diff --git a/multipers/ml/signed_measures.py b/multipers/ml/signed_measures.py
index 0355398..dedd272 100644
--- a/multipers/ml/signed_measures.py
+++ b/multipers/ml/signed_measures.py
@@ -1,5 +1,6 @@
+from collections.abc import Callable, Iterable
 from itertools import product
-from typing import Callable, Iterable, Optional
+from typing import Optional
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -53,15 +54,17 @@ def __init__(
         # homological degrees + None for euler
         degrees: list[int | None] = [],
         rank_degrees: list[int] = [],  # same for rank invariant
-        filtration_grid: Iterable[np.ndarray]
-        # filtration values to consider. Format : [ filtration values of Fi for Fi:filtration values of parameter i]
-        | None = None,
+        filtration_grid: (
+            Iterable[np.ndarray]
+            # filtration values to consider. Format : [ filtration values of Fi for Fi:filtration values of parameter i]
+            | None
+        ) = None,
         progress=False,  # tqdm
         num_collapses: int | str = 0,  # edge collapses before computing
         n_jobs=None,
-        resolution: Iterable[int]
-        | int
-        | None = None,  # when filtration grid is not given, the resolution of the filtration grid to infer
+        resolution: (
+            Iterable[int] | int | None
+        ) = None,  # when filtration grid is not given, the resolution of the filtration grid to infer
         # sparse=True, # sparse output # DEPRECATED TO Ssigned measure formatter
         plot: bool = False,
         filtration_quantile: float = 0.0,  # quantile for inferring filtration grid
@@ -79,7 +82,7 @@ def __init__(
         ] = None,  # Can be significantly faster for some grid strategies, but can drop statistical performance
         enforce_null_mass: bool = False,
         flatten=True,
-        backend:Optional[str]=None,
+        backend: Optional[str] = None,
     ):
         super().__init__()
         self.degrees = degrees
@@ -113,8 +116,32 @@ def __init__(
         self._default_mass_location = None
         self.flatten = flatten
         self.num_parameters: int = 0
+        self._is_minpres: bool | None = None
         return
 
+    @staticmethod
+    def _is_filtered_complex(input):
+        return mp.simplex_tree_multi.is_simplextree_multi(input) or mp.slicer.is_slicer(
+            input, allow_minpres=True
+        )
+
+    def _input_checks(self, X):
+        assert len(X) > 0, "No filtered complex found. Cannot fit."
+        assert self._is_filtered_complex(
+            X[0][0]
+        ), f"X[0] is not a known filtered complex, {X[0]=}, nor X[0][0]."
+        self._num_axis = len(X[0])
+        first = X[0][0]
+        assert (
+            not mp.slicer.is_slicer(first) or self.expand is None
+        ), "Cannot expand slicers."
+        self._is_minpres = mp.slicer.is_slicer(first) and isinstance(
+            first, Union[tuple, list]
+        )
+        assert not (
+            self._is_minpres and self.minpres_degrees is not None
+        ), "Input is already a minpres. Cannot reduce again."
+
     def _infer_filtration(self, X):
         indices = np.random.choice(
             len(X), min(int(self.fit_fraction * len(X)) + 1, len(X)), replace=False
@@ -646,8 +673,8 @@ def _check_axis(self, X):
             self._num_axis = 1
             self._axis_iterator = [slice(None)]
             return
-        assert (  ## vaguely checks that its a signed measure
-            self._check_sm(_sm := X[0][0][0])
+        assert self._check_sm(  ## vaguely checks that its a signed measure
+            _sm := X[0][0][0]
         ), f"Cannot take this input. # data, axis, degrees, sm.\n Got {_sm} of type {type(_sm)}"
 
         self._has_axis = True
@@ -717,9 +744,11 @@ def _infer_grids(self, X):
             filtration_values = [
                 np.concatenate(
                     [
-                        stuff
-                        if isinstance(stuff := x[ax][degree][0], np.ndarray)
-                        else stuff.detach().numpy()
+                        (
+                            stuff
+                            if isinstance(stuff := x[ax][degree][0], np.ndarray)
+                            else stuff.detach().numpy()
+                        )
                         for x in X
                         for degree in range(self._num_degrees)
                     ]
@@ -1168,22 +1197,28 @@ def __init__(
         return
 
     def fit(self, X, y=None):
-        from multipers.ml.sliced_wasserstein import (SlicedWassersteinDistance,
-                                                     WassersteinDistance)
+        from multipers.ml.sliced_wasserstein import (
+            SlicedWassersteinDistance,
+            WassersteinDistance,
+        )
 
         # _DISTANCE = lambda : SlicedWassersteinDistance(num_directions=self.num_directions) if self._sliced else WassersteinDistance(epsilon=self.epsilon, ground_norm=self.ground_norm) # WARNING if _sliced is false, this distance is not CNSD
         if len(X) == 0:
             return self
         num_degrees = len(X[0])
         self._SWD_list = [
-            SlicedWassersteinDistance(
-                num_directions=self.num_directions,
-                n_jobs=self.n_jobs,
-                scales=self.scales,
-            )
-            if self._sliced
-            else WassersteinDistance(
-                epsilon=self.epsilon, ground_norm=self.ground_norm, n_jobs=self.n_jobs
+            (
+                SlicedWassersteinDistance(
+                    num_directions=self.num_directions,
+                    n_jobs=self.n_jobs,
+                    scales=self.scales,
+                )
+                if self._sliced
+                else WassersteinDistance(
+                    epsilon=self.epsilon,
+                    ground_norm=self.ground_norm,
+                    n_jobs=self.n_jobs,
+                )
             )
             for _ in range(num_degrees)
         ]