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01:00:01 up 9 days, 6:19, 0 users, load average: 1.20, 0.92, 0.82 | temp=41.2'C | Start
01:00:09 up 9 days, 6:19, 0 users, load average: 1.35, 0.96, 0.83 | temp=41.2'C | SID plot Finished
Plot of the mains voltage at Howhill on the 27th May 2008. At 1138 there was adeep brownout to 200v followed by a power cut. This was due to part of Longannet and Sizewell B unexpectedly going offline. National Grid reported the loss of 1.51GW of capacity.
14:00:01 up 4 days, 20:31, 0 users, load average: 0.55, 0.73, 0.76 | temp=42.8'C | Start
14:00:09 up 4 days, 20:31, 0 users, load average: 1.01, 0.82, 0.79 | temp=43.3'C | SID plot Finished
Plot de Pvc para suelo técnico en exterior.
Adobe Ilustrator.
Polygroup renovando sus fichas técnicas.
Dubbed "Plot Realignment Pullover" by the family! Many different yarns were used for the module plots, all 49 of them having a different pattern. Box-type pattern, there is no front or back.
08:00:01 up 4 days, 14:31, 0 users, load average: 0.79, 0.82, 0.74 | temp=42.2'C | Start
08:00:09 up 4 days, 14:31, 0 users, load average: 0.89, 0.84, 0.75 | temp=42.2'C | SID plot Finished
19:00:01 up 2 days, 56 min, 0 users, load average: 0.83, 0.78, 0.76 | temp=41.7'C | Start
19:00:09 up 2 days, 56 min, 0 users, load average: 1.08, 0.83, 0.78 | temp=41.7'C | SID plot Finished
Many real-world networks can be represented as large graphs. Computational manipulation of such large graphs is common, but current tools for graph visualization are limited to datasets of a few thousand nodes.
These graphs contain sets of highly connected nodes that we call “communitiesâ€Â. Furthermore, these communities often have their own parts which are more connected than the rest that can be viewed as “sub-communitiesâ€Â. We used the Louvain method to extract communities and sub-communities from a sample network obtained from Arxiv dataset. We also used GUESS which is a graph exploration tool that contains an interpreted language (Gython) combined with a graphical front-end.
Using extracted hierarchical clustering dendrogram from Louvain method, we developed a tool which visualizes different hierarchical partitions of graph. Also, it allows us to manually merge and unmerge nodes into and from a community.
The plot shows the five levels of the decomposition, the smallest graph being the one between the communitiues whose decomposition maximizes the modularity according to Louvain method.
Computing the diameter (i.e. the maximal distance between two nodes) of a huge graph is in many cases too time-consuming to be performed.
In Fast Computation of Empirically Tight Bounds for the Diameter of Massive Graphs we propose several methods to obtain upper and lower bounds for the diameter of a given graph. Although these bounds are guaranteed (it is sure that the diameter is between the bounds), we do not know if the bounds are tight in general. We therefore computed them on a variety of real-world cases; in all of them, the results were excellent: the difference between the upper and lower bounds was a few units only. As the computations needed to compute these bounds are very efficient, this provides an effective solution for diameter estimations when exact computation is out of reach (or not needed).
Our methods to compute upper and lower bounds are not deterministic: if we run them several times, we may obtain different values. As, in all cases, it is guaranteed that the diameter is indeed between the bounds, one may cumpute several bounds and keep the best ones.
The plot above represents the distributions of the obtained bounds for each method, ran 2000 times each on a web graph of approximately 40 million nodes and 800 million links. Each line corresponds to a different method (detailed in the paper)). For upper bounds (the three rightmost lines), we plot for each value x on the horizontal axis the number of times that the method output a bound larger than or equal to x. For lower bounds (the two leftmost lines) we plot for each value x on the horizontal axis the number of times that the method output a bound smaller than or equal to x.
In this case, we therefore reach the conclusion that our 40 million node graph has a diameter between 32 and 33 (it is thereforre 32 or 33, exactly). This is a very precise result, typical of this approach. It is obtained in minutes, while classical methods are unable to handle such massive graphs even within weeks.
My plot in the winter snow. My strawberries are in the blue tub, and there was oregano on tan pot, the back bed has garlic.
To see where on a map i find is fun, in a geeky investigative sort of way ;)
Not very often we get to get a glimpse into where a particular bird has been, and therefore have a good idea of probability as to where it might be going.
It would seem this particular Caspian Tern was likely heading back south after nesting this year, at same location where it was hatched.or ??....
Please feel free to comment, or share your thoughts and insights.
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14:00:01 up 6 days, 19:19, 0 users, load average: 0.68, 0.66, 0.71 | temp=41.7'C | Start
14:00:09 up 6 days, 19:19, 0 users, load average: 0.78, 0.68, 0.72 | temp=41.7'C | SID plot Finished
I'm trying to plot the GPS data recorded during the ski tour on Monday
gaining altitude was mostly with help of cable cars or ski lifts ;-)
I only started tracking at the 1st summit
anybody knows good software to do stuff like that?
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02:00:01 up 35 days, 7:53, 0 users, load average: 0.30, 0.38, 0.40 | temp=41.7'C | Start
02:00:06 up 35 days, 7:53, 0 users, load average: 0.36, 0.39, 0.41 | temp=42.2'C | SID plot Finished
Plots dotting the landscape...don't remember exactly where this was taken...somewhere between T.O and Denver.
traceroute is a tool that gives the internet path followed by your packets towards a given destination, under the form of a series of IP addresses. Consider a given set of destinations, and let us call the result of a traceroute towards each of these destinations a measurement round. We are interested in studying how the set of IP addresses we can observe in a measurement round evolves with time. Therefore, we repeat these measurement rounds periodically (every 15 minutes approximately), using a fixed destination set (for more details about these measurements, see this paper).
For each IP address we see at least once during our measurements, we record two things: the number of rounds in which we have observed it, and its number of appearances, i.e. rounds in which the IP is present, but was not present in the round before. For instance, an IP address that we see at rounds 1, 5,6,7 and 10 has been observed in 5 distinct rounds, and has appeared three times.
We then make the above plot: each dot corresponds to an IP address. The coordinate of the dot on the x-axis is the number of rounds the IP address was observed in; its coordinate on the y-axis is its number of appearances. Surprisingly, the plot exhibits a clear geometric shape: we can see a triangle and a circle-like shape in it. These shapes can however be explained.
By definition of the plot, no dot can appear outside of the triangle: no IP address can appear a larger number of times than the number of rounds it was observed in (therefore we cannot have y > x); conversely, no IP address can appear a larger number of times that the number of rounds it was not observed in, since an appearance is defined as a round in which the IP address is not observed, followed by a round in which it is observed (therefore we cannot have y > 4676 - x, 4676 being the total number of measurement rounds). This defines the borders of the triangle.
The circle is in fact a parabola. Consider an IP address that was observed in exactly x distinct rounds during our measurements. If we suppose the rounds this address was observed in were chosen at random among our 4676 measurement rounds, then we can compute the expected number of appearances of this address. A given round corresponds to an appearance with the probability that the address was observed in this round, multiplied by the probability that it was not observed in the previous round, which gives (x / 4676) * (4676 - x / 4676). To obtain the number of expected number of appearances, just multiply this probability by the total number of rounds, giving the equation of the parabola.
The fact that the parabola can clearly be observed means that a large number of IP addresses seem to behave randomly in our observations: they appear the same number of times as they would if they were observed at random times. Dots above the parabola correspond to addresses that tend to blink on and off more than expected; finally, a large number of dots below the parabola mean that many IP addresses tend to be more stable than expected: they appear fewer times than expected, meaning that when they appear they tend to stay there a large number of consecutive rounds before disappearing.