diff --git a/saba/models.py b/saba/models.py
new file mode 100644
index 0000000..566ba19
--- /dev/null
+++ b/saba/models.py
@@ -0,0 +1,168 @@
+from astropy.modeling.core import FittableModel
+import numpy as np
+
+
+class Split(FittableModel):
+    """
+    Allows inputs to be split into multiple outputs based on indexs
+
+    Parameters
+    ----------
+    split_indexes : tuple
+        A tuple of integers representing indices on which the input
+        array will be split
+    name : str, optional
+        A human-friendly name associated with this model instance
+        (particularly useful for identifying the individual components of a
+        compound model).
+    meta : dict-like
+        Free-form metadata to associate with this model.
+
+    """
+    def __init__(self, split_indexes, name=None, meta=None):
+        self._inputs = tuple('x')
+        self._outputs = tuple('x' + str(idx) for idx in range(len(split_indexes) + 1))
+        self._split_indexes = split_indexes
+        super(Split, self).__init__(name=name, meta=meta)
+
+    def _format_expression(self):
+        first = "x[:{0}]".format(self._split_indexes[0])
+        last = "x[{0}:]".format(self._split_indexes[-1])
+        if self.n_outputs > 1:
+            middle = ["x[{0}:{1}]".format(*indexes) for indexes in zip(self._split_indexes[:-1], self._split_indexes[1:])]
+            return ", ".join([first] + middle + [last])
+        else:
+            return ", ".join([first, last])
+
+    @property
+    def split_indexes(self):
+        """Integers representing indices of the inputs."""
+        return self._split_indexes
+
+    @property
+    def inputs(self):
+        """
+        The name(s) of the input variable(s) on which a model is evaluated.
+        """
+        return self._inputs
+
+    @property
+    def outputs(self):
+        """The name(s) of the output(s) of the model."""
+        return self._outputs
+
+    def __repr__(self):
+        if self.name is None:
+            return '<SplitInput1D {0})>'.format(self._format_expression())
+        else:
+            return '<SplitInput1D({0}, name={1})>'.format(self._format_expression(), self.name)
+
+    def evaluate(self, x):
+        first = x[:self._split_indexes[0]]
+        last = x[self._split_indexes[-1]:]
+        if self.n_outputs>2:
+            middle=[x[indexes[0]:indexes[1]] for indexes in zip(self._split_indexes[:-1], self._split_indexes[1:])]
+            return [first] + middle + [last]
+        else:
+            return [first, last]
+
+    def __call__(self, x):
+        return self.evaluate(x)
+
+
+class Join(FittableModel):
+    """
+    Flattens output
+
+    Parameters
+    ----------
+    n_inputs : int
+        We need to tell it how many inputs to expect.
+    name : str, optional
+        A human-friendly name associated with this model instance
+        (particularly useful for identifying the individual components of a
+        compound model).
+    meta : dict-like
+        Free-form metadata to associate with this model.
+
+    """
+
+    def __init__(self, n_inputs, name=None, meta=None):
+        self._inputs = tuple('x' + str(idx) for idx in range(n_inputs + 1))
+        self._outputs = tuple("x")
+        super(Join, self).__init__(name=name, meta=meta)
+
+    @property
+    def inputs(self):
+        """
+        The name(s) of the input variable(s) on which a model is evaluated.
+        """
+        return self._inputs
+
+    @property
+    def outputs(self):
+        """The name(s) of the output(s) of the model."""
+        return self._outputs
+
+    def evaluate(self, *x):
+        return np.hstack(x)
+
+    def __call__(self, *x):
+        return self.__call__(x)
+
+
+def model_split_join(xvals, models):
+    ''' This returns a compound model which allows the
+    tying of parameters. the input values xvals should be
+    flattened before passing into the returned model.
+
+    Parameters
+    ----------
+    xvals: list of arrays
+        arrays of input coordinates which should then
+        be flattend and input
+
+    models: a list of `astropy.modeling.core.FittableModel`
+        the models which are combined
+
+    Returns
+    -------
+    combined_model: an instance of `astropy.modeling.core.FittableModel`
+    '''
+
+    if len(models) > 1:
+        split_indexes = []
+        for xx in xvals[:-1]:
+            split_indexes.append(len(xx))
+
+        running_total = 0
+        # the xvals should be flattend so.
+        for n, ll in enumerate(split_indexes):
+            running_total += ll
+            split_indexes[n] = running_total
+
+        splitin = Split(split_indexes)
+        joinout = Join(len(split_indexes))
+        mo = models[0]
+        for m in models[1:]:
+            mo &= m
+        return splitin | mo | joinout
+
+
+def split_output(model, x):
+    '''This model takes the output of model
+    and splits it based on thw model which
+    splits the input.
+
+    Parameters
+    ----------
+    model: `astropy.modeling.core.FittableModel`
+        a split| models | join combined_model instance
+    xvals: array
+        a flat array of input values for the model
+
+    Returns
+    -------
+    output: a list of arrays
+    '''
+    return model._submodels[0](model(x))