Ndlayout¶
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- Title
- NdLayout Container
- Dependencies
- Bokeh
- Backends
- Bokeh
- Matplotlib
- Plotly
import numpy as np
import holoviews as hv
hv.extension('bokeh')
An NdLayout
is a multi-dimensional dictionary of HoloViews elements presented side-by-side like a Layout
. An NdLayout
can be considered as a special-case of HoloMap
that can hold any one type of HoloViews container or element as long as it isn't another NdLayout
or Layout
. Unlike a regular Layout
that can be built with the +
operator, the items in an NdOverlay
container have corresponding keys and must all have the same type. See the Building Composite Objects user guide for details on how to compose containers.
NdLayout
holds dictionaries¶
Using the sine_curve
function below, we can declare a dictionary of Curve
elements, where the keys correspond to the frequency values:
frequencies = [0.5, 0.75, 1.0, 1.25]
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]))
curve_dict = {f:sine_curve(0,f) for f in frequencies}
We now have a dictionary where the frequency is the key and the corresponding curve element is the value. We can now turn this dictionary into an NdLayout
by declaring the keys as corresponding to the frequency key dimension:
NdLayout = hv.NdLayout(curve_dict, kdims='frequency')
NdLayout
NdLayout
is multi-dimensional¶
By using tuple keys and making sure each position in the tuple is assigned a corresponding kdim
, NdLayouts
allow visualization of a multi-dimensional space:
curve_dict_2D = {(p,f):sine_curve(p,f) for p in [0, np.pi/2] for f in [0.5, 0.75]}
NdLayout = hv.NdLayout(curve_dict_2D, kdims=['phase', 'frequency'])
NdLayout
NdLayout
is similar to HoloMap
¶
Other than the difference in the visual semantics, whereby NdLayout
displays its contents overlaid, NdLayout
are very similar to HoloMap
(see the HoloMap
notebook for more information).
One way to demonstrate the similarity of these two containers is to cast our NdLayout
object to HoloMap
:
hv.HoloMap(NdLayout)
Download this notebook from GitHub (right-click to download).