diff --git a/ophyd_devices/sim/sim_frameworks.py b/ophyd_devices/sim/sim_frameworks.py
new file mode 100644
index 0000000..ad9f4b6
--- /dev/null
+++ b/ophyd_devices/sim/sim_frameworks.py
@@ -0,0 +1,121 @@
+import numpy as np
+
+from scipy.ndimage import gaussian_filter
+
+from collections import defaultdict
+from ophyd_devices.sim.sim_data import NoiseType
+
+
+class PinholeLookup:
+    """Pinhole lookup table for simulated devices.
+
+    When activated, it will create a lookup table for a simulated camera based on the config.
+    The lookup table will be used to simulate the effect of a pinhole on the camera image.
+    An example config is shown below, for the dev.eiger, with dev.samx and dev.samy as reference motors.
+
+    eiger:
+        cen_off: [0, 0] # [x,y]
+        cov: [[1000, 500], [200, 1000]] # [[x,x],[y,y]]
+        pixel_size: 0.01
+        signal: image
+        ref_motors: [samx, samy]
+        slit_width: [1, 2]
+        motor_dir: [0, 1] # x:0 , y:1, z:2 coordinates
+    """
+
+    def __init__(
+        self,
+        *args,
+        name,
+        device_manager=None,
+        config: dict = None,
+        **kwargs,
+    ):
+        self.name = name
+        self.device_manager = device_manager
+        self.config = config
+        self._enabled = True
+        self._lookup = defaultdict(dict)
+        self._gaussian_blur_sigma = 8
+        self._compile_lookup()
+
+    @property
+    def lookup(self):
+        """lookup property"""
+        return (
+            self._lookup
+            if getattr(self.device_manager.devices, self.name).enabled is True
+            else None
+        )
+
+    def _compile_lookup(self):
+        """Compile the lookup table for the simulated camera."""
+        for device_name in self.config.keys():
+            self.lookup[device_name] = {
+                "obj": self,
+                "method": self._compute,
+                "args": {},
+                "kwargs": {"device_name": device_name},
+            }
+
+    def _compute(self, *args, device_name: str = None, **kwargs) -> np.ndarray:
+        """
+        Compute the lookup table for the simulated camera.
+        It copies the sim_camera bevahiour and adds a mask to simulate the effect of a pinhole.
+
+        Args:
+            device_name (str): Name of the device.
+
+        Returns:
+            np.ndarray: Lookup table for the simulated camera.
+        """
+        device_obj = self.device_manager.devices.get(device_name)
+        params = device_obj.sim._all_params.get("gauss")
+        shape = device_obj.image_shape.get()
+        params.update(
+            {
+                "noise": NoiseType.POISSON,
+                "cov": np.array(self.config[device_name]["cov"]),
+                "cen_off": np.array(self.config[device_name]["cen_off"]),
+            }
+        )
+
+        pos, offset, cov, amp = device_obj.sim._prepare_params_gauss(params, shape)
+        v = device_obj.sim._compute_multivariate_gaussian(pos=pos, cen_off=offset, cov=cov, amp=amp)
+        device_pos = self.config[device_name]["pixel_size"] * pos
+        valid_mask = self._create_mask(
+            device_pos=device_pos,
+            ref_motors=self.config[device_name]["ref_motors"],
+            width=self.config[device_name]["slit_width"],
+            dir=self.config[device_name]["motor_dir"],
+        )
+        valid_mask = self._blur_image(valid_mask, sigma=self._gaussian_blur_sigma)
+        v *= valid_mask
+        v = device_obj.sim._add_noise(v, params["noise"])
+        v = device_obj.sim._add_hot_pixel(v, params["hot_pixel"])
+        return v
+
+    def _blur_image(self, image: np.ndarray, sigma: float = 5) -> np.ndarray:
+        """Blur the image with a gaussian filter.
+
+        Args:
+            image (np.ndarray): Image to be blurred.
+            sigma (float): Sigma for the gaussian filter.
+
+        Returns:
+            np.ndarray: Blurred image.
+        """
+        return gaussian_filter(image, sigma=sigma)
+
+    def _create_mask(
+        self, device_pos: np.ndarray, ref_motors: list[str], width: list[float], dir: list[int]
+    ):
+        mask = np.ones_like(device_pos, dtype=bool)
+        for ii, motor_name in enumerate(ref_motors):
+            motor_pos = self.device_manager.devices.get(motor_name).read()[motor_name]["value"]
+            edges = [motor_pos + width[ii] / 2, motor_pos - width[ii] / 2]
+            mask[..., dir[ii]] = np.logical_and(
+                device_pos[..., dir[ii]] > np.min(edges), device_pos[..., dir[ii]] < np.max(edges)
+            )
+
+        return np.prod(mask, axis=2)