# Image ¶

- Title
- Image Element
- Dependencies
- Matplotlib
- Backends
- Matplotlib
- Bokeh

```
import numpy as np
import holoviews as hv
hv.extension('matplotlib')
```

Like
```
Raster
```

, a HoloViews
```
Image
```

allows you to view 2D arrays using an arbitrary color map. Unlike
```
Raster
```

, an
```
Image
```

is associated with a
2D coordinate system in continuous space
, which is appropriate for values sampled from some underlying continuous distribution (as in a photograph or other measurements from locations in real space).

```
ls = np.linspace(0, 10, 200)
xx, yy = np.meshgrid(ls, ls)
bounds=(-1,-1,1,1) # Coordinate system: (left, bottom, top, right)
img = hv.Image(np.sin(xx)*np.cos(yy), bounds=bounds)
img
```

Slicing, sampling, etc. on an
```
Image
```

all operate in this continuous space, whereas the corresponding operations on a
```
Raster
```

work on the raw array coordinates.

```
img + img[-0.5:0.5, -0.5:0.5]
```

Notice how, because our declared coordinate system is continuous, we can slice with any floating-point value we choose. The appropriate range of the samples in the input numpy array will always be displayed, whether or not there are samples at those specific floating-point values. This also allows us to index by a floating value, since the
```
Image
```

is defined as a continuous space it will snap to the closest coordinate, to inspect the closest coordinate we can use the
```
closest
```

method:

```
%%opts Points (color='black' marker='x' size=20)
closest = img.closest((0.1,0.1))
print('The value at position %s is %s' % (closest, img[0.1, 0.1]))
img * hv.Points([img.closest((0.1,0.1))])
```

We can also easily take cross-sections of the Image by using the sample method or collapse a dimension using the
```
reduce
```

method:

```
img.sample(x=0) + img.reduce(x=np.mean)
```

Download this notebook from GitHub (right-click to download).