# Manipulating temporal networks¶

Crucial functionalities needed for working with temporal networks are any which can also be found in typical video editing software. We want to be able to cut slices out of them, concatenate different networks, and to rescale time.

## Slicing¶

Say we have an experiment conducted between $$8\mathrm{h}\leq t < 48\mathrm{h}$$. We are only interested in the activity during the first night, though, i.e. for $$20\mathrm{h}\leq t < 32\mathrm{h}$$.

We can do

night = tc.slice(temporal_network, new_t0 = 20, new_tmax = 32)


The corresponding function can be found at tacoma.api.slice.

Slicing is illustrated below.

## Concatenating¶

We might have data of two consecutive weeks, but in two files. We can load both files, construct temporal_network_A and temporal_network_B and then concatenate them by

temporal_network = tc.concatenate([temporal_network_A, temporal_network_B])


Attention

• All temporal networks in the list must be of equal type.
• Temporal networks do not have to have the same number of nodes. However, if $$N_A$$ and $$N_B$$ differ, it is useful to provide int_to_node-dictionaries containing unique identifiers for nodes. When concatenating, these dictionaries will be merged and node integers will be remapped accordingly.
• All times $$t$$ of a temporal network will be remapped to have the beginning of the experiment at $$t_\mathrm{max}$$ of the predecessing network in the list.

Concatenation is illustrated below.

The corresponding function can be found at tacoma.api.concatenate.

## Rescaling time¶

We can rescale time to either speed up or slow down all events, or to change the unit of time. For example, to change from seconds to hours, do

ec_hour = tc.rescale_time(ec_seconds,
new_t0 = ec_seconds.t0 / 3600.,
new_tmax = ec_seconds.tmax / 3600.)


To speed up the time by a factor 2, you could do

ec_speedy = tc.rescale_time(ec,
new_t0 = ec.t0,
new_tmax = ec.tmax / 2.)


The corresponding function can be found at tacoma.api.rescale_time.