# Binned and Unbinned gridsΒΆ

Sherpa supports models for both
unbinned and
binned data
sets. The output of a model depends on how it is called
(is it sent just the grid points or the bin edges), how
the `integrate`

flag of
the model component is set, and whether the model supports
both or just one case.

The `Const1D`

model represents
a constant value, which means that for an unbinned
dataset the model evaluates to a single value (the
`c0`

parameter):

```
>>> from sherpa.models.basic import Const1D
>>> mdl = Const1D()
>>> mdl.c0 = 0.1
>>> mdl([1, 2, 3])
array([ 0.1, 0.1, 0.1])
>>> mdl([-4000, 12000])
array([ 0.1, 0.1])
```

The default value for its
`integrate`

flag is
`True`

:

```
>>> mdl.integrate
True
```

which means that this value is multiplied by the bin width when given a binned grid (i.e. when sent in the low and high edges of each bin):

```
>>> mdl([10, 20, 30], [20, 30, 50])
array([ 1., 1., 2.])
```

When the `integrate`

flag is unset, the model no longer
multiplies by the bin width, and so acts similarly to the
unbinned case:

```
>>> mdl.integrate = False
>>> mdl([10, 20, 30], [20, 30, 50])
array([ 0.1, 0.1, 0.1])
```

The behavior in all these three cases depends on the model - for instance some models may raise an exception, ignore the high-edge values in the binned case, or use the mid-point - and so the model documentation should be reviewed.

The following example uses the
`Polynom1D`

class to model the
linear relation
\(y = mx + c\) with the origin at \(x = 1400\),
an offset of 2, and a gradient of 1:

```
>>> from sherpa.models.basic import Polynom1D
>>> poly = Polynom1D()
>>> poly.offset = 1400
>>> poly.c0 = 2
>>> poly.c1 = 1
>>> x = [1391, 1396, 1401, 1406, 1411]
>>> poly(x)
array([ -7., -2., 3., 8., 13.])
```

As the integrate flag is set, the model is integrated across each bin:

```
>>> poly.integrate
True
>>> xlo, xhi = x[:-1], x[1:]
>>> y = poly(xlo, xhi)
>>> y
array([-22.5, 2.5, 27.5, 52.5])
```

Thanks to the easy functonal form chosen for this example, it is easy to confirm that these are the values of the integrated model:

```
>>> (y[:-1] + y[1:]) * 5 / 2.0
array([-22.5, 2.5, 27.5, 52.5])
```

Turning off the `integrate`

flag for this model shows that it
uses the low-edge of the bin when evaluating the model:

```
>>> poly.integrate = False
>>> poly(xlo, xhi)
array([-7., -2., 3., 8.])
```