diff --git a/labcore/data/datagen.py b/labcore/data/datagen.py
new file mode 100644
index 0000000..4e3dd79
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+++ b/labcore/data/datagen.py
@@ -0,0 +1,103 @@
+
+import numpy as np
+
+from dataclasses import dataclass, asdict
+from abc import ABC, abstractmethod
+
+"""
+
+Implementation would work like this given x (n-dimensional array):
+
+sine() = SineDataGen()
+model = sine.generate(x, noise_std = 0.5, A = 2, f = 3)
+
+-------------
+These two lines would create a synthesizer representing a sine wave and generate models 
+for each set of data stored in x.
+
+Std for Gaussian distribution passed to noise() is 0.5 --> maybe add way to have multiple 
+                                                           distributions for different data sets?
+
+A, f are passed to SineDataGen's model() as kwargs
+
+DataGen's generate() then applies the noise to the model and returns an array with
+the same dimension that was passed
+
+"""
+
+"""
+generate should now work such that the following can be done:
+
+x = [np array of coordinates]
+sine = SineDataGen(A = 2, f = 3)
+
+coords = sine.generate() --> uses A = 2, f = 3 
+coords = sine.generate(A = 5) --> uses A = 5, f = 2
+
+"""
+
+@dataclass
+class DataGen(ABC):
+    noise_std : float = 1.0
+    
+    @abstractmethod
+    def model(coordinates, *args, **kwargs):
+        pass
+
+    def generate(self, coordinates, **kwargs): 
+        
+        # updates previously set dataclass fields
+        params = asdict(self) 
+        params.update(kwargs)
+        noise_std = params.pop('noise_std')
+
+        one_d = coordinates.ndim == 1
+        coordinates = np.atleast_2d(coordinates)
+        model_outputs = np.array([self.model(coords, **params) +
+                                  self.noise(coords, noise_std)
+                                  for coords in coordinates])
+        if (one_d):
+            return model_outputs.squeeze()
+
+        return model_outputs
+    
+    @staticmethod
+    def noise(coordinates, std):
+        return np.random.normal(scale = std, size = len(coordinates))
+    
+
+@dataclass
+class ExponentialDataGen(DataGen):
+
+    base: float = np.e
+
+    @staticmethod
+    def model(coordinates, base):
+        return base ** coordinates
+    
+
+
+@dataclass
+class SineDataGen(DataGen):
+
+    A : float = 1
+    f : float = 1
+    phi : float = 0
+    of : float = 0
+
+    @staticmethod
+    def model(coordinates, A, f, phi, of):
+        return A * np.sin(2 * np.pi * coordinates * f + phi) + of
+
+
+@dataclass
+class GaussianDataGen(DataGen):
+
+    x0 : float = 0
+    sigma : float = 1
+    A : float = 1
+    of : float = 0
+
+    @staticmethod
+    def model(coordinates, x0, sigma, A, of):
+        return A * np.exp(-((coordinates - x0) ** 2) / (2 * sigma ** 2)) + of
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