diff --git a/inv_kin_testing.ipynb b/inv_kin_testing.ipynb
index 8e75bae..840efc7 100644
--- a/inv_kin_testing.ipynb
+++ b/inv_kin_testing.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,15 +52,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-243.619306486927, -123.2097721836616, 140.34764917140853, -107.13787698774695, -90.0, 90.0]\n"
-     ]
+     "data": {
+      "text/plain": [
+       "[-243.619306486927,\n",
+       " -123.2097721836616,\n",
+       " 140.34764917140853,\n",
+       " -107.13787698774695,\n",
+       " -90.0,\n",
+       " 90.0]"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -80,7 +88,6 @@
     "    # Get limbs and offsets\n",
     "    offset_x, offset_y, offset_z = (0, 0, 0.14)     # Tool offset\n",
     "    l_bs, l1, l2, l3, l_wt = (0.1333, .425, .39225, .1267, .0997)    # Limb lengths\n",
-    "    #l3=0.15\n",
     "    \n",
     "    # Calculate base angle and r relative to shoulder joint\n",
     "    def calculate_theta(x, y, a):\n",
@@ -141,120 +148,37 @@
     "    # Return adjusted joint positions\n",
     "    return [o+j*i for j, o, i in zip(joints, offsets, inverse)]\n",
     "\n",
-    "# print([math.degrees(deg) for deg in get_joints_from_xyz_rel(0.3, 0.3, 0.3)])\n",
-    "print([math.degrees(deg) for deg in get_joints_from_xyz_abs(0, -0.3, 0.1)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "flip\n",
-      "m: -0.9031146536634159\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "47.914345304219715"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def calculate_theta(x, y, a):\n",
-    "        # Calculate if we need the + or - in our equations\n",
-    "        if (x>a and y>=0) or (x>-a and y<0):\n",
-    "            print('no flip')\n",
-    "            flip = 1\n",
-    "        elif (x<a and y>=0) or (x<-a and y<0):\n",
-    "            print('flip')\n",
-    "            flip = -1\n",
-    "        else: \n",
-    "            print('critical')\n",
-    "            # Critical section (x=a, or x=-a). Infinite slope\n",
-    "            # Return 0 or 180 depending on sign\n",
-    "            return math.degrees(math.atan2(y, 0)-math.pi/2)\n",
-    "        \n",
-    "        # Calculate tangent line y = mx + b\n",
-    "        if abs(a) != abs(x): # If there is no division by 0\n",
-    "            m = (x*y + math.sqrt(x*x*y*y-(x*x-a*a)*(y*y-a*a)))/(x*x-a*a)\n",
-    "        else: # Deal with edge case when x^2=a^2\n",
-    "            m = flip*(-a*a+y*y)/(a*y-flip*abs(a*y))\n",
-    "        \n",
-    "        print('m:',m)\n",
-    "        b = flip * a * math.sqrt(1+m*m)\n",
-    "\n",
-    "        # Calculate equivalent tangent point on circle\n",
-    "        cx = (-flip*m*b)/(1+m*m)\n",
-    "        cy = m*cx + flip*b\n",
-    "\n",
-    "        # Calculate base angle\n",
-    "        theta = math.atan2(cy, cx) + (-math.pi if flip==1 else 0)\n",
-    "\n",
-    "        return math.degrees(theta)\n",
-    "\n",
-    "calculate_theta(-0.1333,0.3,0.1333)"
+    "# Print degree rotation for each joint (robot angles)\n",
+    "[math.degrees(deg) for deg in get_joints_from_xyz_abs(0, -0.3, 0.1)]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Simulate arm and joint angles"
+    "### Simulate Arm and Joint Angles"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 84,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Target position (x,y,z): 0.3 0.3 0.3\n",
-      "R:  0.4243\n",
-      "Angles (base, shoulder, elbow, wrist): [-26.688, 105.6932, 101.1834, 4.5098]\n",
-      "Robot Angles: [-116.688, -105.6932, 101.1834, -85.4902, -90.0, 90.0]\n",
-      "elbow (x,y): -0.114 0.407\n",
-      "wrist (x,y): 0.244 0.435\n",
-      "tool  (x,y): 0.394 0.435\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "def draw_arm(x, y, z):\n",
+    "def draw_arm_side_view(x, y, z, details=False):\n",
     "\n",
     "    # Get joint angles\n",
-    "    l1, l2, l3 = (.422864, .359041, .15)\n",
-    "    offset_x, offset_y, offset_z = (0.145, 0, 0.195)\n",
+    "    l1, l2, l3 = .425, .39225, .1267\n",
+    "    offset_x, offset_y, offset_z = (0, 0, 0.14)\n",
     "    r, theta = cartesian_to_polar(x, y)\n",
     "    base, shoulder, elbow, wrist, _, _ = get_joints_from_xyz_rel(x, y, z)\n",
     "\n",
     "    # Print angles\n",
-    "    print('Target position (x,y,z):', x, y, z)\n",
-    "    print('R: ', round(math.sqrt(x**2+y**2),4))\n",
-    "    print('Angles (base, shoulder, elbow, wrist):', [round(math.degrees(i), 4) for i in [base, shoulder, elbow, wrist]])\n",
-    "    print('Robot Angles:', [round(math.degrees(i), 4) for i in get_joints_from_xyz_abs(x, y, z)])\n",
+    "    if details:\n",
+    "        print('Target position (x,y,z):', x, y, z)\n",
+    "        print('R: ', round(math.sqrt(x**2+y**2),4))\n",
+    "        print('Angles (base, shoulder, elbow, wrist):', [round(math.degrees(i), 4) for i in [base, shoulder, elbow, wrist]])\n",
+    "        print('Robot Angles:', [round(math.degrees(i), 4) for i in get_joints_from_xyz_abs(x, y, z)])\n",
     "\n",
     "    # Calculate each joint's endpoint position\n",
     "    x1, y1 = polar_to_cartesian(l1, shoulder)\n",
@@ -269,9 +193,10 @@
     "    ty = y3 - offset_z\n",
     "\n",
     "    # Print each joint's endpoint position\n",
-    "    print('elbow (x,y):', round(x1,3), round(y1,3))\n",
-    "    print('wrist (x,y):', round(x2,3), round(y2,3))\n",
-    "    print('tool  (x,y):', round(x3,3), round(y3,3))\n",
+    "    if details:\n",
+    "        print('elbow (x,y):', round(x1,3), round(y1,3))\n",
+    "        print('wrist (x,y):', round(x2,3), round(y2,3))\n",
+    "        print('tool  (x,y):', round(x3,3), round(y3,3))\n",
     "\n",
     "    # Draw limbs\n",
     "    plt.plot([0, x1], [0, y1], color='cyan', linewidth=7)\n",
@@ -299,9 +224,7 @@
     "    ax = plt.subplot(111)\n",
     "    ax.spines[['right', 'top']].set_visible(False)\n",
     "    plt.axis('equal')\n",
-    "    plt.show()\n",
-    "\n",
-    "draw_arm(0.3, 0.3, 0.3)"
+    "    plt.show()"
    ]
   },
   {
@@ -310,95 +233,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "draw_arm(0.3, 0.3, 0.3)\n",
-    "draw_arm(-0.3, -0.3, 0.7)\n",
-    "draw_arm(-0.3, 0.4, 0.2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Interactive Arm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ipywidgets import interact, FloatSlider\n",
-    "\n",
-    "# Interactive slider for z coordinate\n",
-    "interact(draw_arm,  x=FloatSlider(min=0, max=1, step=0.01, value=0.3),\n",
-    "                    y=FloatSlider(min=0, max=1, step=0.01, value=0.3),\n",
-    "                    z=FloatSlider(min=0, max=1, step=0.01, value=0.3))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Angles: [153.31198894476785, 105.69315886537287, 101.18338602047919, 4.509772844893658, -90.0, 0.0]\n",
-      "(cx, cy): (-0.11909893611698993, 0.0598693027836566)\n",
-      "-0.06746836046451549 0.16257881075200548\n",
-      "-0.24309496877200426 -0.18679793543865197\n",
-      "-0.29999999999998095 -0.29999999999996196\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Target position (x,y,z): -0.3 -0.3 0.3\n",
-      "R:  0.4243\n",
-      "Angles (base, shoulder, elbow, wrist): [153.312, 105.6932, 101.1834, 4.5098]\n",
-      "Robot Angles: [63.312, -105.6932, 101.1834, -85.4902, -90.0, 90.0]\n",
-      "elbow (x,y): -0.114 0.407\n",
-      "wrist (x,y): 0.244 0.435\n",
-      "tool  (x,y): 0.394 0.435\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "def display_arm(x, y, z):\n",
+    "def draw_arm_top_view(x, y, z, details=False):\n",
     "    # Get joint and position information\n",
     "    angles = get_joints_from_xyz_rel(x, y, z)\n",
-    "    print('Angles:', [math.degrees(angle) for angle in angles])\n",
-    "\n",
     "    offset_x, offset_y, offset_z = (0, 0, 0.14)     # Tool offset\n",
     "    l_bs, l1, l2, l3, l_wt = (0.1333, .425, .39225, .1267, .0997)    # Limb lengths\n",
     "    cx, cy = l_bs*math.cos(angles[0]), l_bs*math.sin(angles[0]) # Base tangent point\n",
     "    line_angle = math.pi/2+angles[0]\n",
-    "    print(f'(cx, cy): ({cx}, {cy})')\n",
+    "\n",
+    "    if details:\n",
+    "        print('Angles:', [round(math.degrees(angle),3) for angle in angles])\n",
+    "        print(f'Circle position (cx, cy): ({round(cx, 3)}, {round(cy, 3)})')\n",
     "\n",
     "\n",
     "    # Plot coordinate system\n",
@@ -425,23 +270,29 @@
     "    # Shoulder\n",
     "    x1, y1 = polar_to_cartesian(l1*math.cos(angles[1]), line_angle)\n",
     "    x1, y1 = cx+x1, cy+y1\n",
-    "    print(x1, y1)\n",
+    "    z1 = l1*math.sin(angles[1])\n",
     "    plt.plot([cx, x1], [cy, y1], color='cyan', linewidth=3)\n",
     "\n",
     "    # Elbow\n",
     "    x2, y2 = polar_to_cartesian(l2*math.cos(angles[1]-angles[2]), line_angle)\n",
     "    x2 += x1\n",
     "    y2 += y1\n",
-    "    print(x2, y2)\n",
+    "    z2 = z1 + l2*math.sin(angles[1]-angles[2])\n",
     "    plt.plot([x1, x2], [y1, y2], color='orange', linewidth=2)\n",
     "\n",
     "    # Wrist\n",
     "    x3, y3 = polar_to_cartesian(l3*math.cos(angles[1]-angles[2]-angles[3]), line_angle)\n",
     "    x3 += x2\n",
-    "    y3 += y2 \n",
-    "    print(x3, y3)\n",
+    "    y3 += y2\n",
+    "    z3 = z2 + l3*math.sin(angles[1]-angles[2]-angles[3]) \n",
     "    plt.plot([x2, x3], [y2, y3], color='red', linewidth=2)\n",
     "\n",
+    "    # Print joint positions\n",
+    "    if details:\n",
+    "        print(f'Shoulder (x, y, z): ({round(x1,3)}, {round(y1,3)}, {round(z1,3)})')\n",
+    "        print(f'Elbow    (x, y, z): ({round(x2,3)}, {round(y2,3)}, {round(z2,3)})')\n",
+    "        print(f'Wrist    (x, y, z): ({round(x3,3)}, {round(y3,3)}, {round(z3,3)})')\n",
+    "\n",
     "    # Display angle\n",
     "    plt.text(0.01, -0.01-cy/abs(cy+0.00001)*0.02, f'{round(math.degrees(angles[0]), 2)}°', fontsize=7)\n",
     "\n",
@@ -465,19 +316,66 @@
     "    # Adjust the position of axis labels\n",
     "    plt.xlabel('+x', horizontalalignment='right', x=1.05)\n",
     "    plt.ylabel('+y', verticalalignment='top', rotation=0, y=1.05)\n",
-    "    plt.show()\n",
-    "\n",
-    "    draw_arm(x,y,z)\n",
-    "\n",
-    "display_arm(-0.3,-0.3,0.3)"
+    "    plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 89,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Target position (x,y,z): 0.5 0.5 0.3\n",
+      "R:  0.7071\n",
+      "Angles (base, shoulder, elbow, wrist): [-34.1339, 65.0453, 57.0325, 8.0128]\n",
+      "Robot Angles: [-124.1339, -65.0453, 57.0325, -81.9872, -90.0, 90.0]\n",
+      "elbow (x,y): 0.179 0.385\n",
+      "wrist (x,y): 0.568 0.44\n",
+      "tool  (x,y): 0.694 0.44\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Angles: [-34.134, 65.045, 57.033, 8.013, -90.0, 0.0]\n",
+      "Circle position (cx, cy): (0.11, -0.075)\n",
+      "Shoulder (x, y, z): (0.211, 0.074, 0.385)\n",
+      "Elbow    (x, y, z): (0.429, 0.395, 0.44)\n",
+      "Wrist    (x, y, z): (0.5, 0.5, 0.44)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def draw_arm(x, y, z, details=False):\n",
+    "    draw_arm_side_view(x,y,z, details=details)\n",
+    "    draw_arm_top_view(x,y,z, details=details)\n",
+    "\n",
+    "draw_arm(0.5,0.5,0.3, details=True)"
+   ]
   }
  ],
  "metadata": {