GRID 2 (v 1.0.85.8679 9 DLC) R.G. Games Repackl
Irta Boccanfuso
XORIGIN, YORIGIN, ZORIGIN, MORIGIN, XYSCALE, ZSCALE, ZORIGIN=value: These parameterscontrol the coordinate precisiongridinside the file geodatabase. The dimensions of the grid aredetermined by the origin, and the scale. The origin defines thelocation of a reference grid point in space. The scale is thereciprocal of the resolution. So, to get a grid with an origin at 0and a resolution of 0.001 on all axes, you would set all the originsto 0 and all the scales to 1000.
Firefox is the only browser with tools built specifically for building and designing with CSS Grid. These tools allow you to visualize the grid, display associated area names, preview transformations on the grid and much more.
You could use packForget():use Tk;my $top = new MainWindow;my $frame = $top->Frame();$frame->Label(-text=>"Can you see me?")->pack();$top->Button(-text=>"Hide",-command=>sub$frame->packForget())->pack(+);$top->Button(-text=>"Show",-command=>sub$frame->pack())->pack();$frame->pack();MainLoop();[download]packForget() removes the widget from the packing order. If you repack it later, it appears at the end of the packing order. You can get the pack configuration with @conf = $frame->packInfo(). The same is possible with grid.Regards, mawe[reply]
[d/l]Re^2: Hiding and Showing part of the Tk GUI
by Ace128 (Hermit) on Jul 19, 2005 at 01:17 UTCLovely! Works like a charm! This is why I love Tk so much! :) So damn neat, and easy to do otherwise seemable tricky things.[reply]Re: Hiding and Showing part of the Tk GUI
by waswas-fng (Curate) on Jul 18, 2005 at 22:20 UTChave you tried changing the container's size to 0 and then repacking?
-Waswas[reply]Re^2: Hiding and Showing part of the Tk GUI
by Ace128 (Hermit) on Jul 19, 2005 at 00:34 UTCWow, now that is smart :) (why didnt I just think of that?) No.. Gotta try that...
Update: Um, doesnt seem to work... $frame->geometry("=0x0+0+0");... damn.. would be neat else...
wrong # args: should be "winfo geometry window" at C:\Perl\site\lib/Tk/Submethod.pm[reply]Back toSeekers of Perl Wisdom
If possible, there are some advantages to compressing after joining files, rather than before. If the scenes are meant to join nicely (like adjacent chunks of terrain) then you need to use the same quantization grid with each file to avoid seams.
AutoDock represents the protein using a Cartesian scoring grid populated with information from an empirically derived energy function. A Lamarckian Genetic Algorithm (LGA) in combination with simulated annealing is then used to optimize both the ligand conformation and position [5].
Glide uses a grid based representation of the protein binding site. A rapid exhaustive search is performed to find generally favorable areas for ligand placement. A size filter is then used to exclude areas without sufficient space for ligand placement. Finally, Monte Carlo Minimization (MCM) of the binding position using the grid based scoring function is performed. The scoring girds themselves are generated using a scoring function derived from ChemScore [6].
The improved initial placement algorithm, here referred to as the Transform algorithm, has two independent components: A modular grid based scoring function, and a MCM based sampling algorithm. The software is implemented so as to allow for the rapid development of new score terms and sampling methodologies and the easy integration of these methods into the existing RosettaLigand modeling pipeline.
Scoring of ligand binding positions in the new Transform algorithm is handled using scoring grids that are controlled by a scoring manager (S1 Fig). Each scoring grid is responsible for computing a single term in the grid based energy function. In this study, a single scoring grid, identical to that used by the TransRot algorithm, was used for scoring. The scoring manager consists of a three-dimensional tensor of floating point values representing Cartesian space, functions to populate the tensor, and functions to score ligands positioned in it. The scoring manager is responsible for keeping the scoring grid up to date with respect to the protein binding position, and for evaluating the score of the ligand based on the grid. For this study, the tensor is a 1000 Å3 cube, with a spacing of 0.25 Å between grid points was used. While the size and density of the grid were not rigorously optimized in this study, we derived the following guidelines for setting parameters: The size of the grid must be sufficiently large that if the ligand is translated as to the maximum allowed range, (5.0 Å in this study) every ligand atom will exist within the grid. RosettaLigand will reject any move that would result in ligand atoms placed outside the grid; hence an overly small grid will artificially constrain ligand sampling. On the other hand, the amount of memory necessary to store a scoring grid increases with the cube of the grid side length. The CPU can handle smaller scoring grids more efficiently; hence a scoring grid that is too large may result in a substantial decrease in algorithm speed. Similarly, the spacing between grid points must be small enough to capture the differences between nearby atoms, but not so small that the grid is too large to be efficiently handled. Overall, a scoring grid should be large enough to encompass the entire protein/ligand binding site, but no larger.
Because the new initial placement algorithm relies on a pre-computed scoring grid, the initial positions of the protein atoms have an impact on the quality of the generated binding positions. To assess the extent of this impact, three sets of input structures were used in docking: the experimental structures provided in the CSAR dataset, repacked structures in which the backbone was held fixed and the side-chains re-optimized without the ligand present, and relaxed structures in which both the side-chain and backbone atoms were minimized within Rosetta in absence of the small molecule. In the case of the experimental and repacked structures, only a single protein structure was used for docking. In the case of the relaxed structures, the ligand was docked into an ensemble of ten models.
It is clear from Fig 4 and Fig 5 that despite using a pre-computed scoring grid during initial placement, RosettaLigand with the new initial placement algorithm is still tolerant of changes to the side-chain and backbone conformations of the protein binding site. In all tested protocols, the success rate of RosettaLigand decreases as the uncertainty associated with the protein side-chain and backbone atoms increases. In other words, after 1000 models have been generated docking ligands into experimental structures (Fig 5A), The TransRot/MCM protocol has successfully docked 81% of models, while the Transform/MCM protocol has successfully docked 87%. When ligands are docked into relaxed models in which both backbone and side-chain atoms are perturbed (Fig 5C), The TransRot/MCM protocol has successfully docked 60% of models, while the Transform/MCM protocol has successfully docked 75%. This decrease in success rate is expected because the addition of side-chain and backbone perturbation effectively adds noise to the protein structure. However, we see that the Transform/MCM protocol results in a 12% decrease in success rate between relaxed and experimental structures, rather than 21% for the TransRot/MCM protocol. The Transform protocol is more tolerant of inaccurate protein structures than the TransRot protocol. Because Transform algorithm is more likely to place the ligand in a high quality binding position, a greater percentage of total docking time is spent in proximity of the correct binding site and binding position. As a result, the sampling density increases and the overall success rate of RosettaLigand increases relative to the TransRot/MCM algorithm.
Black Box is played on a two-dimensional grid. The object of the game is to discover the location of objects ("atoms", represented by metal balls in the Waddingtons game and by yellow balls in the Parker Brothers version) hidden within the grid, by the use of the minimum number of probes ("rays"). The atoms are hidden by a person in a two-player game. In a solitaire game, they are either hidden by a computer or they are pre-hidden; in this case, the results of various probes are resolved by looking them up in a book. The seeker designates where the ray enters the black box and the hider (or computer or book) announces the result (a "hit", "reflection", or "detour"/"miss"). This result is marked by the seeker, who uses these to deduce the position of the atoms in the black box.
There are 32 input positions in an 88 grid, eight each at the top, bottom, right, and left. A beam is "fired" into one of these positions and the result is used to help deduce the location of a known number of hidden atoms.
The final type of interaction of a ray with an atom is a "reflection", designated by an "R". This occurs in two circumstances. If an atom is at the edge of the grid, any ray which is aimed into the grid directly beside it causes a reflection.
Each entry and exit location counts as a point. Hits and reflections therefore cost one point, while detours cost two points. When the seeker guesses the location of the atoms in the grid, each misidentified atom position costs penalty points: ten in the original Waddingtons rules, five in the Parker Brothers version and most computer editions.
The most common variant of Black Box is played on an 88 grid with five (or more) atoms. Five-atom configurations allow for positions that cannot be unambiguously determined by probes. The grid at left shows an example of this.
We have already seen that the number of jobs that RosettaScripts attempts can be controlled with the "-nstruct" (number of structures to generate per input) and "-jd2:ntrials" (number of attempts to make for each structure before giving up on that structure), and with the number of input structures. There are certain special "grid-sampling" movers in Rosetta that can interface with the RosettaScripts job distribution system in a special way: given some sort of parameter space that one might want to sample, a grid sampler can assign each of a regular grid of points to sample in that space to a series of RosettaScripts jobs. This is a departure from the usual Rosetta paradigm, which is to carry out a large number of stochastic trajectories. Here, we are performing a large number of regular, deterministic samples.
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