Would that mean that I can use Ubuntu just like a "normal application" on my
windows PC or would I have to swich the operating system on my PC ? Or in
other words. Is it possible to use it in parallel to my normal windows usage
?
Sorry for that maybe dumb question, i am not that much into
Hi,
I am getting the following error when using get_edges_prob() with
layered SBMs. Minimal example:
import graph_tool.all as gt
import numpy as np
gr=gt.generate_sbm(b=np.array([0]*500+[1]*500),probs=np.array([[1,200],[200,1]]))
etype=gr.new_edge_property('int')
gr.ep.etype=etype
for
Hello,
I think I am having the same problem recently risen. I was just wondering
how I can access to the color tuples of the given mapping (draw) results.
Given that:
>>> g = gt.collection.data["celegansneural"]>>> state =
>>> gt.minimize_nested_blockmodel_dl(g, deg_corr=True)>>>
>>>
Am 16.01.20 um 16:43 schrieb Lietz, Haiko:
>> > How can I access the (r, g, b, alpha) tuples used by default when using
>> > graph_draw()?
>>
>> The default color map used can be accessed via:
>>
>> graph_tool.draw.default_cm
>
>
> I want to extract the exact colors used by default in
You can try using linux subsystem for windows, which is basically Ubuntu
18.04
On Thu, Jan 16, 2020 at 10:32 AM PhilippCoelsch
wrote:
> Hi all,
>
> I am a PhD-student who wants to perform some network analyses using this
> Graph-Tool library.
> Unfortunately I only have access to a Windows 10
Am 16.01.20 um 16:29 schrieb PhilippCoelsch:
> Hi all,
>
> I am a PhD-student who wants to perform some network analyses using this
> Graph-Tool library.
> Unfortunately I only have access to a Windows 10 PC and no Mac OS or Linux
> device. I also cannot install another operating system on my
> > How can I access the (r, g, b, alpha) tuples used by default when using
> > graph_draw()?
>
> The default color map used can be accessed via:
>
> graph_tool.draw.default_cm
I want to extract the exact colors used by default in coloring vertices. For
example vertex_fill_color=0 is
Hi all,
I am a PhD-student who wants to perform some network analyses using this
Graph-Tool library.
Unfortunately I only have access to a Windows 10 PC and no Mac OS or Linux
device. I also cannot install another operating system on my computer.
I tried the installation via Docker as described
Am 16.01.20 um 13:33 schrieb Lietz, Haiko:
> Hi all,
>
>
> How can I access the (r, g, b, alpha) tuples used by default when using
> graph_draw()?
The default color map used can be accessed via:
graph_tool.draw.default_cm
Best,
Tiago
--
Tiago de Paula Peixoto
Hi all,
How can I access the (r, g, b, alpha) tuples used by default when using
graph_draw()?
Thanks
Haiko
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Thanks, for the time being I've solved with a try/except within the function,
so that
def foo():
global pv
[…]
try:
pv = [sl.collect_vertex_marginals(pv[l]) for l, sl in
enumerate(s.get_levels())]
except NameError:
pv = [None] * len(state.get_levels())
(in fact I've tried to
Am 16.01.20 um 11:11 schrieb Davide Cittaro:
> Hi again,
> This is more a python related question, but I'm asking here hoping somebody
> had a similar issue and worked it out.
> I need to collect vertex marginals during equilibrate and when I do it in my
> python console everything works just
Am 16.01.20 um 10:45 schrieb Davide Cittaro:
> Hello Tiago,
> thanks for the answer
>
>> On 16 Jan 2020, at 10:40, Tiago de Paula Peixoto wrote:
>>
>> The difference between the epsilon in mcmc_equilibrate() and in
>> hiearchy_minimize() is explained in the documentation.
>
> Indeed, but I
Am 16.01.20 um 02:13 schrieb Gerion Entrup:
> Hi,
>
> I have seen the algorithm for the dominator tree in graph_tool.
> Is there an algorithm present that calculates the postdominator tree [1] as
> well?
Yes, just reverse the graph and obtain the dominator tree.
--
Tiago de Paula Peixoto
Thank you Tiago!
James
> On 16 Jan 2020, at 09:40, Tiago de Paula Peixoto wrote:
>
> Am 15.01.20 um 11:49 schrieb James Ruffle:
>> Dear Tiago/Community,
>>
>> I am interested in using the SIS (and SIRS) models to simulate dynamic
>> processes.
>> However, rather than pass a singular
> On 16 Jan 2020, at 10:46, Tiago de Paula Peixoto wrote:
>
> The group labels of the projected state will match the node index of the
> original level. So you only need to look into the marginal distribution
> for that level, and copy it to the base level.
Excellent, thanks!
d
Hi again,
This is more a python related question, but I'm asking here hoping somebody had
a similar issue and worked it out.
I need to collect vertex marginals during equilibrate and when I do it in my
python console everything works just fine (as in the cookbook):
pv = [None] *
Hello Tiago,
thanks for the answer
> On 16 Jan 2020, at 10:40, Tiago de Paula Peixoto wrote:
>
> The difference between the epsilon in mcmc_equilibrate() and in
> hiearchy_minimize() is explained in the documentation.
Indeed, but I wanted to know how the epsilon in the hiearchy_minimize
Am 15.01.20 um 18:01 schrieb Davide Cittaro:
> Hi again,
> I'm following the cookbook and I'm collecting vertex marginals during
> mcmc_equilibrate, so
>
> pv = [None] * len(state.get_levels())
> def collect_marginals(s):
> global pv
> pv = [sl.collect_vertex_marginals(pv[l]) for l, sl
Am 15.01.20 um 12:15 schrieb Davide Cittaro:
> Hi again,
> A quick question: does graph-tool support GPU computation seamlessly? Our
> institute is pushing on that kind of hardware, I want to understand if I can
> take advantage of it.
Not yet.
--
Tiago de Paula Peixoto
signature.asc
Am 15.01.20 um 11:49 schrieb James Ruffle:
> Dear Tiago/Community,
>
> I am interested in using the SIS (and SIRS) models to simulate dynamic
> processes.
> However, rather than pass a singular transmission probability, per the
> documentation, I am interested in using edge property maps as
Am 15.01.20 um 11:29 schrieb Davide Cittaro:
> Hi all,
> I'm trying to figure out a decent set of parameters to achieve a good
> trade-off between speed and accuracy.
> On my data, default parameters make comuputation times extremely long
> (especially compared to other community finding
Am 15.01.20 um 11:22 schrieb dawe:
> I have a question related to this
> The documentation example suggests a hierarchy set to 10 levels
>
> bs = state.get_bs() # Get hierarchical partition.
> bs += [np.zeros(1)] * (10 - len(bs))# Augment it to L = 10 with
>
Am 15.01.20 um 11:15 schrieb Davide Cittaro:
> Hello everybody,
> I'm new to graph-tool and nSBM, so forgive my naive question. We are still
> trying to understand how parameter influence our outcome. My first question is
>
> is
>
> state = gt.minimize_nested_blockmodel_dl(g)
>
Am 10.01.20 um 12:15 schrieb Martin Helfrich:
> Hello everyone,
>
> could you please update the precompiled distributions under
>
> |http://downloads.skewed.de/apt/|
>
> to include a version for Ubuntu Eoan? I don't think there is one yet...
> This would be a great help to us. Or maybe, can
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