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..744be72 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);
@@ -500,7 +526,7 @@ bool cMC::thermodynamic_integration(){
     return false;
 }
 
-void cMC::run_spin_dynamics(double kBT, int threads){
+void cMC::run_spin_dynamics(double kBT){
     double mu_s = sqrt(2*constants.damping_parameter*constants.hbar*kBT/constants.delta_t);
     {
         for (int i=0; i<n_tot; i++)
@@ -585,6 +611,7 @@ 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 +621,12 @@ 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 +640,8 @@ 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 +653,9 @@ 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 +667,12 @@ 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 +681,7 @@ 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 +694,10 @@ 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 +708,24 @@ 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 +735,7 @@ 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 +759,30 @@ 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 +793,7 @@ vector<double> cMC::get_histogram(int derivative){
 
 void cMC::reset()
 {
+    // Reset statistics
     acc = 0;
     MC_count = 0;
     E_tot.reset();
@@ -786,11 +840,15 @@ 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 +860,7 @@ 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 +899,19 @@ 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..73a549a 100644
--- a/mamonca/cMC.h
+++ b/mamonca/cMC.h
@@ -21,10 +21,10 @@ 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);
         }
@@ -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..539e7c5 100644
--- a/notebooks/first_steps.ipynb
+++ b/notebooks/first_steps.ipynb
@@ -33,27 +33,14 @@
    "execution_count": 2,
    "id": "a175712e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e11d4810f88740d493d345138f54fad7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pyiron_atomistics.atomistics.structure.factory import StructureFactory"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "id": "8e508b81",
    "metadata": {},
    "outputs": [],
@@ -66,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "b2540f68",
    "metadata": {},
    "outputs": [],
@@ -75,6 +62,8 @@
     "\n",
     "mc = MC(len(bcc))\n",
     "mc.set_heisenberg_coeff(J)\n",
+    "# Use this command to turn on spin dynamics\n",
+    "# mc.switch_spin_dynamics()\n",
     "m_lst = []\n",
     "T_lst = np.arange(0, 1600, 100)\n",
     "for T in T_lst:\n",
@@ -84,23 +73,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "96e733b9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fb01e85abd0>]"
+       "[<matplotlib.lines.Line2D at 0x7f160a4ff2d0>]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -121,7 +110,7 @@
    "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."
+    "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. Here the calculation was done using Monte Carlo, but by setting `mc.switch_spin_dynamics()` you can also turn on spin dynamics (cf. code above). The results after convergence should be more or less the same."
    ]
   },
   {
@@ -142,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 6,
    "id": "110b315b",
    "metadata": {},
    "outputs": [],
@@ -159,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 7,
    "id": "7f61d836",
    "metadata": {},
    "outputs": [],
@@ -170,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 8,
    "id": "a04adf31",
    "metadata": {},
    "outputs": [],
@@ -182,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 9,
    "id": "55a4c4a7",
    "metadata": {},
    "outputs": [],
@@ -193,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 10,
    "id": "12103d66",
    "metadata": {},
    "outputs": [],
@@ -212,13 +201,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 11,
    "id": "d8f7f333",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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3LBaL3nrrrRMes2bNGmVlZSk2NlaDBg3S008/HfpCAQB+gxvD0AF3jcpq6k2uBmgtosJQZWWlRo0apSeffLJd/Xfv3q3Jkydr4sSJysvL0+9+9zvddtttev3110NcKQCgiSvOoXRXrCRpB5fK0AnZzS4gEJMmTdKkSZPa3f/pp59Wv3799Nhjj0mShg0bpk2bNunPf/6zLr/88hBVCQA42pDURBW4a7StsEJZ/XuYXQ7QQkSNDAXqk08+UXZ2dou2iy++WJs2bVJ9fdtDtbW1tSorK2vxAACcnMy0xnlDjAyhE+rSYaiwsFCpqakt2lJTU+XxeFRcXNzmMQsXLpTL5fI/MjIywlEqAHRpQxrnDW1jEjU6oS4dhiTJYrG0eN60JfzR7U3mz58vt9vtf+zduzfkNQJAV+dfUcbIEDqhiJozFKi0tDQVFrbc8bSoqEh2u13JycltHuN0OuV0OsNRHgBEjdNSuslikUoq61RcUaue3fj3LDqPLj0yNH78eOXk5LRoe++99zRmzBg5HA6TqgKA6BMXY1P/HvGS2G8InU9EhaGKigpt3rxZmzdvltSwdH7z5s3Kz8+X1HCJa+bMmf7+c+bM0Z49ezRv3jxt3bpVS5cu1fPPP69f//rXZpQPAFHNP2+IS2XoZCIqDG3atEmjR4/W6NGjJUnz5s3T6NGjdffdd0uSCgoK/MFIkgYOHKjVq1fr3//+t84880w98MADevzxx1lWDwAmYEUZOquImjN03nnn+SdAt+WFF15o1Xbuuefq888/D2FVAID2YEUZOquIGhkCAESuIyNDFcf9D1sg3AhDAICwGJCcIIfNoopajw64a8wuB/AjDAEAwiLGbtWgnt0ksaIMnQthCAAQNkPSWFGGzocwBAAIm8xURobQ+RCGAABhw15D6IwIQwCAsGlaUbajqEJeHyvK0DkQhgAAYZNxSrxiHVbVeXzaU1JpdjmAJMIQACCMrFaL/1IZO1GjsyAMAQDC6shO1BUmVwI0IAwBAMIqk5EhdDKEIQBAWLHXEDobwhAAIKyaRoZ2F1eq1uM1uRqAMAQACLPUJKeSYu3y+gztOsSKMpiPMAQACCuLxdLsDvZcKoP5CEMAgLA7sqKMMATzEYYAAGHHyBA6E8IQACDsuEcZOhPCEAAg7JrC0N7D1aqs9ZhcDaIdYQgAEHY9EmLUK9EpqeGmrYCZCEMAAFP4d6JmEjVMRhgCAJiCeUPoLAhDAABTZKZ1k8SKMpiPMAQAMAV7DaGzIAwBAEwxuDEMFZXX6ofKOpOrQTQjDAEATNHNaVffU+IkcakM5iIMAQBM419RRhiCiQhDAADTDEljRRnMRxgCAJgmk0nU6AQIQwAA0zRfUWYYhsnVIFoRhgAAphnUK0E2q0VlNR4dLKs1uxxEKcIQAMA0sQ6bBiTHS2LeEMxDGAIAmCozjXuUwVyEIQCAqbhHGcxGGAIAmIq9hmA2whAAwFRNew1tP1gun48VZQg/whAAwFT9e8Qrxm5VTb1Pe3+oMrscRKGIC0OLFy/WwIEDFRsbq6ysLK1bt+64/ZcvX65Ro0YpPj5e6enpuu6661RSUhKmagEAJ2K3WXVar26S2HwR5oioMLRy5UrdfvvtWrBggfLy8jRx4kRNmjRJ+fn5bfb/6KOPNHPmTF1//fX65ptvtGrVKn322WeaPXt2mCsHABxPZhrzhmCeiApDixYt0vXXX6/Zs2dr2LBheuyxx5SRkaElS5a02X/Dhg0aMGCAbrvtNg0cOFA//vGPddNNN2nTpk1hrhwAcDxHVpRVmFwJolHEhKG6ujrl5uYqOzu7RXt2drbWr1/f5jETJkzQvn37tHr1ahmGoYMHD+q1117TlClTjvl3amtrVVZW1uIBAAitzLSGy2TsNQQzREwYKi4ultfrVWpqaov21NRUFRYWtnnMhAkTtHz5ck2fPl0xMTFKS0tT9+7d9cQTTxzz7yxcuFAul8v/yMjICOrnAAC01jQytPNQheo8PpOrQbSJmDDUxGKxtHhuGEartiZbtmzRbbfdprvvvlu5ubl65513tHv3bs2ZM+eY7z9//ny53W7/Y+/evUGtHwDQWp/ucermtMvjM/R9SaXZ5SDK2M0uoL169uwpm83WahSoqKio1WhRk4ULF+qcc87Rb37zG0nSGWecoYSEBE2cOFEPPvig0tPTWx3jdDrldDqD/wEAAMdksVg0JLWbPs8v1bbCcv9IERAOETMyFBMTo6ysLOXk5LRoz8nJ0YQJE9o8pqqqSlZry49os9kkNYwoAQA6D1aUwSwRE4Ykad68eXruuee0dOlSbd26VXfccYfy8/P9l73mz5+vmTNn+vtfeumleuONN7RkyRLt2rVLH3/8sW677TaNHTtWvXv3NutjAADa4F9RxiRqhFnEXCaTpOnTp6ukpET333+/CgoKNGLECK1evVr9+/eXJBUUFLTYc2jWrFkqLy/Xk08+qV/96lfq3r27zj//fD388MNmfQQAwDFwjzKYxWJwvei4ysrK5HK55Ha7lZSUZHY5ANBlFVfUasyD78tikbbcd4niYmxml4QIFsj3d0RdJgMAdF09uzmVnBAjw5C+K2LzRYQPYQgA0Gkc2YmaS2UIH8IQAKDTYEUZzEAYAgB0GqwogxkCDkPLli3TqlWrWrWvWrVKL774YlCKAgBEJ/89yhgZQhgFHIYeeugh9ezZs1V7SkqK/vjHPwalKABAdBrcODJU4K6Ru7re5GoQLQIOQ3v27NHAgQNbtffv37/FHj8AAAQqKdah3q5YSdIORocQJgGHoZSUFH355Zet2r/44gslJycHpSgAQPQaksaKMoRXwGHoyiuv1G233aYPP/xQXq9XXq9XH3zwgebOnasrr7wyFDUCAKKIfydqJlEjTAK+HceDDz6oPXv26IILLpDd3nC4z+fTzJkzmTMEADhp7DWEcAs4DMXExGjlypV68MEHtXnzZsXFxWnkyJH++4MBAHAymvYa2lZYLsMwZLFYTK4IXV2Hb9Q6ePBgDR48OJi1AACg01K6yWKRfqiqV3FFnXolOs0uCV1cwHOGrrjiCj300EOt2h955BH9/Oc/D0pRAIDoFeuwaUBygiT2G0J4BByG1qxZoylTprRqv+SSS7R27dqgFAUAiG5DUhs2X2QnaoRDwGGooqJCMTExrdodDofKysqCUhQAILr5V5QxMoQwCDgMjRgxQitXrmzV/sorr2j48OFBKQoAEN3YawjhFPAE6j/84Q+6/PLLtXPnTp1//vmSpH/9619asWJFm/csAwAgUM33GmJFGUIt4DA0depUvfXWW/rjH/+o1157TXFxcTrjjDP0/vvv69xzzw1FjQCAKDOgZ4IcNosq67zaX1qtvqfEm10SurB2XSZ7/PHHVVNTI0nKz8/X5MmT9fHHH6uyslLFxcX64IMPCEIAgKBx2Kw6tRd3sEd4tCsMzZs3zz85euDAgTp06FBIiwIAwL8TdWGFyZWgq2vXZbLevXvr9ddf1+TJk2UYhvbt2+cfKTpav379glogACA6ZaYlSl8wMoTQa1cY+v3vf69f/vKXuvXWW2WxWPSjH/2oVZ+mCW5erzfoRQIAos+RkSHCEEKrXWHoxhtv1FVXXaU9e/b4J0snJyeHujYAQBRrWlH23aEKebw+2W0B7wYDtEu7V5MlJiZqxIgRWrZsmc455xw5nce/V8yKFSs0depUJSQknHSRAIDo0/eUOMU5bKqu92rP4Sr/hGog2AKO2ddee+0Jg5Ak3XTTTTp48GCHigIAwGq1+G/LsZ1LZQihkI05GoYRqrcGAEQJ/7whJlEjhLgACwDotDLTuEcZQo8wBADotFhRhnAgDAEAOq2mkaHvS6pUU8/WLQgNwhAAoNNKSXTKFeeQ12do16FKs8tBFxWyMNS/f385HI5QvT0AIApYLJYjd7Bn3hBCpN1h6NNPP22xu/TRq8Vqa2v16quv+p9//fXXysjICEKJAIBoNiStYXk9K8oQKu0OQ+PHj1dJSYn/ucvl0q5du/zPS0tLddVVVwW3OgBA1POPDDGJGiHS7jB09EhQW/sIsbcQACDY2GsIoRbUOUMWiyWYbwcAgD8M7fuhWhW1HpOrQVfEajIAQKd2SkKMUhIbbgO1g9EhhEBAYWjLli368ssv9eWXX8owDH377bf+5998802oamxh8eLFGjhwoGJjY5WVlaV169Ydt39tba0WLFig/v37y+l06tRTT9XSpUvDUisAIDjYiRqh1O671kvSBRdc0GJe0E9/+lNJDZfHDMMI+WWylStX6vbbb9fixYt1zjnn6JlnntGkSZO0ZcsW9evXr81jpk2bpoMHD+r555/XaaedpqKiInk8DLMCQCQZkpqodTuKta2wwuxS0AW1Owzt3r07lHW0y6JFi3T99ddr9uzZkqTHHntM7777rpYsWaKFCxe26v/OO+9ozZo12rVrl3r06CFJGjBgQDhLBgAEAXsNIZTaHYb69+8fyjpOqK6uTrm5ubrrrrtatGdnZ2v9+vVtHvP2229rzJgx+tOf/qSXXnpJCQkJmjp1qh544AHFxcW1eUxtba1qa2v9z8vKyoL3IQAAHTIkjRVlCJ2ALpOZqbi4WF6vV6mpqS3aU1NTVVhY2OYxu3bt0kcffaTY2Fi9+eabKi4u1s0336zDhw8fc97QwoULdd999wW9fgBAxw1Oadh48VB5rQ5X1qlHQozJFaEribjVZEfPSzreXCWfzyeLxaLly5dr7Nixmjx5shYtWqQXXnhB1dXVbR4zf/58ud1u/2Pv3r1B/wwAgMAkOO3K6NEwos+lMgRbxIShnj17ymaztRoFKioqajVa1CQ9PV19+vSRy+Xytw0bNkyGYWjfvn1tHuN0OpWUlNTiAQAwH/OGECoRE4ZiYmKUlZWlnJycFu05OTmaMGFCm8ecc845OnDggCoqjqw+2L59u6xWq/r27RvSegEAweXfiZrbciDITioMPfTQQyotLQ1SKSc2b948Pffcc1q6dKm2bt2qO+64Q/n5+ZozZ46khktcM2fO9PefMWOGkpOTdd1112nLli1au3atfvOb3+gXv/jFMSdQAwA6J/YaQqic1ATqP/7xj5o2bZq6d+8epHKOb/r06SopKdH999+vgoICjRgxQqtXr/avdCsoKFB+fr6/f7du3ZSTk6Nf/vKXGjNmjJKTkzVt2jQ9+OCDYakXABA8zUeGwrG3HaKHxTiJu6smJibqiy++0KBBg4JZU6dSVlYml8slt9vN/CEAMFGtx6vhd78rr8/QhvkXKM0Va3ZJ6MQC+f6OmDlDAIDo5rTbNLBngiT2G0JwnVQY2rJli+mbMQIAood/RRmTqBFEJxWGMjIyZLPZglULAADH5Z83xMgQgojLZACAiJGZ1rATNSvKEEyEIQBAxBjSbONFn6/D63+AFghDAICI0T85QTF2q2rqfdr7Q5XZ5aCLIAwBACKGzWrx37SVnagRLAGHoSuuuEIPPfRQq/ZHHnlEP//5z4NSFAAAx8I9yhBsAYehNWvWaMqUKa3aL7nkEq1duzYoRQEAcCxD0ppWlFWcoCfQPgGHoYqKCsXExLRqdzgcKisrC0pRAAAcC3sNIdgCDkMjRozQypUrW7W/8sorGj58eFCKAgDgWJpGhnYeqlCdx2dyNegKAr5R6x/+8Addfvnl2rlzp84//3xJ0r/+9S+tWLFCq1atCnqBAAA019sVq25OuypqPfq+pNK/3B7oqIBHhqZOnaq33npL3333nW6++Wb96le/0r59+/T+++/rsssuC0GJAAAcYbFYNCS1YUXZt1wqQxAEPDIkSVOmTGlzEjUAAOGQmZaoz/NLG+YNjTK7GkS6gEeGPvvsM23cuLFV+8aNG7Vp06agFAUAwPFwjzIEU8Bh6JZbbtHevXtbte/fv1+33HJLUIoCAOB42GsIwRRwGNqyZYvOOuusVu2jR4/Wli1bglIUAADH07SiLP9wlarqPCZXg0gXcBhyOp06ePBgq/aCggLZ7R2aggQAQEB6dnMqOSFGhiF9V8Tmizg5AYehiy66SPPnz5fb7fa3lZaW6ne/+50uuuiioBYHAMCx+OcNsaIMJyngoZxHH31U//Ef/6H+/ftr9OjRkqTNmzcrNTVVL730UtALBACgLZlpifpkVwnzhnDSAg5Dffr00Zdffqnly5friy++UFxcnK677jpdddVVcjgcoagRAIBWjqwo4zIZTk6HJvkkJCToxhtvDHYtAAC0W2Zaw8aL3KMMJ6tDYWjbtm164okntHXrVlksFg0dOlS33nqrhg4dGuz6AABo0+DGkaHCshq5q+rliufqBDom4AnUr732mkaMGKHc3FyNGjVKZ5xxhj7//HONHDmSe5MBAMImKdah3q5YSdL2IkaH0HEBjwz99re/1fz583X//fe3aL/nnnt055136uc//3nQigMA4HiGpCXqgLtG2wrL9aMBPcwuBxEq4JGhwsJCzZw5s1X71VdfrcLCwqAUBQBAe7ATNYIh4DB03nnnad26da3aP/roI02cODEoRQEA0B7sNYRgaNdlsrffftv/+9SpU3XnnXcqNzdX48aNkyRt2LBBq1at0n333ReaKgEAaENm2pGRIcMwZLFYTK4IkchiGIZxok5Wa/sGkCwWi7xe70kX1ZmUlZXJ5XLJ7XYrKSnJ7HIAAM3U1Hs17O53ZBjSpwsuUEpirNkloZMI5Pu7XSnH5/O169HVghAAoHOLddg0IDlBkrS9kM0X0TEBzxlqr5EjR2rv3r2hensAACRJQ1IbNl/cxiRqdFDIwtD333+v+vr6UL09AACSmq0oYxI1OihkYQgAgHAYktZ0jzLCEDqGMAQAiGhNI0M7DpbL5zvhmiCgFcIQACCiDeiZIIfNoso6r/aXVptdDiIQYQgAENEcNqtO7dV4B3sulaEDCEMAgIjn34maMIQOCFkYeuaZZ5Samhr09128eLEGDhyo2NhYZWVltXlrkLZ8/PHHstvtOvPMM4NeEwDAXP6dqFlRhg5odxiaPHmy3G63//n//M//qLS01P+8pKREw4cP9z+fMWOGEhISglNlo5UrV+r222/XggULlJeXp4kTJ2rSpEnKz88/7nFut1szZ87UBRdcENR6AACdw5GRITZeRODaHYbeffdd1dbW+p8//PDDOnz4sP+5x+PRtm3bglvdURYtWqTrr79es2fP1rBhw/TYY48pIyNDS5YsOe5xN910k2bMmKHx48eHtD4AgDmGNo4M7SyqkMfrM7kaRJp2h6Gjb2HWjluaBVVdXZ1yc3OVnZ3doj07O1vr168/5nHLli3Tzp07dc8997Tr79TW1qqsrKzFAwDQufXpHqf4GJvqvD59X1JldjmIMBEzgbq4uFher7fVPKTU1FQVFha2ecyOHTt01113afny5bLb7e36OwsXLpTL5fI/MjIyTrp2AEBoWa0WDU49cgd7IBDtDkMWi0UWi6VVW7gd/TcNw2izDq/XqxkzZui+++7TkCFD2v3+8+fPl9vt9j+4vxoARIbMpnuUMYkaAWrfcIkaQsesWbPkdDolSTU1NZozZ45/knTz+USh0LNnT9lstlajQEVFRW2uWisvL9emTZuUl5enW2+9VZLk8/lkGIbsdrvee+89nX/++a2Oczqd/s8IAIgcQxgZQge1Owxde+21LZ5fffXVrfrMnDnz5Cs6hpiYGGVlZSknJ0f/+Z//6W/PycnRz372s1b9k5KS9NVXX7VoW7x4sT744AO99tprGjhwYMhqBQCEXyb3KEMHtTsMLVu2LJR1tMu8efN0zTXXaMyYMRo/fryeffZZ5efna86cOZIaLnHt379ff//732W1WjVixIgWx6ekpCg2NrZVOwAg8jXdo+z74krV1HsV67CZXBEiRbvDUGcwffp0lZSU6P7771dBQYFGjBih1atXq3///pKkgoKCE+45BADomnolOtU93qHSqnrtPFSh03u7zC4JEcJihHuNfIQpKyuTy+WS2+1WUlKS2eUAAI5j2jOf6NPdh/WX6aP0n6P7ml0OTBTI93fELK0HAOBEmi6VbStkJ2q0H2EIANBlDEljRRkCRxgCAHQZR0aGCENoP8IQAKDLGNK48eL+0mqV19SbXA0iBWEIANBldI+PUWpSw8a5O4qYN4T2IQwBALoU/07UXCpDOxGGAABdin/eEJOo0U6EIQBAl8KKMgSKMAQA6FLYawiBIgwBALqUwY0ryooralVSUWtyNYgEhCEAQJcSH2NXvx7xkqTtBxkdwokRhgAAXY5/RRnzhtAOhCEAQJeTmdZwqYwVZWgPwhAAoMthryEEgjAEAOhyMtOO7DVkGIbJ1aCzIwwBALqcQT27yW61qLzGo8KyGrPLQSdHGAIAdDkxdqsG9kyQxB3scWKEIQBAl8RO1GgvwhAAoEtiJ2q0F2EIANAlsdcQ2oswBADokppWlO0oKpfXx4oyHBthCADQJfXrES+n3aqaep/2Hq4yuxx0YoQhAECXZLNa/DdtZSdqHA9hCADQZbETNdqDMAQA6LL8K8oYGcJxEIYAAF0Wew2hPQhDAIAuq2lkaNehStV5fCZXg86KMAQA6LLSXbFKdNrl8RnaXVxpdjnopAhDAIAuy2Kx+C+VMW8Ix0IYAgB0aawow4kQhgAAXVomew3hBAhDAIAujRVlOBHCEACgS2taUZZ/uEpVdR6Tq0FnRBgCAHRpyd2c6tktRoYhfVdUYXY56IQIQwCALq9pEvU2JlGjDYQhAECX519RxrwhtCHiwtDixYs1cOBAxcbGKisrS+vWrTtm3zfeeEMXXXSRevXqpaSkJI0fP17vvvtuGKsFAHQGmf69hrhMhtYiKgytXLlSt99+uxYsWKC8vDxNnDhRkyZNUn5+fpv9165dq4suukirV69Wbm6ufvKTn+jSSy9VXl5emCsHAJiJvYZwPBbDMAyzi2ivs88+W2eddZaWLFnibxs2bJguu+wyLVy4sF3vcfrpp2v69Om6++6729W/rKxMLpdLbrdbSUlJHaobAGCu8pp6jbz3PUnSF3dnyxXvMLkihFog398RMzJUV1en3NxcZWdnt2jPzs7W+vXr2/UePp9P5eXl6tGjxzH71NbWqqysrMUDABDZEmMd6tM9TpK0vYjRIbQUMWGouLhYXq9XqampLdpTU1NVWFjYrvd49NFHVVlZqWnTph2zz8KFC+VyufyPjIyMk6obANA5DGnaiZpLZThKxIShJhaLpcVzwzBatbVlxYoVuvfee7Vy5UqlpKQcs9/8+fPldrv9j7179550zQAA87ETNY7FbnYB7dWzZ0/ZbLZWo0BFRUWtRouOtnLlSl1//fVatWqVLrzwwuP2dTqdcjqdJ10vAKBzyWSvIRxDxIwMxcTEKCsrSzk5OS3ac3JyNGHChGMet2LFCs2aNUsvv/yypkyZEuoyAQCdlH/jxYPliqC1QwiDiBkZkqR58+bpmmuu0ZgxYzR+/Hg9++yzys/P15w5cyQ1XOLav3+//v73v0tqCEIzZ87UX//6V40bN84/qhQXFyeXy2Xa5wAAhN9pKd1ktUilVfU6VF6rlKRYs0tCJxExI0OSNH36dD322GO6//77deaZZ2rt2rVavXq1+vfvL0kqKChosefQM888I4/Ho1tuuUXp6en+x9y5c836CAAAk8Q6bBqQnCCpYXQIaBJR+wyZgX2GAKDrmPNSrt75plC/nzJMsycOMrschFCX3GcIAICTxYoytIUwBACIGv4VZdyjDM0QhgAAUSMzrWHjxR0Hy+XzMUsEDQhDAICo0T85QTE2q6rqvNpfWm12OegkCEMAgKjhsFk1qFfjijI2X0QjwhAAIKpkph3ZfBGQCEMAgCjTtBM1K8rQhDAEAIgq3KMMRyMMAQCiStNlsl2HKlXv9ZlcDToDwhAAIKr06R6n+Bib6rw+7SmpNLscdAKEIQBAVLFaLRrsv1TG5osgDAEAolBmasPmi6wog0QYAgBEIf+KMiZRQ4QhAEAUyuSGrWiGMAQAiDpNy+u/L6lUTb3X5GpgNsIQACDq9Ep0qnu8Qz5D+q6ISdTRjjAEAIg6FouFnajhRxgCAEQl/07UhKGoRxgCAESlIWmsKEMDwhAAICpl+i+TMWco2hGGAABRaUjjxov7S6tVXlNvcjUwE2EIABCVusfHKDXJKYnRoWhHGAIARC1WlEEiDAEAoph/RRmTqKMaYQgAELWGcFsOiDAEAIhimVwmgwhDAIAoNrhxRVlxRZ2KK2pNrgZmIQwBAKJWfIxd/XrES2J0KJoRhgAAUc2/ooxJ1FGLMAQAiGqZaQ2Xyrax11DUIgwBAKIaew2BMAQAiGqZzW7YahiGydXADIQhAEBUG9Szm+xWi8prPSpw15hdDkxAGAIARLUYu1UDeyZIkrZxqSwqEYYAAFFvSBoryqIZYQgAEPWGNt2jjJGhqEQYAgBEPe5RFt0iLgwtXrxYAwcOVGxsrLKysrRu3brj9l+zZo2ysrIUGxurQYMG6emnnw5TpQCASNF0j7IdByvk9bGiLNpEVBhauXKlbr/9di1YsEB5eXmaOHGiJk2apPz8/Db77969W5MnT9bEiROVl5en3/3ud7rtttv0+uuvh7lyAEBnltEjXrEOq2o9PuUfrjK7HISZxYigTRXOPvtsnXXWWVqyZIm/bdiwYbrsssu0cOHCVv3vvPNOvf3229q6dau/bc6cOfriiy/0ySeftPk3amtrVVt75GZ9ZWVlysjIkNvtVlJSUhA/DQCgM7n0iY/01X63nr46S5eMSDO7HJyksrIyuVyudn1/R8zIUF1dnXJzc5Wdnd2iPTs7W+vXr2/zmE8++aRV/4svvlibNm1SfX19m8csXLhQLpfL/8jIyAjOBwAAdGrsRB29IiYMFRcXy+v1KjU1tUV7amqqCgsL2zymsLCwzf4ej0fFxcVtHjN//ny53W7/Y+/evcH5AACATu3IPcoIQ9HGbnYBgbJYLC2eG4bRqu1E/dtqb+J0OuV0Ok+ySgBApOHu9dErYkaGevbsKZvN1moUqKioqNXoT5O0tLQ2+9vtdiUnJ4esVgBA5Gm6R9nu4krVerwmV4NwipgwFBMTo6ysLOXk5LRoz8nJ0YQJE9o8Zvz48a36v/feexozZowcDkfIagUARJ60pFglxtrl8RnaXVxpdjkIo4gJQ5I0b948Pffcc1q6dKm2bt2qO+64Q/n5+ZozZ46khvk+M2fO9PefM2eO9uzZo3nz5mnr1q1aunSpnn/+ef3617826yMAADopi8Xi329oG5fKokpEzRmaPn26SkpKdP/996ugoEAjRozQ6tWr1b9/f0lSQUFBiz2HBg4cqNWrV+uOO+7QU089pd69e+vxxx/X5ZdfbtZHAAB0YkPSErVpzw+sKIsyEbXPkBkC2acAABDZXlz/ve55+xtdOCxVz107xuxycBK65D5DAACEGnsNRSfCEAAAjYakNuw1lH+4SlV1HpOrQbgQhgAAaJTczame3Rr2mttxsMLkahAuhCEAAJphJ+roQxgCAKAZdqKOPoQhAACa8e81xMhQ1CAMAQDQzJA0VpRFG8IQAADNDE5pmDN0sKxWpVV1JleDcCAMAQDQTGKsQ326x0mStrOiLCpE1O04AAAIh8y0RO0vrdb1L3ymPqfEKd0Vq/TucertilW6K07p3WPV2xWnNFesYh02s8vFSSIMAQBwlEkj0vTvbUUqr/Xo28JyfXuclWXJCTFK794Qkno3hqZ0V6x6N/5MTYqVw8aFmM6Me5OdAPcmA4DoVFHr0YHSah0orVaBu0YFpdU64K5RgbtaBaU1OuCuVk2974TvY7VIvRKdDWGpMTQ1D0u9u8epVzenrFZLGD5V9Ajk+5uRIQAA2tDNadeQ1ET/vkNHMwxDpVX1OtAYjgrcjWGpWWgqdNeo3mvoYFmtDpbVavPetv+W3WpRalLskbDUeBmueWjqkRAji4XAFAqEIQAAOsBiseiUhBidkhCj03u72uzj8xkqrqw9EpaOCk0F7hodLKuRx2dof2m19pdWS/qhzfdy2q0Nc5eah6XmP7vHKSnWEcJP3HURhgAACBGr1aKUxFilJMZqVEb3Nvt4vD4Vlde2DEulNUcuz7mrVVxRp1qPT9+XVOn7kqpj/r1Ep12ZaYka0cel03snaUQfl05L6cacpRNgztAJMGcIAGC2Wo9Xhe4af1gqcB8JS00/3dX1bR4bY7dqWFqiTu/j0ojeLo3ok6QhqYldfhUcc4YAAOhCnHab+icnqH9ywjH7VNV5tPdwtbYUuPX1/jJ9vd+tLQfKVF7r0Rf73Ppin9vf1261aEhqokb0SWocRXJpWHqi4mOiMxYwMnQCjAwBACKVz2co/3CVvj7QEJC+OeDWV/vdKq1qPYpktUin9urW4hLb8N5JETsPKZDvb8LQCRCGAABdiWEYOuCu0df73fpmv1tfH2gYRSoqr22z/4Dk+BaX2Eb0dumUhJgwVx04wlAQEYYAANGgqKxG3zQGo6aRpIbVba316R7nHz0a2cel0/skKSUxNswVHx9hKIgIQwCAaPVDZZ2+OVCmrxoD0jf73cdczZaS6NSIPi6N6J3UMJLUx6XerljT9kYiDAURYQgAgCPKauq1pXEEqWkkaeehCvnaSBOnxDv8E7SbLrH16xEflt22CUNBRBgCAOD4quo82lpQrm8OuBsus+0v0/aD5fK0kZASnXYNb7zE1hSQBvXqJluQAxJhKIgIQwAABK7W49X2worG+UcNE7W3FpSpztP6fm7jByVrxY3jgvr32WcIAACYymm3aWRfl0b2PXKrknqvTzsPVfj3QfrmQMOltsy0tu//Fi6EIQAAEBYOm1VD05I0NC1JV2T1lSR5fYZq6r2m1sXNSgAAgGlsVosSnOaOzRCGAABAVCMMAQCAqEYYAgAAUY0wBAAAohphCAAARDXCEAAAiGqEIQAAENUIQwAAIKoRhgAAQFSLmDD0ww8/6JprrpHL5ZLL5dI111yj0tLSY/avr6/XnXfeqZEjRyohIUG9e/fWzJkzdeDAgfAVDQAAOr2ICUMzZszQ5s2b9c477+idd97R5s2bdc011xyzf1VVlT7//HP94Q9/0Oeff6433nhD27dv19SpU8NYNQAA6OwshmEYZhdxIlu3btXw4cO1YcMGnX322ZKkDRs2aPz48fr222+VmZnZrvf57LPPNHbsWO3Zs0f9+vVr1zFlZWVyuVxyu91KSkrq8GcAAADhE8j3d0SMDH3yySdyuVz+ICRJ48aNk8vl0vr169v9Pm63WxaLRd27dz9mn9raWpWVlbV4AACArsvc28S2U2FhoVJSUlq1p6SkqLCwsF3vUVNTo7vuukszZsw4bkJcuHCh7rvvvlbthCIAACJH0/d2ey6AmRqG7r333jaDR3OfffaZJMlisbR6zTCMNtuPVl9fryuvvFI+n0+LFy8+bt/58+dr3rx5/uf79+/X8OHDlZGRccK/AwAAOpfy8nK5XK7j9jE1DN1666268sorj9tnwIAB+vLLL3Xw4MFWrx06dEipqanHPb6+vl7Tpk3T7t279cEHH5zwuqHT6ZTT6fQ/79atm/bu3avExMR2Ba9AlJWVKSMjQ3v37mU+UghxnsOD8xwenOfw4DyHT6jOtWEYKi8vV+/evU/Y19Qw1LNnT/Xs2fOE/caPHy+3261PP/1UY8eOlSRt3LhRbrdbEyZMOOZxTUFox44d+vDDD5WcnBxwjVarVX379g34uEAkJSXxf7Yw4DyHB+c5PDjP4cF5Dp9QnOsTjQg1iYgJ1MOGDdMll1yiG264QRs2bNCGDRt0ww036Kc//WmLlWRDhw7Vm2++KUnyeDy64oortGnTJi1fvlxer1eFhYUqLCxUXV2dWR8FAAB0MhERhiRp+fLlGjlypLKzs5Wdna0zzjhDL730Uos+27Ztk9vtliTt27dPb7/9tvbt26czzzxT6enp/kcgK9AAAEDXFhGrySSpR48e+sc//nHcPs1njA8YMKBdM8jN5HQ6dc8997SYo4Tg4zyHB+c5PDjP4cF5Dp/OcK4jYtNFAACAUImYy2QAAAChQBgCAABRjTAEAACiGmEIAABENcJQiC1evFgDBw5UbGyssrKytG7duuP2X7NmjbKyshQbG6tBgwbp6aefDlOlkS2Q8/zGG2/ooosuUq9evZSUlKTx48fr3XffDWO1kSvQf56bfPzxx7Lb7TrzzDNDW2AXEeh5rq2t1YIFC9S/f385nU6deuqpWrp0aZiqjVyBnufly5dr1KhRio+PV3p6uq677jqVlJSEqdrItHbtWl166aXq3bu3LBaL3nrrrRMeY8r3oIGQeeWVVwyHw2H87W9/M7Zs2WLMnTvXSEhIMPbs2dNm/127dhnx8fHG3LlzjS1bthh/+9vfDIfDYbz22mthrjyyBHqe586dazz88MPGp59+amzfvt2YP3++4XA4jM8//zzMlUeWQM9zk9LSUmPQoEFGdna2MWrUqPAUG8E6cp6nTp1qnH322UZOTo6xe/duY+PGjcbHH38cxqojT6Dned26dYbVajX++te/Grt27TLWrVtnnH766cZll10W5sojy+rVq40FCxYYr7/+uiHJePPNN4/b36zvQcJQCI0dO9aYM2dOi7ahQ4cad911V5v9f/vb3xpDhw5t0XbTTTcZ48aNC1mNXUGg57ktw4cPN+67775gl9aldPQ8T58+3fj9739v3HPPPYShdgj0PP/zn/80XC6XUVJSEo7yuoxAz/MjjzxiDBo0qEXb448/bvTt2zdkNXY17QlDZn0PcpksROrq6pSbm6vs7OwW7dnZ2cfcAfuTTz5p1f/iiy/Wpk2bVF9fH7JaI1lHzvPRfD6fysvL1aNHj1CU2CV09DwvW7ZMO3fu1D333BPqEruEjpznt99+W2PGjNGf/vQn9enTR0OGDNGvf/1rVVdXh6PkiNSR8zxhwgTt27dPq1evlmEYOnjwoF577TVNmTIlHCVHDbO+ByNmB+pIU1xcLK/Xq9TU1BbtqampKiwsbPOYwsLCNvt7PB4VFxcrPT09ZPVGqo6c56M9+uijqqys1LRp00JRYpfQkfO8Y8cO3XXXXVq3bp3sdv5V0x4dOc+7du3SRx99pNjYWL355psqLi7WzTffrMOHDzNv6Bg6cp4nTJig5cuXa/r06aqpqZHH49HUqVP1xBNPhKPkqGHW9yAjQyFmsVhaPDcMo1Xbifq31Y6WAj3PTVasWKF7771XK1euVEpKSqjK6zLae569Xq9mzJih++67T0OGDAlXeV1GIP88+3w+WSwWLV++XGPHjtXkyZO1aNEivfDCC4wOnUAg53nLli267bbbdPfddys3N1fvvPOOdu/erTlz5oSj1Khixvcg/7kWIj179pTNZmv1XxlFRUWtUm+TtLS0Nvvb7XYlJyeHrNZI1pHz3GTlypW6/vrrtWrVKl144YWhLDPiBXqey8vLtWnTJuXl5enWW2+V1PClbRiG7Ha73nvvPZ1//vlhqT2SdOSf5/T0dPXp00cul8vfNmzYMBmGoX379mnw4MEhrTkSdeQ8L1y4UOecc45+85vfSJLOOOMMJSQkaOLEiXrwwQcZuQ8Ss74HGRkKkZiYGGVlZSknJ6dFe05OjiZMmNDmMePHj2/V/7333tOYMWPkcDhCVmsk68h5lhpGhGbNmqWXX36Za/7tEOh5TkpK0ldffaXNmzf7H3PmzFFmZqY2b96ss88+O1ylR5SO/PN8zjnn6MCBA6qoqPC3bd++XVarVX379g1pvZGqI+e5qqpKVmvLr0ybzSZJnf6m4JHEtO/BkE7PjnJNSzeff/55Y8uWLcbtt99uJCQkGN9//71hGIZx1113Gddcc42/f9OSwjvuuMPYsmWL8fzzz7O0vh0CPc8vv/yyYbfbjaeeesooKCjwP0pLS836CBEh0PN8NFaTtU+g57m8vNzo27evccUVVxjffPONsWbNGmPw4MHG7NmzzfoIESHQ87xs2TLDbrcbixcvNnbu3Gl89NFHxpgxY4yxY8ea9REiQnl5uZGXl2fk5eUZkoxFixYZeXl5/i0MOsv3IGEoxJ566imjf//+RkxMjHHWWWcZa9as8b927bXXGueee26L/v/+97+N0aNHGzExMcaAAQOMJUuWhLniyBTIeT733HMNSa0e1157bfgLjzCB/vPcHGGo/QI9z1u3bjUuvPBCIy4uzujbt68xb948o6qqKsxVR55Az/Pjjz9uDB8+3IiLizPS09ON//7v/zb27dsX5qojy4cffnjcf992lu9Bi2EwvgcAAKIXc4YAAEBUIwwBAICoRhgCAABRjTAEAACiGmEIAABENcIQAACIaoQhAAAQ1QhDAAAgqhGGAHRa5513nm6//fZO+TcGDBigxx57LOj1AAg/whAAAIhqhCEAABDVCEMAIsI//vEPjRkzRomJiUpLS9OMGTNUVFTkf/3f//63LBaL3n33XY0ePVpxcXE6//zzVVRUpH/+858aNmyYkpKSdNVVV6mqqqrFe3s8Ht16663q3r27kpOT9fvf/17Nb9tYVFSkSy+9VHFxcRo4cKCWL1/eqr5FixZp5MiRSkhIUEZGhm6++WZVVFSE7oQACBrCEICIUFdXpwceeEBffPGF3nrrLe3evVuzZs1q1e/ee+/Vk08+qfXr12vv3r2aNm2aHnvsMb388sv6v//7P+Xk5OiJJ55occyLL74ou92ujRs36vHHH9df/vIXPffcc/7XZ82ape+//14ffPCBXnvtNS1evLhFEJMkq9Wqxx9/XF9//bVefPFFffDBB/rtb38bknMBIMhO/sb3ABAa5557rjF37tw2X/v0008NSUZ5eblhGIbx4YcfGpKM999/399n4cKFhiRj586d/rabbrrJuPjii1v8jWHDhhk+n8/fdueddxrDhg0zDMMwtm3bZkgyNmzY4H9969athiTjL3/5yzFrf/XVV43k5OSAPi8AczAyBCAi5OXl6Wc/+5n69++vxMREnXfeeZKk/Pz8Fv3OOOMM/++pqamKj4/XoEGDWrQdPaozbtw4WSwW//Px48drx44d8nq92rp1q+x2u8aMGeN/fejQoerevXuL9/jwww910UUXqU+fPkpMTNTMmTNVUlKiysrKk/3oAEKMMASg06usrFR2dra6deumf/zjH/rss8/05ptvSmq4fNacw+Hw/26xWFo8b2rz+Xzt/ttG49yh5mHpaHv27NHkyZM1YsQIvf7668rNzdVTTz0lSaqvr2/33wJgDrvZBQDAiXz77bcqLi7WQw89pIyMDEnSpk2bgvb+GzZsaPV88ODBstlsGjZsmDwejzZt2qSxY8dKkrZt26bS0lJ//02bNsnj8ejRRx+V1drw35ivvvpq0OoDEFqMDAHo9Pr166eYmBg98cQT2rVrl95++2098MADQXv/vXv3at68edq2bZtWrFihJ554QnPnzpUkZWZm6pJLLtENN9ygjRs3Kjc3V7Nnz1ZcXJz/+FNPPVUej8df30svvaSnn346aPUBCC3CEIBOr1evXnrhhRe0atUqDR8+XA899JD+/Oc/B+39Z86cqerqao0dO1a33HKLfvnLX+rGG2/0v75s2TJlZGTo3HPP1X/913/pxhtvVEpKiv/1M888U4sWLdLDDz+sESNGaPny5Vq4cGHQ6gMQWhbDaLaZBgAAQJRhZAgAAEQ1whAAAIhqhCEAABDVCEMAACCqEYYAAEBUIwwBAICoRhgCAABRjTAEAACiGmEIAABENcIQAACIaoQhAAAQ1f4/37nsPFHZv6oAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -236,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "id": "b70a0bb0",
    "metadata": {},
    "outputs": [
@@ -244,7 +233,7 @@
      "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 0.957321594716624 eV\n"
      ]
     }
    ],
@@ -270,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 13,
    "id": "0dee5e1a",
    "metadata": {},
    "outputs": [],
@@ -283,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 14,
    "id": "aa94c95b",
    "metadata": {},
    "outputs": [],
@@ -301,23 +290,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 15,
    "id": "7abfdf91",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fafec87f650>]"
+       "[<matplotlib.lines.Line2D at 0x7f1609b51210>]"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -337,7 +326,7 @@
    "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."
    ]
   },
   {