diff --git a/VERSION b/VERSION
index 5a5831a..d169b2f 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-0.0.7
+0.0.8
diff --git a/driftbench/data_generation/sample.py b/driftbench/data_generation/sample.py
index 2d93431..30f5179 100644
--- a/driftbench/data_generation/sample.py
+++ b/driftbench/data_generation/sample.py
@@ -1,10 +1,17 @@
 import numpy as np
-import jax # noqa
+import jax  # noqa
 from driftbench.data_generation.latent_information import LatentInformation
 from driftbench.data_generation.data_generator import CurveGenerator
 
 
-def sample_curves(dataset_specification, f=None, w0=None, random_state=2024, measurement_scale=None, callback=None):
+def sample_curves(
+    dataset_specification,
+    f=None,
+    w0=None,
+    random_state=2024,
+    measurement_scale=None,
+    callback=None,
+):
     dimensions = dataset_specification["dimensions"]
     drifts = dataset_specification.get("drifts")
     x_scale = dataset_specification.get("x_scale", 0.02)
@@ -12,13 +19,21 @@ def sample_curves(dataset_specification, f=None, w0=None, random_state=2024, mea
     func = _get_func(dataset_specification, f)
     w_init = _get_w_init(dataset_specification, w0)
     rng = np.random.RandomState(random_state)
-    latent_information = _generate_latent_information(dataset_specification, rng, x_scale, y_scale)
+    latent_information = _generate_latent_information(
+        dataset_specification, rng, x_scale, y_scale
+    )
     if drifts is not None:
         latent_information = drifts.apply(latent_information)
     data_generator = CurveGenerator(func, w_init)
     w = data_generator.run(latent_information, callback=callback)
-    x_min = int(np.min(dataset_specification["latent_information"].x0))
-    x_max = int(np.max(dataset_specification["latent_information"].x0))
+    x_range = np.concatenate(
+        (
+            dataset_specification["latent_information"].x0,
+            dataset_specification["latent_information"].x1,
+            dataset_specification["latent_information"].x2,
+        )
+    )
+    x_min, x_max = int(np.min(x_range)), int(np.max(x_range))
     xs = np.linspace(x_min, x_max, dimensions)
     curves = np.array([func(w_i, xs) for w_i in w])
     # Apply a default noise of 5% of the mean of the sampled curves
@@ -34,17 +49,20 @@ def _generate_latent_information(dataset_specification, rng, x_scale, y_scale):
     N = dataset_specification["N"]
     base_latent_information = dataset_specification["latent_information"]
     latent_information = []
+    xis = [f"x{i}" for i in range(3)]
+    yis = [f"y{i}" for i in range(3)]
     for i in range(N):
         # Apply some random noise on the base values
-        x0 = base_latent_information.x0 + rng.normal(size=len(base_latent_information.x0), scale=x_scale)
-        y0 = base_latent_information.y0 + rng.normal(size=len(base_latent_information.y0), scale=y_scale)
-
-        x1 = base_latent_information.x1 + rng.normal(size=len(base_latent_information.x1), scale=x_scale)
-        y1 = base_latent_information.y1 + rng.normal(size=len(base_latent_information.y1), scale=y_scale)
-
-        x2 = base_latent_information.x2 + rng.normal(size=len(base_latent_information.x2), scale=x_scale)
-        y2 = base_latent_information.y2 + rng.normal(size=len(base_latent_information.y2), scale=y_scale)
-        latent_information.append(LatentInformation(y0, x0, y1, x1, y2, x2))
+        latent_dict = {}
+        for xi in xis:
+            latent_dict[xi] = getattr(base_latent_information, xi) + rng.normal(
+                size=len(getattr(base_latent_information, xi)), scale=x_scale
+            )
+        for yi in yis:
+            latent_dict[yi] = getattr(base_latent_information, yi) + rng.normal(
+                size=len(getattr(base_latent_information, yi)), scale=y_scale
+            )
+        latent_information.append(LatentInformation(**latent_dict))
     return latent_information
 
 
@@ -55,9 +73,11 @@ def _get_func(dataset_specification, f):
         func_expr = dataset_specification["func"]
         return eval(f"lambda w, x: {func_expr}")
     else:
-        raise ValueError("""No function provided. Either specify function in yaml
+        raise ValueError(
+            """No function provided. Either specify function in yaml
                 file, or provide a function as argument to the sample
-                function.""")
+                function."""
+        )
 
 
 def _get_w_init(dataset_specification, w0):
@@ -71,5 +91,7 @@ def _get_w_init(dataset_specification, w0):
             w_init = w_init_expr
         return np.array(w_init, dtype=np.float64)
     else:
-        raise ValueError("""No initial guess provided. Either specify initial guess in
-                   yaml file, or provide an inital guess as argument to the sample function.""")
+        raise ValueError(
+            """No initial guess provided. Either specify initial guess in
+                   yaml file, or provide an inital guess as argument to the sample function."""
+        )