diff --git a/ophyd_devices/sim/sim.py b/ophyd_devices/sim/sim.py
index 496710d..58be827 100644
--- a/ophyd_devices/sim/sim.py
+++ b/ophyd_devices/sim/sim.py
@@ -251,7 +251,6 @@ class SimPositioner(Device, PositionerBase):
     low_limit_travel = Cpt(SetableSignal, value=0, kind=Kind.omitted)
     unused = Cpt(Signal, value=1, kind=Kind.omitted)
 
-    # TODO add short description to these two lines and explain what this does
     SUB_READBACK = "readback"
     _default_sub = SUB_READBACK
 
diff --git a/ophyd_devices/sim/sim_data.py b/ophyd_devices/sim/sim_data.py
index 22dbc8f..eab7618 100644
--- a/ophyd_devices/sim/sim_data.py
+++ b/ophyd_devices/sim/sim_data.py
@@ -1,6 +1,9 @@
-from abc import ABC, abstractmethod
+from __future__ import annotations
+
 from collections import defaultdict
+
 import enum
+import inspect
 import time as ttime
 import numpy as np
 
@@ -28,6 +31,13 @@ class NoiseType(str, enum.Enum):
     POISSON = "poisson"
 
 
+class HotPixelType(str, enum.Enum):
+    """Type of hot pixel to add to simulated data."""
+
+    CONSTANT = "constant"
+    FLUCTUATING = "fluctuating"
+
+
 class SimulatedDataBase:
     USER_ACCESS = [
         "get_sim_params",
@@ -38,12 +48,42 @@ class SimulatedDataBase:
 
     def __init__(self, *args, parent=None, device_manager=None, **kwargs) -> None:
         self.parent = parent
-        self.sim_state = defaultdict(lambda: {})
-        self._all_params = defaultdict(lambda: {})
+        self.sim_state = defaultdict(dict)
+        self._all_params = defaultdict(dict)
         self.device_manager = device_manager
         self._simulation_type = None
+        self.lookup_table = getattr(self.parent, "lookup_table", None)
         self.init_paramaters(**kwargs)
         self._active_params = self._all_params.get(self._simulation_type, None)
+        # self.register_in_lookup_table()
+
+    #     self.lookup_table = self.update_lookup_table()
+
+    # def update_lookup_table(self) -> None:
+    #     """Update the lookup table with the new value for the signal."""
+    #     table = getattr(self.device_manager.lookup_table, self.parent.name, None)
+
+    #     return getattr(self.device_manager.lookup_table, self.parent.name, None)
+
+    # def register_in_lookup_table(self) -> None:
+    #     """Register the simulated device in the lookup table."""
+    #     self.device_manager.lookup_table[self.parent.name] = {"obj": self, "method": "_compute_sim_state", "args": (), "kwargs": {}}
+
+    def execute_simulation_method(self, *args, method=None, **kwargs) -> any:
+        """Execute the method from the lookup table."""
+
+        if self.lookup_table and self.parent.name in self.lookup_table:
+            # obj = self.parent.lookup_table[self.parent.name]["obj"]
+            method = self.lookup_table[self.parent.name]["method"]
+            args = self.lookup_table[self.parent.name]["args"]
+            kwargs = self.lookup_table[self.parent.name]["kwargs"]
+            # Do I need args and kwargs! Why!!
+
+        if method is not None:
+            method_arguments = list(inspect.signature(method).parameters.keys())
+            if all([True for arg in method_arguments if arg in args or arg in kwargs]):
+                return method(*args, **kwargs)
+        raise SimulatedDataException(f"Method {method} is not available for {self.parent.name}")
 
     def init_paramaters(self, **kwargs):
         """Initialize the parameters for the Simulated Data
@@ -119,13 +159,16 @@ class SimulatedDataBase:
         self.sim_state[signal_name]["value"] = value
         self.sim_state[signal_name]["timestamp"] = ttime.time()
 
-    def _update_init_params(self, sim_type_default: SimulationType) -> None:
+    def _update_init_params(
+        self,
+        sim_type_default: SimulationType,
+    ) -> None:
         """Update the initial parameters of the simulated data with input from deviceConfig.
 
         Args:
             sim_type_default (SimulationType): Default simulation type to use if not specified in deviceConfig.
         """
-        init_params = self.parent.init_sim_params
+        init_params = getattr(self.parent, "init_sim_params", None)
         for sim_type in self._all_params.values():
             for sim_type_config_element in sim_type:
                 if init_params:
@@ -191,10 +234,13 @@ class SimulatedDataMonitor(SimulatedDataBase):
             signal_name (str): Name of the signal to update.
         """
         if self.get_sim_type() == SimulationType.CONSTANT:
-            value = self._compute_constant()
+            method = "_compute_constant"
+            # value = self._compute_constant()
         elif self.get_sim_type() == SimulationType.GAUSSIAN:
-            value = self._compute_gaussian()
+            method = "_compute_gaussian"
+            # value = self._compute_gaussian()
 
+        value = self.execute_simulation_method(method=getattr(self, method))
         self.update_sim_state(signal_name, value)
 
     def _compute_constant(self) -> float:
@@ -210,12 +256,10 @@ class SimulatedDataMonitor(SimulatedDataBase):
             v = self._active_params["amp"]
             return v
         else:
-            # TODO Propagate msg to client!
-            logger.warning(
+            raise SimulatedDataException(
                 f"Unknown noise type {self._active_params['noise']}. Please choose from 'poisson',"
-                " 'uniform' or 'none'. Returning 0."
+                " 'uniform' or 'none'."
             )
-            return 0
 
     def _compute_gaussian(self) -> float:
         """Computes return value for sim_type = "gauss".
@@ -243,12 +287,9 @@ class SimulatedDataMonitor(SimulatedDataBase):
                 v += np.random.uniform(-1, 1) * params["noise_multiplier"]
             return v
         except SimulatedDataException as exc:
-            # TODO Propagate msg to client!
-            logger.warning(
-                f"Could not compute gaussian for {params['ref_motor']} with {exc} raised."
-                "Returning 0 instead."
-            )
-            return 0
+            raise SimulatedDataException(
+                f"Could not compute gaussian for {self.parent.name} with {exc} raised. Deactivate eiger to continue."
+            ) from exc
 
 
 class SimulatedDataCamera(SimulatedDataBase):
@@ -282,13 +323,27 @@ class SimulatedDataCamera(SimulatedDataBase):
                 "amp": 100,
                 "noise": NoiseType.POISSON,
                 "noise_multiplier": 0.1,
+                "hot_pixel": {
+                    "coords": np.array([[100, 100], [200, 200]]),
+                    "type": [HotPixelType.CONSTANT, HotPixelType.FLUCTUATING],
+                    "value": [1e6, 1e4],
+                },
             },
             SimulationType.GAUSSIAN: {
-                "amp": 100,
+                "amp": 500,
                 "cen_off": np.array([0, 0]),
                 "cov": np.array([[10, 5], [5, 10]]),
                 "noise": NoiseType.NONE,
                 "noise_multiplier": 0.1,
+                "hot_pixel": {
+                    "coords": np.array([[240, 240], [50, 20], [40, 400]]),
+                    "type": [
+                        HotPixelType.FLUCTUATING,
+                        HotPixelType.CONSTANT,
+                        HotPixelType.FLUCTUATING,
+                    ],
+                    "value": np.array([1e4, 1e6, 1e4]),
+                },
             },
         }
         # Update init parameters and set simulation type to Gaussian if not specified otherwise in init_sim_params
@@ -304,36 +359,33 @@ class SimulatedDataCamera(SimulatedDataBase):
             signal_name (str): Name of the signal to update.
         """
         if self.get_sim_type() == SimulationType.CONSTANT:
-            value = self._compute_constant()
+            method = "_compute_constant"
+            # value = self._compute_constant()
         elif self.get_sim_type() == SimulationType.GAUSSIAN:
-            value = self._compute_gaussian()
+            method = "_compute_gaussian"
+            # value = self._compute_gaussian()
+
+        value = self.execute_simulation_method(method=getattr(self, method))
 
         self.update_sim_state(signal_name, value)
 
     def _compute_constant(self) -> float:
         """Compute a return value for sim_type = Constant."""
-
-        # tuple with shape
-        shape = self.sim_state[self.parent.image_shape.name]["value"]
-        v = self._active_params["amp"] * np.ones(shape, dtype=np.uint16)
-        if self._active_params["noise"] == NoiseType.POISSON:
-            v = np.random.poisson(np.round(v), v.shape)
-            return v
-        if self._active_params["noise"] == NoiseType.UNIFORM:
-            multiplier = self._active_params["noise_multiplier"]
-            v += np.random.randint(-multiplier, multiplier, v.shape)
-            return v
-        if self._active_params["noise"] == NoiseType.NONE:
-            return v
-        # TODO Propagate msg to client!
-        logger.warning(
-            f"Unknown noise type {self._active_params['noise']}. Please choose from 'poisson',"
-            " 'uniform' or 'none'. Returning 0."
-        )
-        return 0
+        try:
+            shape = self.sim_state[self.parent.image_shape.name]["value"]
+            v = self._active_params["amp"] * np.ones(shape, dtype=np.uint16)
+            return self._add_noise(v, self._active_params["noise"])
+        except SimulatedDataException as exc:
+            raise SimulatedDataException(
+                f"Could not compute constant for {self.parent.name} with {exc} raised. Deactivate eiger to continue."
+            ) from exc
 
     def _compute_multivariate_gaussian(
-        self, pos: np.ndarray, cen_off: np.ndarray, cov: np.ndarray
+        self,
+        pos: np.ndarray | list,
+        cen_off: np.ndarray | list,
+        cov: np.ndarray | list,
+        amp: float,
     ) -> np.ndarray:
         """Computes and returns the multivariate Gaussian distribution.
 
@@ -345,16 +397,80 @@ class SimulatedDataCamera(SimulatedDataBase):
         Returns:
             np.ndarray: Multivariate Gaussian distribution.
         """
-
+        if isinstance(pos, list):
+            pos = np.array(pos)
+        if isinstance(cen_off, list):
+            cen_off = np.array(cen_off)
+        if isinstance(cov, list):
+            cov = np.array(cov)
         dim = cen_off.shape[0]
         cov_det = np.linalg.det(cov)
         cov_inv = np.linalg.inv(cov)
-        N = np.sqrt((2 * np.pi) ** dim * cov_det)
+        norm = np.sqrt((2 * np.pi) ** dim * cov_det)
         # This einsum call calculates (x-mu)T.Sigma-1.(x-mu) in a vectorized
         # way across all the input variables.
         fac = np.einsum("...k,kl,...l->...", pos - cen_off, cov_inv, pos - cen_off)
+        v = np.exp(-fac / 2) / norm
+        v *= amp / np.max(v)
+        return v
 
-        return np.exp(-fac / 2) / N
+    def _prepare_params_gauss(self, params: dict, shape: tuple) -> tuple:
+        """Prepare the positions for the gaussian.
+
+        Args:
+            params (dict): Parameters for the gaussian.
+            shape (tuple): Shape of the image.
+        Returns:
+            tuple: Positions, offset and covariance matrix for the gaussian.
+        """
+        x, y = np.meshgrid(
+            np.linspace(-shape[0] / 2, shape[0] / 2, shape[0]),
+            np.linspace(-shape[1] / 2, shape[1] / 2, shape[1]),
+        )
+        pos = np.empty((*x.shape, 2))
+        pos[:, :, 0] = x
+        pos[:, :, 1] = y
+
+        offset = params["cen_off"]
+        cov = params["cov"]
+        amp = params["amp"]
+        return pos, offset, cov, amp
+
+    def _add_noise(self, v: np.ndarray, noise: NoiseType) -> np.ndarray:
+        """Add noise to the simulated data.
+
+        Args:
+            v (np.ndarray): Simulated data.
+            noise (NoiseType): Type of noise to add.
+        """
+        if noise == NoiseType.POISSON:
+            v = np.random.poisson(np.round(v), v.shape)
+            return v
+        if noise == NoiseType.UNIFORM:
+            multiplier = self._active_params["noise_multiplier"]
+            v += np.random.uniform(-multiplier, multiplier, v.shape)
+            return v
+        if self._active_params["noise"] == NoiseType.NONE:
+            return v
+
+    def _add_hot_pixel(self, v: np.ndarray, hot_pixel: dict) -> np.ndarray:
+        """Add hot pixels to the simulated data.
+
+        Args:
+            v (np.ndarray): Simulated data.
+            hot_pixel (dict): Hot pixel parameters.
+        """
+        for coords, hot_pixel_type, value in zip(
+            hot_pixel["coords"], hot_pixel["type"], hot_pixel["value"]
+        ):
+            if coords[0] < v.shape[0] and coords[1] < v.shape[1]:
+                if hot_pixel_type == HotPixelType.CONSTANT:
+                    v[coords[0], coords[1]] = value
+                elif hot_pixel_type == HotPixelType.FLUCTUATING:
+                    maximum = np.max(v) if np.max(v) != 0 else 1
+                    if v[coords[0], coords[1]] / maximum > 0.5:
+                        v[coords[0], coords[1]] = value
+        return v
 
     def _compute_gaussian(self) -> float:
         """Computes return value for sim_type = "gauss".
@@ -367,41 +483,15 @@ class SimulatedDataCamera(SimulatedDataBase):
         Returns: float
         """
 
-        params = self._active_params
-        shape = self.sim_state[self.parent.image_shape.name]["value"]
         try:
-            X, Y = np.meshgrid(
-                np.linspace(-shape[0] / 2, shape[0] / 2, shape[0]),
-                np.linspace(-shape[1] / 2, shape[1] / 2, shape[1]),
-            )
-            pos = np.empty((*X.shape, 2))
-            pos[:, :, 0] = X
-            pos[:, :, 1] = Y
+            params = self._active_params
+            shape = self.sim_state[self.parent.image_shape.name]["value"]
+            pos, offset, cov, amp = self._prepare_params_gauss(self._active_params, shape)
 
-            v = self._compute_multivariate_gaussian(
-                pos=pos, cen_off=params["cen_off"], cov=params["cov"]
-            )
-            # divide by max(v) to ensure that maximum is params["amp"]
-            v *= params["amp"] / np.max(v)
-
-            # TODO add dependency from motor position -> #transmission factor, sigmoidal form from 0 to 1 as a function of motor pos
-            # motor_pos = self.device_manager.devices[params["ref_motor"]].obj.read()[
-            #     params["ref_motor"]
-            # ]["value"]
-
-            if params["noise"] == NoiseType.POISSON:
-                v = np.random.poisson(np.round(v), v.shape)
-                return v
-            if params["noise"] == NoiseType.UNIFORM:
-                multiplier = params["noise_multiplier"]
-                v += np.random.uniform(-multiplier, multiplier, v.shape)
-                return v
-            if self._active_params["noise"] == NoiseType.NONE:
-                return v
+            v = self._compute_multivariate_gaussian(pos=pos, cen_off=offset, cov=cov, amp=amp)
+            v = self._add_noise(v, params["noise"])
+            return self._add_hot_pixel(v, params["hot_pixel"])
         except SimulatedDataException as exc:
-            # TODO Propagate msg to client!
-            logger.warning(
-                f"Could not compute gaussian for {params['ref_motor']} with {exc} raised."
-                "Returning 0 instead."
-            )
-            return 0
+            raise SimulatedDataException(
+                f"Could not compute gaussian for {self.parent.name} with {exc} raised. Deactivate eiger to continue."
+            ) from exc