12:21 AM
11 hours later…
11:49 AM
Here's for example the result from 100 epidemic simulations on a graph (SIR model): cloud.mpi-cbg.de/index.php/s/hVgqBtaC4tdxsan
How would I extract just one (e.g. just I) from the TemporalData, without disassembling the whole thing first?
12:22 PM
2 hours later…
2:57 PM
@halirutan @Kuba Yes, the vendor readme that I linked to lists all the packages they use for syntax highlighting. So if we want to improve the Github syntax highlighting for WL, the package that we need to improve or replace is this one, as listed by the vendor readme: github.com/shadanan/mathematica-tmbundle
3:32 PM
@Szabolcs I think a minimal rewording of your problem is "how to get
TemporalData[{{1, 10}, {2, 1}, {3, 30}}]
from TemporalData[{{1, {10, 11, 6}}, {2, {1, 15, 7}}, {3, {30, 5, 1}}}]
without disassembling". After having read some kilometers of documentation, I think this is badly documented. The solution is : TemporalData[{{1, {10, 11, 6}}, {2, {1, 15, 7}}, {3, {30, 5, 1}}}]["PathComponent", 1]
.
To select several values, one can do
TemporalData[{{1, {10, 11, 6}}, {2, {1, 15, 7}}, {3, {30, 5, 1}}}]["PathComponent",{ 1,2}]
. See here
2 hours later…
6:01 PM
Thank you @andre314 for that! @Szabolcs I use the Time Series and Random Function routines only infrequently. But part of that infrequency of use is the meager documentation (and for me, confusing documentation: I see the initial definitions of the functions and the parameters but then some of the examples (again, maybe it's just me) don't seem to match the parameter patterns).
My other issue with the time series/random functions is that there appears to be two kinds one functions: one includes standard errors (
TimeSeriesModelFit
) and one just includes the parameter estimates (EstimatedProcess
). I have the same issue with NonlinearModelFit
(which has standard errors) and FindFit
(which only gives parameter estimates).
@ChrisK I was exposing igraph's SIR model functionality in IGraph/M and I was looking for a good data structure to return. In principle, TemporalData is perfect, but I was not at all experienced with it.
Here's the documentation I wrote up so far (it's just one function): cloud.mpi-cbg.de/index.php/s/JHF70KzRp6KpMSN
@CarlLange The out of ordinary thing I want to do is to let it extrapolate into the future by assuming that values no longer change
4
I am working with a large number of TimeSeries of irregular length. This means that if I sum them, or compute the mean, it cuts off at the terminus of the shortest series. Is there a convenient way to make it compute to the end of the longest series, assuming that the missing data did not change?...
igraph's SIR function produces many simulation runs simultaneously. The time points of events are different in each run. The events are not on a regular temporal grid. Therefore, we do need interpolation.
The values are integers (number of infected nodes), so ideally we should not have higher-than-0 order interpolation.
This happens at different times for each run. For some runs, it may happen after just one event (with some probability, the single infected person recovers used as initial condition recovers before he can spread the disease)
2 hours later…
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