1. Basics
2. Complex recipes
3. Handling multiple unknowns
4. No solution found
5. Linear programming
Recipes consist of three major things: A crafting time, one or more products, and one or more ingredients. Products and ingredients consist of an amount and an item.
The vast majority of the recipes in the game will produce a single instance of a single item. Several will make multiple copies of an item at a time, and a bare handful will make multiple distinct items at once. The recipe for the electronic circuit, shown above, will produce a single electronic circuit each time it is completed.
Copper cables and iron plates have their own recipes, in turn.
And then copper cables require copper plate, the recipe for which looks like this:
Iron ore and copper ore don't have a recipe, and are mined directly from deposits in the game world.
If one were to represent the complete tree of items and the ratios required of each of their prerequisites, it would look something like this:
The relationship between crafting times and ratios is straightforward, but deserves mentioning. The ratios shown above are best thought of as a series of rates. For instance, if you wanted to produce one electronic circuit per second, then you would also need to make three copper cables per second, one-and-a-half copper plates per second, and so on. These rates would remain constant regardless of the crafting times of the individual recipes. Recipes with longer crafting times will simply require more assemblers or furnaces working in parallel in order to satisfy the desired rate.
(There is the additional detail that different types of assemblers and furnaces have different crafting speeds. Working through the arithmetic here is outside the scope of this post, however.)
A tree similar to the above is sufficient for calculating the production ratios between the majority of the recipes in the game. However, the complete recipe graph hides a few complex corner-cases, and the technique shown above will fail once it runs into the weirder parts of the graph.
But, inevitably, you will run into the oil production pipeline.
 These recipes violate a number of assumptions that it would be easy to make if you looked exclusively at the non-oil parts of the recipe graph.
Notice how the fluids produced by these recipes (heavy oil, light oil, and petroleum gas; collectively referred to as the oil products) are each created by multiple recipes. The basic and advanced oil processing recipes are also in that exclusive class of recipes which produce multiple, distinct items.
There are some other recipes closely related to the oil pipeline which add to the annoyance. Each of the three oil products may be turned into solid fuel, via three additional recipes. There is also a system in which crude oil may be placed into barrels, and then the full barrels may be emptied again. This is accomplished via a pair of recipes that depend on one another. As a consequence, the recipe graph is not a DAG. Any algorithm that wants to work with the recipe graph in a general sense must be prepared to deal with cycles.
So: Suppose we want to make an item that depends on one of the oil products. A good example would be plastic.
If we want to produce plastic bars at a given rate, we will require petroleum gas at 1.5 times that rate. However, we can obtain petroleum gas from three different recipes, and some of those recipes ultimately depend on each other, and they also end up producing the other oil products at the same time, which we can turn into more petroleum gas via the cracking recipes.
How can we calculate the optimal selection of recipes to produce this with? And what if we actually want to use more than one oil product, at some specific ratio?
It turns out that this is a straightforward problem of linear algebra. The key is representing the recipes in a useful way. We can encode the recipe graph using a matrix like the following.
| -40 | 0 | 30 | 10 | 0 | 0 | |
| 30 | -30 | 30 | 45 | 0 | 0 | |
| 0 | 20 | 40 | 55 | 0 | 0 | |
| -30 | -30 | 0 | -50 | 1 | 0 | |
| 0 | 0 | -100 | -100 | 0 | 1 |
Now let's use this matrix to determine the optimum ratios for some series of oil products. Completely arbitrarily, let's say we want to produce 45 units of petroleum gas, and 10 units of heavy oil, per unit time. We can represent this as an item vector:
| 10 | |
| 0 | |
| 45 | |
| 0 | |
| 0 |
| result | ||||||
|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | -13/400 | -23/12 |
| 0 | 1 | 0 | 0 | 0 | -7/400 | -1/4 |
| 0 | 0 | 1 | 0 | 0 | -3/50 | -10/3 |
| 0 | 0 | 0 | 1 | 0 | 1/20 | 10/3 |
| 0 | 0 | 0 | 0 | 1 | 1 | 305/3 |
To get a better sense of what this looks like, let's plot the system of equations represented by the above matrix.
■ ■ ■ ■ ■
In mathematical terms, let's call the recipe matrix A, the vector of ratios we want to solve for x, and the vector of desired outputs b. Together, these satisfy the relationship Ax = b. However, it is very important that all of the components of x are positive. A negative value in x would represent trying to operate a recipe in reverse, which is not something that makes sense in the context of the game.
Thus, the vertical lines in the above plot represent the minimum and maximum values for xcrude oil such that all of the values in x will be greater than or equal to zero. Or, in game terms, they represent the minimum and maximum amounts of oil we could spend in order to produce 45 units of petroleum gas and 1 unit of heavy oil, with no other oil products left over.
In order to choose a value for xcrude oil, we need to establish an order of priorities. Ultimately, we are trying to find the most efficient production chain for the items we want to make, where efficiency is defined as using as few resources as possible. But, as we can see with water and crude oil in the above plot, sometimes resources are used in inverse proportion to one another. Using less crude oil would mean using more water, and vice versa.
Given that crude oil can be annoying to acquire, and water is effectively free, it makes sense to define, a priori, that optimizing for crude oil has a higher priority than optimizing for water. Therefore, we can say that the optimum value for xcude oil is the minimum possible one, as shown in the plot above.
Taking that value as a solution, our final value for x is this.
| 0.00 | 0.78 | 0.21 | 0.38 | 42.69 | 58.97 |
Let's start with a more constrained example. Let's take the oil production matrix above, and add the recipes for solid fuel.
| -40 | 0 | 30 | 10 | -20 | 0 | 0 | 0 | 0 | |
| 30 | -30 | 30 | 45 | 0 | -10 | 0 | 0 | 0 | |
| 0 | 20 | 40 | 55 | 0 | 0 | -20 | 0 | 0 | |
| 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | |
| -30 | -30 | 0 | -50 | 0 | 0 | 0 | 1 | 0 | |
| 0 | 0 | -100 | -100 | 0 | 0 | 0 | 0 | 1 |
| result | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | 0 | 5/2 | -1/20 | -33/400 | -50 |
| 0 | 1 | 0 | 0 | 0 | 0 | 5/22 | -3/220 | -137/4400 | -450/11 |
| 0 | 0 | 1 | 0 | 0 | 0 | 18/11 | -1/55 | -43/550 | -600/11 |
| 0 | 0 | 0 | 1 | 0 | 0 | -18/11 | 1/55 | 3/44 | 600/11 |
| 0 | 0 | 0 | 0 | 1 | 0 | -37/11 | 9/110 | 9/110 | 500/11 |
| 0 | 0 | 0 | 0 | 0 | 1 | 48/11 | -9/110 | -9/110 | 600/11 |
Items come from recipes. In the normal case, each item is produced by one recipe. The oil production pipeline violates this assumption, by having three items that are produced by four recipes. Thus, its recipe matrix is not square, and calculations involving oil end up with a single unknown. In practice, regardless of the order in which you happen to arrange the columns of the matrix, this unknown is really asking: Which oil product do I need to use the least?
To put that another way: Having more recipes than items results from having multiple ways of making an item. The unknown that results from trying to solve the resulting recipe matrix represents the choice between those recipes.
There are three possibilties when trying to choose between these recipes.
We saw this in the last section, when the ratio for heavy oil cracking turned out to be zero. This result can be understood intuitively: We required heavy oil as part of the output, and advanced oil processing produces heavy oil at an extremely low ratio, while making the other products at higher ratios. It therefore follows that production of heavy oil would be the bottleneck, and we would have no excess heavy oil to pass on to the heavy oil cracking recipe.
There is always a bottleneck.
Applying that same intuition to the result above, we can guess which three recipes will end up being zero. First, notice that light oil produces solid fuel at a vastly more advantageous ratio (10:1) than either of the other two products (20:1). Then notice that the ratio for converting from heavy oil to light oil (40:30) is more advantageous than converting from heavy oil directly to solid fuel.
So: The first zero recipe is the solid fuel from heavy oil recipe. It's more efficient to convert all of our heavy oil to light oil.
On the other hand, we have petroleum gas, which we will be producing as an unavoidable byproduct of the oil processing recipes, and so we're forced to make a certain proportion of our solid fuel from it. Converting our light oil into petroleum gas would be much less efficient than just converting all of the light oil directly into solid fuel, and so the second zero recipe is light oil cracking.
Finally, given that we really want all of the light oil we can get our hands on, we can look back at the oil processing recipes and note that basic oil processing makes less of it than advanced oil processing does. Thus, the third zero recipe is basic oil processing.
But how can we calculate this automatically?
In the previous section, we essentially determined the optimum value for x by trying to zero out each value in x, and checking whether the result was valid. This yielded two valid results (the minimum and maximum shown in the graph), from which we chose the optimum one.
This can be generalized for multiple unknowns by choosing every combination of N elements from x, finding solutions for the rest of x when those elements are zero, and then seeing if that solution is valid (i.e. if it contains no negative elements). This will, more than likely, yield multiple valid solutions, and so the final step is to choose the most optimal solution.
This process can be optimized slightly by only considering those recipes that have at least one product that is produced by multiple recipes. The only recipes that can be considered to be zero in this process are those which have alternatives. The game's complete recipe graph has 215 recipes with 11 unknowns. Computing this naively would yield over 8×1017 possibilties. However, the game only has 22 recipes which actually need to be considered, which brings the number of possibilities down to 705,432. Further reductions in the number of possibilities are possible as well, since some combinations of zeros are mutually exclusive, but this is outside the scope of this post. (The bulk of these excess recipes come from the fluid barreling system, which can largely be ignored as redundant in most production chains.)
For the purposes of our example, all this means is that we do not need to consider the possibility that water or crude oil will be zero (although they may be zero as a natural consequence of other columns being zero). If we take our 7 remaining recipes and set three of them to zero at a time, we get 35 possible solutions, of which the following are valid:
| 0.00 | 0.00 | 0.00 | 12.90 | 6.45 | 58.06 | 35.48 | 645.16 | 1290.32 | The "don't crack anything" solution. |
| 0.00 | 0.00 | 15.38 | 0.00 | 23.08 | 46.15 | 30.77 | 0.00 | 1538.46 | The same, but with basic oil processing. |
| 0.00 | 31.58 | 0.00 | 21.05 | 10.53 | 0.00 | 89.47 | 2000.00 | 2105.26 | Crack all the light oil to petroleum gas. |
| 0.00 | 22.22 | 22.22 | 0.00 | 33.33 | 0.00 | 66.67 | 666.67 | 2222.22 | The same, but with basic oil processing. |
| 3.12 | 0.00 | 0.00 | 12.50 | 0.00 | 65.62 | 34.38 | 718.75 | 1250.00 | The winner! |
| 10.34 | 0.00 | 13.79 | 0.00 | 0.00 | 72.41 | 27.59 | 310.34 | 1379.31 | Crack all the heavy oil, but use basic oil processing. |
| 5.56 | 38.89 | 0.00 | 22.22 | 0.00 | 0.00 | 100.00 | 2444.44 | 2222.22 | All petroleum gas, all the time. |
| 20.00 | 46.67 | 26.67 | 0.00 | 0.00 | 0.00 | 100.00 | 2000.00 | 2666.67 | The same, but with basic oil processing. |
If we think of the recipe matrix as a vector space, there are some places we just can't reach. In the vanilla game, this is entirely the fault of the oil processing recipes.
To illustrate this, here is a somewhat simplified oil processing recipe graph, with only heavy oil and light oil.
| -40 | 30 | 10 | |
| 30 | 30 | 45 |
The situation with the complete recipe graph is analogous, except with a third dimension representing petroleum gas.
This can be overcome by introducing new vectors which can "waste" any given item. So long as these matrix columns are treated with the lowest priority, they should be used only when required.
The chief difficulty of representing a Factorio recipe graph as a linear program lies in crafting the objective function. The linear algebra approach described in the previous sections has the advantage of returning each possible solution as a tuple of costs, which may be compared to each other in whichever way is found most convenient.
When specifying the recipe graph as a linear program, however, the costs have to be specified as a linear combination, which ultimately has to yield a scalar value. There is a way to do this, but first let's go through the relatively simple example of the oil processing pipeline.
Here's a matrix we used earlier:
| output | |||||||
|---|---|---|---|---|---|---|---|
| -40 | 0 | 30 | 10 | 0 | 0 | 10 | |
| 30 | -30 | 30 | 45 | 0 | 0 | 0 | |
| 0 | 20 | 40 | 55 | 0 | 0 | 45 | |
| -30 | -30 | 0 | -50 | 1 | 0 | 0 | |
| 0 | 0 | -100 | -100 | 0 | 1 | 0 |
Minimize C = xcrude oil subject to:
-40x1 + 30x3 + 10x4 ≥ 10Note that, at this point, we are specifying crude oil as the only cost because we know ahead of time that we want it to be so. I will address the general case of determining a cost function later.
30x1 - 30x2 + 30x3 + 45x4 ≥ 0
20x2 + 40x3 + 55x4 ≥ 45
-30x1 - 30x2 - 50x4 + xwater ≥ 0
-100x3 - 100x4 + xcrude oil ≥ 0
∀i: xi ≥ 0
The tableau for our linear program looks like this:
| s1 | s2 | s3 | s4 | s5 | C | answer | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| -40 | 0 | 30 | 10 | 0 | 0 | -1 | 0 | 0 | 0 | 0 | 0 | 10 |
| 30 | -30 | 30 | 45 | 0 | 0 | 0 | -1 | 0 | 0 | 0 | 0 | 0 |
| 0 | 20 | 40 | 55 | 0 | 0 | 0 | 0 | -1 | 0 | 0 | 0 | 45 |
| -30 | -30 | 0 | -50 | 1 | 0 | 0 | 0 | 0 | -1 | 0 | 0 | 0 |
| 0 | 0 | -100 | -100 | 0 | 1 | 0 | 0 | 0 | 0 | -1 | 0 | 0 |
| 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| s1 | s2 | s3 | s4 | s5 | C | answer | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| -400/13 | 0 | 0 | 0 | 0 | 1 | -50/39 | -80/117 | -40/39 | 0 | -1 | 0 | 2300/39 |
| -7/13 | 1 | 0 | 0 | 0 | 0 | 1/390 | 5/234 | -7/390 | 0 | 0 | 0 | 61/78 |
| 400/13 | 0 | 0 | 0 | 1 | 0 | 21/13 | 5/39 | -17/13 | -1 | 0 | 0 | 555/13 |
| 20/13 | 0 | 0 | 1 | 0 | 0 | 2/65 | -2/195 | -1/65 | 0 | 0 | 0 | 5/13 |
| -24/13 | 0 | 1 | 0 | 0 | 0 | -17/390 | 2/585 | 1/195 | 0 | 0 | 0 | 8/39 |
| 400/13 | 0 | 0 | 0 | 0 | 0 | 50/39 | 80/117 | 40/39 | 0 | 1 | 1 | -2300/39 |
| 0.00 | 0.78 | 0.21 | 0.38 | 42.69 | 58.97 |
Now let's circle back to the cost function.
Ultimately, we want to prioritize our resource usage according to an a priori notion of how "difficult" each resource is to acquire. Crude oil is somewhat difficult to obtain compared to water, which is effectively free. Therefore, our cost function should value crude oil more highly than water. The question is: How much more?
Let's see what happens to the above solution if we define water to have a fixed cost of 1, while adjusting the cost of crude oil:
As we increase the cost coefficient of oil, the solution jumps, discontinuously, from one value for x to another. Past a certain point, it settles on a single solution, which will remain the same as the cost coefficient increases to infinity. This solution is the same one we get when we set water's cost to 0 and crude oil's cost to 1.
The cost function is a linear function, and the ratio of these costs defines the slope of the resulting line. As that slope changes, the cost function will reach different vertices on the boundary of the feasible region. Once the slope exceeds that final critical value, the vertex it will reach will remain the same no matter how much steeper that slope becomes, all the way up to infinity (a completely vertical line).
It follows, then, that the ratio in the costs between these resources may be any value that we know to be greater than or equal to this highest critical ratio. Actually calculating this critical value basically requires a linear program of its own, but it turns out we can assume that it will always be less than the ratio between the largest absolute value in the linear program and the smallest. In the example we've been using, that ratio is 10.
To restate our goal, we want our cost function to represent a lexicographical ordering of our resources: First minimize our use of crude oil, then (if multiple solutions would use equal amounts of crude oil) minimize our use of water. Given this, we can revise our cost function to:
C = xwater + 10xcrude oilThe resulting solution remains the same as above.
Now let's discuss the surplus variables. These represent quantities of items which we produce as surplus, in addition to the amounts we requested. While our priority is still, first and foremost, to minimize our resource expenditures, we also want to avoid making extra items that we don't need to.
For example, if a solution requires producing surplus petroleum gas, this cost function means that converting all of that excess petroleum gas into solid fuel and then claiming that as surplus instead will be an equal solution.
We can avoid this by introducing a new pseudo-item that it costs all recipes to use, and minimizing that use of this value at the lowest priority. Let's call this the recipe tax.
C = 10xwater + 100xcrude oil + xtaxAnd our tableau with this new pseudo-item and cost function ends up looking like this:
| tax | s1 | s2 | s3 | s4 | s5 | C | answer | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| -40 | 0 | 30 | 10 | 0 | 0 | 0 | -1 | 0 | 0 | 0 | 0 | 0 | 10 |
| 30 | -30 | 30 | 45 | 0 | 0 | 0 | 0 | -1 | 0 | 0 | 0 | 0 | 0 |
| 0 | 20 | 40 | 55 | 0 | 0 | 0 | 0 | 0 | -1 | 0 | 0 | 0 | 45 |
| -30 | -30 | 0 | -50 | 1 | 0 | 0 | 0 | 0 | 0 | -1 | 0 | 0 | 0 |
| 0 | 0 | -100 | -100 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | -1 | 0 | 0 |
| 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 0 | 0 | 0 | 0 | 10 | 100 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
One possible additional feature is placing multiple input items at the same priority level. If we define the ratio described above as R, and the number of items at each priority level as N, then the coefficient for each priority level can be defined something like this:
Pn = Pn - 1 × R × Nn - 1Where P1 is defined to be 1.


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ActiveTcl User Guide
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tk_getOpenFile, tk_getSaveFile - pop up a dialog box for the user to select a file to open or save.
tk_getOpenFile ?option value ...?
tk_getSaveFile ?option value ...?
The procedures tk_getOpenFile and tk_getSaveFile pop up a dialog box for the user to select a file to open or save. The tk_getOpenFile command is usually associated with the Open command in the File menu. Its purpose is for the user to select an existing file only. If the user enters an non-existent file, the dialog box gives the user an error prompt and requires the user to give an alternative selection. If an application allows the user to create new files, it should do so by providing a separate New menu command.
The tk_getSaveFile command is usually associated with the Save as command in the File menu. If the user enters a file that already exists, the dialog box prompts the user for confirmation whether the existing file should be overwritten or not.
The following option-value pairs are possible as command line arguments to these two commands:
If the user selects a file, both tk_getOpenFile and tk_getSaveFile return the full pathname of this file. If the user cancels the operation, both commands return the empty string.
The filePatternList value given by the -filetypes option is a list of file patterns. Each file pattern is a list of the form
typeName {extension ?extension ...?} ?{macType ?macType ...?}?
typeName is the name of the file type described by this file pattern and is the text string that appears in the File types listbox. extension is a file extension for this file pattern. macType is a four-character Macintosh file type. The list of macTypes is optional and may be omitted for applications that do not need to execute on the Macintosh platform.
Several file patterns may have the same typeName, in which case they refer to the same file type and share the same entry in the listbox. When the user selects an entry in the listbox, all the files that match at least one of the file patterns corresponding to that entry are listed. Usually, each file pattern corresponds to a distinct type of file. The use of more than one file patterns for one type of file is necessary on the Macintosh platform only.
On the Macintosh platform, a file matches a file pattern if its name matches at least one of the extension(s) AND it belongs to at least one of the macType(s) of the file pattern. For example, the C Source Files file pattern in the sample code matches with files that have a .c extension AND belong to the macType TEXT. To use the OR rule instead, you can use two file patterns, one with the extensions only and the other with the macType only. The GIF Files file type in the sample code matches files that EITHER have a .gif extension OR belong to the macType GIFF.
On the Unix and Windows platforms, a file matches a file pattern if its name matches at at least one of the extension(s) of the file pattern. The macTypes are ignored.
On the Unix and Macintosh platforms, extensions are matched using glob-style pattern matching. On the Windows platforms, extensions are matched by the underlying operating system. The types of possible extensions are: (1) the special extension * matches any file; (2) the special extension "" matches any files that do not have an extension (i.e., the filename contains no full stop character); (3) any character string that does not contain any wild card characters (* and ?).
Due to the different pattern matching rules on the various platforms, to ensure portability, wild card characters are not allowed in the extensions, except as in the special extension *. Extensions without a full stop character (e.g, ~) are allowed but may not work on all platforms.
set types { {{Text Files} {.txt} } {{TCL Scripts} {.tcl} } {{C Source Files} {.c} TEXT} {{GIF Files} {.gif} } {{GIF Files} {} GIFF} {{All Files} * } } set filename [tk_getOpenFile -filetypes $types] if {$filename != ""} { # Open the file ... }
tk_chooseDirectory
Copyright © 1996 Sun Microsystems, Inc. Copyright © 1995-1997 Roger E. Critchlow Jr.


Proposal - Symbolic Shape Inference And Partial Data Propagation¶
Note: This proposal was accepted and implemented in ONNX 1.10. Following PRs implemented this proposal: 3518, 3551, 3593, 3580
Introduction¶
ONNX provides an implementation of shape inference on ONNX graphs. Shape inference is computed using the operator level shape inference functions. The inferred shape of an operator is used to get the shape information without having to launch the model in a session. Such static shape inference can be used to catch obvious errors before runtime, eliminate run-time checks which are otherwise guaranteed to pass, improve static memory planning and improve model visualization experience. For pytorch exporter and compiler-based execution providers like Nuphar, shape inference is required (rank inference is minimum requirement), and they cannot work with unknown shapes.
This document explains the limitations of shape inference and lays out a proposal for addressing these limitations.
Current onnx shape inference limitations (Pre ONNX 1.10)¶
Today, ONNX shape inference is not guaranteed to be complete. Wherever possible we fall back to rank inference however, there are scenarios when rank inference is not possible either. Here are the various limitations which block the completion of shape inference:
Some dynamic behaviors block the flow of shape inference, and the shape inference stops. For example, reshape to a dynamically computed shape.
Shape inference works only with constants and simple variables. It does not support arithmetic expressions containing variables nor does it support symbol generation. For example, concatenation on tensors of shapes (5, 2) and (7, 2) can be inferred to produce a result of shape (12, 2), but concatenation on tensors of shapes (5, 2) and (N, 2) will simply produce (?, 2), where “?” represents a dimension with neither dim value nor dim param, rather than containing a representation of N+5 or generating a new symbol (M, 2). In such scenarios shape propagation stops.
All operators are not required to have a shape inference implementation. When such an op is encountered the shape inference stops. There are also cases when rank inference is not done as a fallback mechanism. (Note: We are working on an ongoing basis to identify and fix such issues. The current document does not focus on this limitation)
Goals and Non-Goals¶
Our goal is to fix the shape inference gap in scenarios where:
Shape computations are done in branches (refer to limitation 1)
Symbolic dimensions are present (refer to limitation 2)
By fixing these gaps we aim to:
Unblock pytorch exporter from exporting models when exporting stops because of absence of shape information.
Improve static memory planning in the runtimes.
Enable pre-allocating output buffers outside of the runtimes so that its lifetime can be managed by the caller itself.
Non-goals¶
Add symbolic expressions to ONNX standard: This is not necessary for accomplishing our goals. There are advantages to having this capability, for example this can significantly reduce the number of symbols introduced and it can also provide more deterministic shape calculations in certain special cases. However, the tradeoff is the added complexity. So, at this point we are not considering it. This can be considered in future iterations.
Enable data computation and propagation for older operator sets. (details in the proposal section)
Note: This work will benefit Nuphar as well but right now there is no plan to move Nuphar to use this solution.
Terminology¶
Shape inference can be broken into 2 parts:
Node level shape inference: This refers to operator specific shape inference functions. They are defined with the operator schema itself.
Graph-level shape inference: This refers to the higher-level logic which walks through the entire graph, gets the inferred shape from node level shape inference functions and then makes decisions on merging these inferred shapes with existing shapes so that they are available for downstream nodes.
Proposal¶
Extend current shape inference to allow:
Symbol generation and propagation
Partial data computation and propagation
Extend shape op to generate slice of the shape to facilitate simplifying shape computations.
Extend shape inference¶
Symbol generation and propagation¶
Extend graph level shape inference to maintain a graph level view of symbols and generate new symbols where necessary. This will enable us to continue the shape inference of the downstream nodes.
Example:
For an op like “Concat” if its inputs have shapes “[M]” and “[N]” current shape-inference returns “[?]” where “?” is to indicate a dimension with neither dim-value nor dim-param set. Now, suppose the output X of “Concat” is input to a unary-op Op1() whose output Y is then input to another unary-op Op2() whose output is Z, etc. The shape “[?]” is propagated further. We infer that Y and Z have shape “[?]”. However, we do not infer that X, Y, and Z have the same shape because two “?” cannot be considered equal.
Per the current proposal, “[?]” in inferred shapes will be replaced by a new unique symbol by the graph level shape inference so the downstream nodes can use the symbolic shapes to carry out shape inference. In the current example, “Concat” will produce “[?]” as the shape which will then be replaced by “[K]”, then subsequent shape inference will infer that X, Y, and Z all have the same shape “[K]”. Runtimes can use this information to reuse memory for these tensors.
Partial data computation and propagation¶
When shape inputs are computed dynamically, shape inference post a reshape node stops. This can be prevented by making this data available to the reshape node during shape inference. We propose computation and propagation of data for operators which are used in shape computation.
It is called “partial” data computation and propagation because this will only be done for shape computations. It is not meant to be a full-fledged kernel for the operator. For the same reasons data computations will be implemented for a limited set of operators. While we will increase the coverage in the future iterations it is important to note that for some operators like LSTM, convolution ops, pooling ops etc. data propagation function will never be added because such ops are not used in shape computations.
The following operators will be picked in the first phase. (These operators are generally used for shape computations.)
Ops |
|---|
Add |
Sub |
Mul |
Cast |
Concat |
Gather |
Reshape |
Shape |
Slice |
Size |
Squeeze |
UnSqueeze |
The OpSchema class will be extended to include an optional “PartialDataPropagationFunction” like the existing TypeAndShapeInferenceFunction. This function will provide data computation for the operators which will then be propagated to the downstream operators by the graph level shape inference. PartialDataPropagationFunction will be called by the graph level shape inference after TypeAndShapeInference runs for the node because the output shape is required for partial data computation.
A new interface “DataPropagationContext” will be added to allow PartialDataPropagationFunction to access all the information required to propagate shape data for the given node and allow writing of the computed data.
Example:
using DataPropagationFunction = std::function<void(DataPropagationContext&)>
class OpSchema final {
public:
.
.
.
OpSchema& PartialDataPropagationFunction(DataPropagationFunction dataPropagationFunction) {
partial_data_propagation_function_ = std::move(dataPropagationFunction);
return *this;
}
DataPropagationFunction GetDataPropagationFunction() const {
return partial_data_propagation_function_ ? partial_data_propagation_function_ : dummyDataPropogator;
}
}
// Operator schema example
ONNX_OPERATOR_SET_SCHEMA(
Shape,
13,
OpSchema()
.SetDoc(“”)
.Input(0, "data", "An input tensor.", "T", . . .)
.Output(0, "shape", "Shape of the input tensor", "T1", . . .)
.TypeConstraint("T", OpSchema::all_tensor_types())
.TypeConstraint("T1", {"tensor(int64)"})
.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
. . .
})
.PartialDataPropagationFunction([](DataPropagationContext& ctx) {
TensorShapeProto tp;
// compute output data for shape operator
// add computed data to DataPropagationContext for propagating it downstream
ctx.addOutputData(0, std::move(tp));
}));
The symbol generation will happen at the graph level shape inference, therefore all the models (older opsets as well as the latest opset versions) can benefit from this enhancement. However, the data computation and propagation are tied to the OpScehma and will happen at node level. To begin with these functions will only be added to the latest op schemas. Older schemas can be extended to support data computation later, on a case by case basis to support some high priority scenarios. What this means is that older opset models will not benefit from shape inference improvements because of this enhancement.
Special Cases¶
This section considers some edge cases and proposes a solution to handle them.
Broadcasting with symbolic dims¶
If we have a broadcast between two unknown dimensions “M” and “N” we cannot infer that both M and N should have the same value. The runtime semantics allows for one of the two symbols to have the value 1 and the other to have a value different from 1. So, merging M and N and treating them as the same value is potentially unsound. In this case, a new symbol will be generated for the output shape and the shape inference will continue.
Inferred shape does not match output shape¶
Inferred and existing shapes can be mismatched. Although failing shape inference in such cases seems like the correct approach it may not always be practical. By default, shape inference will fail when such a case is encountered however callers will have an option to override existing types with inferred types. When this option is enabled, shape inference will continue with the inferred type.
Handling symbolic dimensions with data propagation¶
When the shape contains symbolic dimensions, we try and propagate them downstream, however in cases where some arithmetic operations are performed on these symbolic dims we create new symbols and propagate them instead.
Output shape is dependent on input data¶
There are certain nodes like NonZero where the output shape depends on the input data. In this case it is not possible to infer the shape completely hence a new symbolic shape will be created using the inferred rank and shape inference will continue.


Example of unordered lists:
The list-style-position Property
list-style-position: outside (default):
- Coffee - A brewed drink prepared from roasted coffee beans, which are the seeds oA brewed drink prepared from roasted coffee beans, which are the seedsA brewed drink prepared from roasted coffee beans, which are the seedsA brewed drink prepared from roasted coffee beans, which are the seedsA brewed drink prepared from roasted coffee beans, which are the seedsf berries from the Coffea plant
- Tea - An aromatic beverage commonly prepared by pouringA brewed drink prepared from roasted coffee beans, which are the seedsA brewed drink prepared from roasted coffee beans, which are the seedsA brewed drink prepared from roasted coffee beans, which are the seeds hot or boiling water over cured leaves of the Camellia sinensis, an evergreen shrub (bush) native to Asia
list-style-position: inside:
- Coffee - A brewed drink prepared from roasted coffee beans, which aA brewed drink prepared from roasted coffee beans, which are the seedsA brewed drink prepared from roasted coffee beans, which are the seedsA brewed drink prepared from roasted coffee beans, which are the seedsre the seeds of berries from the Coffea plant
- Tea - An aromatic beverage commonly prepared by pouring hot or boiling water over cured leaves of the Camellia sinensis, an evergreen shrub (bush) native toA brewed drink prepared from roasted coffee beans, which are the seedsA brewed drink prepared from roasted coffee beans, which are the seedsA brewed drink prepared from roasted coffee beans, which are the seeds Asia
shortland
list-style: square inside url("sqpurple.gif");
type - position - image
Remove bullets, margin and padding:
ul.demo {
list-style-type: none;
margin: 0;
padding: 0;
}
List with a red left border:


stow
Symlink manager.
Often used to manage dotfiles.
More information: home page
- Symlink all files recursively to a given directory:
stow --target=path/to/target_directory file1 directory1 file2 directory2
- Delete symlinks recursively from a given directory:
stow --delete --target=path/to/target_directory file1 directory1 file2 directory2
- Simulate to see what the result would be like:
stow --simulate --target=path/to/target_directory file1 directory1 file2 directory2
- Delete and resymlink:
stow --restow --target=path/to/target_directory file1 directory1 file2 directory2
- Exclude files matching a regular expression:
stow --ignore=regular_expression --target=path/to/target_directory file1 directory1 file2 directory2


Front End Dev.
Hi, i am Martin. If you like my work's contact me.
About me
Hello ,My name is Martin Tsvein. I started programming as a hobby. Over time, I decided that I wanted to become something more. Then I started to code more seriously and started my career as a programmer.


Despite modern alternatives like Slack, the ancient [IRC][irc-info] is still hugely popular as an online interactive chat platform.
This may be because there are IRC clients for almost every operating system and device, from the Commodore Amiga to your smartphone, and the technology behind IRC is reassuringly simplistic - it really is just raw text and a few control characters being bumped around the network.
Online servers, such as those offered by [Freenode][freenode], are wonderful for both public and private channels. But it’s equally easy to run your own private IRC server, giving you complete control over your data, logs and configuration settings whilst avoiding all the risks and frustrations of dealing with IRC spammers and bots.
In this tutorial, we’ll cover installing the [InspIRCd][inspircd] IRC server on Ubuntu, from installing its dependencies and building the latest version from GitHub, to configuration and execution.
This tutorial is recommended for users who are comfortable using the terminal.
For the first step, we’ll install the dependencies needed to build and run InspIRCd, starting with git:
sudo apt-get install git
Next is Perl so we can run the configuration script included with InspIRCd:
sudo apt-get install perl
sudo apt-get install g++
And finally we need to make sure that make is installed:
sudo apt-get install make
The latest version of InspIRCd can be downloaded from: https://github.com/inspircd/inspircd/releases/latest
As we’re going to be building the latest version from the source code, we need to grab the tar.gz archive either with your browser or from the command line. To take version 2.0.25
as an example, you could use the following command to download the archive:
wget https://github.com/inspircd/inspircd/archive/v2.0.25.tar.gz
Use tar to extract the download:
tar xvf ./v2.0.25.tar.gz
With the source code downloaded and extracted, we can now configure how we want InspIRCd built.
First, enter the installation directory:
cd inspircd-2.0.25
The version number above needs to correspond with your downloaded version.
To begin configuring the installation, enter the following:
perl ./configure
You are now asked a series of questions. When unsure, press return to answer with the default values.
The final question will ask whether you’d like to check for updates to third-party modules, and you should answer y
for yes.
The final output should be similar to the following:
Ok, 144 modules.
Writing inspircd_config.h
Writing GNUmakefile ...
Writing BSDmakefile ...
Writing inspircd ...
Writing cache file for future ./configures ...
To build your server with these settings, please run 'make' now.
*** Remember to edit your configuration files!!! ***
We can now proceed with the build step.
The server can now be built by executing make
in the installation directory. This process will take around 10 minutes, depending on your system speed, so feel free to step away from the computer.
With that complete, type make install
to move the executable files into the locations configured earlier. An overview of this process is output upon completion:
*************************************
* BUILDING INSPIRCD *
* *
* This will take a *long* time. *
* Why not read our wiki at *
* http://wiki.inspircd.org *
* while you wait for make to run? *
*************************************
*************************************
* INSTALL COMPLETE! *
*************************************
Paths:
Base install: /home/javier/build/inspircd-2.0.25/run
Configuration: /home/javier/build/inspircd-2.0.25/run/conf
Binaries: /home/javier/build/inspircd-2.0.25/run/bin
Modules: /home/javier/build/inspircd-2.0.25/run/modules
Data: /home/javier/build/inspircd-2.0.25/run/data
To start the ircd, run: /home/javier/build/inspircd-2.0.25/run/inspircd start
Remember to create your config file: /home/javier/build/inspircd-2.0.25/run/conf/inspircd.conf
Examples are available at: /home/javier/build/inspircd-2.0.25/run/conf/examples/
With InspIRCd fully installed, we can configure the server.
We can now proceed with the build step.
From the build directory, create a text file called run/config/inspircd.conf
and insert the following:
<config format="xml">
<define name="bindip" value="<IP_ADDRESS>">
<define name="localips" value="&bindip;/24">
####### SERVER CONFIGURATION #######
<server
name="SERVER_HOSTNAME/FQDN"
description="SERVER_DESCRIPTION"
id="SERVER_SID"
network="NETWORK_NAME">
####### ADMIN INFO #######
<admin
name="ADMIN_NAME"
nick="ADMIN_NICK"
email="ADMIN_EMAIL">
####### PORT CONFIGURATION #######
<bind
address="SERVER_IP"
port="SERVER_PORT"
type="SERVER_TYPE">
Change the following values in the above text to reflect your own configuration:
The configuration file should now look something like this:
<config format="xml">
<define name="bindip" value="<IP_ADDRESS>">
<define name="localips" value="&bindip;/24">
####### SERVER CONFIGURATION #######
<server
name="tutorials.ubuntu.com"
description="Welcome to Ubuntu Tutorials"
id="97K"
network="tutorials.ubuntu.com">
####### ADMIN INFO #######
<admin
name="tutorial ubuntu"
nick="tutorial"
<EMAIL_ADDRESS>####### PORT CONFIGURATION #######
<bind
address="23.54.785.654"
port="6697"
type="clients">
Make sure you save your changes!
It’s now time to start up InspIRCd for the first time!
In your the terminal window, type:
./inspircd start
./inspircd status
If successful, you will see the following output:
InspIRCd is running (PID: 13301)
Congratulations! Your server is now online!
Any IRC client capable of accessing your server will now be able to connect to your IRC server.
.conf
fileChanges to your IRC server need to be made to the config file and a list of supported commands and other useful information can be found at InspIRCd’s Wiki.
.config
file is correct


Edit Client
Company Name
Company Representative
Email
Phone
Phone Type
Address
Address Two
City
State
Zip Code
County
Start Date


You have confirmed your identity
You are about to login to view:
Booking reference number 0002 3345 3348
Check for a new email.
This will contain your login access code
Need help logging in?
When you opted for email referrals, you should have been asked to confirm the postdcode you will use to identify youreself here. This will usually be the postcode of the address registered with your GP.
If you are having difficulty, contact your referrer to confirm the address and postcode they have for you.
Alternatively call the e-Referral Service Appointments Line and provide your booking reference number.
Phone: 0345 60 88 88 8
Textphone: 0345 8 50 22 50
Appointments Line opening hours are: weekdays 8am to 8pm; weekends and bank holidays 8am to 4pm (closed on Christmas Day).


For quite a long time now I'm working as a 'full-stack' developer with main emphasis on UI development. We were adopting Angular technology from the very early days until now and never regret it. It's a perfect fit for our company and also for my private work.
Most of our time we are working on SPAs for mobile/web.  Requirements were for us it is best to go for Java/server-side rendering.
And there's the problem: We have to learn/remember two totally different rendering engines, syntaxes etc. It's a pain to do exactly the same thing, producing HTML, in totally different ways. I wish I had reusable components/classes with a well defined API which I could just throw in and use.
I wanted to have the Angular experience on the JVM and so ngoy was born.
ngoy is pronounced 'njoy - enjoy with a silent e.
Angular developers should be familiar with ngoy from the very beginning. Binding syntax, components, templates, pipes, services etc. are all the same.
The modern SPA concept fits very well into a 'classic' server-side environment.
I know it's nothing really new...like most of todays modern software. But it's a fresh look at server-side rendering, which is still heavily used in the industrie.
Thank you.


Since I’ve been thinking about deployment I’ve been thinking a lot more about what "configuration management" means, how it should work, what it should do.
I guess my quick summary of configuration management is that it is setting up a server correctly. "Correct" is an ambiguous term, but given that there are so many to configuration management the solutions are also ambiguous.
Silver Lining includes configuration management of a sort. It is very simple. Right now it is simply a bunch of files to rsync over, and one shell script (you can see the files here and the script here — at least until I move them and those links start 404ing). Also each "service" (e.g., a database) has a simple setup script. I’m sure this system will become more complicated over time, but it’s really simple right now, and I like that.
The other system I’ve been asked about the most about lately is Puppet. Puppet is a real configuration management system. The driving forces are very different: I’m just trying to get a system set up that is in all ways acceptable for web application deployment. I want one system set up for one kind of task; I am completely focused on that end, and I care about means only insofar as I don’t want to get distracted by those means. Puppet is for people who care about the means, not just the ends. People who want things to work in a particular way; I only care that they work.
That’s the big difference between Puppet and Silver Lining. The smaller difference (that I want to talk about) is "push" vs. "pull". Grig wrote up some notes on two approaches. Silver Lining uses a "push" system (though calling it a "system" is kind of overselling what it does) while Puppet is "pull". Basically Silver Lining runs these commands (from your personal computer):
$ rsync -r <silverlining>/server-files/serverroot/ root@server:/
$ ssh root@server "$(cat <silverlining>/server-files/update-server-script.sh)"
This is what happens when you run silver setup-node server: it pushes a bunch of files over to the server, and runs a shell script. If you update either of the files or the shell script you run silver setup-node again to update the server. This is "push" because everything is initiated by the "master" (in this case, the developer’s personal computer).
Puppet uses a pull model. In this model there is a daemon running on every machine, and these machines call in to the master to see if there’s any new instructions for them. If there are, the daemon applies those instructions to the machine it is running on.
Grig identifies two big advantages to this pull model:
- When a new server comes up it can get instructions from the master and start doing things. You can’t push instructions to a server that isn’t there, and the server itself is most aware of when it is ready to do stuff.
- If a lot of servers come up, they can all do the setup work on their own, they only have to ask the master what to do.
But… I don’t buy either justification.
First: servers don’t just do things when they start up. To get this to work you have to create custom images with Puppet installed, and configured to know where the master is, and either the image or the master needs some indication of what kind of server you intended to create. All this is to avoid polling a server to see when it comes online. Polling a server is lame (and is the best Silver Lining can do right now), but avoiding polling can be done with something a lot simpler than a complete change from push to pull.
Second: there’s nothing unscalable about push. Look at those commands: one rsync and one ssh. The first is pretty darn cheap, and the second is cheap on the master and expensive on the remote machine (since it is doing things like installing stuff). You need to do it on lots of machines?  This is not complicated stuff.
It is possible to write a push system that is hard to scale, if the master is doing lots of work. But just don’t do that. Upload your setup code to the remote server/slave and run it there. Problem fixed!
What are the advantages of push?
- Easy to bootstrap. A bare server can be setup with push, no customization needed. Any customization is another kind of configuration, and configuration should be automated, and… well, this is why it’s a bootstrap problem.
 Weird behavior is also synchronous; can’t tell why servers are doing something? Run the commands and watch the output.
- Development is sensible: if you have a change to your setup scripts, you can try it out from your machine. You don’t need to do anything exceptional, your machine doesn’t have to accept connections from the slave, you don’t need special instructions to keep the slave from setting itself up as a production machine, there’s no daemon that might need modifications… none of that. You change the code, you run it, it works.
 If you don’t start thinking about push and pull and other design choices, it simply becomes: do the obvious and easy thing.
In conclusion: push is sensible, pull is needless complexity.
Automatically generated list of related posts:
- Treating configuration values as templates A while back I described fassembler and one of the...
I used an rsync-based automated deployment tool before (slack, created at google), but it was a ‘pull’ system, where the client machines were rsync-ing from a central master. We ran into issues when we had to rsync thousands of files and there were hundreds of clients involved.
In your case, you’re pushing the rsync from the master to all your other nodes, and the push is under your control, so you won’t run into this issue necessarily, but it’s something to keep in mind.
Grig
I am, admittedly, still working on attaining just the first order of magnitude (1 server to 10), and have not even gotten close to the next order of magnitude. I have no idea how far this can go… I guess I could spin up a hundred servers and try, but… eh.
Naah, wait for your user reports to come in ;-) They’ll run into scenarios you can never even start to imagine.
Also, speaking of large scale file synchronization problems, here’s how twitter does it: http://github.com/lg/murder + Capistrano. Interesting use of BitTorrent.
This is essentially the “user-data” that you can push in to a Debian or Ubuntu image starting up on EC2 http://alestic.com/2009/06/ec2-user-data-scripts , something it gets right. If the first two characters of the user-data are “#!” it treats it as a script and executes it, and the stdout is directed to /var/log/syslog, which you can tail and make sure everything worked (use “set -e -x”!). This is enormously flexible and powerful, and cheap and easy. I much prefer having a nice, tidy library of useful Bash functions that install packages and sed/awk various config scripts, that I understand exactly what they are going to do, than going, okay, I need a centralized config server, an AMI which has the client — before I can do anything — ugh, my heart sinks …
Couldn’t agree more. This is why I used a push model for collective.hostout. I wanted something that made as little assumptions about the server, it’s firewalls and what other servers exist (e.g. svn/git/puppet) servers. You create a VPS and push the bootstrap and code up.
Push systems are called “agentless” in the world of Enterprise (i.e. expensive, bloated yet underwhelming) network and systems monitoring. Commercial vendors tend to disparage agentless systems, usually because they like the idea of selling lots of licenses for the agents. Granted, in the world of Windows where SSH is not ubiquitous, you need agents to bootstrap the monitoring infrastructure.
Congratulations on the Mozilla job, BTW!
Unfortunately Ian, you’re completely wrong. I don’t think I’ve ever read a bigger misunderstanding of Puppet. It’s for people who want things to work “a certain way”?  I’m sure for setting up one or two servers something like Silver Lining might work but it won’t scale at all and it covers only the very, very, very basics of what configuration management. (Hello, dependencies? What do you do when one service needs another service before it can start?)  Thanks for that at least.
I think I was pretty clear that Silver Lining is a very simple and limited approach to the problem of configuration management. It actually “handles” things like dependencies and starting services in the right order, because Ubuntu and Debian maintainers have been doing that work for ages now, and Silver Lining uses that work. If you are willing to use and trust a quality distribution like Ubuntu, then configuration management becomes a much more approachable project. If you want to be distribution- or OS-agnostic, or you disagree with the way the packagers arranged those packages, then you have to do a lot more work. Puppet does that work. I don’t consider the tradeoff worth it, but I can understand why some people would feel differently. That’s a [different argument](https://ianbicking.org/2010/02/10/why-toppcloud-not-agnostic/) for a different time.
Puppet also uses a pull model. It would be entirely possible to use a push model to the same effect.
There is one additional advantage for the push model, at least for me: security. With pull somehow it’s the production server that must connect to the developers’ citadel. I might be a bit paranoid, but this always means that you must let a way to for the deployment server to connect to the citadel, even if for just a little anonymous ftp account or an open port in the firewall. This means that if the production server is compromised it has a channel that could be abused to try and compromise the citadel.
I don’t feel at ease with this scheme. I’d rather have my completely firewalled developers’ citadel that connects to the production server. (BTW this holds for the backup as well. I never have the deployment server connect to the backup server, but the other way round.)
I think flexibility is the key point here.
Specially at the moment of wanting to build a server from scratch. Sometimes, you do not need an extremely efficient (yet overcomplicated) piece of software that can handle 1,000 servers at the same time when all you really want/need is get one up and running.
I like simplicity.
I like getting things done.
And Puppet does not seem neither simple or easy to get started with.
For a few months I have been developing something along the lines of Silver Lining, but more oriented to internal infrastructure System Administration (nothing to do with the Cloud) called Pacha (http://code.google.com/p/pacha)
Wish I could have stayed for the Silver Lining sprint after PyCon…
Sorry about being slightly off topic, but how do you view Silver lining’s overlap with fabric?
http://cloudsilverlining.org/design.html#fabric
It’s in the docs http://cloudsilverlining.org/design.html#fabric
IMO Fabric’s problem is that it expect you to run a lot of stuff on the server which makes it a messier approach.
Interesting Usenix paper from 1998. They swear by the pull mechanism too:
http://www.infrastructures.org/papers/bootstrap/bootstrap.html
Totally agree. This is the main reason I was first turned on to toppcloud/silverlining having push combined with “a setup that does one thing and does it well” is an excellent combination. Will it scale? we’ll see that when we need it. For now we are more than happy for running each app entirely on one box.
A pull-based system is useful for test machines that constantly reboot to ensure a clean test environment. Wouldn’t it be easier to have the machine come up, pull its configuration, start testing than to have it come up, and wait for another server to configure it and start the tests?
Your example makes more sense to me than most use cases I’ve heard. The way Paul Smith mentions that EC2 instances can be started with a script to be run on boot seems kind of like a hybrid. It’s not exactly push, as the commands are executed offline on restart. But it’s not pull, in that everything is initiated when the server is requisitioned, and initiated by the requisitioner. I guess it’s async push…?
There are certainly unscalable aspects of push and pull, but in fact they have different optimizations. Push is easier on the server, since it can more easily manage its resource caching, but it is highly unpredictable as you don’t know whether the clients are available or not. Moreover, a server is usually equipped to deal with the load where as a client isn’t. With push you are letting one system drive the availability of many, which makes adaptation and fault tolerance very difficult. Pull can automatically perform distributed load balancing and each machine can download stuff at its convenience. This especially applies to hosts that are down at the moment of push.
Another problem with push is that the clients have no say in what they get from the “pusher”. This is a potential security disaster waiting to happen. Clients are in a better position to decide when and what to download than a central pusher is to decide when and what they need from its single viewpoint. So adaptive behaviour makes a strong case for pull based (subscription based) services.
As for the rest, Cfengine introduced pull and is of course far superior to Puppet both in capabilities and implementation ;-)


Chunky Comic Reader - iPad
This requires the Pro version, which is unlocked with a $4 in-app purchase.
To add the feed, tap the
cloud button at the top.
Edit then tap the
+ next to
Calibre / Ubooquity / OPDS .
Server:
Username/Password: Use your logins from here.
The
All comics list is massive, I'd recommend using the
Folders entry instead (and then tap the pin to bookmark it).
You can then download comics by tapping the down arrow next to the issue, or the
All at the bottom (to download all in the visible list).
Challenger Comics Viewer / Chrome - Android
While Challenger also supports OPDS feeds, like Chunky, the support isn't quite as smooth/doesn't support larger comic databases. Instead, I suggest the following:
In Challenger, choose the
Add Folder option.
Choose your
Internal SD card.
Click the
Download folder and hit Select.
Now open this page in your browser (Chrome should work best), choose whatever comic you'd like to read and tap
Download.

Voila, comics!


Previous drug experiences include alcohol, cannabis, synthetic cannabinoids, mephedrone, cocaine, piperazines, bk-MDMA, methylphenidate, diazepam, zopiclone, dextromethorphan, diphenhydramine, and lysergamides.
Several months ago, I began using Sudafed (pseudoephedrine) tablets to deal with nasal congestion. At this time I had not consumed any drugs within at least two months, other than regularly drinking alcohol. I would generally consume one 60mg pill every morning. I soon began to notice unusual effects; a couple times I became extremely drowsy. More reliably, after consuming pseudoephedrine I would intermittently experience a very euphoric 'rushing' sensation which would make my whole body tingle. Enjoying the increased energy and rushes, I took higher doses on occasion (up to about 240mg, perhaps once or twice a week), while continuing the medicinal use.
After nearly a month of daily use, however, the experience began to change. There was one day in which I took only one 60mg pill in the morning, and that evening while sitting calmly, the skin-tingling rushing sensation hit me, but not like it had before. There was no euphoria. My entire body was tingling intensively (uncomfortably so) for at least thirty seconds, during which time I had severe tunnel vision. I thought I was going to lose consciousness, but luckily it passed. In retrospect, this seems to have been a withdrawal effect; in my defence, neither the pharmacist nor the manufacturer warned against long-term use.
Unfazed by this experience, I continued using pseudoephedrine as usual, perhaps 120mg per day on average.
A few days later, I again took 60mg in the morning. A couple hours after I took an over-the-country energy supplement (contains 'traces' of phenylalanine, which indirectly increases production of dopamine, adrenaline and noradrenaline - this may have contributed). I didn't eat that day after about 2pm. Around 7pm, I began feeling very dizzy and unwell, so I sat on a chair and tried to remain calm. I was noticeably tachycardic at this point, and within a few moments the rush hit me again. Extreme thirst, dizziness, static-like tunnel vision, chest pains, tremors, acute changes in temperature, intense tingling sensation. I fell to the floor, thinking I was having a heart attack. I may have lost consciousness briefly.
An ambulance was called, which took nearly an hour to arrive. In this time I slowly began to recover, though I sat in hospital all night still feeling disorientated. Multiple tests conducted (ECG, x-rays, blood, etc), and no explanation was found. Discharged the next morning, and a couple days later I was feeling fine.
Looking back, I know this was a panic attack; I have had a couple more since. Worryingly, this chemical seems to have altered my brain chemistry, as my body now reacts very badly to adrenaline. I can't exercise or take any drugs (even alcohol) as any moderate adrenaline release causes tremors, accompanied by fear and chest pains (most likely stress related).
Take care with pseudoephedrine; it seems to have some scary unknown side effects.
[Reported Dose: '60-240mg daily']
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


NEWS.md
As of January 2021:
As of December 2020:
delta
slot has been added to the txshift
class to record the shift.txshift_msm
class.summary
method has been removed, with the functionality now supported by the print
methods for the txshift
and txshift_msm
classes.plot
method has been amended to support simultaneous confidence bands.As of October 2020:
C
to C_samp
for the indicator of inclusion in the second-stage sample.C_cens
to denote censoring due to loss to follow-up, i.e., prior to the occurrence of the outcome.C_cens
and adjusts the estimation procedure so as to use inverse censoring weights in the full-data EIF procedure (NOTE: these are not updated in the two-phase sampling correction).est_g
to est_g_exp
for the exposure mechanism density estimationest_ipcw
to est_samp
for the two-phase sampling mechanismest_g_cens
for the loss to follow-up censoring mechanismAs of September 2020:
sl3
dependency to an Enhances
designation for CRAN submission.sl3
from Remotes
and added installation safety checks.As of June 2020:
msm_vimshift
).plot
method for MSM summarization (msm_vimshift
).msm_vimshift
for computing CIs for binary outcomes by switching from manually computing CIs to internally using custom confint
method.msm_vimshift
for building lm
model objects through weighted regression; move models from plot
method to msm_vimshift
.As of April 2020:
onestep_txshift
and tmle_txshift
).txshift
, vimshift_msm
).assert_that
checks.fit_spec
terminology to fit_ext
for external fits.As of March 2020:
As of March 2020:
eif
, est_Hn
, est_Q
, est_g
, est_ipcw
, fit_fluctuation
, ipcw_eif_update
).sl3
).sl3
>= v1.3.7)As of December 2019:
hal9001::fit_hal
in pseudo-outcome regression for efficient estimation by explicitly including max_degree = NULL
.sl3
dependency to a specific tag (formerly v1.2.0).sl3
>= v1.3.6 and hal9001
>= v0.2.5.As of October 2019:
as.data.table
to data.table
in internal functions to catch up with changes in dependencies.As of September 2019:
As of September 2019:
drtmle
and/or survtmle
.sl3::Lrnr_glm
instead of sl3::Lrnr_glm_fast
.As of July 2019:
As of June 2019:
sl3
dependency to version 1.2.0 of that package for stability.As of June 2019:
hal9001::fit_hal
for pseudo-outcome EIF regression.tx_shift
to shift_additive
.As of June 2019:
As of June 2019:
bound_propensity
function to bound the propensity score away from zero by a factor 1/n, rather than to numerical precision.As of April 2019:
sl3
.As of March 2019:
As of February 2019:


The dependencies display from the last step uses a default XML profile.
We can improve it's appearance and function by creating a custom profile.
Fortunately, you don't have to start with a blank file.
Whenever the Technology Explorer for DB2 deals with a table that doesn't have a profile, it creates a default using
the pureXML capabilities built into the DB2 data server.
(That is how it can display the contents of any table in the Tables page.)
To create a default that you can edit, select the XML reference column in the table catalog view of the Technology Explorer.
Goal:
Find the 'SYSCAT.TABDEP' table and save the XML table definition file associated with it.
Steps to complete:
- On the menu bar of the table page to the right
- Select 'Schema' from the 'Column' drop down
- Select '=' from the 'Condition' drop down
- Select 'SYSCAT' from the drop down
- Click on the 'Add condition' button
- Select 'Table' from the 'Column' drop down
- Select '=' from the 'Condition' drop down
- Enter in the value 'TABDEP' in the right hand field (case is sensitive)
- Click the 'Apply search' button
- If you do not see the table try searching again
- Click the icon in the XML column
- Right-click in the XML document and select 'This Frame' -> 'Save Frame As...'
- Navigate to the folder /var/www/tableDefinitions/
- Save the file with the name 'tableDependCat.xml' (case is sensitive)


Navigation Object
The Navigation object contains functions for building presentation navigational elements, as well as controls for navigation functionality.
- Source:
- framework.js, line 778
Properties:
| Name | Type | Description |
|---|---|---|
active |
{string} | - Currently active page id |
Methods
<static> eDetailer.NAV.buttonBar(button, prefix) → {string}
Buttonbar Builder
Builds the bottom button bar navigation based on supplied array within the sitemap.
Parameters:
| Name | Type | Argument | Default | Description |
|---|---|---|---|---|
button |
{array} | - Array of buttons from the sitemap | ||
prefix |
{string} |
optional |
"bbar-" | - Prefix for all IDs generated (should be unique) |
Returns:
- Source:
- framework.js, line 937
- To Do:
- This should be unnecessary... modify NAV.generate to accomodate
<static> eDetailer.NAV.generate(pages, level, prefix, accordian, hidden, buttons) → {string}
Menu Builder
Recursive function to build the page menu hierarchies based on supplied array of pages. Used for Navigation and ToC
Parameters:
| Name | Type | Argument | Default | Description |
|---|---|---|---|---|
pages |
{array} | - Array of page objects | ||
level |
{int} | - Current build level of hierarchal menu | ||
prefix |
{string} |
optional |
"nav-" | - Prefix for all IDs generated (should be unique) |
accordian |
{boolean} |
optional |
false | - Include accordian functionality |
hidden |
{boolean} |
optional |
false | - Include sitemap entries with a type of "hidden" |
buttons |
{boolean} |
optional |
false | - Include sitemap entries with a type of "button" |
Returns:
- Source:
- framework.js, line 862
- To Do:
- update parameters to accept array of types
<static> eDetailer.NAV.hide()
Close the Navigation Menu
Removes the class "open" from the navigation menu and the class "active" from the navigation button
- Source:
- framework.js, line 832
<static> eDetailer.NAV.itemToggle()
Navigation Menu Item Toggle
Toggles the open/close state of accordian menu items
- Source:
- framework.js, line 799
<static> eDetailer.NAV.show()
Open the Navigation Menu
Adds the class "open" to the navigation menu and the class "active" to the navigation button
- Source:
- framework.js, line 818
<static> eDetailer.NAV.toggle()
Navigation Visibility Toggle
Toggles the open/close state of the navigation based on whether the navigation element has a class of "open"
- Source:
- framework.js, line 788


These nodes are used extensively for building the UI, especially the Explorer. Using the Nodes API, it is possible to create a variety of custom nodes and entire subhierarchies, and manipulate and search existing nodes. In conjunction with the Datasystems API, nodes may also represent persistent data and can present the user interface to this data. Cookies may also be attached to nodes.


WITNESS LIST Revenue & Public Education Funding, Select Committee February 18, 1997-5:00P HB 4 On: Craymer, Dale (Governor's Office) Daugherty, Craig (Comptroller's Office) Hamilton, Billy (Comptroller's Office) Hawkins, Albert (Governor's Office) Keel, John (LBB) Le Bas, James (Governor's Office) Madrigal, Sergio (LBB) Sorgee, Richard (LBB) Wilson, Brian (Governor's Office)


Islamabad (/ɪzˈlɑːməˌbɑːd/; Urdu: اسلام آباد, Islāmābād, Urdu pronunciation: [ɪsˌlɑːmɑˈbɑːd̪]) is the capital city of Pakistan located within the Islamabad Capital Territory.
It has a population of 2 million[3] and together with its neighboring twin city of Rawalpindi, the greater Islamabad-Rawalpindi metropolitan area is the third largest in Pakistan with a population of over 4.5 million inhabitants
Calendar
Here we are making sweet AJAX-powered tabs with CSS3 and the newly released version 1.4 of jQuery.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer luctus quam quis nibh fringilla sit amet consectetur lectus malesuada. Sed nec libero erat. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nunc mi nisi, rhoncus ut vestibulum ac, sollicitudin quis lorem. Duis felis dui, vulputate nec adipiscing nec, interdum vel tortor. Sed gravida, erat nec rutrum tincidunt, metus mauris imperdiet nunc, et elementum tortor nunc at eros. Donec malesuada congue molestie. Suspendisse potenti. Vestibulum cursus congue sem et feugiat. Morbi quis elit odio.
Weather
The climate of Islamabad has a humid subtropical climate (Köppen: Cwa), with five seasons: Winter (Nov−Feb), Spring (March−April), Summer (May−June), Rainy Monsoon (July−August) and Autumn (September−October). The wettest month is July, with heavy rainfall and evening thunderstorms with the possibility of cloudburst. The coolest month is January, with temperatures variable by location. In Islamabad, temperatures vary from cold to mild, routinely dropping below zero. In the hills there is sparse snowfall. The weather ranges from a minimum of −3.9 °C (25.0 °F) in January to a maximum of 46.1 °C (115.0 °F) in June.[1] The average low is 2 °C (35.6 °F) in January, while the average high is 38.1 °C (100.6 °F) in June.[2] The highest temperature recorded was 46.5 °C (115.7 °F) in June, while the lowest temperature was −4 °C (24.8 °F) in January.[3] On 23 July 2001, Islamabad received a record breaking 620 millimetres (24 in) of rain fell in just 10 hours.
It was the heaviest rainfall in 24 hours in Islamabad and at any locality in Pakistan during the past 100 years. The hottest month is June, where average highs routinely exceed 38 °C (100.4 °F).


Final Euro NCAP results ends with eight five stars
Eight of the 12 models tested earning the maximum five-stars
by Charl BoschIn what is expected to be its final release for 2019, the European New Car Assessment Programme, otherwise known as Euro NCAP, has released its latest batch of vehicle assessments with eight of the 12 models tested earning the maximum five-stars.
Of the top rated models, only the Audi Q7 and Subaru Forester are offered in South Africa, with the incoming Peugeot 2008 bagging four stars, but with an additional star added when equipped with the optional safety package.
Bar the new Renault Captur and Porsche Taycan arriving next year, as well as the Ford Kuga that is still to be confirmed, the other models to receive safety’s highest grading included the recently revised Ford Mondeo, the new Skoda Octavia and the all-electric Tesla Model X, while the updated Volkswagen Sharan and its Seat equivalent, the Alhambra, both obtained four stars. The facelift Jeep Renegade’s lack of autonomous emergency braking however saw it receiving only three stars, the lowest of any model tested this year.
“Well done to Tesla and the other five stars performers for not only bringing best in class performance in testing but also making safety systems accessible and standard across the European market. It is also good to see manufacturers – Ford and Volkswagen in this case – standardising AEB and keeping safety up-to-date even for older cars, to the benefit of their customers,” Euro NCAP Secretary General, Michiel van Ratingen, said in a statement.
“It’s a pity that not everyone follows this example, and by prioritising looks over substance Jeep ended up at the bottom of the list of this year’s rated cars. The good news is that consumers have more choice than ever to find a safer alternative”.
| CAR | STARS | ADULT | CHILD | PEDESTRIAN | ASSIST | NOTE |
| Audi A1 |
|
95% | 85% | 73% | 80% | |
| Audi Q7 |
|
92% | 86% | 71% | 72% | |
| Audi e-tron |
|
91% | 85% | 71% | 76% | |
| BMW 1 Series |
|
83% | 87% | 76% | 72% | |
| BMW 3 Series |
|
97% | 87% | 87% | 76% | |
| BMW Z4 |
|
97% | 87% | 91% | 76% | |
| Citroën C5 Aircross |
|
87% | 86% | 58% | 75% | |
| Citroën C5 Aircross |
|
89% | 86% | 67% | 82% | Safety Pack |
| DS3 Crossback |
|
87% | 86% | 54% | 63% | |
| DS3 Crossback |
|
96% | 86% | 64% | 76% | Safety Pack |
| Ford Explorer |
|
87% | 86% | 61% | 76% | |
| Ford Focus |
|
96% | 87% | 72% | 75% | |
| Ford Kuga |
|
92% | 86% | 82% | 73% | |
| Ford Mondeo |
|
96% | 87% | 70% | 73% | |
| Honda CR-V |
|
93% | 83% | 70% | 76% | |
| Kia Ceed |
|
88% | 85% | 52% | 68% | |
| Kia Ceed |
|
88% | 85% | 68% | 73% | Safety Pack |
| Jeep Cherokee |
|
80% | 78% | 56% | 69% | |
| Jeep Renegade |
|
82% | 84% | 55% | 58% | |
| Lexus UX |
|
96% | 85% | 82% | 77% | |
| Mazda3 |
|
98% | 87% | 81% | 73% | |
| Mazda CX-30 |
|
99% | 86% | 80% | 77% | |
| Mercedes-Benz B-Class |
|
96% | 90% | 78% | 75% | |
| Mercedes-Benz CLA |
|
96% | 91% | 91% | 75% | |
| Mercedes-Benz EQC |
|
96% | 90% | 75% | 75% | |
| Mercedes-Benz GLB |
|
92% | 88% | 78% | 74% | |
| Mercedes-Benz G-Class |
|
90% | 83% | 78% | 72% | |
| Mercedes-Benz GLE |
|
91% | 90% | 78% | 78% | |
| Range Rover Evoque |
|
94% | 87% | 72% | 73% | |
| Renault Captur |
|
96% | 83% | 70% | 74% | |
| Renault Clio |
|
96% | 89% | 72% | 75% | |
| Opel/Vauxhall Corsa |
|
84% | 86% | 66% | 69% | |
| Peugeot 208 |
|
91% | 86% | 56% | 71% | |
| Peugeot 2008 |
|
91% | 84% | 62% | 68% | |
| Peugeot 2008 |
|
93% | 84% | 73% | 73% | Safety Pack |
| Porsche Taycan |
|
85% | 83% | 70% | 73% | |
| Seat Alhambra |
|
89% | 78% | 59% | 62% | |
| Seat Tarraco |
|
97% | 84% | 79% | 79% | |
| Skoda Kamiq |
|
96% | 85% | 80% | 76% | |
| Skoda Octavia |
|
92% | 88% | 73% | 79% | |
| Skoda Scala |
|
97% | 87% | 81% | 76% | |
| SsangYong Korando |
|
88% | 85% | 68% | 74% | |
| Subaru Forester |
|
97% | 91% | 80% | 78% | |
| Tesla Model X |
|
98% | 81% | 72% | 94% | |
| Tesla Model 3 |
|
95% | 86% | 74% | 94% | |
| Toyota Corolla |
|
95% | 84% | 86% | 77% | |
| Toyota RAV4 |
|
93% | 87% | 85% | 77% | |
| Volkswagen Sharan |
|
89% | 78% | 59% | 62% | |
| Volkswagen T-Cross |
|
97% | 86% | 81% | 80% |


Speakers and panels concerning workforce topics with a mission to inspire
Breakout sessions on specific professional and personal topics catered to the individual
Professionals who agree to disagree on controversial topics
Departmental and student organization representatives showcasing their opportunities for students
Leadership in Your Career ▪ Can I have a Career and a Family? ▪ Is Striving for Diversity a Good Thing? ▪ What is Unconscious Bias? ▪ Getting Hired ▪ Starting Your Own Business ▪ Professional Opportunities in Engineering ▪ What's Next: Work or Grad School? ▪ Increasing Your Personal Productivity ▪ Growing Confidence in Your Career ▪ Social Impact Training ▪ Handling the Money You Earn ▪ How Mentorship Can Change Your Life ▪ Introduction to the College of Engineering


{{- partial "_shared/head.html" . -}}
{{- block "header" . }}{{- end }}
{{- block "main" . }}{{- end }}
{{- block "footer" . }}{{- end }}
{{- partial "_shared/js.html" . -}} {{ template "_internal/google_analytics.html" . }}


I love the vibe that comes with developing and editing.Got me in love with coding and photography.I enjoy taking photos, the angles to it and the finesse shots.The ninja comes out while coding. I am a fan of TMNT especially Donnie.
Well a creative needs to eat well and get into that zone : No pizza no ninja
Some of my photography works are in the


A C D E F G I L M O P R S T U W
All Classes All Packages
All Classes All Packages
All Classes All Packages
S
- setApellido1(String) - Method in class ejercicio01.Cuenta
- setApellido2(String) - Method in class ejercicio01.Cuenta
- setCaptureRawRecord(boolean) - Method in class com.csvreader.CsvReader
- setComment(char) - Method in class com.csvreader.CsvReader
-
Sets the character to use as a comment signal.
- setComment(char) - Method in class com.csvreader.CsvWriter
- setDelimiter(char) - Method in class com.csvreader.CsvReader
-
Sets the character to use as the column delimiter.
- setDelimiter(char) - Method in class com.csvreader.CsvWriter
-
Sets the character to use as the column delimiter.
- setDni(String) - Method in class ejercicio01.Cuenta
- setEscapeMode(int) - Method in class com.csvreader.CsvReader
-
Sets the current way to escape an occurance of the text qualifier inside qualified data.
- setEscapeMode(int) - Method in class com.csvreader.CsvWriter
- setForceQualifier(boolean) - Method in class com.csvreader.CsvWriter
-
Use this to force all fields to be surrounded by the text qualifier even if the qualifier is not necessarily needed to escape this field.
- setHeaders(String[]) - Method in class com.csvreader.CsvReader
- setIdOficina(String) - Method in class ejercicio01.Oficina
- setNombre(String) - Method in class ejercicio01.Cuenta
- setNumCuenta(String) - Method in class ejercicio01.Cuenta
- setPin(String) - Method in class ejercicio01.Cuenta
- setRecordDelimiter(char) - Method in class com.csvreader.CsvReader
-
Sets the character to use as the record delimiter.
- setRecordDelimiter(char) - Method in class com.csvreader.CsvWriter
-
Sets the character to use as the record delimiter.
- setRutaFicheroCuentas(String) - Method in class ejercicio01.Oficina
- setSafetySwitch(boolean) - Method in class com.csvreader.CsvReader
-
Safety caution to prevent the parser from using large amounts of memory in the case where parsing settings like file encodings don't end up matching the actual format of a file.
- setSaldo(String) - Method in class ejercicio01.Cuenta
- setSkipEmptyRecords(boolean) - Method in class com.csvreader.CsvReader
- setTextQualifier(char) - Method in class com.csvreader.CsvReader
-
Sets the character to use as a text qualifier in the data.
- setTextQualifier(char) - Method in class com.csvreader.CsvWriter
-
Sets the character to use as a text qualifier in the data.
- setTrimWhitespace(boolean) - Method in class com.csvreader.CsvReader
-
Sets whether leading and trailing whitespace characters should be trimmed from non-textqualified column data or not.
- setUseComments(boolean) - Method in class com.csvreader.CsvReader
-
Sets whether comments are being looked for while parsing or not.
- setUseTextQualifier(boolean) - Method in class com.csvreader.CsvReader
-
Sets whether text qualifiers will be used while parsing or not.
- setUseTextQualifier(boolean) - Method in class com.csvreader.CsvWriter
-
Sets whether text qualifiers will be used while writing data or not.
- skipLine() - Method in class com.csvreader.CsvReader
-
Skips the next line of data using the standard end of line characters and does not do any column delimited parsing.
- skipRecord() - Method in class com.csvreader.CsvReader
-
Skips the next record of data by parsing each column. Does not increment
getCurrentRecord()
.
All Classes All Packages


About
Projects
Contact
Hi i'm Damian
a Data Analyst
A few of my projects
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Here are additional links to learn more
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It works!
Your Django-nonrel installation is working fine. Now go and build something useful with it. :)
Also, make sure you follow the Django-nonrel & webdev blog, so you don't miss any new features and improvements.
If you're new to Django you should take a look at the Django documentation and the Django book. You'll also find djangosnippets very useful.
If you search for reusable apps that work with Django-nonrel here's a list to get you started:
- django-mediagenerator: Combines and compresses your JS and CSS files
- django-dbindexer: Adds extra DB features (e.g., __iexact, JOINs, etc.) beyond the native DB API
- nonrel-search: A simple full-text search engine for App Engine and other non-relational DBs
- django-filetransfers: Generic API for file uploads/downloads (e.g. via App Engine Blobstore)
- allbuttonspressed: The simple CMS/blog that powers the Django-nonrel website
- ...and our other projects...
Finally, take a look at the apps listed on Django Packages. Many simple Django apps (e.g., django-registration) should work unmodified. If you want to use django-debug-toolbar you need to disable the SQLDebugPanel in your settings.


| rem comment Use for comment lines. The keyword rem makes the script program ignor any text that follows on the line. popup comment This command pauses the script program, brings up a small form and waits for the user to press the OK button before proceeding. Any comment text that follows the popup command is displayed on the pop up form. Main |


7 Logistics
7.1 Lab Meetings 5
We hold lab meetings once a week during the semester and at the discretion of the PI during the summer. Lab meetings usually involve check-ins on recruiting and testinge fforts, data blitzes, presentations (lab internal and external) and feedback on projects. Location and time vary by semester and pandemic status.
7.2 Snacks [on hold during pandemic]
The lab manager is entrusted to get snacks for each lab meeting. The lab usually allots a budget of $20/wk for bagels and coffee. After buying said snacks, the lab manager is reimbursed from the PI’s research funds.
Before the semester begins, the lab manager will contact the lab managers/PI of either lab spaces to check availability and block of a time slot that works best for lab members.↩


About Me
Hi there, I'm a manufacturing technician transitioning into full stack web developing. In my previous work I was responsible for maintaining and repairing large and often dangerous equipment. Due to this, I developed a keen sense of detail and organization as overlooking some detail, even a seemingly minor one, could have a major effect. In addition I often had to work very closely with either maintenance or the regular machine operators or both! Communication was key to making sure we arrived at the correct solution in a timely and efficient manner. Now I’m bringing both my communication skills and sense of detail orientation to my budding web development skills.
Download Cory's<EMAIL_ADDRESS>(740)814-5702


Most excellent Christmas. Mega Bloks for Coraline, awesome pics Amber did up, and watching Cora chew wrapping paper were the highlights
12/25/2008, 9:00:19 PM
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Most excellent Christmas. Mega Bloks for Coraline, awesome pics Amber did up, and watching Cora chew wrapping paper were the highlights
12/25/2008, 9:00:19 PM
Favs: 0
Retweets: 0
link


HOME
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scroll down to see more
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This file describes the content of the different files including in this repository to reproduce results from [1] and some of its supplementary results.
20151016_Functions_remainder.csv
Functions measured in [2]. The relevant quantities used in [1] are labelled with "7", and include:
samples_metadata_time0.tsv
Dist_GPS-Haversine.dat
Haversine (spatial) distances between samples
corMat-SparCC_20151016_OTU_remainder.clean.samples.txt
Matrix of correlations between samples computed with SparCC
distMat_ShannonJensen_Samples_Time0.clean.dat
Distance matrix computed with Jensen-Shannon divergence.
SEMmodels.zip
Results for the Structural Equation Models analysed. The structure of the folders follows the one available at the repository of the project .
TaxaSummaries.zip
The file contains one folder for each community-class, with matrices in different formats (biom and txt) computing the relative abundances of the OTUs at different taxonomic levels (labelled L2 being the proxy for Phylum to L6, the proxy of species). These matrices can be visualized interactively opening with a web browser the files area_charts.html.
[1] Pascual-García, A., & Bell, T. (2019). Community-level signatures of ecological succession in natural bacterial communities. bioRxiv, 636233
[2] Rivett, Damian W., and Thomas Bell. Abundance determines the functional role of bacterial phylotypes in complex communities." Nature microbiology 3.7 (2018): 767.


- DestroyInGameScreen() - Method in class ViewLayer.SwingUICore
-
Dereferences the InGameScreen instance.
- DGO_Default_MapTexture_On - Static variable in class ControlLayer.SharedResources
-
- DGO_Default_Music_On - Static variable in class ControlLayer.SharedResources
-
- DGO_Default_Sound_On - Static variable in class ControlLayer.SharedResources
-
- DisplayCarSelections() - Method in class ViewLayer.Screens.LaunchScr.CarSelectionPanel
-
Instantiates and adds the selection of cars to the containing JPanel
- DisplayGameOverPopup() - Method in class ViewLayer.SwingUICore
-
Displays game over messages.
- DisplayHeader() - Method in class ViewLayer.Screens.LaunchScr.MapSelectionPanel
-
Adds a title text (header) to the map selection panel's top.
- DisplayMapSelections() - Method in class ViewLayer.Screens.LaunchScr.MapSelectionPanel
-
Adds each available map's (name and thumbnail image) as JLabels with to the panel.
- DrawMap(Graphics) - Method in class ViewLayer.MapVL.EasyMapVL
-
Draws out the easy/default map without texture enhancement.
- DrawMap(Graphics) - Method in class ViewLayer.MapVL.MapView
-
Draws out the map without texture/enhancement.
- DrawMap(Graphics) - Method in class ViewLayer.MapVL.MediumMapVL
-
Draws out the easy/default map without texture enhancement.
- DrawMap(Graphics) - Method in class ViewLayer.Screens.InGameScr.InGameScreen
-


#Simple-keyboard-layouts-switcher-GEO-ENG-RUS
#simple-keyboard
#simple-keyboard-layouts
MORE DEMO
KA-EN
EN-RU
RU-EN
Simple-Keyboard Language Switcher: EN-KA


Tom Brady Tribute
The GOAT of the NFL
Tom Brady Career Highlights
- 7x Super Bowl Champion
- 5x Super Bowl MVP
- 3x NFL MVP(Most Valuable Player)
- 14x Chosen to be in the Pro Bowl
- Most career regular season wins
- Longest touchdown pass: 99 yards (tie)
- Still an active quarterback at a age of 43


{% extends "global/Page.html" %} {% load otree static %} {% block title %} Order Assignment {% endblock %} {% block content %}
Character Michael always comes with special power-- he can decide of all characters in his group, who goes first, second, and etc. Whenever a player is assigned to play the character Michael, the player will be prompted to, first, submit the order assignment and then, according to the order assignment, submit prediction at the corresponding turn.
{% next_button %}(If the image is not loading properly, please right click on the image icon, and select "Open Image in New Tab".)
{% endblock %}