diff --git a/src/tavi/data/fit.py b/src/tavi/data/fit.py
index c86421b..fc4872e 100644
--- a/src/tavi/data/fit.py
+++ b/src/tavi/data/fit.py
@@ -6,34 +6,6 @@
 
 from tavi.data.scan_data import ScanData1D
 
-# @dataclass
-# class FitParam:
-#     name: str
-#     value: Optional[float] = None
-#     vary: bool = True
-#     min: float = -np.inf
-#     max: float = np.inf
-#     expr: Optional[str] = None
-
-
-# brute_step: Optional[float] = None
-
-
-# @dataclass
-# class FitData1D:
-#     x: np.ndarray
-#     y: np.ndarray
-#     fmt: dict = {}
-
-
-class FitData1D(object):
-
-    def __init__(self, x: np.ndarray, y: np.ndarray) -> None:
-
-        self.x = x
-        self.y = y
-        self.fmt: dict = {}
-
 
 class Fit1D(object):
     """Fit a 1D curve
@@ -83,7 +55,6 @@ def __init__(
         self._num_backgrounds = 0
         self._num_signals = 0
         self.result: Optional[ModelResult] = None
-        self.fit_data: Optional[FitData1D] = None
 
         self.PLOT_COMPONENTS = False
         self.nan_policy = nan_policy
@@ -202,7 +173,7 @@ def model(self):
         compposite_model = np.sum(self._signal_models + self._background_models)
         return compposite_model
 
-    def x_to_plot(self, num_of_pts: Optional[int]):
+    def x_to_plot(self, num_of_pts: Optional[int] = 100):
         if num_of_pts is None:
             x_to_plot = self.x
         elif isinstance(num_of_pts, int):
@@ -211,18 +182,8 @@ def x_to_plot(self, num_of_pts: Optional[int]):
             raise ValueError(f"num_of_points={num_of_pts} needs to be an integer.")
         return x_to_plot
 
-    def eval(self, pars: Optional[Parameters], num_of_pts: Optional[int] = 100, x=None) -> FitData1D:
-        if pars is None:
-            pars = self.result.params
-
-        if x is not None:
-            return self.model.eval(pars, x=x)
-
-        else:
-            x_to_plot = self.x_to_plot(num_of_pts)
-            y_to_plot = self.model.eval(pars, x=x_to_plot)
-
-            return FitData1D(x_to_plot, y_to_plot)
+    def eval(self, pars: Parameters, x: np.ndarray) -> np.ndarray:
+        return self.model.eval(pars, x=x)
 
     def fit(self, pars: Parameters) -> ModelResult:
 
diff --git a/src/tavi/plotter.py b/src/tavi/plotter.py
index df95c9f..219d5c4 100644
--- a/src/tavi/plotter.py
+++ b/src/tavi/plotter.py
@@ -6,12 +6,22 @@
 from mpl_toolkits.axisartist.grid_finder import MaxNLocator
 from mpl_toolkits.axisartist.grid_helper_curvelinear import GridHelperCurveLinear
 
-from tavi.data.fit import Fit1D, FitData1D
+from tavi.data.fit import Fit1D
 from tavi.data.scan_data import ScanData1D, ScanData2D
 from tavi.instrument.resolution.ellipse import ResoEllipse
 
 
+class FitData1D(object):
+
+    def __init__(self, x: np.ndarray, y: np.ndarray) -> None:
+
+        self.x = x
+        self.y = y
+        self.fmt: dict = {}
+
+
 class ResoBar(object):
+
     def __init__(self, pos: tuple[float, float], fwhm: float) -> None:
 
         self.pos = pos
@@ -77,9 +87,12 @@ def _add_fit_from_fitting(self, fit_data: Fit1D, num_of_pts: Optional[int] = 100
         for key, val in kwargs.items():
             data.fmt.update({key: val})
 
-    def add_fit(self, fit_data: Union[FitData1D, Fit1D], num_of_pts: Optional[int] = 100, **kwargs):
-        if isinstance(fit_data, FitData1D):
-            self._add_fit_from_eval(fit_data, **kwargs)
+    def add_fit(
+        self, fit_data: Union[tuple[np.ndarray, np.ndarray], Fit1D], num_of_pts: Optional[int] = 100, **kwargs
+    ):
+        if isinstance(fit_data, tuple):
+            x, y = fit_data
+            self._add_fit_from_eval(FitData1D(x, y), **kwargs)
         elif isinstance(fit_data, Fit1D):
             self._add_fit_from_fitting(fit_data, num_of_pts, **kwargs)
         else:
diff --git a/tests/test_fit.py b/tests/test_fit.py
index d4a338e..48046e7 100644
--- a/tests/test_fit.py
+++ b/tests/test_fit.py
@@ -78,15 +78,16 @@ def test_guess_initial(fit_data):
     f1.add_signal(model="Gaussian")
     f1.add_background(model="Constant")
     pars = f1.guess()
-    inital = f1.eval(pars, num_of_pts=50)
+    x = f1.x_to_plot(num_of_pts=100)
+    y = f1.eval(pars, x)
     # fit_result = f1.fit(pars)
-    assert inital.y.shape == (50,)
-    assert inital.y.min() > 6
+    assert y.shape == (100,)
+    assert y.min() > 6
 
     if PLOT:
         p1 = Plot1D()
         p1.add_scan(s1_scan, fmt="o", label="data")
-        p1.add_fit(inital, label="guess", color="C1", marker="s", linestyle="dashed", linewidth=2, markersize=4)
+        p1.add_fit((x, y), label="guess", color="C1", marker="s", linestyle="dashed", linewidth=2, markersize=4)
 
         fig, ax = plt.subplots()
         p1.plot(ax)
@@ -101,8 +102,8 @@ def test_fit_single_peak_internal_model(fit_data):
     f1.add_signal(model="Gaussian")
     f1.add_background(model="Constant")
     pars = f1.guess()
-    fit_result = f1.fit(pars)
-    assert np.allclose(fit_result.redchi, 37.6, atol=1)
+    result = f1.fit(pars)
+    assert np.allclose(result.redchi, 37.6, atol=1)
 
     if PLOT:
         p1 = Plot1D()