diff --git a/examples/BESSY2_example/bessy2-chroma.ipynb b/examples/BESSY2_example/bessy2-chroma.ipynb
index b8d67556..4a3e02c7 100644
--- a/examples/BESSY2_example/bessy2-chroma.ipynb
+++ b/examples/BESSY2_example/bessy2-chroma.ipynb
@@ -10,8 +10,14 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "jlp-py312",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "name": "python",
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,
diff --git a/examples/BESSY2_example/bessy2-orbit.ipynb b/examples/BESSY2_example/bessy2-orbit.ipynb
index 03fa4727..4356697d 100644
--- a/examples/BESSY2_example/bessy2-orbit.ipynb
+++ b/examples/BESSY2_example/bessy2-orbit.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 40,
    "id": "747364f7",
    "metadata": {},
    "outputs": [],
@@ -24,14 +24,12 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
-    "from pyaml.accelerator import Accelerator\n",
-    "from pyaml.tuning_tools.orbit_response_matrix import ConfigModel as ORM_ConfigModel\n",
-    "from pyaml.tuning_tools.orbit_response_matrix import OrbitResponseMatrix"
+    "from pyaml.accelerator import Accelerator"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "id": "aff4d20f",
    "metadata": {},
    "outputs": [
@@ -39,7 +37,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "27 Jan% 2026, 16:57:24 | WARNING | PyAML OA control system binding (0.1.1) initialized with name 'live' and prefix='a3744:'\n"
+      "23 Apr 2026, 08:36:50 | WARNING | PyAML OA control system binding (0.1.1) initialized with name 'live' and prefix='pons:'\n",
+      "23 Apr 2026, 08:36:50 | INFO | Setting the rf_weight by default to inf.\n",
+      "23 Apr 2026, 08:36:50 | INFO | Setting the rf_weight by default to inf.\n",
+      "23 Apr 2026, 08:36:50 | INFO | Setting the rf_weight by default to inf.\n",
+      "Controls:\n",
+      "    live\n",
+      "    .\n",
+      "\n",
+      "Simulators:\n",
+      "    design\n",
+      "    .\n",
+      "\n",
+      "Arrays:\n",
+      "    BPM                   (pyaml.arrays.bpm_array)                size=123\n",
+      "    HCorr                 (pyaml.arrays.magnet_array)             size=48\n",
+      "    VCorr                 (pyaml.arrays.magnet_array)             size=64\n",
+      "    .\n",
+      "\n",
+      "Tools:\n",
+      "    DEFAULT_ORBIT_RESPONSE_MATRIX (pyaml.tuning_tools.orbit_response_matrix)\n",
+      "    DEFAULT_DISPERSION (pyaml.tuning_tools.dispersion)\n",
+      "    DEFAULT_ORBIT_CORRECTION (pyaml.tuning_tools.orbit)\n",
+      "    .\n"
      ]
     }
    ],
@@ -47,7 +67,8 @@
     "# Load the configuration\n",
     "# Remember to change the prefix for the live mode to the one matching your virtual\n",
     "# accelerator before loading.\n",
-    "sr = Accelerator.load(\"BESSY2Orbit.yaml\")"
+    "sr = Accelerator.load(\"BESSY2Orbit.yaml\")\n",
+    "print(sr.yellow_pages)"
    ]
   },
   {
@@ -63,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "id": "9caca764",
    "metadata": {},
    "outputs": [],
@@ -74,28 +95,8 @@
     "\n",
     "# if the ORM is not present measure it\n",
     "if sr.design.orbit.response_matrix is None:\n",
-    "    orm = OrbitResponseMatrix(\n",
-    "        cfg=ORM_ConfigModel(\n",
-    "            bpm_array_name=\"BPM\",\n",
-    "            hcorr_array_name=\"HCorr\",\n",
-    "            vcorr_array_name=\"VCorr\",\n",
-    "            corrector_delta=1e-6,\n",
-    "        ),\n",
-    "        element_holder=SR,  # Measurement target\n",
-    "    )\n",
-    "    orm.measure(set_wait_time=0.0 if SR == sr.design else 2.0)\n",
-    "    orm_data = orm.get()\n",
-    "\n",
-    "    # Save the data to json\n",
-    "    ORM_data = {\n",
-    "        \"type\": \"pyaml.tuning_tools.response_matrix\",\n",
-    "        \"matrix\": orm_data[\"matrix\"],\n",
-    "        \"input_names\": orm_data[\"input_names\"],\n",
-    "        \"output_names\": orm_data[\"output_names\"],\n",
-    "        \"input_planes\": orm_data[\"input_planes\"],\n",
-    "        \"output_planes\": orm_data[\"output_planes\"],\n",
-    "    }\n",
-    "    json.dump(ORM_data, open(\"orm.json\", \"w\"))"
+    "    sr.design.orm.measure(set_wait_time=0.0 if SR == sr.design else 2.0)\n",
+    "    sr.design.orm.save(\"orm.json\")"
    ]
   },
   {
@@ -110,18 +111,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 43,
    "id": "50c2e8a4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23 Apr 2026, 08:37:08 | INFO | Setting the rf_weight by default to inf.\n"
+     ]
+    }
+   ],
    "source": [
     "# Load the ORM for the live mode\n",
-    "sr.live.orbit.load_response_matrix(\"orm.json\")"
+    "sr.live.orbit.load(\"orm.json\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 44,
+   "id": "38dc01be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reset corrector to 0 on the twin\n",
+    "sr.live.get_magnets(\"HCorr\").strengths.set(np.zeros(48))\n",
+    "sr.live.get_magnets(\"VCorr\").strengths.set(np.zeros(64))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "id": "d6e7ba16",
    "metadata": {},
    "outputs": [],
@@ -129,9 +150,8 @@
     "# Set orbit for design\n",
     "\n",
     "std_kick = 100e-6\n",
-    "# corr_d = sr.design.get_magnet(\"VS3M2D1R\")\n",
-    "corr_d = sr.design.get_magnet(\"HS4M2D1R\")\n",
-    "corr_d.strength.set(0)\n",
+    "corr_d = sr.design.get_magnet(\"VS3M2D1R\")\n",
+    "corr_d.strength.set(std_kick)\n",
     "\n",
     "orbit_d = sr.design.get_bpms(\"BPM\").positions"
    ]
@@ -162,31 +182,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "f7f03793",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x78e6a9792bd0>]"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot comparison\n",
     "plt.plot(orbit_d.get()[:, 1] * 1e3)\n",
@@ -195,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "c2bd7949",
    "metadata": {},
    "outputs": [],
@@ -216,139 +215,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "484fc6fc",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Lattice([Sextupole('S3M2D1R', 0.16, -21.185, Corrector='V', KickAngle=array([0., 0.]), PolynomA=array([0.000625, 0.      , 0.      ]))], name='BESSY II', energy=1718500000.0, particle=Particle('relativistic'), periodicity=1, harmonic_number=400, beam_current=np.float64(0.0), nbunch=np.int64(1))"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ring[\"S3M2D1R\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "74ff1047",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Lattice([Sextupole('S4M2D1R', 0.16, 16.68, Corrector='H', KickAngle=array([0., 0.]), PolynomB=array([-6.250e-04,  0.000e+00,  1.668e+01]))], name='BESSY II', energy=1718500000.0, particle=Particle('relativistic'), periodicity=1, harmonic_number=400, beam_current=np.float64(0.0), nbunch=np.int64(1))"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ring[\"S4M2D1R\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "id": "b3502dd4",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0.]\n",
-      "[0. 0. 0.]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import at\n",
     "\n",
@@ -361,24 +251,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "6b263174",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.0001 0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.    ]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get the devices\n",
     "hcorr = sr.live.get_magnets(\"HCorr\")\n",
@@ -403,24 +279,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "0b5e533b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.0001 0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.    ]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get the devices\n",
     "hcorr_d = sr.design.get_magnets(\"HCorr\")\n",
@@ -453,185 +315,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "fcef2603",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.0001 0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
-      " 0.     0.     0.     0.    ]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(vcorr.strengths.get())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "9092481a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 1.65776421e+04 -3.29277182e+05]\n",
-      " [ 1.14783316e+04 -6.58563827e+05]\n",
-      " [ 7.49089817e+03 -5.36161825e+05]\n",
-      " [ 9.21487725e+03 -3.03525308e+05]\n",
-      " [ 4.11466188e+03 -3.56953722e+05]\n",
-      " [-1.53967578e+03 -2.42608835e+05]\n",
-      " [ 1.07766612e+03 -4.44624244e+03]\n",
-      " [ 8.69857692e+03  3.36731888e+05]\n",
-      " [ 1.20123184e+04  4.85083728e+05]\n",
-      " [ 1.13642223e+04  6.37629044e+05]\n",
-      " [-3.91707340e+03  5.35212285e+05]\n",
-      " [-6.21057992e+03  3.07125577e+05]\n",
-      " [-3.33147658e+03  3.66247747e+05]\n",
-      " [ 1.32745896e+04  2.61593627e+05]\n",
-      " [ 2.24719436e+04  5.85778342e+04]\n",
-      " [ 1.86012890e+04 -5.58502662e+05]\n",
-      " [ 1.02869400e+04 -1.00434189e+06]\n",
-      " [-1.28587069e+04 -9.62103668e+05]\n",
-      " [-1.96933300e+04 -6.69440017e+05]\n",
-      " [-9.94795398e+03 -9.43225399e+05]\n",
-      " [ 2.06995210e+04 -8.93036312e+05]\n",
-      " [ 1.85725518e+04 -4.85220644e+05]\n",
-      " [-1.11676377e+04  4.61542293e+05]\n",
-      " [-1.22077469e+04  4.94653610e+05]\n",
-      " [-1.32478561e+04  5.27764928e+05]\n",
-      " [-1.41700863e+04  5.57123629e+05]\n",
-      " [-1.60283541e+04  9.50424642e+05]\n",
-      " [ 1.00422635e+04  9.42736975e+05]\n",
-      " [ 1.84686920e+04  6.70289495e+05]\n",
-      " [ 1.29285427e+04  9.58914185e+05]\n",
-      " [ 3.85330531e+02  9.41224793e+05]\n",
-      " [-4.21499707e+03  5.02520191e+05]\n",
-      " [-1.68555967e+04 -1.80768549e+05]\n",
-      " [-1.16650963e+04 -4.62437730e+05]\n",
-      " [ 6.66057739e+03 -5.40634428e+05]\n",
-      " [ 1.35715417e+04 -4.51009371e+05]\n",
-      " [ 9.77209548e+03 -7.11730964e+05]\n",
-      " [ 2.84691536e+03 -7.76570791e+05]\n",
-      " [-1.09066046e+03 -5.62545798e+05]\n",
-      " [-1.42092040e+04  1.29201722e+05]\n",
-      " [-1.28826017e+04  4.35192996e+05]\n",
-      " [ 1.84450699e+04  5.41791325e+05]\n",
-      " [ 2.88399763e+04  4.68569395e+05]\n",
-      " [ 1.67558437e+04  7.53335297e+05]\n",
-      " [-1.73979972e+04  8.50089084e+05]\n",
-      " [-3.06293403e+04  5.43291110e+05]\n",
-      " [-2.52445516e+04  4.01574306e+05]\n",
-      " [-1.27084447e+04  5.58497104e+05]\n",
-      " [ 2.36921602e+04  4.22844202e+05]\n",
-      " [ 3.54718217e+04  2.08063311e+05]\n",
-      " [ 1.93666439e+04  2.05571840e+05]\n",
-      " [-2.61583686e+04  7.76875754e+04]\n",
-      " [-2.45587144e+04 -1.05503643e+05]\n",
-      " [ 1.69548449e+04 -4.40812290e+05]\n",
-      " [ 2.12725425e+04 -5.13643256e+05]\n",
-      " [-7.89703123e+03 -3.96759734e+05]\n",
-      " [-1.75316883e+04 -1.97592104e+05]\n",
-      " [-1.26121442e+04 -1.98517164e+05]\n",
-      " [-3.14624644e+03 -8.22567161e+04]\n",
-      " [-2.64918690e+02  4.82577265e+04]\n",
-      " [ 1.62699560e+04  5.92441435e+05]\n",
-      " [ 1.27606932e+04  1.03460272e+06]\n",
-      " [-6.68396259e+03  9.74010995e+05]\n",
-      " [-1.56530099e+04  6.64882599e+05]\n",
-      " [-1.06646101e+04  9.23777435e+05]\n",
-      " [-7.12795027e+03  8.57341713e+05]\n",
-      " [-4.06681008e+03  4.45098748e+05]\n",
-      " [ 1.88673655e+04 -5.90835152e+05]\n",
-      " [ 1.91759996e+04 -9.74606434e+05]\n",
-      " [-2.71839129e+04 -9.50903499e+05]\n",
-      " [-4.49823614e+04 -6.64537031e+05]\n",
-      " [-2.58575005e+04 -9.38891200e+05]\n",
-      " [ 1.71386355e+04 -9.07066145e+05]\n",
-      " [ 2.87663857e+04 -4.78290729e+05]\n",
-      " [ 2.81803024e+04 -4.20562552e+05]\n",
-      " [ 2.49800583e+04 -1.77326236e+05]\n",
-      " [ 2.44780515e+04 -8.23757115e+04]\n",
-      " [ 2.52923178e+04  1.89849594e+05]\n",
-      " [ 1.35210640e+04  5.73129828e+05]\n",
-      " [-2.96230171e+04  6.45207042e+05]\n",
-      " [-4.61411996e+04  5.23226543e+05]\n",
-      " [-2.55135854e+04  8.12740313e+05]\n",
-      " [ 2.27992836e+04  8.58918783e+05]\n",
-      " [ 2.56026937e+04  3.94440789e+05]\n",
-      " [ 7.32343793e+03  8.13499779e+04]\n",
-      " [-1.08929735e+04 -3.28609269e+05]\n",
-      " [-1.42419333e+04 -6.47273733e+05]\n",
-      " [-6.93273210e+03 -7.20460709e+05]\n",
-      " [-8.04057392e+03 -5.74444396e+05]\n",
-      " [-2.39688586e+03 -8.83612463e+05]\n",
-      " [ 2.94321556e+03 -9.50823406e+05]\n",
-      " [-3.86889926e+02 -5.78451528e+05]\n",
-      " [-1.41673576e+04 -2.53133765e+05]\n",
-      " [-1.03004767e+04 -2.83804988e+05]\n",
-      " [-1.00292158e+04 -1.54772636e+05]\n",
-      " [-1.27818673e+04 -1.81497944e+04]\n",
-      " [-5.69379645e+03  6.55544894e+04]\n",
-      " [ 6.55613049e+03  1.83975611e+05]\n",
-      " [ 3.17701619e+03  2.57625221e+05]\n",
-      " [-1.05793747e+04  2.88170913e+05]\n",
-      " [-1.65609594e+04  3.01452859e+05]\n",
-      " [-1.56853435e+04  2.61082287e+05]\n",
-      " [ 6.16387981e+03  1.38231684e+05]\n",
-      " [ 9.67044832e+03  7.59860264e+03]\n",
-      " [ 5.49196082e+03 -7.93864872e+04]\n",
-      " [-1.74063151e+04 -1.98626458e+05]\n",
-      " [-2.98180725e+04 -2.04491491e+05]\n",
-      " [-2.46534395e+04 -5.79202128e+05]\n",
-      " [-1.21151122e+04 -9.69676163e+05]\n",
-      " [ 3.93627408e+03 -8.79685527e+05]\n",
-      " [ 3.34500796e+03 -5.73946216e+05]\n",
-      " [ 1.15071760e+03 -7.69990653e+05]\n",
-      " [-2.32418139e+04 -6.76540753e+05]\n",
-      " [-2.26893942e+04 -2.91651506e+05]\n",
-      " [ 1.34439211e+04  6.07177681e+05]\n",
-      " [ 1.74402122e+04  9.57298923e+05]\n",
-      " [ 2.07983969e+03  8.96389444e+05]\n",
-      " [-4.02649838e+02  5.94704266e+05]\n",
-      " [-1.37931806e+03  8.08261510e+05]\n",
-      " [ 2.52219883e+03  7.41249206e+05]\n",
-      " [ 7.05916567e+03  3.73478678e+05]\n",
-      " [ 8.70739421e+03  2.51788797e+05]\n",
-      " [ 9.05922428e+03  2.25812934e+05]]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x78e62f9a3e90>,\n",
-       " <matplotlib.lines.Line2D at 0x78e62f84a850>]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "orbit_initial = orbit.get()\n",
     "print(orbit_initial)\n",
@@ -644,31 +341,7 @@
    "execution_count": null,
    "id": "1f51011c",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'plot' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[13]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mplot\u001b[49m(orbit.get()[:,\u001b[32m1\u001b[39m])\n",
-      "\u001b[31mNameError\u001b[39m: name 'plot' is not defined"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "CA.Client.Exception...............................................\n",
-      "    Warning: \"Virtual circuit disconnect\"\n",
-      "    Context: \"nb-56j0b44.basisit.de:5064\"\n",
-      "    Source File: modules/ca/src/client/cac.cpp line 1238\n",
-      "    Current Time: Tue Jan 27 2026 16:57:55.905285510\n",
-      "..................................................................\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.plot(orbit.get()[:, 1])"
    ]
@@ -830,7 +503,7 @@
     "# If you are using the ORM measured on design to correct on live you need to set the\n",
     "# gain since the unit for the BPMs are not the same for both modes yet.\n",
     "\n",
-    "sr.live.orbit.correct(gain=1e-9)\n",
+    "sr.live.orbit.correct(gain=1e-9, singular_values_H=48, singular_values_V=64)\n",
     "# sr.design.orbit.correct()\n",
     "# sr.live.orbit.correct()\n",
     "\n",
@@ -865,8 +538,8 @@
     "ax1.plot(orbit_after[:, 0] * 1e-6, label=\"Orbit after correction\")\n",
     "ax2.plot(orbit_after[:, 1] * 1e-6, label=\"Orbit after correction\")\n",
     "\n",
-    "# ax3.plot(hcorr.strengths.get(), label=\"H Steerers\")\n",
-    "# ax3.plot(vcorr.strengths.get(), label=\"V Steerers\")\n",
+    "ax3.plot(hcorr.strengths.get(), label=\"H Steerers\")\n",
+    "ax3.plot(vcorr.strengths.get(), label=\"V Steerers\")\n",
     "\n",
     "ax1.set_ylabel(\"Horizontal pos. [mm]\")\n",
     "ax2.set_ylabel(\"Vertical pos. [mm]\")\n",
@@ -892,7 +565,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pyaml-tango",
+   "display_name": "jlp-py312",
    "language": "python",
    "name": "python3"
   },
@@ -906,7 +579,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.14.2"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,
diff --git a/examples/BESSY2_example/bessy2-orbit.py b/examples/BESSY2_example/bessy2-orbit.py
index 5b900f12..3d755b54 100644
--- a/examples/BESSY2_example/bessy2-orbit.py
+++ b/examples/BESSY2_example/bessy2-orbit.py
@@ -32,18 +32,18 @@
 # SR = sr.live
 
 # if the ORM is not present measure it
-if sr.design.orbit.response_matrix is None:
+if SR.orbit.response_matrix is None:
     SR.orm.measure(sleep_between_step=0.0 if SR == sr.design else 2.0)
     SR.orm.save("orm.json")
-    # Load it on live
-    sr.live.orbit.load("orm.json")
+    # Load it on orbit correction tool
+    SR.orbit.load("orm.json")
 
-# ----- Correct the orbit -----
+# ----- Correct the orbit on live -----
 
 # Get the devices
-hcorr = sr.live.get_magnets("HCorr")
-vcorr = sr.live.get_magnets("VCorr")
-orbit = sr.live.get_bpms("BPM").positions
+hcorr = SR.get_magnets("HCorr")
+vcorr = SR.get_magnets("VCorr")
+orbit = SR.get_bpms("BPM").positions
 
 # Create an initial orbit with errors
 std_kick = 10e-6
@@ -57,9 +57,11 @@
 # If you are using the ORM measured on design to correct on live you need to set the
 # gain since the unit for the BPMs are not the same for both modes yet.
 
-sr.live.orbit.correct(gain=1e-9)
-# sr.design.orbit.correct()
-# sr.live.orbit.correct()
+if SR == sr.design:
+    SR.orbit.correct()
+else:
+    # BPM are in nm, work around with a 1e-9 weight
+    SR.orbit.correct(gain=1e-9)
 
 time.sleep(3)
 orbit_after = orbit.get()
diff --git a/examples/BESSY2_example/bessy2-tune.ipynb b/examples/BESSY2_example/bessy2-tune.ipynb
index 5ea99d53..5ca7f42b 100644
--- a/examples/BESSY2_example/bessy2-tune.ipynb
+++ b/examples/BESSY2_example/bessy2-tune.ipynb
@@ -23,8 +23,7 @@
     "import numpy as np\n",
     "\n",
     "from pyaml.accelerator import Accelerator\n",
-    "from pyaml.common.constants import ACTION_MEASURE\n",
-    "from pyaml.magnet.magnet import Magnet"
+    "from pyaml.common.constants import Action"
    ]
   },
   {
@@ -52,10 +51,10 @@
     "# This callback is used to print output during the tune response measurement.\n",
     "\n",
     "\n",
-    "def tune_callback(step: int, action: int, m: Magnet, dtune: np.array):\n",
-    "    if action == ACTION_MEASURE:\n",
+    "def tune_callback(action: int, cb_data: dict):\n",
+    "    if action == Action.MEASURE:\n",
     "        # On action measure, the measured dq / dk is passed as argument\n",
-    "        print(f\"Tune response: #{step} {m.get_name()} {dtune}\")\n",
+    "        print(f\"Tune response: #{cb_data['step']} {cb_data['magnet']} {cb_data['tune']}\")\n",
     "    return True"
    ]
   },
@@ -73,26 +72,14 @@
     "\n",
     "# Choose which backend to use.\n",
     "SR = sr.design\n",
+    "# SR = sr.live\n",
     "\n",
-    "tune_adjust = sr.design.tune\n",
-    "tune_adjust.response.measure(\n",
-    "    callback=tune_callback, set_wait_time=0.0 if SR == sr.design else 2.0\n",
-    ")\n",
-    "tune_adjust.response.save_json(\"tune-response.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f7777a47",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ----- Load the response matrix -----\n",
-    "# The example does the correction for the live mode\n",
-    "# but it can also be done on the design mode.\n",
-    "\n",
-    "sr.live.tune.response.load_json(\"tune-response.json\")"
+    "# if the TRM is not present measure it\n",
+    "if SR.tune.response_matrix is None:\n",
+    "    SR.trm.measure(sleep_between_step=0.0 if SR == sr.design else 2.0, callback=tune_callback)\n",
+    "    SR.trm.save(\"trm.json\")\n",
+    "    # Load it on tune tuning tool\n",
+    "    SR.tune.load(\"trm.json\")"
    ]
   },
   {
@@ -106,13 +93,13 @@
     "\n",
     "print(\"\\nRun tune correction:\")\n",
     "\n",
-    "initial_tunes = np.array2string(sr.live.tune.readback(), precision=6, floatmode=\"fixed\")\n",
+    "initial_tunes = np.array2string(SR.tune.readback(), precision=6, floatmode=\"fixed\")\n",
     "print(f\"Initial tunes: {initial_tunes}\")\n",
     "\n",
-    "sr.live.tune.set([0.83, 0.84], iter=2, wait_time=3)\n",
+    "SR.tune.set([0.83, 0.84], iter=2, wait_time=3)\n",
     "time.sleep(3)\n",
     "\n",
-    "final_tunes = np.array2string(sr.live.tune.readback(), precision=6, floatmode=\"fixed\")\n",
+    "final_tunes = np.array2string(SR.tune.readback(), precision=6, floatmode=\"fixed\")\n",
     "print(f\"Final tunes: {final_tunes}\")"
    ]
   }
diff --git a/examples/BESSY2_example/bessy2-tune.py b/examples/BESSY2_example/bessy2-tune.py
index f293078f..2a997cb8 100644
--- a/examples/BESSY2_example/bessy2-tune.py
+++ b/examples/BESSY2_example/bessy2-tune.py
@@ -39,23 +39,24 @@ def tune_callback(action: int, cb_data: dict):
 
 # Choose which backend to use.
 SR = sr.design
+# SR = sr.live
 
 # if the TRM is not present measure it
 if sr.design.tune.response_matrix is None:
     SR.trm.measure(sleep_between_step=0.0 if SR == sr.design else 2.0, callback=tune_callback)
     SR.trm.save("trm.json")
-    # Load it on live
-    sr.live.tune.load("trm.json")
+    # Load it on tune tuning tool
+    SR.tune.load("trm.json")
 
 # ----- Correct the tune -----
 
 print("\nRun tune correction:")
 
-initial_tunes = np.array2string(sr.live.tune.readback(), precision=6, floatmode="fixed")
+initial_tunes = np.array2string(SR.tune.readback(), precision=6, floatmode="fixed")
 print(f"Initial tunes: {initial_tunes}")
 
-sr.live.tune.set([0.83, 0.84], iter=2, wait_time=3)
+SR.tune.set([0.83, 0.84], iter=2, wait_time=3)
 time.sleep(3)
 
-final_tunes = np.array2string(sr.live.tune.readback(), precision=6, floatmode="fixed")
+final_tunes = np.array2string(SR.tune.readback(), precision=6, floatmode="fixed")
 print(f"Final tunes: {final_tunes}")
diff --git a/pyaml/tuning_tools/chromaticity.py b/pyaml/tuning_tools/chromaticity.py
index 27a4d892..a57457cd 100644
--- a/pyaml/tuning_tools/chromaticity.py
+++ b/pyaml/tuning_tools/chromaticity.py
@@ -88,7 +88,7 @@ def response_matrix(self) -> ResponseMatrixData | None:
 
     def load(self, load_path: Path):
         """
-        Dyanmically loads a response matrix.
+        Dynamically loads a response matrix.
 
         Parameters
         ----------