diff --git a/src/modelskill/comparison/_collection.py b/src/modelskill/comparison/_collection.py
index 3d1cad9bd..40bb97f06 100644
--- a/src/modelskill/comparison/_collection.py
+++ b/src/modelskill/comparison/_collection.py
@@ -107,9 +107,6 @@ def _name(self) -> str:
     def _unit_text(self) -> str:
         # Picking the first one is arbitrary, but it should be the same for all
         # we could check that they are all the same, but let's assume that they are
-        # for cmp in self:
-        #     if cmp._unit_text != text:
-        #         warnings.warn(f"Unit text is inconsistent: {text} vs {cmp._unit_text}")
         return self[0]._unit_text
 
     @property
diff --git a/src/modelskill/comparison/_comparison.py b/src/modelskill/comparison/_comparison.py
index 8838184e5..f0bf5d536 100644
--- a/src/modelskill/comparison/_comparison.py
+++ b/src/modelskill/comparison/_comparison.py
@@ -116,12 +116,6 @@ def _parse_dataset(data: xr.Dataset) -> xr.Dataset:
             data.attrs["gtype"] = str(GeometryType.NODE)
         else:
             data.attrs["gtype"] = str(GeometryType.POINT)
-    # assert "gtype" in data.attrs, "data must have a gtype attribute"
-    # assert data.attrs["gtype"] in [
-    #     str(GeometryType.POINT),
-    #     str(GeometryType.TRACK),
-    # ], f"data attribute 'gtype' must be one of {GeometryType.POINT} or {GeometryType.TRACK}"
-
     if "color" not in data["Observation"].attrs:
         data["Observation"].attrs["color"] = "black"
 
@@ -614,17 +608,6 @@ def time(self) -> pd.DatetimeIndex:
         """time of compared data as pandas DatetimeIndex"""
         return self.data.time.to_index()
 
-    # TODO: Should we keep these? (renamed to start_time and end_time)
-    # @property
-    # def start(self) -> pd.Timestamp:
-    #     """start pd.Timestamp of compared data"""
-    #     return self.time[0]
-
-    # @property
-    # def end(self) -> pd.Timestamp:
-    #     """end pd.Timestamp of compared data"""
-    #     return self.time[-1]
-
     @property
     def x(self) -> Any:
         """x-coordinate"""
diff --git a/src/modelskill/settings.py b/src/modelskill/settings.py
index 73e7063ba..28d03fc5b 100644
--- a/src/modelskill/settings.py
+++ b/src/modelskill/settings.py
@@ -192,19 +192,6 @@ def _describe_option_short(pat: str = "", _print_desc: bool = True) -> str | Non
     return s
 
 
-def _describe_option(pat: str = "", _print_desc: bool = True) -> str | None:
-    keys = _select_options(pat)
-    if len(keys) == 0:
-        raise OptionError("No such keys(s)")
-
-    s = "\n".join([_build_option_description(k) for k in keys])
-
-    if _print_desc:
-        print(s)
-        return None
-    return s
-
-
 def reset_option(pat: str = "", silent: bool = False) -> None:
     """Reset one or more options (matching a pattern) to the default value
 
@@ -337,11 +324,6 @@ def _build_option_description(k: str) -> str:
     return s
 
 
-# temporary disabled
-# get_option = _get_option
-# set_option = _set_option
-# reset_option = _reset_option
-# describe_option = _describe_option
 options = OptionsContainer(_global_settings)
 
 
@@ -453,22 +435,11 @@ def inner(x) -> None:
 )  # a list can be used as a tuple
 
 
-def is_callable(obj) -> bool:
-    if not callable(obj):
-        raise ValueError("Value must be a callable")
-    return True
-
-
 def is_positive(value) -> None:
     if not (np.isreal(value) and value > 0):
         raise ValueError("Value must be a number greater than 0")
 
 
-def is_nonnegative(value) -> None:
-    if not (np.isreal(value) and value >= 0):
-        raise ValueError("Value must be a non-negative number")
-
-
 def is_between_0_and_1(value) -> None:
     if not (np.isreal(value) and value >= 0 and value <= 1):
         raise ValueError("Value must be a number between 0 and 1")
diff --git a/src/modelskill/skill.py b/src/modelskill/skill.py
index d16bda8dd..b41cf75d1 100644
--- a/src/modelskill/skill.py
+++ b/src/modelskill/skill.py
@@ -56,21 +56,6 @@ def _get_plot_df(self, level: int | str = 0) -> pd.DataFrame:
             df = ser.to_frame()
         return df
 
-    # TODO hide this for now until we are certain about the API
-    # def map(self, **kwargs):
-    #     if "model" in self.skillarray.data.index.names:
-    #         n_models = len(self.skillarray.data.reset_index().model.unique())
-    #         if n_models > 1:
-    #             raise ValueError(
-    #                 "map() is only possible for single model skill. Use .sel(model=...) to select a single model."
-    #             )
-
-    #     gdf = self.skillarray.to_geodataframe()
-    #     column = self.skillarray.name
-    #     kwargs = {"marker_kwds": {"radius": 10}} | kwargs
-
-    #     return gdf.explore(column=column, **kwargs)
-
     def __call__(self, *args: Any, **kwds: Any) -> Any:
         raise NotImplementedError(
             "It is not possible to call plot directly (has no default)! Use one of the plot methods explicitly e.g. plot.line() or plot.bar()"
diff --git a/src/modelskill/timeseries/_timeseries.py b/src/modelskill/timeseries/_timeseries.py
index bba5d78f7..c53de27d0 100644
--- a/src/modelskill/timeseries/_timeseries.py
+++ b/src/modelskill/timeseries/_timeseries.py
@@ -274,10 +274,6 @@ def _coordinate_values(self, coord: str) -> None | float | np.ndarray:
         vals = self.data[coord].values
         return np.atleast_1d(vals)[0] if vals.ndim == 0 else vals
 
-    @property
-    def _is_modelresult(self) -> bool:
-        return bool(self.data[self.name].attrs["kind"] == "model")
-
     @property
     def values(self) -> np.ndarray:
         """Values as numpy array"""
diff --git a/src/modelskill/utils.py b/src/modelskill/utils.py
index 43435dd93..892d6ae89 100644
--- a/src/modelskill/utils.py
+++ b/src/modelskill/utils.py
@@ -4,7 +4,7 @@
 import numpy as np
 import pandas as pd
 import xarray as xr
-from collections.abc import Hashable, Iterable
+from collections.abc import Hashable
 
 _RESERVED_NAMES = ["Observation", "time", "x", "y", "z"]
 
@@ -67,52 +67,6 @@ def rename_coords_pd(df: pd.DataFrame) -> pd.DataFrame:
     return df.rename(columns=mapping)
 
 
-# def get_item_name_and_idx(
-#     item_names: List[str], item: int | str | None = None
-# ) -> Tuple[str, int]:
-#     """Returns the name and index of the requested variable, provided
-#     either as either a str or int.
-
-#     Examples
-#     --------
-#     >>> get_item_name_and_idx(['a', 'b', 'c'], 1)
-#     ('b', 1)
-#     >>> get_item_name_and_idx(['a', 'b', 'c'], 'a')
-#     ('a', 0)
-#     >>> get_item_name_and_idx(['a', 'b', 'c'], -1)
-#     ('c', 2)
-#     """
-#     n_items = len(item_names)
-#     if item is None:
-#         if n_items == 1:
-#             return item_names[0], 0
-#         else:
-#             raise ValueError(
-#                 f"item must be specified when more than one item available. Available items: {item_names}"
-#             )
-#     if isinstance(item, int):
-#         if item < 0:  # Handle negative indices
-#             item = n_items + item
-#         if (item < 0) or (item >= n_items):
-#             raise IndexError(f"item {item} out of range (0, {n_items-1})")
-#         return item_names[item], item
-#     elif isinstance(item, str):
-#         if item not in item_names:
-#             raise KeyError(f"item must be one of {item_names}, got {item}.")
-#         return item, item_names.index(item)
-#     else:
-#         raise TypeError("item must be int or string")
-
-
-def is_iterable_not_str(obj):
-    """Check if an object is an iterable but not a string."""
-    if isinstance(obj, str):
-        return False
-    if isinstance(obj, Iterable):
-        return True
-    return False
-
-
 def make_unique_index(
     df_index: pd.DatetimeIndex, offset_duplicates: float = 0.001, warn: bool = True
 ) -> pd.DatetimeIndex: