diff --git a/notebooks/V09_zustandsrekonstruktion_mittels_beobachter.ipynb b/notebooks/V09_zustandsrekonstruktion_mittels_beobachter.ipynb
index 9f5f595cb81bd19a3b34dc76c77ad66d6e4cd4c9..f61c6b1c06e65c04110fd97833961a8bcfaaaeab 100644
--- a/notebooks/V09_zustandsrekonstruktion_mittels_beobachter.ipynb
+++ b/notebooks/V09_zustandsrekonstruktion_mittels_beobachter.ipynb
@@ -8,43 +8,45 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 15,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## <span style='color:Gray'>Beispiel #1 </span>"
+    "from systheo2functions import *\n",
+    "%matplotlib inline"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div style=\"font-family: 'times'; font-size: 13pt; text-align: justify\">\n",
-    "Für das System:"
+    "## <span style='color:Gray'>Beispiel #1 </span>"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "pkg load control"
+    "<div style=\"font-family: 'times'; font-size: 13pt; text-align: justify\">\n",
+    "Für das System:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "A = [-2 1;\n",
-    "      0 -1];\n",
+    "A = np.array([[-2,1],\n",
+    "              [0,-1]])\n",
     "  \n",
-    "B = [1; 1];\n",
+    "B = np.array([[1],\n",
+    "              [1]])\n",
     "\n",
-    "C = [1 0];\n",
+    "C = np.array([[1,0]])\n",
     "\n",
-    "D = 0"
+    "D = np.array([0])"
    ]
   },
   {
@@ -57,22 +59,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
-    "poles = [-10+1j*5 -10-1j*5]\n",
-    "% po = [-5+1j*5 -5-1j*5]  % Beobachterpole"
+    "poles = [-10+1j*5,-10-1j*5]\n",
+    "# po = [-5+1j*5 -5-1j*5]  % Beobachterpole"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "So = [C; C*A]\n",
-    "rank(So)"
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "So:\n",
+      "[[ 1  0]\n",
+      " [-2  1]]\n",
+      "Rang: 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "row1 = C\n",
+    "row2 = np.matmul(C,A)\n",
+    "So = np.r_[row1,row2]\n",
+    "#So = (C  )\n",
+    "#     (C*A)\n",
+    "print(\"So:\\n\"+str(So))\n",
+    "\n",
+    "print_rank(So)"
    ]
   },
   {
@@ -85,11 +104,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G:[[ 17. 106.]]\n"
+     ]
+    }
+   ],
    "source": [
-    "G = place(A',C',poles)"
+    "G = signal.place_poles(A.T, C.T, poles).gain_matrix\n",
+    "print(\"G:\"+str(G))"
    ]
   },
   {
@@ -104,21 +132,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ao:\n",
+      "[[ -19.    1.]\n",
+      " [-106.   -1.]]\n",
+      "Eigenwerte:\n",
+      "(-10+5j)\n",
+      "(-10-5j)\n"
+     ]
+    }
+   ],
    "source": [
-    "Ao = A-G'*C\n",
-    "eigs(Ao)"
+    "Ao = A-np.matmul(G.T,C)\n",
+    "print(\"Ao:\\n\"+str(Ao))\n",
+    "print_eig(Ao)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bo:\n",
+      "[[  1.  17.]\n",
+      " [  1. 106.]]\n"
+     ]
+    }
+   ],
    "source": [
-    "Bo = [B G']"
+    "column1 = B\n",
+    "column2 = G.T\n",
+    "Bo = np.c_[column1,column2]\n",
+    "print(\"Bo:\\n\"+str(Bo))"
    ]
   },
   {
@@ -143,33 +198,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
-    "% Simulink model A\n",
-    "% Set the Octave Engine to run the simulation\n",
-    "% Simulation Parameters\n",
-    "addpath(\"./Octsim\");\n",
-    "% Start time\n",
-    "tini = 0;\n",
-    "% End time\n",
-    "tfinal = 10;\n",
-    "% Time Step\n",
-    "dt = 0.001;\n",
-    "% Number of data flows in the schematic\n",
-    "nflows_1 = 12;\n",
-    "% Initial conditions\n",
-    "xo_1 = [2; 1];\n",
-    "xo_2 = [0; 0];\n",
+    "tini = 0 # Start time\n",
+    "tfinal = 10 # End time\n",
+    "dt = 0.001 # Time Step\n",
+    "nflows_1 = 13 # Number of data flows in the schematic\n",
     "\n",
-    "% Matrices\n",
-    "C2 = eye(2);\n",
-    "D2 = [0;0];\n",
-    "Do = zeros(2,2);\n",
+    "xo_1 = np.array([[2],\n",
+    "                 [1]])\n",
+    "xo_2 = np.array([[0],\n",
+    "                 [0]])\n",
     "\n",
-    "% Instance of the simulation schematic\n",
-    "sc1 = Schema(tini,tfinal,dt,nflows_1);"
+    "#Matrices:\n",
+    "C2 = np.array([[1,0],\n",
+    "               [0,1]]);\n",
+    "D2 = np.array([[0],\n",
+    "               [0]]);\n",
+    "Do = np.array([[0,0],\n",
+    "               [0,0]]);\n",
+    "\n",
+    "sc1 = Schema(tini,tfinal,dt,nflows_1) # Instance of the simulation schematic"
    ]
   },
   {
@@ -184,67 +235,119 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
-    "c1{1} = SquareSignal(1,10,0,1,0.5); %input"
+    "#c1{1} = SquareSignal(1,10,0,1,0.5); %input"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
-    "c1{2} = StateSpace(1,[2 3],A,B,C2,D2,xo_1); % original system\n",
+    "c1 = SquareSignal(1,10,0,1,0.5) #input\n",
     "\n",
-    "c1{3} = StateSpace(1,[4 5],A,B,C2,D2,xo_2); % erroneous system\n",
-    "c1{4} = StateSpace([1 8],[6 7],Ao,Bo,C2,Do,xo_2); % observer system\n",
+    "c2 = StateSpace([1],[2,3],A,B,C2,D2,xo_1) #original system\n",
+    "c3 = StateSpace([1],[4,5],A,B,C2,D2,xo_2) #erroneous system\n",
+    "c4 = StateSpace([1,8],[6,7],Ao,Bo,C2,Do,xo_2) #observer system\n",
+    "c5 = Gain([2,3],[8],C)\n",
+    "c6 = Sum(2,4,9,1,-1)\n",
+    "c7 = Sum(3,5,10,1,-1)\n",
+    "c8 = Sum(2,6,11,1,-1)\n",
+    "c9 = Sum(3,7,12,1,-1)\n",
     "\n",
-    "c1{5} = Gain([2 3],8,C);\n",
+    "sc1.AddListComponents(np.array([c1,c2,c3,c4,c5,c6,c7,c8,c9]))\n",
     "\n",
-    "c1{6} = Sum(2,4,9,1,-1);\n",
-    "c1{7} = Sum(3,5,10,1,-1);\n",
-    "c1{8} = Sum(2,6,11,1,-1);\n",
-    "c1{9} = Sum(3,7,12,1,-1);\n",
-    "sc1.AddListComponents(c1);\n",
+    "#Run the schematic and plot:\n",
+    "out1 = sc1.Run(np.array([1,2,3,4,5,6,7,8,9,10,11,12]))\n",
     "\n",
-    "% Run the schematic and plot\n",
-    "out1 = sc1.Run([1:12]);\n",
-    "time1 = out1(1,:);"
+    "time1 = out1[0,:]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "plot(time1,out1(3,:), time1,out1(4,:));\n",
-    "legend('State-Space 1, x_{dot}','State-Space 1, y')"
+    "%matplotlib inline\n",
+    "plt.plot(time1,out1[2,:], time1,out1[3,:])\n",
+    "plt.legend(['Signal 2: State-Space x_{dot}','Signal 3: State-Space y'])\n",
+    "plt.grid()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 26,
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "plot(time1,out1(10,:),'r',time1,out1(11,:),'b');\n",
-    "legend('Reale System','System mit falsche Bedingungen vor t0')\n",
-    "grid on"
+    "%matplotlib inline\n",
+    "plt.plot(time1,out1[9,:],'r', time1,out1[10,:],'b')\n",
+    "plt.legend(['Reales System','System mit falscher Bedingungen vor t0'])\n",
+    "plt.grid()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 27,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "plot(time1,out1(12,:),'r',time1,out1(13,:),'b');\n",
-    "legend('Offene Kreis','System mit Beobachter')\n",
-    "grid on"
+    "%matplotlib inline\n",
+    "plt.plot(time1,out1[11,:],'r', time1,out1[12,:],'b')\n",
+    "plt.legend(['Offener Kreis','System mit Beobachter'])\n",
+    "plt.grid()\n",
+    "plt.show()"
    ]
   },
   {
@@ -282,18 +385,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
-    "A = [-2 1;\n",
-    "      0 -1];\n",
+    "A = np.array([[-2,1],\n",
+    "              [0,-1]])\n",
     "  \n",
-    "B = [0; 1];\n",
+    "B = np.array([[0],\n",
+    "              [1]])\n",
     "\n",
-    "C = [1 0];\n",
+    "C = np.array([[1,0]])\n",
     "\n",
-    "D = 0;"
+    "D = np.array([0])"
    ]
   },
   {
@@ -306,12 +410,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sc:\n",
+      "[[ 0  1]\n",
+      " [ 1 -1]]\n",
+      "Rang: 2\n"
+     ]
+    }
+   ],
    "source": [
-    "Sc = [B A*B]\n",
-    "rank(Sc)"
+    "column1 = B\n",
+    "column2 = np.matmul(A,B)\n",
+    "Sc = np.c_[column1,column2] #Sc = (B A*B)\n",
+    "print(\"Sc:\\n\"+str(Sc))\n",
+    "\n",
+    "print_rank(Sc)"
    ]
   },
   {
@@ -324,12 +443,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "So:\n",
+      "[[ 1  0]\n",
+      " [-2  1]]\n",
+      "Rang: 2\n"
+     ]
+    }
+   ],
    "source": [
-    "So = [C; C*A]\n",
-    "rank(So)"
+    "row1 = C\n",
+    "row2 = np.matmul(C,A)\n",
+    "So = np.r_[row1,row2]\n",
+    "print(\"So:\\n\"+str(So))\n",
+    "\n",
+    "print_rank(So)"
    ]
   },
   {
@@ -342,12 +476,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "K:[[34.  7.]]\n"
+     ]
+    }
+   ],
    "source": [
-    "pc = [-5+1j*5 -5-1j*5]\n",
-    "K = place(A,B,pc)"
+    "pc = [-5+1j*5,-5-1j*5]\n",
+    "K = signal.place_poles(A, B, pc).gain_matrix\n",
+    "print(\"K:\"+str(K))"
    ]
   },
   {
@@ -360,11 +503,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
-    "csi = 0.8;"
+    "csi = 0.8"
    ]
   },
   {
@@ -377,11 +520,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
-    "omega0 =10;"
+    "omega0 =10"
    ]
   },
   {
@@ -395,12 +538,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G:[[13. 85.]]\n"
+     ]
+    }
+   ],
    "source": [
-    "poles = [-csi*omega0+1j*omega0*sqrt(1-csi*csi) -csi*omega0-1j*omega0*sqrt(1-csi*csi)]\n",
-    "G = place(A',C',poles)"
+    "poles = [-csi*omega0+1j*omega0*sqrt(1-csi*csi),-csi*omega0-1j*omega0*sqrt(1-csi*csi)]\n",
+    "G = signal.place_poles(A.T, C.T, poles).gain_matrix\n",
+    "print(\"G:\"+str(G))"
    ]
   },
   {
@@ -413,21 +565,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-2  1]\n",
+      " [ 0 -1]]\n",
+      "[[13.]\n",
+      " [85.]]\n",
+      "[[1 0]]\n",
+      "Ao:\n",
+      "[[-15.   1.]\n",
+      " [-85.  -1.]]\n",
+      "Eigenwerte:\n",
+      "(-8+6j)\n",
+      "(-8-6j)\n"
+     ]
+    }
+   ],
    "source": [
-    "Ao = A-G'*C\n",
-    "eigs(Ao)"
+    "print(A)\n",
+    "print(G.T)\n",
+    "print(C)\n",
+    "Ao = A-np.matmul(G.T,C)\n",
+    "print(\"Ao:\\n\"+str(Ao))\n",
+    "print_eig(Ao)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bo:\n",
+      "[[ 0. 13.]\n",
+      " [ 1. 85.]]\n"
+     ]
+    }
+   ],
    "source": [
-    "Bo = [B G']"
+    "column1 = B\n",
+    "column2 = G.T\n",
+    "Bo = np.c_[column1,column2]\n",
+    "print(\"Bo:\\n\"+str(Bo))"
    ]
   },
   {
@@ -452,73 +639,113 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 37,
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "% Simulink model A\n",
-    "% Set the Octave Engine to run the simulation\n",
-    "% Simulation Parameters\n",
-    "addpath(\"./Octsim\");\n",
-    "% Start time\n",
-    "tini = 0;\n",
-    "% End time\n",
-    "tfinal = 5;\n",
-    "% Time Step\n",
-    "dt = 0.01;\n",
-    "% Number of data flows in the schematic\n",
-    "nflows_2 = 11;\n",
-    "% Initial conditions\n",
-    "xo_1 = [2; 1];\n",
-    "xo_2 = [0; 0];\n",
+    "tini = 0 # Start time\n",
+    "tfinal = 5 # End time\n",
+    "dt = 0.01 # Time Step\n",
+    "nflows_2 = 12 # Number of data flows in the schematic\n",
+    "\n",
+    "xo_1 = np.array([[2],\n",
+    "                 [1]])\n",
+    "xo_2 = np.array([[0],\n",
+    "                 [0]])\n",
+    "\n",
+    "#Matrices:\n",
+    "C2 = np.array([[1,0],\n",
+    "               [0,1]]);\n",
+    "D2 = np.array([[0],\n",
+    "               [0]]);\n",
+    "Do = np.array([[0,0],\n",
+    "               [0,0]]);\n",
+    "\n",
+    "sc2 = Schema(tini,tfinal,dt,nflows_2) # Instance of the simulation schematic\n",
     "\n",
-    "% Matrices\n",
-    "C2 = eye(2);\n",
-    "D2 = [0;0];\n",
-    "Do = zeros(2,2);\n",
+    "c2_1 = StateSpace([3],[1,2],A,B,C2,D2,xo_1)\n",
+    "c2_2 = Gain([1,2],[3],-1*K)\n",
     "\n",
-    "% Instance of the simulation schematic\n",
-    "sc2 = Schema(tini,tfinal,dt,nflows_2);\n",
+    "c2_3 = StateSpace([6],[4,5],A,B,C2,D2,xo_1)\n",
+    "c2_4 = Gain([7,8],[6],-1*K)\n",
     "\n",
-    "c2{1} = StateSpace(3,[1 2],A,B,C2,D2,xo_1);\n",
-    "c2{2} = Gain([1 2],3,-K);\n",
+    "c2_5 = StateSpace([6,9],[7,8],Ao,Bo,C2,Do,xo_2)\n",
+    "c2_6 = Gain([4,5],[9],C)\n",
     "\n",
-    "c2{3} = StateSpace(6,[4 5],A,B,C2,D2,xo_1);\n",
-    "c2{4} = Gain([7 8],6,-K);\n",
+    "#print(Ao)\n",
+    "#print(Bo)\n",
+    "#print(C2)\n",
+    "#print(Do)\n",
+    "#print(\"xo_2:\")\n",
+    "#print(xo_2)\n",
     "\n",
-    "c2{5} = StateSpace([6 9],[7 8],Ao,Bo,C2,Do,xo_2);\n",
-    "c2{6} = Gain([4 5],9,C);\n",
+    "c2_7 = Sum(1,4,10,1,-1)\n",
+    "c2_8 = Sum(2,5,11,1,-1)\n",
     "\n",
-    "c2{7} = Sum(1,4,10,1,-1);\n",
-    "c2{8} = Sum(2,5,11,1,-1);\n",
+    "sc2.AddListComponents(np.array([c2_1,c2_2,c2_3,c2_4,c2_5,c2_6,c2_7,c2_8]))\n",
     "\n",
-    "sc2.AddListComponents(c2);\n",
+    "#Run the schematic and plot:\n",
+    "out2 = sc2.Run(np.array([1,2,3,4,5,6,7,8,9,10,11]))\n",
     "\n",
-    "% Run the schematic and plot\n",
-    "out2 = sc2.Run([1:11]);\n",
-    "time2 = out2(1,:);"
+    "time2 = out2[0,:]\n",
+    "#print(out2[7,:])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "plot(time2,out2(2,:),'r',time2,out2(3,:),'b');\n",
-    "legend('State-Space:1','State-Space:2')\n",
-    "grid on"
+    "%matplotlib inline\n",
+    "plt.plot(time2,out2[1,:],'r', time2,out2[2,:],'b')\n",
+    "plt.legend(['1','2'])\n",
+    "plt.grid()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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czf7mrtSKOLEH8ougcl5q7Zzt4s86FzNtW+tuuyKS1TwX5MaG6QkZCvJ81E4pHrnD0A2YB3tHvPSBJTUArHszxV55yx6oWuSMbbtp1kUweyW8+mMYWhxMRGQcHgzyED2DhrqqYvJ8MS6NPzW0MvIGzNXlAVbOm8K6VMfJT+yFqvNSa2M0l/8VtB2AXZqKKCLx8WCQh+kOGeZNizE+Ds6VnTDioqAh1y+tYefRTt5pjf36uAZ6oOMdmJ6mID//T2HaIt2gWUTi5r0gj4ToGjTUTimKvcPQ0EqMMXI4Y3hle5IXB7Xsc7ZV5yZ3/HiGbtB8dBsc2JCe9xCRrOK5ICcSJhgZZXwczviwc+TQCsCcqcUsm12R/PBKy15nm64eOcAFf+Ysj6sbNItIHDwX5JFIiBD5Y/TIo0MuMT7sHHLd0hq2Hm7naEdf4gWc2OPc2GLqwsSPjVd+Ibz7S3DoRXj75fS9j4hkBc8FuY2ECZE3epD7Y88jP9P1S53hlaQuDmrdD1PmQX5B4scmov7TUFoDz/69xspFZEyeC3ITCTFo88YYWhk/yBdML+Xc6tLkgvzkQZgyP/HjElVQAu/7JjS+RlXLK+l/PxHxrPxMF5CwSBiT76eiyB/79fxCZ+hjjCAHuG7pTH70/D5auvupKi2M772thbZDqd1MYty3sDR1BDl4ooc2s5orShZQvedhfvbSrRQGAkwvK2R6aSEzKwJMLSkYuWiYiOQcV4LcGHMd8EMgD3jQWvs9N9qNxWdDBArGCF5joisgjj298PqlNdy/fh9Pbz/Gxy+L8wrN3jbo74Cp7vbIewdCPL39GOt3NfPHt1o42Xt6nZgrfbfxnwXfp2Xdd/lh+MPDjisP5LNgeikLp5eyYHoJC6eXsGB6KfOmFVOYn+dqjSNEIjDQ5ayhHgo6FzDlF0JeoTPslB84dW9VEUmvlIPcGJMH/G/gGqAR2GiMedJauzPVtmPJI0xRIDD2TgUlo85aGfKumjLOmVHKr18/En+QnzzobF0aWjneGeTfGt7isc2NdPWHmFFWyNXnV3NhbQXnVJdRVVrI1JJrOPLTV/hK65PcfvuXaSqcz4mufo6093HgRDcHTvTw0v4TPLal8VS7PuPMzllQ5QT7guklzK8qoaY8wIzyAKWFCfyzBzvh6FZnVcZjb0LrW9B1FLqOQWRw7GMLSqFoirNme9FUKJpCpGgKkcJKIoEKbGEFBJzvCVRih74Ky+gLWbr7QzGbHfobZOiPERN9JtYfJ6Ptc7oNE7PNM18bk7VgI87XuMZpz4bHuaJ3jOPHq1V/uWU1N3rklwD7rbUHAIwxa4GbANeD/NUffYpLCVJUNMr4+BB/8bhDK8YYbl1Zy3fX7R59SdyztUWDfOqCOCuOrW8gzP3P7+Ohlw4SsZYbLpjFRy+dy8q5U4bfSDrqD+d8ltk925m54a+Z+enfgX/KiH26goMcaunlrRPdHDjRzVstPRw40cPLB1oJDg4PmZKCPGaUBygv8lNamEdpYT6lhX4K/T58BqqDB1nc8SKLul6ltns7eTjh0pE/jWMF82jznU9b2So6KKfP+umzfkLWQHiQfDuALzKAPxykONRNWUcX5e1dlHGUCruPCtNNJT0UmLGXILjK5tP/op8B/PTjp9862zCj/6VhsBjAEMEAPiL4sNHnLT6s85xxPjween34Pqef49QxzvNDbZ96zrj7IfRqgBdcbdI1ETv6L4JUfgrvBcI5drlEx7xvwerVrrbpRpDPBg6f8bgRuPTsnYwxa4A1ANXV1TQ0NCT8Rh1F5/Lb4puh5vIxj185YOk/+g7bx3mP6v4IPgM/ePyP3Hbe+LNQ5h1az3zg92++TSQvuXnou1rDPLS9nxN9lstn5XPzIj/Ti9vpOdTO7w/FPqZ7wMebCz7Psu3f4ehPPsqe8/5y1B5WJbCiAFbMAmZBxAY4GbQc77W091vagxFn2x+kty/I8S7L2yFLJDTAVZGX+bB5nouMM1d+h63jwcgH2cz57PfV0RGqJD8Cfh/k+Qz5BvJ9kOeDfJ8hzx99bJzH+Sb6mnH2P/W9sQQIUhTpoSjSQ3Gkm6JIb3TbQ2GkFxMKUuiLkG8H8VvnF0S+HcTH0C+l4fEx9CgyLILN6S9jhsWxjca9Nb7o8c5rNvqctdE2jDn9/FDUGwM2RptjdZjHTTtLJBzGlxf7F5UZIy7H72uP/eZjtT3e8an288PhMHmjnHO2GiyYklT+jcWNII/1bzniX95a+wDwAEB9fb1dncxvpNWraWhoYNxjD1RT5ssffz/gv49uZFNTB/dfcWXstVvO9Ou1UDaLK67+QNwlD4lELD/asJ97N+2lbloJP/rEMi5dMC2uYxsaGlh2w9dgWpiZL3yfmRde7cwzd0Pzbtj8H7DtUWf53GmLYOU/wQUfYUnpDFxcqDchDQ0NvNflXstkF9d/21lG5+wON4K8EZhzxuNaoMmFdpNXUAJ9J+Pa9daVtazf3UzDnmauPr967J3bDiY1rNIZHOQvH3mdF/ae4EPLZ/GdW5ZRXJDEj/7Krztrof/um+Dzw6VrEm8DYDAIO59wAvydl522Ft8IKz8Fde/ReKqIx7gR5BuBc4wx84EjwO3AR11oN3kFJdDROP5+wPsXV1NTHuAnLx2MI8gPwLnXJlRKS3c/n3zoNfYc6+IfP7SUj106N/kpgz4f3PoQ/L9Pwbq/c5bTvfYfwT/KxVFna94NWx6GbY84v+imLoBr/ics/xiUVCVXk4hkXMpBbq0NGWO+DPwOZ/rhQ9baHSlXlgp/yairH47YNc/Hp1bV8d11u9nR1MGSWRWxd+zvhp7mhHrkh9t6+fOHXuNoRx8PfrKe1efNiPvYUeUXwkcehufuhpd/BPuehSv+DpZ++PQ6M0OsdWaZ7HsG3vgvZ/aJLx/edQPUfwrqrnB/TXURmXCuzCO31j4FPOVGW66IY/rhmW6/ZC73r9/Hgy8e5N4/Wx57p5OHnG2cUw/3Hu/iEz95leBghF989jJWzhs50yRpefnwgX+Cc6+Dp78BT34ZnvoqzFoB5bMACz0tcGI3dB93jqlZBh/4Liy7DUqnu1eLiGSc967sjEfB+NMPz1RR5OfPLp7Lwy8f4m+uOZc5U2NMb2w74GzjuBho89sn+fRPN1KY7+OXn38359WUxV1LQua/F77wIhx6CXb/1ulxH9kMxgeBClh4NcxeAYuuTnnKpIhMXlka5KUQ7odwyOm9xmHNFQt45LW3+Zdn9vDD2y8auUP72852St2Y7byw9wRf+NlmZpQX8vPPXBr7l4KbjHECff570/s+IjJpZecA6amlbOPvlddUBPjMe+bzxNYmth5uH7lDxxHnF0SgctQ2ntzWxGf/cyN1VSX86guXpz/ERUTI1iAf53Zvo/nClQupLi/krl9toz901pWHnY1QPjvm1DxrLQ/8/i3+6tHXuWjuFP7r85cxvSzOhbhERFKUnUE+zu3eRlMW8PO9Wy5g7/Fuvr9uz/AXOxqhYvaIY8IRyz88uYPvPLWbDy6bycOfvoTygBaLEpGJk6Vj5GPf7m0s73vXDP7i8joe+sNB5k0r5pOX1zkvdBxxZn6c4WhHH3eu3cprB9v47Hvm880/OT/mWikiIumUpUE+/u3exvLtGxbTeLKPu5/cQVNHH3+9eh6BnmYorwVgIBThvza+w78+u5eBUIR/ve1CPryy1q3qRUQSkp1BHsft3saS5zP828dX8PdP7OD/vnCAjVu28DiwvimfZ371But3H6ele4DLFkzlOzcvY0E8KyeKiKRJdgZ5HLd7G48/z8d3b1nGjRfO4oVnHofj8ND2QXYEjnHZ/GnccelcrjinSnfoEZGMU5CP490Lp/Huy8vg1/Afd96Cf8Y5Cm8RmVSyO8iTHCMfIboAV8GUWq0MKCKTTpZOPxzqkSc+ayWmjkbnVmVnL0olIjIJZGeQ5wcA48rQCgCdR2LOIRcRmQyyM8iNcS4KSvDKzlF1HIGKOePvJyKSAdkZ5BBdAdGloZWhy/NFRCahLA7yEnc+7Ozvcu5lqaEVEZmksjvI3Rgj7zjibMt15aaITE7ZG+R+l4K8M3rvzwoFuYhMTtkb5G73yDW0IiKTVBYHeWK3extVRyNgoGxm6m2JiKRBFgd5aUJ3CBpV5xEoq4E8rTEuIpNTFgd5CfS7MP2wo1Hj4yIyqWVvkBeWuTOPvPOI5pCLyKSW3UEeHoBQf/JtWKseuYhMelkc5OXOtr8r+TZ62yAUVI9cRCa1LA7yMmfb35l8G5pDLiIekANBnkKPXHPIRcQDFORjid5QQpfni8hkpiAfS2cj+PxQMt2dmkRE0iCLg9yFDzs7ojeU8GXvj0lEvC+lhDLG/LMxZrcx5g1jzK+NMZUu1ZU6Vz7sPKJhFRGZ9FLtaj4LLLXWXgDsBb6RekkucWuMXB90isgkl1KQW2ufsdaGog9fASZP9zU/AL785IM8EobOJs0hF5FJz83B308D61xsLzXGOL3yZIO8+zjYsOaQi8ikZ6y1Y+9gzHNATYyXvmWtfSK6z7eAeuAWO0qDxpg1wBqA6urqlWvXrk2q4O7ubkpLS+Pa99JXPkdHxRJ2n/+VhN+nvGMPK16/izeWfZu2afUJH++mRM45W+icc4POOTHve9/7NltrRwaStTalL+CTwMtAcbzHrFy50iZrw4YN8e/8fy639tGPJvdGbz5m7d3l1h59M7njXZTQOWcJnXNu0DknBthkY2Rqfgq/WDDGXAd8DbjSWuvCnY5dVliW/KyVTl3VKSLekOoY+Y+AMuBZY8xWY8yPXajJPYVlEEwyyDuOODenCFS6WpKIiNtS6pFbaxe5VUhaFJZB61vJHdtx2JmxYoy7NYmIuCy7L1lMZdZK5xENq4iIJyjIR9OhOwOJiDdkeZCXQ6gPwoOJHRfqh55mqJiTnrpERFyU5UGe5GX6mrEiIh6SI0Ge4MyVoRtKaGhFRDwgu4M8UOFsgx2JHXeqR67L80Vk8svyIK90tn3tiR136s5A6pGLyOSX3UFeNMXZBtsTO66jEYqmQkGx6yWJiLgty4O80tkm2iPXHHIR8ZDsDvJTQysnEzuuQ3cGEhHvyO4gLyhxbi6R6NBKZ6M+6BQRz8juIDfG6ZUnMrTS3+XMctHQioh4RHYHOTgfeCbSIz81h1w9chHxhhwI8srEeuSd0amH6pGLiEdkf5AHKhP7sLNDFwOJiLdkf5AXVSY4tNIIGCibmaaCRETclf1BnuiHnR2NTojn+dNVkYiIq7I/yIumOLNQIpH49u84DJVavlZEvCMHgrwSsNAf58JZ7e9oHXIR8ZTsD/JEFs6KhJ3L89UjFxEPyf4gH1pvJZ4PPLuOQSSkHrmIeEr2B3kiPfKOw862cm66qhERcV32B/nQUrbxzCVvjwa5euQi4iHZH+TFU51tb+v4+3a842w1Ri4iHpIDQV4FGOhpGX/f9sPRG0qUpL0sERG3ZH+Q5+U7vfKe5vH31RxyEfGg7A9ygJLp0B1HkLcf1vi4iHhO7gT5eEMr1kZ75JqxIiLekhtBXjpj/KGV3jYY7FWPXEQ8JzeCvGQ6dJ8Yex/NWBERj8qdIB/ogsG+0ffRHHIR8ShXgtwY81VjjDXGVLnRnutKZzjbnjF65ScPOdsp89JejoiIm1IOcmPMHOAa4J3Uy0mTkunOdqzhlbYDzhzyoStBRUQ8wo0e+b3AXYB1oa30KImjR952AKYumJh6RERclJ/KwcaYG4Ej1tptxpjx9l0DrAGorq6moaEhqffs7u5O+NjCYDPvBnZveZFjRwMx97msaScdFYvZlWRd6ZTMOXudzjk36JxdYq0d8wt4Dtge4+sm4FWgIrrfIaBqvPastaxcudIma8OGDYkfNNBr7d3l1v7+X0Z5vc/auyus3fDdpOtKp6TO2eN0zrlB55wYYJONkanj9sitte+P9bwxZhkwHxjqjdcCW4wxl1hrj6X268Vl/iIoKBv96s72twGroRUR8aSkh1astW8CM4YeG2MOAfXW2jhWp8qA8lnQ2RT7tbYDzlZBLiIelBvzyAEqZkNHY+zXFOQi4mGuBbm1tm7S9sYBKmpHD/LW/c6dhIbWLhcR8ZAc6pHPcdZbGQyOfO34Tphx/sTXJCLighwK8lpn23lk+PPWQvMumLF44msSEXFB7gR5ZfTS+6FL8Yd0NEJ/B1QvmfCSRETckDtBPm2hs219a/jzzTudrYJcRDwqd4K8tBoKSqHtrCA/vt3ZaoxcRDwqd4LcGKdX3rp/+PPHd0LFXAhUZKYuEZEU5U6QA1SdC827hz937A0Nq4iIp+VWkM+8EDoboafVedzbBi17Yc7Fma1LRCQFuRfkAMe2OdvDrzrbOZdlph4RERfkVpDXXADGB++84jzevx78xTB7RWbrEhFJQW4FeVElzK6Hfc9CJAJ71sHCq5zVEUVEPCq3ghzg3A9A0xb4w33OePmSmzNdkYhISnIvyOs/7axNvv4emDIfFn8o0xWJiKQkpVu9eVLxVPjE47D1EVh1J+Tl3o9ARLJLbqbYnEucLxGRLJB7QysiIllGQS4i4nEKchERj1OQi4h4nIJcRMTjFOQiIh6nIBcR8TgFuYiIxxlr7cS/qTEngLeTPLwKaHGxHC/QOecGnXNuSOWc51lrp5/9ZEaCPBXGmE3W2vpM1zGRdM65QeecG9JxzhpaERHxOAW5iIjHeTHIH8h0ARmgc84NOufc4Po5e26MXEREhvNij1xERM6gIBcR8TjPBLkx5jpjzB5jzH5jzNczXc9EMMY8ZIxpNsZsz3QtE8EYM8cYs8EYs8sYs8MYc2ema0o3Y0zAGPOaMWZb9JzvyXRNE8UYk2eMed0Y85tM1zJRjDGHjDFvGmO2GmM2udauF8bIjTF5wF7gGqAR2AjcYa3dmdHC0swYcwXQDTxsrV2a6XrSzRgzE5hprd1ijCkDNgMfyuZ/Z2OMAUqstd3GGD/wEnCntfaVDJeWdsaYvwHqgXJr7Q2ZrmciGGMOAfXWWlcvgvJKj/wSYL+19oC1dgBYC9yU4ZrSzlr7e6At03VMFGvtUWvtluj3XcAuYHZmq0ov6+iOPvRHvyZ/7ypFxpha4IPAg5muJRt4JchnA4fPeNxIlv8PnuuMMXXARcCrGS4l7aJDDFuBZuBZa23WnzNwH3AXEMlwHRPNAs8YYzYbY9a41ahXgtzEeC7rey25yhhTCjwGfMVa25npetLNWhu21i4HaoFLjDFZPYxmjLkBaLbWbs50LRmwylq7Arge+FJ0+DRlXgnyRmDOGY9rgaYM1SJpFB0nfgz4hbX28UzXM5Gste1AA3BdZitJu1XAjdHx4rXAVcaYn2e2pIlhrW2KbpuBX+MMG6fMK0G+ETjHGDPfGFMA3A48meGaxGXRD/5+Auyy1v4g0/VMBGPMdGNMZfT7IuD9wO6MFpVm1tpvWGtrrbV1OP8vP2+t/XiGy0o7Y0xJ9EN8jDElwLWAKzPSPBHk1toQ8GXgdzgfgP3SWrsjs1WlnzHmUeBl4DxjTKMx5jOZrinNVgGfwOmhbY1+/Ummi0qzmcAGY8wbOB2WZ621OTMdL8dUAy8ZY7YBrwG/tdY+7UbDnph+KCIio/NEj1xEREanIBcR8TgFuYiIxynIRUQ8TkEuIuJxCnIREY9TkIuIeNz/B3R3jyPSfLucAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "plot(time2,out2(8,:),'r',time2,out2(9,:),'b');\n",
-    "legend('State-Space 2:1','State-Space 2:2')\n",
-    "grid on"
+    "%matplotlib inline\n",
+    "plt.plot(time2,out2[7,:],time2,out2[8,:])\n",
+    "plt.legend(['7','8'])\n",
+    "plt.grid()\n",
+    "plt.show()"
    ]
   },
   {
@@ -526,80 +753,117 @@
    "metadata": {},
    "source": [
     "<div style=\"font-family: 'times'; font-size: 13pt; text-align: justify\">\n",
-    "<b> Aufgabe </b>: Wiederholen Sie nun die Simulation mit $\\omega_0$ = 1, d.h. langsamer!"
+    "    Im Folgenden lässt sich  $\\omega_0$ mittels Schieberegler einstellen.<br>\n",
+    "<b> Aufgabe:</b> Wiederholen Sie nun die Simulation mit $\\omega_0$ = 1, d.h. langsamer!<br>\n",
+    "    Probieren Sie auch gerne andere Werte für  $\\omega_0$ aus."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 40,
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59ded2cc6a794a3dbe19ecccecaa982e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c433a3a231544ff0a0086900cfbd981f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=1, description='omega', max=20, min=1), Dropdown(description='scope', in…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "% Update values with new omega\n",
-    "omega1 =1;\n",
-    "poles = [-csi*omega1+1j*omega1*sqrt(1-csi*csi) -csi*omega1-1j*omega1*sqrt(1-csi*csi)];\n",
-    "G = place(A',C',poles);\n",
-    "Ao = A-G'*C;\n",
-    "eigs(Ao);\n",
-    "Bo = [B G'];\n",
+    "#Gleiche Simulation mit anderem omega:\n",
+    "%matplotlib widget\n",
+    "fig, ax = plt.subplots(figsize=(9, 5))\n",
+    "ax.set_ylim([-10, 15])\n",
+    "ax.grid(True)\n",
     "\n",
-    "% Start time\n",
-    "tini = 0;\n",
-    "% End time\n",
-    "tfinal = 5;\n",
-    "% Time Step\n",
-    "dt = 0.001;\n",
-    "% Number of data flows in the schematic\n",
-    "nflows_2 = 11;\n",
-    "% Initial conditions\n",
-    "xo_1 = [2; 1];\n",
-    "xo_2 = [0; 0];\n",
+    "dic = {\"Regler ohne Beobachter 1&2\":[1,2],\"Abweichung 10&11\":[10,11],\"Beobachter 4&5\":[4,5],\n",
+    "       \"Regler mit Beobachter 7&8\":[7,8]}\n",
+    "@widgets.interact(omega=(1, 20, 1), scope = [\"Regler ohne Beobachter 1&2\",\"Abweichung 10&11\",\n",
+    "                                             \"Beobachter 4&5\",\"Regler mit Beobachter 7&8\"])\n",
     "\n",
-    "% Matrices\n",
-    "C2 = eye(2);\n",
-    "D2 = [0;0];\n",
-    "Do = zeros(2,2);\n",
+    "def update(omega = 1, scope=\"Regler mit Beobachter 7&8\"):\n",
+    "    \"\"\"Remove old lines from plot and plot new one\"\"\"\n",
+    "    [l.remove() for l in ax.lines]\n",
+    "    [l.remove() for l in ax.lines]\n",
+    "    omega0 =omega\n",
+    "    poles = [-csi*omega0+1j*omega0*sqrt(1-csi*csi),-csi*omega0-1j*omega0*sqrt(1-csi*csi)]\n",
+    "    G = signal.place_poles(A.T, C.T, poles).gain_matrix\n",
+    "    #print(\"G:\"+str(G))\n",
+    "    Ao = A-np.matmul(G.T,C)\n",
+    "    #print(\"Ao:\\n\"+str(Ao))\n",
+    "    #print_eig(Ao)\n",
+    "    column1 = B\n",
+    "    column2 = G.T\n",
+    "    Bo = np.c_[column1,column2]\n",
+    "    #print(\"Bo:\\n\"+str(Bo))\n",
     "\n",
-    "% Instance of the simulation schematic\n",
-    "sc2 = Schema(tini,tfinal,dt,nflows_2);\n",
+    "    tini = 0 # Start time\n",
+    "    tfinal = 5 # End time\n",
+    "    dt = 0.001 # Time Step\n",
+    "    nflows_2 = 12 # Number of data flows in the schematic\n",
     "\n",
-    "c2{1} = StateSpace(3,[1 2],A,B,C2,D2,xo_1);\n",
-    "c2{2} = Gain([1 2],3,-K);\n",
+    "    xo_1 = np.array([[2],[1]])\n",
+    "    xo_2 = np.array([[0],[0]])\n",
     "\n",
-    "c2{3} = StateSpace(6,[4 5],A,B,C2,D2,xo_1);\n",
-    "c2{4} = Gain([7 8],6,-K);\n",
+    "    #Matrices:\n",
+    "    C2 = np.array([[1,0],\n",
+    "               [0,1]]);\n",
+    "    D2 = np.array([[0],[0]]);\n",
+    "    Do = np.array([[0,0],\n",
+    "               [0,0]]);\n",
     "\n",
-    "c2{5} = StateSpace([6 9],[7 8],Ao,Bo,C2,Do,xo_2);\n",
-    "c2{6} = Gain([4 5],9,C);\n",
+    "    sc2 = Schema(tini,tfinal,dt,nflows_2) # Instance of the simulation schematic\n",
     "\n",
-    "c2{7} = Sum(1,4,10,1,-1);\n",
-    "c2{8} = Sum(2,5,11,1,-1);\n",
+    "    c2_1 = StateSpace([3],[1,2],A,B,C2,D2,xo_1);\n",
+    "    c2_2 = Gain([1,2],[3],-K);\n",
     "\n",
-    "sc2.AddListComponents(c2);\n",
+    "    c2_3 = StateSpace([6],[4,5],A,B,C2,D2,xo_1);\n",
+    "    c2_4 = Gain([7,8],[6],-K);\n",
     "\n",
-    "% Run the schematic and plot, now having omega:=1\n",
-    "out2 = sc2.Run([1:11]);\n",
-    "time2 = out2(1,:);\n",
-    "plot(time2,out2(8,:),'r',time2,out2(9,:),'b');\n",
-    "legend('State-Space 2:1','State-Space 2:2')\n",
-    "grid on"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style=\"font-family: 'times'; font-size: 13pt; text-align: justify\">\n",
-    "Differenz der Systeme:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(time2,out2(11,:),'r',time2,out2(12,:),'b');"
+    "    c2_5 = StateSpace([6,9],[7,8],Ao,Bo,C2,Do,xo_2);\n",
+    "    c2_6 = Gain([4,5],[9],C);\n",
+    "\n",
+    "    c2_7 = Sum(1,4,10,1,-1);\n",
+    "    c2_8 = Sum(2,5,11,1,-1);\n",
+    "\n",
+    "    sc2.AddListComponents(np.array([c2_1,c2_2,c2_3,c2_4,c2_5,c2_6,c2_7,c2_8]))\n",
+    "\n",
+    "    #Run the schematic and plot:\n",
+    "    out2 = sc2.Run(np.array([1,2,3,4,5,6,7,8,9,10,11]))\n",
+    "\n",
+    "    time2 = out2[0,:]\n",
+    "    \n",
+    "    ax.plot(time2,out2[dic[scope][0],:],'r', time2,out2[dic[scope][1],:],'b')\n",
+    "    plt.show()\n",
+    "    \n"
    ]
   },
   {
@@ -619,18 +883,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
-    "A = [-2 1;\n",
-    "      0 -1];\n",
+    "A = np.array([[-2,1],\n",
+    "              [0,-1]])\n",
     "  \n",
-    "B = [0; 1];\n",
+    "B = np.array([[0],\n",
+    "              [1]])\n",
     "\n",
-    "C = [1 0];\n",
+    "C = np.array([[1,0]])\n",
     "\n",
-    "D = 0;"
+    "D = np.array([0])"
    ]
   },
   {
@@ -651,12 +916,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sc:\n",
+      "[[ 0  1]\n",
+      " [ 1 -1]]\n",
+      "Rang: 2\n"
+     ]
+    }
+   ],
    "source": [
-    "Sc = [B A*B]\n",
-    "rank(Sc)"
+    "column1 = B\n",
+    "column2 = np.matmul(A,B)\n",
+    "Sc = np.c_[column1,column2] #Sc = (B A*B)\n",
+    "print(\"Sc:\\n\"+str(Sc))\n",
+    "\n",
+    "print_rank(Sc)"
    ]
   },
   {
@@ -669,16 +949,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ad:\n",
+      "[[0.98019867 0.00985116]\n",
+      " [0.         0.99004983]]\n",
+      "Bd:\n",
+      "[[4.95029042e-05]\n",
+      " [9.95016625e-03]]\n"
+     ]
+    }
+   ],
    "source": [
-    "Ts = 0.01;\n",
+    "Ts = 0.01\n",
     "\n",
-    "pc = [-5+1j*5 -5-1j*5];\n",
-    "pcd = exp(pc.*Ts);\n",
-    "Ad = expm(A*Ts)\n",
-    "Bd = (Ad-eye(2))*inv(A)*B"
+    "pc = np.array([-5+1j*5,-5-1j*5])\n",
+    "pcd = exp(Ts*pc)\n",
+    "Ad = linalg.expm(Ts*A)\n",
+    "print(\"Ad:\\n\"+str(Ad))\n",
+    "Bd = matmul_loop([Ad-[[1,0],[0,1]],inv(A),B]) #(Ad-Einheitsmatrix)*inv(A)*B\n",
+    "print(\"Bd:\\n\"+str(Bd))"
    ]
   },
   {
@@ -691,11 +986,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "K:[[32.4989039   6.89018096]]\n"
+     ]
+    }
+   ],
    "source": [
-    "K = place(Ad,Bd,pcd) "
+    "K = signal.place_poles(Ad, Bd, pcd).gain_matrix\n",
+    "print(\"K:\"+str(K))"
    ]
   },
   {
@@ -708,12 +1012,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "So:\n",
+      "[[1.         0.        ]\n",
+      " [0.98019867 0.00985116]]\n",
+      "Rang: 2\n"
+     ]
+    }
+   ],
    "source": [
-    "So = [C; C*Ad]\n",
-    "rank(So)"
+    "row1 = C\n",
+    "row2 = np.matmul(C,Ad)\n",
+    "So = np.r_[row1,row2]\n",
+    "print(\"So:\\n\"+str(So))\n",
+    "\n",
+    "print_rank(So)"
    ]
   },
   {
@@ -726,11 +1045,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
-    "poles = [0 0];"
+    "poles = [0,0]"
    ]
   },
   {
@@ -747,20 +1066,44 @@
    "source": [
     "<div style=\"font-family: 'times'; font-size: 13pt; text-align: justify\">\n",
     "Beobachterpole:\n",
-    "<br><span style='color:Gray'>Hinweis</span>: Verwende Funktion <code>acker</code> anstelle von <code>place</code>!"
+    "<br><span style='color:Gray'>Hinweis</span>: Verwende Funktion <code>control.acker</code> anstelle von <code>signal.place_poles</code>!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gd:[[ 1.97024851 99.50083333]]\n",
+      "Aod:\n",
+      "[[-9.90049834e-01  9.85116044e-03]\n",
+      " [-9.95008333e+01  9.90049834e-01]]\n",
+      "Eigenwerte:\n",
+      "0j\n",
+      "-0j\n",
+      "Bod:\n",
+      "[[4.95029042e-05 1.97024851e+00]\n",
+      " [9.95016625e-03 9.95008333e+01]]\n"
+     ]
+    }
+   ],
    "source": [
-    "Gd = acker(Ad',C',poles) \n",
-    "\n",
-    "Aod = Ad-Gd'*C\n",
-    "eigs(Aod)\n",
-    "Bod = [Bd Gd'];"
+    "#Gd = acker(Ad',C',poles)\n",
+    "Gd = control.acker(Ad.T, C.T, poles)\n",
+    "print(\"Gd:\"+str(Gd))\n",
+    "       \n",
+    "Aod = Ad-np.matmul(Gd.T,C)\n",
+    "print(\"Aod:\\n\"+str(Aod))\n",
+    "print_eig(Aod)\n",
+    "       \n",
+    "column1 = Bd\n",
+    "column2 = Gd.T\n",
+    "Bod = np.c_[column1,column2]\n",
+    "print(\"Bod:\\n\"+str(Bod))"
    ]
   },
   {
@@ -777,54 +1120,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 48,
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "% Simulink model A\n",
-    "% Number of data flows in the schematic\n",
-    "% Start time\n",
-    "tini = 0;\n",
-    "% End time\n",
-    "tfinal = 3;\n",
-    "% Time Step\n",
-    "dt = 0.001;\n",
-    "% Number of data flows in the schematic\n",
-    "nflows_3 = 13;\n",
-    "% Initial conditions\n",
-    "xo_1 = [2; 1];\n",
-    "xo_2 = [0; 0];\n",
+    "tini = 0 # Start time\n",
+    "tfinal = 3 # End time\n",
+    "dt = 0.001 # Time Step\n",
+    "nflows_3 = 14 # Number of data flows in the schematic\n",
+    "\n",
+    "xo_1 = np.array([[2],[1]])\n",
+    "xo_2 = np.array([[0],[0]])\n",
     "\n",
-    "% Matrices\n",
-    "C2 = eye(2);\n",
-    "D2 = [0;0];\n",
-    "Do = zeros(2,2);\n",
+    "#Matrices:\n",
+    "C2 = np.array([[1,0],\n",
+    "               [0,1]]);\n",
+    "D2 = np.array([[0],\n",
+    "               [0]]);\n",
+    "Do = np.array([[0,0],\n",
+    "               [0,0]]);\n",
     "\n",
-    "% Instance of the simulation schematic\n",
-    "sc3 = Schema(tini,tfinal,dt,nflows_3);\n",
+    "sc3 = Schema(tini,tfinal,dt,nflows_3) # Instance of the simulation schematic\n",
     "\n",
-    "c3{1} = ZOH(4,1,Ts); %ZOH arguments: in_ID,out_ID,T\n",
-    "c3{2} = StateSpace(1,[2 3],A,B,C2,D2,xo_1); \n",
-    "c3{3} = Gain([2 3],4,-K);\n",
+    "c3_1 = ZOH(4,1,Ts); #ZOH arguments: in_ID,out_ID,T\n",
+    "c3_2 = StateSpace([1],[2,3],A,B,C2,D2,xo_1); \n",
+    "c3_3 = Gain([2,3],[4],-K);\n",
     "\n",
-    "c3{4} = ZOH(11,5,Ts);\n",
-    "c3{5} = StateSpace(5,[6 7],A,B,C2,D2,xo_1);\n",
+    "c3_4 = ZOH(11,5,Ts);\n",
+    "c3_5 = StateSpace([5],[6,7],A,B,C2,D2,xo_1);\n",
     "\n",
-    "c3{6} = Sum(2,6,12,1,-1);\n",
-    "c3{7} = Sum(3,7,13,1,-1);\n",
+    "c3_6 = Sum(2,6,12,1,-1);\n",
+    "c3_7 = Sum(3,7,13,1,-1);\n",
     "\n",
-    "c3{8} = Gain([6 7],8,C);\n",
+    "c3_8 = Gain([6,7],[8],C);\n",
     "\n",
-    "% Notice, that we introduce the discrete StateSpace Object here: DTStateSpace(in,out,A,B,C,D,T,xo)\n",
-    "c3{9} = DTStateSpace([11 8],[9 10],Aod,Bod,C2,Do,Ts,xo_2);\n",
+    "# Notice, that we introduce the discrete StateSpace Object here: DTStateSpace(in,out,A,B,C,D,xo,Ty)\n",
+    "c3_9 = DTStateSpace([11,8],[9,10],Aod,Bod,C2,Do,xo_2,Ts);\n",
     "\n",
-    "c3{10} = Gain([9 10],11,-K);\n",
+    "c3_10 = Gain([9,10],[11],-K);\n",
     "\n",
-    "sc3.AddListComponents(c3);\n",
+    "sc3.AddListComponents(np.array([c3_1,c3_2,c3_3,c3_4,c3_5,c3_6,c3_7,c3_8,c3_9,c3_10]))\n",
     "\n",
-    "% Run the schematic and plot\n",
-    "out3 = sc3.Run([1:13]);\n",
-    "time3 = out3(1,:);"
+    "#Run the schematic and plot:\n",
+    "out3 = sc3.Run(np.array([1,2,3,4,5,6,7,8,9,10,11,12,13]))\n",
+    "\n",
+    "time3 = out3[0,:]"
    ]
   },
   {
@@ -836,13 +1181,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "plot(time3,out3(3,:),'r',time3,out3(4,:),'b');\n",
-    "legend('State-Space:1','State-Space:2')\n",
-    "grid on"
+    "%matplotlib inline\n",
+    "plt.plot(time3,out3[2,:],'r', time3,out3[3,:],'b')\n",
+    "plt.legend(['State-Space:1','State-Space:2'])\n",
+    "plt.grid()\n",
+    "plt.show()"
    ]
   },
   {
@@ -854,13 +1214,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "plot(time3,out3(7,:),'r',time3,out3(8,:),'b');\n",
-    "legend('State-Space 1:1','State-Space 1:2')\n",
-    "grid on"
+    "%matplotlib inline\n",
+    "plt.plot(time3,out3[6,:],'r', time3,out3[7,:],'b')\n",
+    "plt.legend(['State-Space 1:1','State-Space 1:2'])\n",
+    "plt.grid()\n",
+    "plt.show()"
    ]
   },
   {
@@ -872,31 +1247,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "plot(time3,out3(13,:),'r',time3,out3(14,:),'b');\n",
-    "legend('Discrete State-Space:1','Discrete State-Space:2')\n",
-    "grid on"
+    "%matplotlib inline\n",
+    "plt.plot(time3,out3[12,:],'r', time3,out3[13,:],'b')\n",
+    "plt.legend(['Discrete State-Space:1','Discrete State-Space:2'])\n",
+    "plt.grid()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "plot(time3,out3(10,:),'r',time3,out3(11,:),'b');\n",
-    "legend('Discrete State-Space:1','Discrete State-Space:2')\n",
-    "grid on"
+    "%matplotlib inline\n",
+    "plt.plot(time3,out3[9,:],'r', time3,out3[10,:],'b')\n",
+    "plt.legend(['Discrete State-Space:1','Discrete State-Space:2'])\n",
+    "plt.grid()\n",
+    "plt.show()"
    ]
   }
  ],
-
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -910,7 +1314,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,