# Gridspace ¶

Title
GridSpace Container
Dependencies
Matplotlib
Backends
Matplotlib
Bokeh
In [1]:
import numpy as np
import holoviews as hv
hv.extension('matplotlib')


A  GridSpace  is a two-dimensional dictionary of HoloViews objects presented onscreen as a grid. In one sense, due to the restriction on it's dimensionality, a  GridSpace  may be considered a special-case of  HoloMap  . In another sense,  GridSpace  may be seen as more general as a  GridSpace  can hold a  HoloMap  but the converse is not permitted; see the Building Composite Objects user guide for details on how to compose containers.

###  GridSpace  holds two-dimensional dictionaries ¶

Using the  sine_curve  function below, we can declare a two-dimensional dictionary of  Curve  elements, where the keys are 2-tuples corresponding to (phase, frequency) values:

In [2]:
def sine_curve(phase, freq):
xvals = [0.1* i for i in range(100)]
return hv.Curve((xvals, [np.sin(phase+freq*x) for x in xvals]))

phases      = [0, np.pi/2, np.pi, 3*np.pi/2]
frequencies = [0.5, 0.75, 1.0, 1.25]
curve_dict_2D = {(p,f):sine_curve(p,f) for p in phases for f in frequencies}


We can now pass this dictionary of curves to  GridSpace  :

In [3]:
gridspace = hv.GridSpace(curve_dict_2D, kdims=['phase', 'frequency'])
gridspace

Out[3]:

###  GridSpace  is similar to  HoloMap  ¶

Other than the difference in the visual semantics, whereby  GridSpaces  display their contents together in a two-dimensional grid,  GridSpaces  are very similar to  HoloMap  s (see the  HoloMap  notebook for more information).

One way to demonstrate the similarity of these two containers is to cast our  gridspace  object to  HoloMap  and back to a  GridSpace  :

In [4]:
hmap = hv.HoloMap(gridspace)
hmap + hv.GridSpace(hmap)

Out[4]: