMIAwBANAOI0OxhzAEAEA7I9rC0KG6Zh1uaIlwNQgHwhAAAO2k2KzK7ZsoiUtlsSKqtuMAACAc8rJTVXb4qK5/aaP62xOUY0/UgPQE5aQnKsfu+XOAPVED0xOVlmiVyWSKdMk4DYQhAACO871xA7V2V5VanG7tP3JU+48c7bZvUrylLRx5QlNOuic4DfS2pScqIc4SxuoRKPYmOwn2JgOA2OR0uVVZ16xyx1EdqGnSgZqjKnc06duao742f+cU9U2OV056gm80aYA9QQPSEzWwrS0r1SarhZkrwRTI9zcjQwAAdMFqMXsui6UnKn9I132aWl2+kHSgxhOQyh1H2wKTp62xxaXDDS063NCiv39b2+X7WMwmZafa2kaVEpWT3nZprm1kKSc9UX2S4rgcFyKEIQAATlFCnEXD+6VoeL+ULl83DEO1R5064DjqCUttAam8LTgdcBxVhaNJTrfhec3RJO070s3vMh+7DOcLSZ6RJe/PSfF8rZ8KzhoAACFiMplkT4qTPSlOowd0fanG5TZUVd/ceWSpLSwdqGlSVX2zmlrd2lPVoD1VDd3+vj5JcTorx65xuekaPzhd43LTldG21xq6x5yhk2DOEAAg0pqdLlU4mtrNXTqqb9uC04G24FTX7Ozy2MF9kzR+cLrG56Zr3OA+GjMgTfHW3j8/KZDvb8LQSRCGAADRoLapVaXVjfpyf41KSmu0paxG/6is79Qv3mrWWTlpGp/bxzd6NKhPYq+bj0QYCiLCEAAgWjmOtuqrtnBUUnpEW8pqdKSxtVO/zBSb79La+MHpOmdQulJs0T2ThjAURIQhAEBvYRiG9lU3qqTsiLaU1qikrEZbD9TK6e4YBcwmzx5txwJSH53ZL0Vmc/SMHhGGgogwBADozZpaXfr6gMMzelRWoy2lNfq2pvMikyk2q87NbZucndtH4wanK7MHT84mDAURYQgAEGsqa5tUUuade3REX+13qLHF1alfbt9ETzBqG0Eak5Mmm7VnrLZNGAoiwhAAINY5XW7tqqzvMPdoV1eTsy1mjclJ811aGx/BydmEoSAiDAEA0FltU6u+KnOopPSI5/JaWU2X25NkpsS3jRx5wtE5ueGZnE0YCiLCEAAAJ2cYhkoPN2pL2bG717aW16rV1TFmmEzSyKzjJmdnpcgS5MnZhKEgIgwBAHBqPJOza9sC0hGVdDM5e8rwDK24eXJQfzcbtQIAgIhLiLMof0gf5Q/pI2mYJKmyrsl3W/+W0hp9ub+m261KwoUwBAAAwiYrNUEFZ/VXwVn9JXn2Zjva2vlOtXDq/ZuTAACAHstiNkV8tWvCEAAAiGmEIQAAENMIQwAAIKYRhgAAQEwjDAEAgJhGGAIAADGNMAQAAGIaYQgAAMS0qAlDR44c0fXXXy+73S673a7rr79eNTU13fZvbW3V3XffrbPPPlvJycnKyclRYWGhDhw4EL6iAQBAjxc1YWjOnDnasmWL3nvvPb333nvasmWLrr/++m77NzY26osvvtAvf/lLffHFF3rrrbe0c+dOzZw5M4xVAwCAni4qdq3ftm2bxowZow0bNmjSpEmSpA0bNmjKlCnavn278vLy/Hqfzz//XBMnTtS+ffs0ePBgv45h13oAAKJPIN/fUTEy9Omnn8put/uCkCRNnjxZdrtd69ev9/t9HA6HTCaT0tPTu+3T3Nys2traDg8AANB7RUUYqqioUFZWVqf2rKwsVVRU+PUeTU1NuueeezRnzpwTJsSioiLfvCS73a7c3NxTrhsAAPR8Ed0m9sEHH9RDDz10wj6ff/65JMlkMnV6zTCMLtuP19raqmuvvVZut1tLliw5Yd9FixZp4cKFvucOh0ODBw9mhAgAgCji/d72ZzZQRMPQ7bffrmuvvfaEfYYOHaqvvvpKBw8e7PTaoUOHlJ2dfcLjW1tbNWvWLO3du1cffvjhSa8b2mw22Ww233PvyWSECACA6FNXVye73X7CPhENQ5mZmcrMzDxpvylTpsjhcOizzz7TxIkTJUkbN26Uw+HQ1KlTuz3OG4R27dqljz76SBkZGQHXmJOTo7KyMqWmpvo1ChWI2tpa5ebmqqysjMnZIcR5Dg/Oc3hwnsOD8xw+oTrXhmGorq5OOTk5J+0b0TDkr9GjR+uyyy7Tj3/8Y73wwguSpJtvvlnf/e53O9xJNmrUKBUVFen73/++nE6nrrnmGn3xxRf63//9X7lcLt/8or59+yo+Pt6v3202mzVo0KDgf6h20tLS+I8tDDjP4cF5Dg/Oc3hwnsMnFOf6ZCNCXlExgVqSli9frrPPPlsFBQUqKCjQOeeco1deeaVDnx07dsjhcEiS9u/fr3fffVf79+/XuHHjNGDAAN8jkDvQAABA7xYVI0OSZzTn1VdfPWGf9pOkhg4d6tekKQAAENuiZmSoN7LZbHrggQc6TNhG8HGew4PzHB6c5/DgPIdPTzjXUbECNQAAQKgwMgQAAGIaYQgAAMQ0whAAAIhphCEAABDTCEMhtmTJEg0bNkwJCQnKz8/XunXrTth/zZo1ys/PV0JCgoYPH67nn38+TJVGt0DO81tvvaXp06erX79+SktL05QpU/T++++HsdroFejfZ69PPvlEVqtV48aNC22BvUSg57m5uVn33nuvhgwZIpvNpjPOOENLly4NU7XRK9DzvHz5cp177rlKSkrSgAEDdOONN6q6ujpM1UantWvX6sorr1ROTo5MJpPeeeedkx4Tke9BAyHzxz/+0YiLizN+//vfG1u3bjUWLFhgJCcnG/v27euy/549e4ykpCRjwYIFxtatW43f//73RlxcnPHGG2+EufLoEuh5XrBggfH4448bn332mbFz505j0aJFRlxcnPHFF1+EufLoEuh59qqpqTGGDx9uFBQUGOeee254io1ip3KeZ86caUyaNMkoLi429u7da2zcuNH45JNPwlh19An0PK9bt84wm83Gf/7nfxp79uwx1q1bZ5x11lnGVVddFebKo8vq1auNe++913jzzTcNScbbb799wv6R+h4kDIXQxIkTjfnz53doGzVqlHHPPfd02f/nP/+5MWrUqA5tt9xyizF58uSQ1dgbBHqeuzJmzBjjoYceCnZpvcqpnufZs2cb9913n/HAAw8QhvwQ6Hn+05/+ZNjtdqO6ujoc5fUagZ7n3/zmN8bw4cM7tD311FPGoEGDQlZjb+NPGIrU9yCXyUKkpaVFmzdvVkFBQYf2goKCbrcD+fTTTzv1v/TSS7Vp0ya1traGrNZodirn+Xhut1t1dXXq27dvKErsFU71PC9btky7d+/WAw88EOoSe4VTOc/vvvuuJkyYoF//+tcaOHCgRo4cqZ/+9Kc6evRoOEqOSqdynqdOnar9+/dr9erVMgxDBw8e1BtvvKErrrgiHCXHjEh9D0bNdhzRpqqqSi6XS9nZ2R3as7OzfRvGHq+ioqLL/k6nU1VVVRowYEDI6o1Wp3Kej/fEE0+ooaFBs2bNCkWJvcKpnOddu3bpnnvu0bp162S18n81/jiV87xnzx59/PHHSkhI0Ntvv62qqirdeuutOnz4MPOGunEq53nq1Klavny5Zs+eraamJjmdTs2cOVNPP/10OEqOGZH6HmRkKMRMJlOH54ZhdGo7Wf+u2tFRoOfZa8WKFXrwwQe1cuVKZWVlhaq8XsPf8+xyuTRnzhw99NBDGjlyZLjK6zUC+fvsdrtlMpm0fPlyTZw4UTNmzNDixYv18ssvMzp0EoGc561bt+qOO+7Q/fffr82bN+u9997T3r17NX/+/HCUGlMi8T3IP9dCJDMzUxaLpdO/MiorKzulXq/+/ft32d9qtSojIyNktUazUznPXitXrtS8efO0atUqXXLJJaEsM+oFep7r6uq0adMmlZSU6Pbbb5fk+dI2DENWq1UffPCBLrroorDUHk1O5e/zgAEDNHDgQNntdl/b6NGjZRiG9u/frxEjRoS05mh0Kue5qKhI559/vn72s59Jks455xwlJydr2rRpevTRRxm5D5JIfQ8yMhQi8fHxys/PV3FxcYf24uJiTZ06tctjpkyZ0qn/Bx98oAkTJiguLi5ktUazUznPkmdEaO7cuXrttde45u+HQM9zWlqa/va3v2nLli2+x/z585WXl6ctW7Zo0qRJ4So9qpzK3+fzzz9fBw4cUH19va9t586dMpvNGjRoUEjrjVancp4bGxtlNnf8yrRYLJKOjVzg9EXsezCk07NjnPfWzZdeesnYunWrceeddxrJycnGN998YxiGYdxzzz3G9ddf7+vvvaXwrrvuMrZu3Wq89NJL3Frvh0DP82uvvWZYrVbj2WefNcrLy32PmpqaSH2EqBDoeT4ed5P5J9DzXFdXZwwaNMi45pprjK+//tpYs2aNMWLECOOmm26K1EeICoGe52XLlhlWq9VYsmSJsXv3buPjjz82JkyYYEycODFSHyEq1NXVGSUlJUZJSYkhyVi8eLFRUlLiW8Kgp3wPEoZC7NlnnzWGDBlixMfHG+edd56xZs0a32s33HCDccEFF3To/9e//tUYP368ER8fbwwdOtR47rnnwlxxdArkPF9wwQWGpE6PG264IfyFR5lA/z63RxjyX6Dnedu2bcYll1xiJCYmGoMGDTIWLlxoNDY2hrnq6BPoeX7qqaeMMWPGGImJicaAAQOMH/7wh8b+/fvDXHV0+eijj074/7c95XvQZBiM7wEAgNjFnCEAABDTCEMAACCmEYYAAEBMIwwBAICYRhgCAAAxjTAEAABiGmEIAADENMIQAACIaYQhAD3WhRdeqDvvvLNH/o6hQ4fqySefDHo9AMKPMAQAAGIaYQgAAMQ0whCAqPDqq69qwoQJSk1NVf/+/TVnzhxVVlb6Xv/rX/8qk8mk999/X+PHj1diYqIuuugiVVZW6k9/+pNGjx6ttLQ0XXfddWpsbOzw3k6nU7fffrvS09OVkZGh++67T+23baysrNSVV16pxMREDRs2TMuXL+9U3+LFi3X22WcrOTlZubm5uvXWW1VfXx+6EwIgaAhDAKJCS0uLHnnkEX355Zd65513tHfvXs2dO7dTvwcffFDPPPOM1q9fr7KyMs2aNUtPPvmkXnvtNf3f//2fiouL9fTTT3c45g9/+IOsVqs2btyop556Sr/73e/04osv+l6fO3euvvnmG3344Yd64403tGTJkg5BTJLMZrOeeuop/f3vf9cf/vAHffjhh/r5z38eknMBIMhOf+N7AAiNCy64wFiwYEGXr3322WeGJKOurs4wDMP46KOPDEnGn//8Z1+foqIiQ5Kxe/duX9stt9xiXHrppR1+x+jRow232+1ru/vuu43Ro0cbhmEYO3bsMCQZGzZs8L2+bds2Q5Lxu9/9rtvaX3/9dSMjIyOgzwsgMhgZAhAVSkpK9L3vfU9DhgxRamqqLrzwQklSaWlph37nnHOO7+fs7GwlJSVp+PDhHdqOH9WZPHmyTCaT7/mUKVO0a9cuuVwubdu2TVarVRMmTPC9PmrUKKWnp3d4j48++kjTp0/XwIEDlZqaqsLCQlVXV6uhoeF0PzqAECMMAejxGhoaVFBQoJSUFL366qv6/PPP9fbbb0vyXD5rLy4uzvezyWTq8Nzb5na7/f7dRtvcofZh6Xj79u3TjBkzNHbsWL355pvavHmznn32WUlSa2ur378LQGRYI10AAJzM9u3bVVVVpccee0y5ubmSpE2bNgXt/Tds2NDp+YgRI2SxWDR69Gg5nU5t2rRJEydOlCTt2LFDNTU1vv6bNm2S0+nUE088IbPZ82/M119/PWj1AQgtRoYA9HiDBw9WfHy8nn76ae3Zs0fvvvuuHnnkkaC9f1lZmRYuXKgdO3ZoxYoVevrpp7VgwQJJUl5eni677DL9+Mc/1saNG7V582bddNNNSkxM9B1/xhlnyOl0+up75ZVX9PzzzwetPgChRRgC0OP169dPL7/8slatWqUxY8boscce029/+9ugvX9hYaGOHj2qiRMn6rbbbtNPfvIT3Xzzzb7Xly1bptzcXF1wwQX613/9V918883KysryvT5u3DgtXrxYjz/+uMaOHavly5erqKgoaPUBCC2TYbRbTAMAACDGMDIEAABiGmEIAADENMIQAACIaYQhAAAQ0whDAAAgphGGAABATCMMAQCAmEYYAgAAMY0wBAAAYhphCAAAxDTCEAAAiGn/D6Nc958He9KaAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -244,7 +409,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The free energy difference between bcc and fcc at 300 K is 0.9558411678020047 eV\n"
+      "The free energy difference between bcc and fcc at 300 K is 0.9565999950953374 eV\n"
      ]
     }
    ],
@@ -265,59 +430,52 @@
    "id": "a992c792-8fe8-41be-ba54-cc8f726cee5f",
    "metadata": {},
    "source": [
-    "[Metadynamics](https://en.wikipedia.org/wiki/Metadynamics) is a simulation method which allows for the free energy distribution along a collective variable defined by the user. In mamonca, you can use the total magnetization as the collective variable, meaning you can obtain the free energy distribution along the total magnetization. For this, you only need to call `set_metadynamics` before `run`, where you must also specify the maximum magnetization value. In order to obtain a reliable result, you should also test the robustness by varying `energy_increment` and `length_scale` (cf. [Wikipedia](https://en.wikipedia.org/wiki/Metadynamics#Algorithm), where `energy_increment` is called $\\omega$ and `length_scale` is called $\\sigma$)"
+    "[Metadynamics](https://en.wikipedia.org/wiki/Metadynamics) is a simulation method which allows for the free energy distribution along a collective variable defined by the user. In mamonca, you can use the total magnetization as the collective variable, meaning you can obtain the free energy distribution along the total magnetization. For this, you only need to call `set_metadynamics` before `run`, where you must also specify the maximum magnetization value. In order to obtain a reliable result, you should also test the robustness by varying `energy_increment` and `length_scale` (cf. [Wikipedia](https://en.wikipedia.org/wiki/Metadynamics#Algorithm), where `energy_increment` is called $\\omega$ and `length_scale` is called $\\sigma$)\n",
+    "\n",
+    "Here, we run 10 calculations and get the average to ensure that the results are representative."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 19,
    "id": "0dee5e1a",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Create bcc structure (which is the ground state of iron)\n",
     "bcc = StructureFactory().bulk(\"Fe\", cubic=True).repeat(10)\n",
-    "\n",
     "neigh = bcc.get_neighbors(num_neighbors=8)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 20,
    "id": "aa94c95b",
    "metadata": {},
    "outputs": [],
    "source": [
     "J = 0.05 * neigh.get_shell_matrix()[0]\n",
     "\n",
-    "mc = MC(len(bcc))\n",
-    "mc.set_heisenberg_coeff(J)\n",
-    "temperature = 1000\n",
-    "mc.set_metadynamics(max_range=1)\n",
-    "mc.run(temperature=temperature, number_of_iterations=100000)\n",
-    "\n",
-    "meta = mc.get_metadynamics_free_energy()"
+    "F_list = []\n",
+    "for _ in range(10):\n",
+    "    mc = MC(len(bcc))\n",
+    "    mc.set_heisenberg_coeff(J)\n",
+    "    temperature = 1000\n",
+    "    mc.set_metadynamics(max_range=1)\n",
+    "    mc.run(temperature=temperature, number_of_iterations=100000)\n",
+    "    meta = mc.get_metadynamics_free_energy()\n",
+    "    F_list.append(meta[\"free_energy\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 22,
    "id": "7abfdf91",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fafec87f650>]"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -329,7 +487,7 @@
    "source": [
     "plt.xlabel(\"Magnetization\")\n",
     "plt.ylabel(\"Free energy eV\")\n",
-    "plt.plot(meta[\"magnetization\"], meta[\"free_energy\"])"
+    "plt.plot(meta[\"magnetization\"], np.mean(F_list, axis=0));"
    ]
   },
   {
@@ -337,7 +495,7 @@
    "id": "b8bf7959-f89f-43a1-ba9f-ca4e30678c2d",
    "metadata": {},
    "source": [
-    "The free energy minimum shows the most stable state. In this case it is around 0.5. Metadynamics, however, often requires a meticulous sampling, meaning with the standard energy increment of 0.001, you might overshoot and miss the global minimum. For a real measurement, it is recommended to make it as small as possible, meaning you should estimate the amount of time needed to run your calculation with Metadynamics with a small energy increment value and see what would be an acceptable total computation time."
+    "The free energy minimum shows the most stable state. In this case it is around 0.6. Metadynamics, however, often requires a meticulous sampling, meaning with the standard energy increment of 0.001, you might overshoot and miss the global minimum. For a real measurement, it is recommended to make it as small as possible, meaning you should estimate the amount of time needed to run your calculation with Metadynamics with a small energy increment value and see what would be an acceptable total computation time."
    ]
   },
   {
@@ -365,7 +523,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/fitting.ipynb b/notebooks/fitting.ipynb
new file mode 100644
index 0000000..89ab13c
--- /dev/null
+++ b/notebooks/fitting.ipynb
@@ -0,0 +1,544 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5db03a01",
+   "metadata": {},
+   "source": [
+    "# Fitting of Heisenberg parameters using pyiron\n",
+    "\n",
+    "In this notebook, I explain how to find the Heisenberg parameters for bcc Fe up to the second shell. It has the following two (obvious) steps:\n",
+    "\n",
+    "1. Launch DFT calculations with ferromagnetic and anti-ferromagnetic states\n",
+    "2. Fit the Heisenberg parameters using Linear Regression\n",
+    "\n",
+    "Note:\n",
+    "\n",
+    "Although this notebook presents a complete workflow, several points require more detailed analysis. Refer to the conclusion section for further information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5f44407e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyiron_atomistics import Project\n",
+    "import numpy as np\n",
+    "import matplotlib.pylab as plt\n",
+    "from tqdm.auto import tqdm\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ad16cf28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pr = Project(\"FIT\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6d7d086a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bulk = pr.create.structure.bulk(\"Fe\", cubic=True, a=2.83)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e695b505",
+   "metadata": {},
+   "source": [
+    "## Creation of dataset\n",
+    "\n",
+    "In this notebook, we are going to create three types of magnetic structures: ferromagnetic, anti-ferromagnetic and double-layer anti-ferromagnetic structures. These structures deliver different energy values, and therefore would allow us to fit different $J_{ij}$ values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a24aae54",
+   "metadata": {},
+   "source": [
+    "### First case: ferromagnetic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "46819bc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jovyan/dev/pyiron_base/pyiron_base/interfaces/lockable.py:59: LockedWarning: __setitem__ called on <class 'pyiron_atomistics.sphinx.input_writer.Group'>, but object is locked!\n",
+      "  warnings.warn(\n",
+      "/home/jovyan/dev/pyiron_base/pyiron_base/interfaces/lockable.py:59: LockedWarning: __setattr__ called on <class 'pyiron_atomistics.sphinx.input_writer.Group'>, but object is locked!\n",
+      "  warnings.warn(\n",
+      "2024-06-14 06:46:44,514 - pyiron_log - WARNING - The job ferro is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
+      "/home/jovyan/dev/pyiron_base/pyiron_base/interfaces/lockable.py:97: UserWarning: Unlock previously locked object!\n",
+      "  self.owner.read_only = False\n",
+      "/home/jovyan/dev/pyiron_base/pyiron_base/interfaces/lockable.py:320: UserWarning: Unlock previously locked object!\n",
+      "  it.read_only = False\n"
+     ]
+    }
+   ],
+   "source": [
+    "spx_f = pr.create.job.Sphinx(\"ferro\")\n",
+    "spx_f.structure = bulk.copy()\n",
+    "spx_f.structure.set_initial_magnetic_moments(len(bulk) * [2])\n",
+    "spx_f.set_encut(500)\n",
+    "spx_f.set_kpoints(k_mesh_spacing=0.15)\n",
+    "spx_f.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94d07faf",
+   "metadata": {},
+   "source": [
+    "### Second case: anti-ferromagnetic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ad47a7c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jovyan/dev/pyiron_base/pyiron_base/interfaces/lockable.py:59: LockedWarning: __setitem__ called on <class 'pyiron_atomistics.sphinx.input_writer.Group'>, but object is locked!\n",
+      "  warnings.warn(\n",
+      "/home/jovyan/dev/pyiron_base/pyiron_base/interfaces/lockable.py:59: LockedWarning: __setattr__ called on <class 'pyiron_atomistics.sphinx.input_writer.Group'>, but object is locked!\n",
+      "  warnings.warn(\n",
+      "2024-06-14 06:46:50,358 - pyiron_log - WARNING - The job antiferro is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
+      "/home/jovyan/dev/pyiron_base/pyiron_base/interfaces/lockable.py:97: UserWarning: Unlock previously locked object!\n",
+      "  self.owner.read_only = False\n",
+      "/home/jovyan/dev/pyiron_base/pyiron_base/interfaces/lockable.py:320: UserWarning: Unlock previously locked object!\n",
+      "  it.read_only = False\n"
+     ]
+    }
+   ],
+   "source": [
+    "spx_af = pr.create.job.Sphinx(\"antiferro\")\n",
+    "spx_af.structure = bulk.copy()\n",
+    "layers = spx_af.structure.analyse.get_layers(planes=[0, 0, 1])\n",
+    "spx_af.structure.set_initial_magnetic_moments(2 - 4 * (layers % 2 == 1))\n",
+    "spx_af.set_encut(500)\n",
+    "spx_af.set_kpoints(k_mesh_spacing=0.15)\n",
+    "spx_af.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a0222db8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Make sure that the magnetic moments were stable\n",
+    "\n",
+    "plt.xlabel(\"Steps\")\n",
+    "plt.ylabel(\"Magnetic moment\")\n",
+    "plt.grid()\n",
+    "plt.plot(spx_af.output.generic.dft.atom_scf_spins[0]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f418b225-1c08-4a41-9507-26fcb172fac7",
+   "metadata": {},
+   "source": [
+    "### Last case: Non-magnetic\n",
+    "\n",
+    "Let's take also a non-magnetic one into account"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "76b937b3-4688-4f08-a7b2-c0f03ee11630",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-06-14 06:46:55,008 - pyiron_log - WARNING - The job nonmag is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
+     ]
+    }
+   ],
+   "source": [
+    "spx_n = pr.create.job.Sphinx(\"nonmag\")\n",
+    "spx_n.structure = bulk.copy()\n",
+    "spx_n.set_encut(500)\n",
+    "spx_n.set_kpoints(k_mesh_spacing=0.15)\n",
+    "spx_n.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca50aded",
+   "metadata": {},
+   "source": [
+    "## Evaluation of results\n",
+    "\n",
+    "In the following, we are going to obtain the $J_{ij}$ parameters by fitting the DFT results to our model. First, let's come back to the Heisenberg model:\n",
+    "\n",
+    "$$\\mathcal H = -\\frac{1}{2}\\sum_{ij} J_{ij}m_im_j$$\n",
+    "\n",
+    "This equation can be rewritten as:\n",
+    "\n",
+    "$$\\mathcal H = -\\frac{1}{2}\\sum_s J_s\\sum_{ij} D_{s,ij}m_im_j$$\n",
+    "\n",
+    "where $J_s$ is the Heisenberg parameter for the shell $s$ and the shell matrix $D_{s,ij}$ is defined as:\n",
+    "\n",
+    "\\begin{align}\n",
+    "D_{s,ij} =\n",
+    "\\begin{cases}\n",
+    "1 & \\text{if $i$ and $j$ are neighbors of shell $s$}\\\\\n",
+    "0 & \\text{else}\n",
+    "\\end{cases}\n",
+    "\\end{align}\n",
+    "\n",
+    "$D_{s,ij}$ can be obtained from `get_shell_matrix` in the `Neighbors` class in pyiron. It returns all the shell matrices in a list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "05151e81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class EvaluateJob:\n",
+    "    def __init__(self, job, n_shells=1):\n",
+    "        self.ref_job = job\n",
+    "        self.n_shells = n_shells\n",
+    "\n",
+    "    @property\n",
+    "    def _neighbors(self):\n",
+    "        return self.ref_job.structure.get_neighbors(\n",
+    "            num_neighbors=100\n",
+    "        )\n",
+    "    \n",
+    "    @property\n",
+    "    def _shell_matrices(self):\n",
+    "        return self._neighbors.get_shell_matrix()[:self.n_shells]\n",
+    "\n",
+    "    @property\n",
+    "    def _magmoms(self):\n",
+    "        if \"atom_spins\" in job[\"output/generic/dft\"]:\n",
+    "            return self.ref_job.output.generic.dft.atom_spins[-1]\n",
+    "        else:\n",
+    "            return np.array(len(self.ref_job.structure) * [0])\n",
+    "    \n",
+    "    @property\n",
+    "    def values(self):\n",
+    "        m = self._magmoms\n",
+    "        # len(m) is required to intercept the per-atom energy\n",
+    "        J = [-mat.dot(m).dot(m) / 2 for mat in self._shell_matrices]\n",
+    "        A = [np.sum(m**ii) for ii in [2, 4, 6]]\n",
+    "        return np.array(J + A)\n",
+    "\n",
+    "    @property\n",
+    "    def derivatives(self):\n",
+    "        m = self._magmoms\n",
+    "        dJ = [-ss.dot(m) for ss in self._shell_matrices]\n",
+    "        dA = [ii * m**(ii - 1) for ii in [2, 4, 6]]\n",
+    "        return np.array(dJ + dA).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "86177998",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69dc892a5c594225a2cd583002a7cb0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jovyan/dev/pyiron_base/pyiron_base/interfaces/lockable.py:59: LockedWarning: __setitem__ called on <class 'pyiron_atomistics.sphinx.input_writer.Group'>, but object is locked!\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "job_nonmag = pr.load(\"nonmag\")\n",
+    "\n",
+    "E_nonmag = job_nonmag[\"output/generic/energy_pot\"][-1] / len(job_nonmag.structure)\n",
+    "\n",
+    "x_lst, y_lst = [], []\n",
+    "for job in pr.iter_jobs(job=\"^(?!nonmag$)\", mode=\"regex\"):\n",
+    "    ej = EvaluateJob(job)\n",
+    "    x_lst.append(ej.values / len(job.structure))\n",
+    "    y_lst.append(job[\"output/generic/energy_pot\"][-1] / len(job.structure) - E_nonmag)\n",
+    "    x_lst.append(ej.derivatives[0])\n",
+    "    y_lst.append(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7280b36a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "53914f3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reg = LinearRegression(fit_intercept=False).fit(x_lst, y_lst)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "800811f8-d192-4d5d-b921-768f679c336a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "m = np.linspace(-3, 3, 1000)\n",
+    "plt.ylabel(\"Energy eV\")\n",
+    "plt.xlabel(\"Magnetic moment\")\n",
+    "plt.grid()\n",
+    "plt.plot(m, reg.coef_[1] * m**2 + reg.coef_[2] * m**4);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4ada1b24-37c4-48aa-b696-22f8c0e753b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.ylabel(\"Predicted energy eV\")\n",
+    "plt.xlabel(\"Measured energy eV\")\n",
+    "plt.grid()\n",
+    "plt.plot(*2 * [[min(y_lst[::2]), 0]])\n",
+    "plt.scatter(y_lst[::2], np.einsum(\"ni,i->n\", x_lst, reg.coef_)[::2]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25769ca1",
+   "metadata": {},
+   "source": [
+    "## Monte Carlo simulation for the magnetization curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "4f1d238d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mamonca import MC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e028a56b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "large_structure = bulk.repeat(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "eb9de0cc-3d7e-4acb-bbd6-f5fee6e09f7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 14 neighbors, so that the first shell (8 atoms) and second shell (6 atoms) would be included.\n",
+    "neigh = large_structure.get_neighbors(num_neighbors=14)\n",
+    "\n",
+    "mc = MC(len(large_structure))\n",
+    "for ss, J in zip(neigh.get_shell_matrix()[:1], reg.coef_[:1]):\n",
+    "    mc.set_heisenberg_coeff(J * ss)\n",
+    "\n",
+    "mc.set_landau_coeff(reg.coef_[1], 2)\n",
+    "mc.set_landau_coeff(reg.coef_[2], 4)\n",
+    "# mc.set_landau_coeff(reg.coef_[3], 6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5ba0110c-33d7-47ee-ab53-ad11a9fe93ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a1ef3b18f624fad8dd0f98877829d87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/19 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "temperatures = np.arange(100, 2000, 100)\n",
+    "\n",
+    "magnetization_lst = []\n",
+    "for T in tqdm(temperatures):\n",
+    "    mc.run(T, 20000)\n",
+    "    magnetization_lst.append(mc.get_magnetization()[-1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "053eea35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.xlabel(\"Temperature K\")\n",
+    "plt.ylabel(\"Magnetization\")\n",
+    "plt.grid()\n",
+    "plt.plot(temperatures, magnetization_lst);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d90162ce",
+   "metadata": {},
+   "source": [
+    "As you can see in the figure above, the transition temperature is fairly different from the experimental Curie temperature (~1,000 K). But I list a few items that probably have played a role, with varying effects.\n",
+    "\n",
+    "- Only the first shell was considered\n",
+    "- Data set consisted of only 3 points\n",
+    "- Temperature effects (vibration, thermal expansion) not included\n",
+    "\n",
+    "Probably by properly taking care of the first three points, we can achive much better results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de926480",
+   "metadata": {},
+   "source": [
+    "# Discussion\n",
+    "\n",
+    "In this notebook, I created small boxes with simple magnetic states, but obviously we can also include more complex magnetic states. With the evaluation class that I wrote above, all the $J_{ij}$ parameters would be properly taken into account. Furthermore, it might be also interesting to think about including longitudinal components. There are already some extended models that can do it. Also in the next step, we should also try to take chemical species in consideration. The functionality is already included in the `Neighbors` class.\n",
+    "\n",
+    "The fitting was done using linear regression here. If the number of descriptors grows, it might be interesting to look into other regression schemes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87ab0a64",
+   "metadata": {},
+   "outputs": [],
+   "source": []
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