diff --git a/Anime.py b/Anime.py
deleted file mode 100644
index 628624c..0000000
--- a/Anime.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import numpy as np
-import torch
-import matplotlib.pyplot as plt
-import matplotlib.animation
-
-
-class Anime:
-    def __init__(self) -> None:
-        super().__init__()
-
-    def show(
-        self,
-        input: torch.Tensor | np.ndarray,
-        mask: torch.Tensor | np.ndarray | None = None,
-        vmin: float | None = None,
-        vmax: float | None = None,
-        cmap: str = "hot",
-        axis_off: bool = True,
-        show_frame_count: bool = True,
-        interval: int = 100,
-        repeat: bool = False,
-        colorbar: bool = True,
-        vmin_scale: float | None = None,
-        vmax_scale: float | None = None,
-    ) -> None:
-        assert input.ndim == 3
-
-        if isinstance(input, torch.Tensor):
-            input_np: np.ndarray = input.cpu().numpy()
-            if mask is not None:
-                mask_np: np.ndarray | None = (mask == 0).cpu().numpy()
-            else:
-                mask_np = None
-        else:
-            input_np = input
-            if mask is not None:
-                mask_np = mask == 0  # type: ignore
-            else:
-                mask_np = None
-
-        if vmin is None:
-            vmin = float(np.where(np.isfinite(input_np), input_np, 0.0).min())
-        if vmax is None:
-            vmax = float(np.where(np.isfinite(input_np), input_np, 0.0).max())
-
-        if vmin_scale is not None:
-            vmin *= vmin_scale
-
-        if vmax_scale is not None:
-            vmax *= vmax_scale
-
-        fig = plt.figure()
-        image = np.nan_to_num(input_np[0, ...], copy=True, nan=0.0)
-        if mask_np is not None:
-            image[mask_np] = float("NaN")
-        image_handle = plt.imshow(
-            image,
-            cmap=cmap,
-            vmin=vmin,
-            vmax=vmax,
-        )
-
-        if colorbar:
-            plt.colorbar()
-
-        if axis_off:
-            plt.axis("off")
-
-        def next_frame(i: int) -> None:
-            image = np.nan_to_num(input_np[i, ...], copy=True, nan=0.0)
-            if mask_np is not None:
-                image[mask_np] = float("NaN")
-
-            image_handle.set_data(image)
-            if show_frame_count:
-                bar_length: int = 10
-                filled_length = int(round(bar_length * i / input_np.shape[0]))
-                bar = "\u25A0" * filled_length + "\u25A1" * (bar_length - filled_length)
-                plt.title(f"{bar} {i} of {int(input_np.shape[0]-1)}", loc="left")
-            return
-
-        _ = matplotlib.animation.FuncAnimation(
-            fig,
-            next_frame,
-            frames=int(input.shape[0]),
-            interval=interval,
-            repeat=repeat,
-        )
-
-        plt.show()