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diff --git a/Project1/System.py b/Project1/System.py
index cbbc0354c7be59d4e0b68e69c2471e956c6e520e..e729f76ec0f90610b8c60f02eac00b4332f07b57 100644
--- a/Project1/System.py
+++ b/Project1/System.py
@@ -332,289 +332,3 @@ class System:
         fig.tight_layout()
 
         return fig, axs
-
-class SystemGPT(System):
-    def f(self, u, sign):
-        """
-        Flux function based on sign.
-        """
-        if sign == '+':
-            return u * np.exp(-self.A * u)
-        elif sign == '-':
-            return -u * np.exp(-self.A * u)
-        else:
-            raise ValueError('Sign must be one of: "+", "-"')
-
-    def compute_flux(self, u, sign):
-        """
-        Compute numerical flux F_{j+1/2} using exponential upwind scheme.
-        u: array of upwind variables (n_plus or n_minus)
-        sign: '+' for n_plus, '-' for n_minus
-        Returns:
-            F: Flux array at interfaces (length: len(u) - 1)
-        """
-        f_u = self.f(u[:-1], sign)
-        f_up = self.f(u[1:], sign)
-        F = 0.5 * (f_u + f_up) - 0.5 * self.M * (u[1:] - u[:-1])
-        return F
-
-    def compute_flux_boundary(self, u, sign, side='left'):
-        """
-        Compute numerical flux at the boundary.
-        side: 'left' or 'right'
-        Returns:
-            Flux at the boundary interface.
-        """
-        if side == 'left':
-            # At the left boundary, u[0] is the ghost cell
-            f_u = self.f(u[0], sign)
-            f_up = self.f(u[1], sign)
-            F_boundary = 0.5 * (f_u + f_up) - 0.5 * self.M * (u[1] - u[0])
-        elif side == 'right':
-            # At the right boundary, u[-1] is the ghost cell
-            f_u = self.f(u[-2], sign)
-            f_up = self.f(u[-1], sign)
-            F_boundary = 0.5 * (f_u + f_up) - 0.5 * self.M * (u[-1] - u[-2])
-        else:
-            raise ValueError("Side must be 'left' or 'right'")
-        return F_boundary
-
-    def Upwind_n_plus(self, n_plus_old, type='Standard'):
-        """
-        Compute upwind terms for n_plus.
-        For 'Standard', it's a simple upwind scheme.
-        For 'Exponential', uses the exponential upwind fluxes.
-        """
-        if type == 'Standard':
-            # Upwind based on positive advection (from left)
-            return n_plus_old[1:], n_plus_old[:-1]
-        elif type == 'Exponential':
-            # Compute fluxes at all interfaces
-            F = self.compute_flux(n_plus_old, '+')  # Length: len(n_plus_old) -1
-
-            # Compute flux differences
-            # F_{j+1/2} - F_{j-1/2} corresponds to F[j] - F[j-1]
-            # To compute F_{j+1/2} for j=1 to N, we shift F by one to the right
-            F_shifted = np.zeros_like(F, dtype=float)
-            F_shifted[:-1] = F[1:]
-            # Compute F_shifted for j=0 separately using boundary flux
-            F_shifted[-1] = self.compute_flux_boundary(n_plus_old, '+', side='right')
-            return F, F_shifted
-        
-    def Upwind_n_minus(self, n_minus_old, type='Standard'):
-        """
-        Compute upwind terms for n_minus.
-        For 'Standard', it's a simple upwind scheme.
-        For 'Exponential', uses the exponential upwind fluxes.
-        """
-        if type == 'Standard':
-            # Upwind based on negative advection (from right)
-            return n_minus_old[:-1], n_minus_old[1:]
-            # return n_minus_old[1:], n_minus_old[:-1]
-        elif type == 'Exponential':
-            # Compute fluxes at all interfaces
-            F = self.compute_flux(n_minus_old, '-')  # Length: len(n_minus_old) -1
-
-            # Compute flux differences
-            # F_{j+1/2} - F_{j-1/2} corresponds to F[j] - F[j-1]
-            # To compute F_{j-1/2}, shift F by one to the right
-            F_shifted = np.zeros_like(F, dtype=float)
-            F_shifted[1:] = F[:-1]
-            # Compute F_shifted for j=0 separately using boundary flux
-            F_shifted[0] = self.compute_flux_boundary(n_minus_old, '-', side='left')
-            return F, F_shifted
-
-    def Upwind(self, n_plus_old, n_minus_old, type='Standard'):
-        """
-        Compute upwind terms for both n_plus and n_minus.
-        Returns tuple of (F_plus, F_plus_shifted), (F_minus, F_minus_shifted)
-        """
-        F_plus, F_plus_shifted = self.Upwind_n_plus(n_plus_old, type)
-        F_minus, F_minus_shifted = self.Upwind_n_minus(n_minus_old, type)
-        return (F_plus, F_plus_shifted), (F_minus, F_minus_shifted)
-
-    def solve_System(self, exchange='None', check_propagation=False, check_stability=True, atol=1e-3, rtol=1e-3, type='Standard'):
-        """
-        Solve the system using the specified discretization type.
-        Parameters:
-            exchange: 'None', 'SourceTerms', 'ReactionTerms', 'FullTerms'
-            check_propagation: whether to check for propagation convergence
-            check_stability: whether to enforce stability conditions
-            atol, rtol: tolerances for convergence
-            type: 'Standard' or 'Exponential'
-        """
-        x, n_init, n_plus_init, n_minus_init = self.Initialization()
-        n = n_init.copy()
-        n_plus = n_plus_init.copy()
-        n_minus = n_minus_init.copy()
-
-        n_vec = [n.copy()]
-        n_plus_vec = [n_plus_init.copy()]
-        n_minus_vec = [n_minus_init.copy()]
-
-        # Initial flux calculation (excluding ghost cells)
-        nx = (n[2] - 2 * n[1] + n[0]) / self.dx**2  # Central difference
-        J_flux = [n_plus.copy() + n_minus.copy() - self.D0 * nx]
-
-        t = 0.0
-        t_vec = [t]
-
-        stability = self.check_stability()
-        if check_stability and not stability:
-            raise ValueError('Stability conditions not satisfied')
-
-        for j in range(self.NTime):
-            # Check for propagation convergence if enabled
-            if check_propagation:
-                n_m_left = n_minus[-1]
-                n_p_right = n_plus[0]
-
-                if (np.isclose(n_p_right, self.N0 * self.sigma_0, rtol=rtol, atol=atol) and
-                    np.isclose(n_m_left, self.NL * self.sigma_L, rtol=rtol, atol=atol)):
-                    print(f'Convergence reached after {j} iterations')
-                    print(f'Needed time for full propagation: {t}')
-                    print(f'Tolerance is: atol={atol}, rtol={rtol}')
-                    self.t_propagation = t
-                    break
-
-            # Retrieve old states
-            n_old, n_plus_old, n_minus_old = n_vec[-1], n_plus_vec[-1], n_minus_vec[-1]
-
-            # Compute fluxes
-            (F_plus, F_plus_shifted), (F_minus, F_minus_shifted) = self.Upwind(n_plus_old, n_minus_old, type=type)
-            # For Standard upwind, F_plus and F_minus are direct flux differences
-            # F_plus_shifted and F_minus_shifted correspond to F_{j-1/2}
-            flux_diff_plus = F_plus - F_plus_shifted
-            flux_diff_minus = F_minus - F_minus_shifted # ? F_minus_shifted - F_minus
-
-            # Update with derivatives
-            n[1:-1] += self.dt * (
-                self.D0 / self.dx**2 * (
-                    n_old[2:] - 2 * n_old[1:-1] + n_old[:-2]
-                )
-            )
-            n_plus[1:] -= (self.dt / self.dx) * flux_diff_plus
-            n_minus[:-1] -= (self.dt / self.dx) * flux_diff_minus
-
-            # Apply exchange terms if any
-            if exchange == 'None':
-                pass
-            elif exchange == 'SourceTerms':
-                n_new, n_plus, n_minus = self.update_SourceTerms(n_new, n_plus, n_minus)
-            elif exchange == 'ReactionTerms':
-                n_new, n_plus, n_minus = self.update_Reaction(n_new, n_plus, n_minus)
-            elif exchange == 'FullTerms':
-                n_new, n_plus, n_minus = self.update_SourceTerms(n_new, n_plus, n_minus)
-                n_new, n_plus, n_minus = self.update_Reaction(n_new, n_plus, n_minus)
-            else:
-                raise ValueError('Exchange method not recognized, must be one of: None, SourceTerms, ReactionTerms, FullTerms')
-
-            # Apply boundary conditions
-            n, n_plus, n_minus = self.apply_bcs(n, n_plus, n_minus)
-
-            # Store updated values
-            n_vec.append(n.copy())
-            n_plus_vec.append(n_plus.copy())
-            n_minus_vec.append(n_minus.copy())
-
-            # Recalculate nx for flux computation (optional, based on your needs)
-            # nx = (n[2:-0] - 2 * n[1:-1] + n[:-2]) / self.dx**2
-            J_flux.append(n_plus.copy() + n_minus.copy() - self.D0 * nx)
-
-            # Update time
-            t += self.dt
-            t_vec.append(t)
-
-        # Convert lists to numpy arrays for easier handling
-        t_vec = np.array(t_vec)
-        n_vec = np.array(n_vec)
-        n_plus_vec = np.array(n_plus_vec)
-        n_minus_vec = np.array(n_minus_vec)
-        # J_flux = np.array(J_flux)  # Uncomment if you compute J_flux
-
-        # Assign to class attributes for access after simulation
-        self.t_vec = t_vec
-        self.n_vec = n_vec
-        self.n_plus_vec = n_plus_vec
-        self.n_minus_vec = n_minus_vec
-        self.J_flux = J_flux  # Assign if needed
-
-        # return {
-        #     'x': self.x,
-        #     't': self.t_vec,
-        #     'n': self.n_vec,
-        #     'n_plus': self.n_plus_vec,
-        #     'n_minus': self.n_minus_vec
-        #     # 'J_flux': self.J_flux  # Return if needed
-        # }
-
-    # Optional: Visualization methods can be added below
-
-    def plot_initial_conditions(self):
-        x, n_init, n_plus_init, n_minus_init = self.Initialization()
-        fig, axs = plt.subplots()
-        fig.suptitle('Initial Solution')
-        axs.plot(self.x, n_init, label='n')
-        axs.plot(x, n_plus_init, label='n+')
-        axs.plot(x, n_minus_init, label='n-')
-        axs.grid()
-        axs.legend()
-        fig.tight_layout()
-        return fig, axs
-    
-    def plot_analytical_solution(self, t):
-        x, n_init, n_plus_init, n_minus_init = self.Initialization()
-        fig, axs = plt.subplots()
-        fig.suptitle(f'Analytical Solution at t={t}')
-        axs.plot(x, self.n_plus_ana(x, t), '--o', label='n+')
-        axs.plot(x, self.n_minus_ana(x, t), '--o', label='n-')
-        axs.grid()
-        fig.legend()
-        fig.tight_layout()
-        return fig, axs
-    
-    def plot_solution(self, timeindex, analytical=False):
-        fig, axs = plt.subplots(ncols=3, figsize=(15, 5))
-        fig.suptitle(f'Solution at t={self.t_vec[timeindex]}')
-        axs[0].plot(self.x, self.n_vec[-1], color=self.colors[0])
-        axs[1].plot(self.x, self.n_plus_vec[-1], color=self.colors[0], label='Numerical')
-        axs[2].plot(self.x, self.n_minus_vec[-1], color=self.colors[0])
-        [ax.grid() for ax in axs]
-        [ax.set_title(t) for ax, t in zip(axs, ['n', 'n+', 'n-'])]
-        [ax.set_xlabel('x [-]') for ax in axs]
-        [ax.set_ylabel(t) for ax, t in zip(axs, ['n [-]', 'n+ [-]', 'n- [-]'])]
-        if analytical:
-            axs[1].plot(self.x, self.n_plus_ana(self.x, self.t_vec[timeindex]), '--', color=self.colors[0], label='Analytical')
-            axs[2].plot(self.x, self.n_minus_ana(self.x, self.t_vec[timeindex]), '--', color=self.colors[0])
-            fig.legend()
-        fig.tight_layout()
-
-        return fig, axs
-    
-    def plot_solution_contourf(self, timeindex):
-        # contourfigures over time for each
-        fig, axs = plt.subplots(ncols=3, figsize=(15, 5))
-        fig.suptitle(f'Solution at t={self.t_vec[timeindex]}')
-        axs[0].contourf(self.x, self.t_vec, self.n_vec, cmap='coolwarm')
-        axs[1].contourf(self.x, self.t_vec, self.n_plus_vec, cmap='coolwarm')
-        axs[2].contourf(self.x, self.t_vec, self.n_minus_vec, cmap='coolwarm')
-        [ax.grid() for ax in axs]
-        [ax.set_title(t) for ax, t in zip(axs, ['n', 'n+', 'n-'])]
-        [ax.set_xlabel('x [-]') for ax in axs]
-        [ax.set_ylabel('t [-]') for ax in axs]
-        fig.tight_layout()
-
-        return fig, axs
-    
-    def plot_flux(self):
-        # flux
-        fig, axs = plt.subplots()
-        fig.suptitle('Flux')
-        cfig = axs.contourf(self.x, self.t_vec, self.J_flux, cmap='coolwarm')
-        fig.colorbar(cfig, ax=axs)
-        axs.grid()
-        axs.set_xlabel('x [-]')
-        axs.set_ylabel('t [-]')
-        fig.tight_layout()
-
-        return fig, axs
\ No newline at end of file
diff --git a/Project1/Task1_class.ipynb b/Project1/Task1_class.ipynb
index f75c7ea7c3d6368955f02016bbc4c8e1326487f2..e3fb607914395d6afffc603dfefb779e94e23963 100644
--- a/Project1/Task1_class.ipynb
+++ b/Project1/Task1_class.ipynb
@@ -2,19 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np \n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "from System import System, SystemGPT"
+    "from System import System"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,14 +58,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/2908415207.py:4: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/2908415207.py:4: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n"
      ]
     },
@@ -89,14 +89,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/1581121392.py:4: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/1581121392.py:4: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n"
      ]
     },
@@ -120,18 +120,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/2250181504.py:11: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/2250181504.py:11: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n",
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/2250181504.py:16: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/2250181504.py:16: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n",
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/2250181504.py:21: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/2250181504.py:21: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n"
      ]
     },
@@ -192,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -208,9 +208,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/886973050.py:19: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/886973050.py:19: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n",
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/886973050.py:24: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/886973050.py:24: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n"
      ]
     },
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -299,11 +299,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/683968515.py:55: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/683968515.py:55: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n",
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/683968515.py:64: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/683968515.py:64: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
       "  fig.legend()\n",
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/683968515.py:66: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/683968515.py:66: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n",
       "/Users/janhabscheid/Documents/git/ddm/Project1/System.py:205: RuntimeWarning: overflow encountered in multiply\n",
       "  self.D0 / self.dx**2 * (\n",
@@ -467,14 +467,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/32072315.py:20: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/32072315.py:20: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n"
      ]
     },
@@ -514,14 +514,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/2884526579.py:20: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/2884526579.py:20: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n"
      ]
     },
@@ -561,18 +561,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/1435854478.py:17: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/1435854478.py:17: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n",
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/1435854478.py:22: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/1435854478.py:22: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n",
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/1435854478.py:27: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/1435854478.py:27: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n"
      ]
     },
@@ -639,24 +639,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/2572615971.py:21: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/3019376010.py:21: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n",
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/2572615971.py:26: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/3019376010.py:26: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n",
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/2572615971.py:31: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/3019376010.py:31: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n"
      ]
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1500x500 with 3 Axes>"
       ]
@@ -666,7 +666,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1500x500 with 3 Axes>"
       ]
@@ -676,7 +676,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -694,7 +694,7 @@
     "    sigma_0=sigma_0, sigma_L=sigma_L,\n",
     "    k_p=k_p, k_n=k_n,\n",
     "    dx=dx,\n",
-    "    t_end=int(t_end*100), NTime=int(NTime*100)\n",
+    "    t_end=t_end, NTime=NTime\n",
     ")\n",
     "MySystem.solve_System(\n",
     "    check_propagation=True, \n",
@@ -721,18 +721,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/2873779185.py:17: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/2873779185.py:17: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n",
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/2873779185.py:22: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/2873779185.py:22: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n",
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/2873779185.py:27: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/2873779185.py:27: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n"
      ]
     },
@@ -799,7 +799,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -849,14 +849,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/2597085326.py:33: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/2597085326.py:33: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n"
      ]
     },
@@ -909,14 +909,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_80194/2796571549.py:34: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_92991/2796571549.py:34: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
       "  fig.show()\n"
      ]
     },