diff --git a/src/scgenerator/variableparameters.py b/src/scgenerator/variableparameters.py
new file mode 100644
index 0000000..9799bcb
--- /dev/null
+++ b/src/scgenerator/variableparameters.py
@@ -0,0 +1,425 @@
+import itertools
+import warnings
+from dataclasses import dataclass, field
+from typing import Callable, Iterator, Protocol, Sequence, TypeVar
+
+import numpy as np
+
+T = TypeVar("T")
+
+
+def debase(num: int, base: tuple[int, ...]) -> tuple[int, ...]:
+    indices = []
+    _, *base = base
+    for i in range(len(base)):
+        ind, num = divmod(num, np.prod(base[i:]))
+        indices.append(ind)
+    indices.append(num)
+    return tuple(indices)
+
+
+def normalize_condition(v: slice | range | int | tuple, num: int) -> tuple[int, ...]:
+    if isinstance(v, slice):
+        return tuple(range(num)[v])
+    elif isinstance(v, Sequence):
+        return tuple(v)
+    elif isinstance(v, int):
+        return (v,)
+    raise TypeError(f"condition {v!r} of type {type(v)} not valid")
+
+
+def get_geometric(id, b_min, b_max, b_num):
+    if b_num == 1:
+        return b_min
+    return 0 if id == 0 else b_min * (b_max / b_min) ** ((id - 1) / (b_num - 2))
+
+
+def get_linear(id, d_min, d_max, d_num) -> float:
+    if d_num == 1:
+        return d_min
+    return d_min + (d_max - d_min) * (id / (d_num - 1))
+
+
+def int_linear(id, d_min, d_max, d_num) -> int:
+    if d_num == 1:
+        return d_min
+    return d_min + id * ((d_max - d_min) // (d_num - 1))
+
+
+def unit_formatter(unit: str, decimals: int = 1) -> Callable[[float | int], str]:
+    if not unit:
+
+        def formatter(val):
+            return f"{val:.{decimals}g}"
+
+    else:
+
+        def formatter(val):
+            base, true_exp = format(val, f".{3+decimals}e").split("e")
+            true_exp = int(true_exp)
+            exp, mult = divmod(true_exp, 3)
+            prefix = "yzafpnµm kMGTPEZY"[8 + exp] if exp else ""
+            return f"{float(base)*10**mult:.{decimals}f}{prefix}{unit}"
+
+    return formatter
+
+
+class Variable(Protocol[T]):
+    place: int
+
+    def __call__(self, _: int, local: bool = False) -> T:
+        ...
+
+    def __repr__(self) -> str:
+        ...
+
+    def __len__(self) -> int:
+        ...
+
+    def format(self, id: int, local: bool = False):
+        return f"{self.name}={self.formatter(self(id, local))}"
+
+    def parse(self, _: str) -> T:
+        ...
+
+    def filter(self, local_id) -> tuple[list[int], tuple[int, ...]]:
+        """
+        filter based on the desired local id.
+
+        Parameters
+        ----------
+        local_id : int
+            index of the desired value
+
+        Returns
+        -------
+        list[int]
+            list of all global indices point to instances of this variable parameter that have the
+            same value
+        tuple[int, ...]
+            resulting shape formed by all other variable parameters
+        """
+        shape = self.all_nums.copy()
+        shape[self.place] = 1
+        indices = [
+            i
+            for i, indices in enumerate(itertools.product(*(range(k) for k in self.all_nums)))
+            if indices[self.place] == local_id
+        ]
+        return indices, tuple(shape)
+
+
+@dataclass
+class FuncVariable(Variable[float]):
+    func: Callable[[int, float, float, int], float]
+    args: tuple[float, float, int]
+    all_nums: list[int]
+    place: int
+    name: str
+    suffix: str = ""
+    decimals: int = 4
+    formatter: Callable[[int], str] = field(init=False)
+
+    def __getstate__(self):
+        return {k: v for k, v in self.__dict__.items() if k != "formatter"}
+
+    def __setstate__(self, d):
+        self.__dict__ |= d
+        self.__post_init__()
+
+    def __post_init__(self):
+        self.formatter = unit_formatter(self.suffix, self.decimals)
+
+    def __call__(self, id: int, local: bool = False) -> float:
+        if not local:
+            if not isinstance(id, (int, np.integer)):
+                raise TypeError(f"id {id!r} is not an integer")
+            if None in self.all_nums:
+                raise ValueError("at least one VariableParameter has not been configured")
+            id = debase(id, self.all_nums)[self.place]
+            if id >= self.args[2]:
+                raise ValueError(
+                    f"id {id} is too big for variable parameter of size {self.args[2]}"
+                )
+        return self.func(id, *self.args)
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}({', '.join(format(el) for el in self.args)})"
+
+    def __len__(self) -> int:
+        return self.args[2]
+
+    def parse(self, s: str):
+        return NotImplemented
+
+
+@dataclass
+class ArrayVariable(Variable[T]):
+    values: Sequence[T]
+    all_nums: list[int]
+    place: int
+    name: str
+    suffix: str
+    decimals: int
+
+    def __call__(self, id: int, local: bool = False) -> T:
+        if not local:
+            id = debase(id, self.all_nums)[self.place]
+        if id >= len(self.values):
+            raise ValueError(
+                f"id {id} is too big for variable parameter of size {len(self.values)}"
+            )
+        return self.values[id]
+
+    def __len__(self) -> int:
+        return 1
+
+    def format(self, _: int, local: bool = False) -> str:
+        return self.formatted
+
+    def parse(self, _: str) -> float:
+        return NotImplemented
+
+
+@dataclass
+class Constant(Variable[T]):
+    value: float | int
+    place: int
+    name: str
+    suffix: str
+    decimals: int
+
+    def __post_init__(self):
+        self.formatted = f"{self.name}={unit_formatter(self.suffix, self.decimals)(self.value)}"
+
+    def __call__(self, _: int, local: bool = False) -> T:
+        return self.value
+
+    def __len__(self) -> int:
+        return 1
+
+    def local(self, _: int) -> T:
+        return self.value
+
+    def format(self, _: int, local: bool = False) -> str:
+        return self.formatted
+
+    def parse(self, _: str) -> float:
+        return NotImplemented
+
+
+@dataclass(unsafe_hash=True)
+class VariableParameter:
+    func: Callable[[int, float, float, int], float] = get_linear
+    default: float | int | None = None
+    suffix: str = ""
+    decimals: int = 4
+    default_callable: Variable | None = field(init=False)
+    place: int | None = field(default=None, init=False)
+    public_name: str = field(init=False)
+    private_name: str = field(init=False)
+
+    def __set_name__(self, owner: type, name: str):
+        self.public_name = name
+        self.private_name = "_" + name
+        if self.default is not None:
+            self.default_callable = Constant(
+                value=self.default,
+                place=self.place,
+                name=self.public_name,
+                suffix=self.suffix,
+                decimals=self.decimals,
+            )
+        else:
+            self.default_callable = None
+
+    def __set__(self, instance, value):
+        all_nums = instance.__variables_nums__
+
+        if value is self:
+            if self.default_callable is None:
+                raise self._error_no_default()
+            var_obj = self.default_callable
+            var_num = 1
+        elif isinstance(value, (float, int, complex)):
+            var_obj = Constant(value, self.place, self.public_name, self.suffix, self.decimals)
+            var_num = 1
+        elif isinstance(value, Sequence):
+            if isinstance(value[0], Sequence):
+                value = value[0]
+                var_obj = FuncVariable(
+                    func=self.func,
+                    value=value,
+                    all_nums=all_nums,
+                    place=self.place,
+                    name=self.public_name,
+                    suffix=self.suffix,
+                    decimals=self.decimals,
+                )
+                var_num = value[2]
+            else:
+                var_obj = ArrayVariable(
+                    values=value,
+                    place=self.place,
+                    name=self.public_name,
+                    suffix=self.suffix,
+                    decimals=self.decimals,
+                )
+        else:
+            raise TypeError(f"value {value!r} of type {type(value)} not recognized")
+
+        all_nums[self.place] = var_num
+        instance.__dict__[self.private_name] = var_obj
+
+    def __get__(self, instance, _):
+        if instance is None:
+            return self
+        if self.private_name not in instance.__dict__:
+            if self.default_callable is None:
+                raise self._error_no_default()
+            return self.default_callable
+
+        return instance.__dict__[self.private_name]
+
+    def _error_no_default(self) -> ValueError:
+        return ValueError(
+            f"Variable parameter {self.public_name!r} has not been initialized "
+            "and no default has been set"
+        )
+
+
+@dataclass
+class Filter:
+    indices: list[int]
+    shape: tuple[int, ...]
+
+    def __iter__(self) -> Iterator[int]:
+        yield from self.indices
+
+    def __call__(
+        self, arr: Sequence | np.ndarray, axis: int = 0, squeeze: bool = True
+    ) -> np.ndarray:
+        """
+        filters and reshapes an array that has already been filtered
+
+        Parameters
+        ----------
+        arr : Sequence | np.ndarray, shape (..., n, ...)
+                                 at index `axis` ^
+            array to reshape
+        axis : int, optional
+            which axis to reshape, by default 0
+        squeeze : bool, optional
+            squeeze the array to get rid of axes of size 1
+
+        Returns
+        -------
+        arr : np.ndarray, shape (..., *shape, ...) where `np.prod(shape) == n`
+        """
+        arr = np.asarray(arr)
+        arr = arr[*((slice(None),) * axis), self.indices]
+        return self.reshape(arr, axis, squeeze)
+
+    def __len__(self) -> int:
+        return len(self.indices)
+
+    def __getitem__(self, key):
+        return self.indices[key]
+
+    def reshape(
+        self, arr: Sequence | np.ndarray, axis: int = 0, squeeze: bool = True
+    ) -> np.ndarray:
+        """
+        reshapes an array that has already been filtered
+
+        Parameters
+        ----------
+        arr : Sequence | np.ndarray, shape (..., n, ...)
+                                 at index `axis` ^
+            array to reshape
+        axis : int, optional
+            which axis to reshape
+        squeeze : bool, optional
+            squeeze the array to get rid of axes of size 1
+
+        Returns
+        -------
+        arr : np.ndarray, shape (..., *shape, ...) where `np.prod(shape) == n`
+        """
+        arr = np.asanyarray(arr)
+        if axis < 0:
+            axis = len(self.shape) + axis
+        arr = arr.reshape(*arr.shape[:axis], *self.shape, *arr.shape[axis + 1 :])
+        if squeeze:
+            arr = arr.squeeze()
+        return arr
+
+
+def vfield(
+    func: Callable[[int, float, float, int], float] = get_linear,
+    default: float | int | None = None,
+    suffix: str = "",
+    decimals: int = 4,
+):
+    return field(default=VariableParameter(func, default, suffix, decimals))
+
+
+def create_filter(_vfields: dict[str, VariableParameter]):
+    def filter(self, **kwargs):
+        """
+        call Config.filter(param_a=0, param_b=(2, 4, 6))
+        """
+        if any(k not in _vfields for k in kwargs):
+            extra = set(kwargs) - set(_vfields)
+            raise NameError(f"class {self.__class__.__name__} has no attribute(s) {extra}")
+        all_local_indices = {
+            k: tuple(range(p_num)) for k, p_num in zip(_vfields, self.__variables_nums__)
+        }
+        conditions = {
+            k: normalize_condition(v, all_local_indices[k][-1] + 1) for k, v in kwargs.items()
+        }
+
+        conditions = list((all_local_indices | conditions).values())
+        all_indices = []
+        for global_index, local_indices in enumerate(
+            itertools.product(*all_local_indices.values())
+        ):
+            if all(j in cond for j, cond in zip(local_indices, conditions)):
+                all_indices.append(global_index)
+
+        shape = tuple(len(s) for s in conditions)
+
+        return Filter(all_indices, shape)
+
+    return filter
+
+
+def vdataclass(cls: type[T]) -> type[T]:
+    _vfields = {
+        k: v.default
+        for k, v in cls.__dict__.items()
+        if isinstance(getattr(v, "default", None), VariableParameter)
+    }
+    if len(_vfields) == 0:
+        warnings.warn(
+            f"class {cls.__qualname__} doesn't contain any variable parameter,"
+            "building normal dataclass instead"
+        )
+        return dataclass(cls)
+    # put __variables_nums__ at the begining of the dict, as other fields need it
+    cls.__annotations__ = dict(__variables_nums__="list[int]") | cls.__annotations__
+    cls.__variables_nums__ = field(
+        init=False, repr=False, default_factory=(lambda: [None] * len(_vfields))
+    )
+
+    cls = dataclass(cls)
+    for i, v in enumerate(_vfields.values()):
+        v.place = i
+
+    def _config_len(self):
+        return np.prod(self.__variables_nums__)
+
+    setattr(cls, "filter", create_filter(_vfields))
+    setattr(cls, "__len__", _config_len)
+
+    return cls
diff --git a/tests/test_variableparameters.py b/tests/test_variableparameters.py
new file mode 100644
index 0000000..9e39e13
--- /dev/null
+++ b/tests/test_variableparameters.py
@@ -0,0 +1,64 @@
+from dataclasses import field
+
+import pytest
+
+from scgenerator.variableparameters import Variable, vdataclass, vfield
+
+
+def test_vfield_identification():
+    """only vfields and not normal fields count"""
+
+    class Conf:
+        x: int
+
+    with pytest.warns(UserWarning):
+        vdataclass(Conf)
+
+    @vdataclass
+    class Conf2:
+        x: Variable = vfield(default=5)
+
+    assert hasattr(Conf2, "filter")
+
+
+def test_constant():
+    """classes with constant fields don't increase size and always return the constant"""
+
+    @vdataclass
+    class Conf:
+        x: Variable = vfield(default=3)
+        y: Variable = vfield(default=4)
+
+    conf = Conf()
+    assert len(conf) == 1
+    assert conf.x(0) == 3
+    assert conf.y(0) == 4
+
+    with pytest.raises(ValueError):
+        conf.x(1)
+    with pytest.raises(ValueError):
+        conf.y(1)
+
+
+def test_decoration():
+    """proper construction of the class"""
+
+
+def test_name_error():
+    """name errors are raised when accessing inexistent variable (direct, filter, ...)"""
+
+
+def test_defaults():
+    """default values are respected, of proper type and of size one"""
+
+
+def test_formatting():
+    """formatting is always respected"""
+
+
+def test_param_parsing():
+    """parsing a string or path returns the correct values"""
+
+
+def test_config_parsing():
+    """parsing a string returns the exact id corresponding to the string/path"""