Dynamic Map ¶
The Containers Tutorial introduced the HoloMap , a core HoloViews data structure that allows easy exploration of parameter spaces. The essence of a HoloMap is that it contains a collection of Elements (e.g. Images and Curves) that you can easily select and visualize.
HoloMaps hold fully constructed Elements at specifically sampled points in a multidimensional space. Although HoloMaps are useful for exploring highdimensional parameter spaces, they can very quickly consume huge amounts of memory to store all these Elements. For instance, a hundred samples along four orthogonal dimensions would need a HoloMap containing a hundred
million
Elements, each of which could be a substantial object that takes time to create and costs memory to store. Thus
HoloMaps
have some clear limitations:
 HoloMaps may require the generation of millions of Elements before the first element can be viewed.
 HoloMaps can easily exhaust all the memory available to Python.
 HoloMaps can even more easily exhaust all the memory in the browser when displayed.
 Static export of a notebook containing HoloMaps can result in impractically large HTML files.
The
DynamicMap
addresses these issues by computing and displaying elements dynamically, allowing exploration of much larger datasets:
 DynamicMaps generate elements on the fly, allowing the process of exploration to begin immediately.
 DynamicMaps do not require fixed sampling, allowing exploration of parameters with arbitrary resolution.
 DynamicMaps are lazy in the sense they only compute only as much data as the user wishes to explore.
Of course, these advantages come with some limitations:
 DynamicMaps require a live notebook server and cannot be fully exported to static HTML.
 DynamicMaps store only a portion of the underlying data, in the form of an Element cache and their output is dependent on the particular version of the executed code.
 DynamicMaps (and particularly their element caches) are typically stateful (with values that depend on patterns of user interaction), which can make them more difficult to reason about.
In addition to the different computational requirements of
DynamicMaps
, they can be used to build sophisticated, interactive vizualisations that cannot be achieved using only
HoloMaps
. This notebook demonstrates some basic examples and the
Streams
notebook follows on by introducing the streams system. The
Linked Streams
tutorial shows how you can directly interact with your plots when using the Bokeh backend.
When DynamicMap was introduced in version 1.6, it support multiple different 'modes' which have now been deprecated. This notebook demonstrates the simpler, more flexible and more powerful DynamicMap introduced in version 1.7. Users who have been using the previous version of DynamicMap should be unaffected as backwards compatibility has been preserved for the most common cases.
All this will make much more sense once we've tried out some
DynamicMaps
and showed how they work, so let's create one!
This tutorial assumes that it will be run in a live notebook environment.
When viewed statically, DynamicMaps will only show the first available Element,
and will thus not have any slider widgets, making it difficult to follow the descriptions below.
It's also best to run this notebook one cell at a time, not via "Run All",
so that subsequent cells can reflect your dynamic interaction with widgets in previous cells.
DynamicMap
¶
Let's start by importing HoloViews and loading the extension:
import holoviews as hv
import numpy as np
hv.notebook_extension()
We will now create
DynamicMap
similar to the
HoloMap
introduced in the
Containers Tutorial
. The
HoloMap
in that tutorial consisted of
Image
elements defined by a function returning NumPy arrays called
sine_array
. Here we will define a
waves_image
function that returns an array pattern parameterized by arbitrary
alpha
and
beta
parameters inside a HoloViews
Image
element:
xvals = np.linspace(4,0,202)
yvals = np.linspace(4,0,202)
xs,ys = np.meshgrid(xvals, yvals)
def waves_image(alpha, beta):
return hv.Image(np.sin(((ys/alpha)**alpha+beta)*xs))
waves_image(0,0) + waves_image(0,4)
Now we can demonstrate the possibilities for exploration enabled by the simplest declaration of a
DynamicMap
.
Basic
DynamicMap
declaration
¶
A simple
DynamicMap
declaration looks identical to that needed to declare a
HoloMap
. Instead supplying some initial data, we will supply the
waves_image
function instead with key dimensions simply declaring the arguments of that function:
dmap = hv.DynamicMap(waves_image, kdims=['alpha', 'beta'])
dmap
This object is created instantly, but because it doesn't generate any
hv.Image
objects initially it only shows the printed representation of this object along with some information about how to display it. We will refer to a
DynamicMap
that doesn't have enough information to display itself as 'unbounded'.
The textual representation of all
DynamicMaps
look similar, differing only in the listed dimensions until they have been evaluated at least once.
Explicit indexing ¶
Unlike a corresponding
HoloMap
declaration, this simple unbounded
DynamicMap
cannot yet visualize itself. To view it, we can follow the advice in the warning message. First we will explicitly index into our
DynamicMap
in the same way you would access a key on a
HoloMap
:
dmap[0,1] + dmap.select(alpha=1, beta=2)
Note that the declared kdims are specifying the arguments
by position
as they do not match the argument names of the
waves_image
function. If you
do
match the argument names
exactly
, you can map a kdim position to any argument position of the callable. For instance, the declaration
kdims=['freq', 'phase']
would index first by frequency, then phase without mixing up the arguments to
waves_image
when indexing.
Setting dimension ranges ¶
The second suggestion proposed by the warning was to supply dimension ranges using the
redim.range
method:
dmap.redim.range(alpha=(0,5.0), beta=(1,5.0))
Here each
hv.Image
object visualizing a particular sine ring pattern with the given parameters is created dynamically, whenever the slider is set to that value. Any value in the allowable range can be requested by dragging the sliders or by tweaking the values using the left and right arrow keys.
Of course, we didn't have to use the
redim.range
method and we could have simply declared the ranges right away using explicit
hv.Dimension
objects. This would allow us to declare other dimension properties such as the step size used by the sliders: by default each slider can select around a thousand distinct values along its range but you can specify your own step value via the dimension
step
parameter. If you use integers in your range declarations, integer stepping will be assumed with a step size of one.
It is important to note that whenever the
redim
method is used, a new
DynamicMap
is returned with the updated dimensions. In other words, the original
dmap
remains unbounded with default dimension objects.
Setting dimension values ¶
This
DynamicMap
above allows exploration of
any
phase and frequency within the declared range unlike an equivalent
HoloMap
which would have to be composed of a finite set of samples. We can achieve a similar discrete sampling using
DynamicMap
by setting the
values
parameter on the dimensions:
dmap.redim.values(alpha=[0,1,2], beta=[0.1, 1.0, 2.5])
The sliders now snap to the specified dimension values and if you are running this tutorial in a live notebook, the above cell should look like the
HoloMap
in the
Containers Tutorial
.
DynamicMap
is in fact a subclass of
HoloMap
with some crucial differences:
 You can now pick as many values of alpha or beta as allowed by the slider.
 What you see in the cell above will not be exported in any HTML snapshot of the notebook
We will now explore how
DynamicMaps
relate to
HoloMaps
including conversion operations between the two types. As we will see, there are other ways to display a
DynamicMap
without using explicit indexing or redim.
Interaction with
HoloMap
s
¶
To explore the relationship between
DynamicMap
and
HoloMap
, let's declare another callable to draw some shapes we will use in a new
DynamicMap
:
def shapes(N, radius=0.5): # Positional keyword arguments are fine
paths = [hv.Path([[(radius*np.sin(a), radius*np.cos(a))
for a in np.linspace(np.pi, np.pi, n+2)]],
extents=(1,1,1,1))
for n in range(N,N+3)]
return hv.Overlay(paths)
Sampling
DynamicMap
from a
HoloMap
¶
When combining a
HoloMap
with a
DynamicMap
, it would be very awkward to have to match the declared dimension
values
of the DynamicMap with the keys of the
HoloMap
. Fortunately you don't have to:
%%opts Path (linewidth=1.5)
holomap = hv.HoloMap({(N,r):shapes(N, r) for N in [3,4,5] for r in [0.5,0.75]}, kdims=['N', 'radius'])
dmap = hv.DynamicMap(shapes, kdims=['N','radius'])
holomap + dmap
Here we declared a
DynamicMap
without using
redim
, but we can view its output because it is presented alongside a
HoloMap
which defines the available keys. This convenience is subject to three particular restrictions:

You cannot display a layout consisting of unbounded
DynamicMaps
only, because at least one HoloMap is needed to define the samples.  The HoloMaps provide the necessary information required to sample the DynamicMap.
Note that there is one way
DynamicMap
is less restricted than
HoloMap
: you can freely combine bounded
DynamicMaps
together in a
Layout
, even if they don't share key dimensions.
Also notice that the
%%opts
cell magic allows you to style DynamicMaps can be styled in exactly the same way as HoloMaps. We will now use the
%opts
line magic to set the linewidths of all
Path
elements in the rest of the notebook:
%opts Path (linewidth=1.5)
Converting from
DynamicMap
to
HoloMap
¶
Above we mentioned that
DynamicMap
is an instance of
HoloMap
. Does this mean it has a
.data
attribute?
dtype = type(dmap.data).__name__
length = len(dmap.data)
print("DynamicMap 'dmap' has an {dtype} .data attribute of length {length}".format(dtype=dtype, length=length))
This is exactly the same sort of
.data
as the equivalent
HoloMap
, except that its values will vary according to how much you explored the parameter space of
dmap
using the sliders above. In a
HoloMap
,
.data
contains a defined sampling along the different dimensions, whereas in a
DynamicMap
, the
.data
is simply the
cache
.
The cache serves two purposes:
 Avoids recomputation of an element should we revisit a particular point in the parameter space. This works well for categorical or integer dimensions, but doesn't help much when using continuous sliders for realvalued dimensions.

Records the space that has been explored with the
DynamicMap
for any later conversion to aHoloMap
up to the allowed cache size.
We can always convert
any
DynamicMap
directly to a
HoloMap
as follows:
hv.HoloMap(dmap)
This is in fact equivalent to declaring a HoloMap with the same parameters (dimensions, etc.) using
dmap.data
as input, but is more convenient. Note that the slider positions reflect those we sampled from the
HoloMap
in the previous section.
Although creating a HoloMap this way is easy, the result is poorly controlled, as the keys in the DynamicMap cache are usually defined by how you moved the sliders around. If you instead want to specify a specific set of samples, you can easily do so by using the same keyselection semantics as for a
HoloMap
to define exactly which elements are to be sampled and put into the cache:
hv.HoloMap(dmap[{(2,0.3), (2,0.6), (3,0.3), (3,0.6)}])
Here we index the
dmap
with specified keys to return a
new
DynamicMap with those keys in its cache, which we then cast to a
HoloMap
. This allows us to export specific contents of
DynamicMap
to static HTML which will display the data at the sampled slider positions.
The key selection above happens to define a Cartesian product, which is one of the most common way to sample across dimensions. Because the list of such dimension values can quickly get very large when enumerated as above, we provide a way to specify a Cartesian product directly, which also works with
HoloMaps
. Here is an equivalent way of defining the same set of four points in that twodimensional space:
samples = hv.HoloMap(dmap[{2,3},{0.5,1.0}])
samples
samples.data.keys()
The default cache size of 500 Elements is relatively high so that interactive exploration will work smoothly, but you can reduce it using the
cache_size
parameter if you find you are running into issues with memory consumption. A bounded
DynamicMap
with
cache_size=1
requires the least memory, but will recompute a new Element every time the sliders are moved, making it less responsive.
Converting from
HoloMap
to
DynamicMap
¶
We have now seen how to convert from a
DynamicMap
to a
HoloMap
for the purposes of static export, but why would you ever want to do the inverse?
Although having a
HoloMap
to start with means it will not save you memory, converting to a
DynamicMap
does mean that the rendering process can be deferred until a new slider value requests an update. You can achieve this conversion using the
Dynamic
utility as demonstrated here by applying it to the previously defined
HoloMap
called
samples
:
from holoviews.util import Dynamic
dynamic = Dynamic(samples)
print('After apply Dynamic, the type is a {dtype}'.format(dtype=type(dynamic).__name__))
dynamic
In this particular example, there is no real need to use
Dynamic
as each frame renders quickly enough. For visualizations that are slow to render, using
Dynamic
can result in more responsive visualizations.
The
Dynamic
utility is very versatile and is discussed in more detail in the
Dynamic Operations
tutorial.
Slicing
DynamicMaps
¶
As we have seen we can either declare dimension ranges directly in the kdims or use the
redim.range
convenience method:
dmap = hv.DynamicMap(shapes, kdims=['N','radius']).redim.range(N=(2,20), radius=(0.5,1.0))
The declared dimension ranges define the absolute limits allowed for exploration in this continuous, bounded DynamicMap . That said, you can use the soft_range parameter to view subregions within that range. Setting the soft_range parameter on dimensions can be done conveniently using slicing:
sliced = dmap[4:8, :]
sliced
Notice that N is now restricted to the range 4:8. Open slices are used to release any
soft_range
values, which resets the limits back to those defined by the full range:
sliced[:, 0.8:1.0]
The
[:]
slice leaves the soft_range values alone and can be used as a convenient way to clone a
DynamicMap
. Note that mixing slices with any other object type is not supported. In other words, once you use a single slice, you can only use slices in that indexing operation.
Using groupby to discretize a DynamicMap ¶
A DynamicMap also makes it easy to partially or completely discretize a function to evaluate in a complex plot. By grouping over specific dimensions that define a fixed sampling via the Dimension values parameter, the DynamicMap can be viewed as a
GridSpace
,
NdLayout
, or
NdOverlay
. If a dimension specifies only a continuous range it can't be grouped over, but it may still be explored using the widgets. This means we can plot partial or completely discretized views of a parameter space easily.
Partially discretize ¶
The implementation for all the groupby operations uses the
.groupby
method internally, but we also provide three higherlevel convenience methods to group dimensions into an
NdOverlay
(
.overlay
),
GridSpace
(
.grid
), or
NdLayout
(
.layout
).
Here we will evaluate a simple sine function with three dimensions, the phase, frequency, and amplitude. We assign the frequency and amplitude discrete samples, while defining a continuous range for the phase:
xs = np.linspace(0, 2*np.pi)
def sin(ph, f, amp):
return hv.Curve((xs, np.sin(xs*f+ph)*amp))
kdims=[hv.Dimension('phase', range=(0, np.pi)),
hv.Dimension('frequency', values=[0.1, 1, 2, 5, 10]),
hv.Dimension('amplitude', values=[0.5, 5, 10])]
waves_dmap = hv.DynamicMap(sin, kdims=kdims)
Next we define the amplitude dimension to be overlaid and the frequency dimension to be gridded:
%%opts GridSpace [show_legend=True fig_size=200]
waves_dmap.overlay('amplitude').grid('frequency')
As you can see, instead of having three sliders (one per dimension), we've now laid out the frequency dimension as a discrete set of values in a grid, and the amplitude dimension as a discrete set of values in an overlay, leaving one slider for the remaining dimension (phase). This approach can help you visualize a large, multidimensional space efficiently, with full control over how each dimension is made visible.
Fully discretize ¶
Given a continuous function defined over a space, we could sample it manually, but here we'll look at an example of evaluating it using the groupby method. Let's look at a spiral function with a frequency and first and secondorder phase terms. Then we define the dimension values for all the parameters and declare the DynamicMap:
%opts Path (linewidth=1 color=Palette('Blues'))
def spiral_equation(f, ph, ph2):
r = np.arange(0, 1, 0.005)
xs, ys = (r * fn(f*np.pi*np.sin(r+ph)+ph2) for fn in (np.cos, np.sin))
return hv.Path((xs, ys))
spiral_dmap = hv.DynamicMap(spiral_equation, kdims=['f','ph','ph2']).\
redim.values(f=np.linspace(1, 10, 10),
ph=np.linspace(0, np.pi, 10),
ph2=np.linspace(0, np.pi, 4))
Now we can make use of the
.groupby
method to group over the frequency and phase dimensions, which we will display as part of a GridSpace by setting the
container_type
. This leaves the second phase variable, which we assign to an NdOverlay by setting the
group_type
:
%%opts GridSpace [xaxis=None yaxis=None] Path [bgcolor='w' xaxis=None yaxis=None]
spiral_dmap.groupby(['f', 'ph'], group_type=hv.NdOverlay, container_type=hv.GridSpace)
This grid shows a range of frequencies
f
on the x axis, a range of the first phase variable
ph
on the
y
axis, and a range of different
ph2
phases as overlays within each location in the grid. As you can see, these techniques can help you visualize multidimensional parameter spaces compactly and conveniently.
DynamicMaps and normalization ¶
By default, a
HoloMap
normalizes the display of elements according the minimum and maximum values found across the
HoloMap
. This automatic behavior is not possible in a
DynamicMap
, where arbitrary new elements are being generated on the fly. Consider the following examples where the arrays contained within the returned
Image
objects are scaled with time:
%%opts Image {+axiswise}
ls = np.linspace(0, 10, 200)
xx, yy = np.meshgrid(ls, ls)
def cells(time):
return hv.Image(time*np.sin(xx+time)*np.cos(yy+time), vdims=['Intensity'])
dmap = hv.DynamicMap(cells, kdims=['time']).redim.range(time=(1,20))
dmap + dmap.redim.range(Intensity=(0,10))
Here we use
+axiswise
to see the behavior of the two cases independently. We see in
A
that when only the time dimension is given a range, no automatic normalization occurs (unlike a
HoloMap
). In
B
we see that normalization is applied, but only when the value dimension ('Intensity') range has been specified.
In other words,
DynamicMaps
cannot support automatic normalization across their elements, but do support the same explicit normalization behavior as
HoloMaps
. Values that are generated outside this range are simply clipped according the usual semantics of explicit value dimension ranges.
Note that we can always have the option of casting a
DynamicMap
to a
HoloMap
in order to automatically normalize across the cached values, without needing explicit value dimension ranges.
Using DynamicMaps in your code ¶
As you can see,
DynamicMaps
let you use HoloViews with a very wide range of dynamic data formats and sources, making it simple to visualize ongoing processes or very large data spaces.
Given unlimited computational resources, the functionality covered in this tutorial would match that offered by
HoloMap
but with fewer normalization options.
DynamicMap
actually enables a vast range of new possibilities for dynamic, interactive visualizations as covered in the
Streams
tutorial. Following on from that, the
Linked Streams
tutorial shows how you can directly interact with your plots when using the Bokeh backend.
Download this notebook from GitHub (rightclick to download).