Stacks On Stacks On Stacks
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[toc] Stacks on Stacks is a helpful mod for increasing the number of stacks you obtain every time you get Charged with Light. With the right loadout, you can stay charged with light consistently with Stacks on Stacks.
You create orbs of light via your Super or masterwork weapons. You gain 2x Charged with light for every orb you pick up, constantly being charged with light. This can be helpful with mods that gain increased benefits the more stacks you have (such as Protective Light).
As your infrastructure grows, common patterns can emerge in which you declare the same components in multiple templates. You can separate out these common components and create dedicated templates for them. Then use the resource in your template to reference other templates, creating nested stacks.
For example, assume that you have a load balancer configuration that you use for most of your stacks. Instead of copying and pasting the same configurations into your templates, you can create a dedicated template for the load balancer. Then, you just use the resource to reference that template from within other templates.
Nested stacks can themselves contain other nested stacks, resulting in a hierarchy of stacks, as in the diagram below. The root stack is the top-level stack to which all the nested stacks ultimately belong. In addition, each nested stack has an immediate parent stack. For the first level of nested stacks, the root stack is also the parent stack. in the diagram below, for example:
Certain stack operations, such as stack updates, should be initiated from the root stack rather than performed directly on nested stacks themselves. Also, in some cases, nested stacks affect how stack operations are performed. For more information, refer to the following topics:
The first is that we require $\\mathcal{X}$ to be a stack in groupoids in the fppf topology, whereas in many references the étale topology is used. It somehow seems to us that the fppf topology is the natural topology to work with. In the end the resulting $2$-category of algebraic stacks ends up being the same. This is explained in Criteria for Representability, Section 96.19.
Definition 93.12.3. Let $S$ be a scheme contained in $\\mathit{Sch}_{fppf}$. The $2$-category of algebraic stacks over $S$ is the sub $2$-category of the $2$-category of categories fibred in groupoids over $(\\mathit{Sch}/S)_{fppf}$ (see Categories, Definition 4.35.6) defined as follows:
In other words this $2$-category is the full sub $2$-category of $\\textit{Cat}/(\\mathit{Sch}/S)_{fppf}$ whose objects are algebraic stacks. Note that every $2$-morphism is automatically an isomorphism. Hence this is actually a $(2, 1)$-category and not just a $2$-category.
Similar to the remark above the $2$-category of algebraic stacks over $S$ is a full sub $2$-category of the $2$-category of categories fibred in groupoids over $(\\mathit{Sch}/S)_{fppf}$. It turns out that it is closed under equivalences. Here is the precise statement.
When you run the cdk synth command for an app with multiple stacks, the cloud assembly includes a separate template for each stack instance. Even if the two stacks are instances of the same class, the AWS CDK emits them as two individual templates.
Like any other construct, stacks can be composed together into groups. The following code shows an example of a service that consists of three stacks: a control plane, a data plane, and monitoring stacks. The service construct is defined twice: once for the beta environment and once for the production environment.
The physical names of the AWS CloudFormation stacks are automatically determined by the AWS CDK based on the stack's construct path in the tree. By default, a stack's name is derived from the construct ID of the Stack object. However, you can specify an explicit name by using the stackName prop (in Python, stack_name), as follows.
The NestedStack construct offers a way around the AWS CloudFormation 500-resource limit for stacks. A nested stack counts as only one resource in the stack that contains it. However, it can contain up to 500 resources, including additional nested stacks.
At synthesis time, the nested stack is synthesized to its own AWS CloudFormation template, which is uploaded to the AWS CDK staging bucket at deployment. Nested stacks are bound to their parent stack and are not treated as independent deployment artifacts. They aren't listed by cdk list, and they can't be deployed by cdk deploy.
This tutorial will describe how three major Stacks parameters function to control the de novo assembly of loci by the ustacks and cstacks programs. All three of these parameters can be specifiedto the pipeline wrapper, denovo_map.pl, which will then passthem through to the appropriate pipeline component. Or, if you are running the pipeline components by hand, two of them are specified to ustacks (and are applied toeach sample processed by Stacks) and one of them to cstacks, which builds the catalog of loci for the population of samples. The following table summarizes the three parameters:
The ustacks program is executed on each sample in a population (each of the parents and progeny in a mapping cross, or each individual sample or pooled sample in a population analysis) and loci from that sample are reconstructed. This is done in two major steps, first, exactly matching reads are stacked together out of the raw data. These stacks can be thought of generally as representing alleles, although some of them will represent errors.
Once a set of exactly matching stacks has been generated, the second stage of the algorithm seeks to match putative alleles together into a locus. The distance allowed between stacks parameter represents the number of nucleotides that may be different between two stacks in order to merge them. These nucleotide differences may be due to polymorphisms present between two alleles, or they may be due to sequencing error.
In Figure 2, Stack 1 and Stack 2 are found to have fewer nucleotide mismatches than allowed by the distance parameter and are merged into polymorphic Locus 1. Stack 3 and Stack X are found to be monomorphic and are converted to individual Locus 2 and Locus X. The large grey set of stacks represents a set of repetitive sequence that has too many alleles to be biologically correct. The pipeline detects these lociand blacklists them from the rest of the pipeline.
Once the loci are formed, the secondary reads are brought back into the analysis and are aligned against the assembled loci using a morepermissive nucleotide mismatch value (you can control this value with the -N parameter to ustacks or denovo_map.pl). This process provides more depth which aides the SNP callingmodel in detecting polymorphisms. A locus with a single polymorphism is outlined in Figure 3.
The ustacks program will be executed on each individual sample in the data set to build loci. Once this is complete,the data from each individual will be merged into a catalog (by the cstacks program), which is meant to contain all the loci and alleles in the population. In the case of a mapping cross, then the catalog can be built soley from the parental loci. In the case of a population, the catalog will be constructed from the loci in each individual in the population.
Rarely in the case of a mapping cross, but frequently in the case of a population, there will be monomorphic, or fixed loci in two or more individuals in the population. However, if you compare these loci to one another, you will find that the are differentially fixed versionsof the same locus and should be merged into a single locus in the catalog. If the distance between catalog loci parameter is greater than 0, then cstacks will use the consensus sequence from each locus to attempt to merge loci togetheracross samples.
The Page Stats window shows how many stacks you're using on each page. Each stack on the page is listed, grouped, and sorted by how much it's used. And a single click on any item will find and display the stack in your layout.
A stack can be easily implemented either through an array or a linked list, as stacks are just special cases of lists. What identifies the data structure as a stack, in either case, is not the implementation but the interface: the user is only allowed to pop or push items onto the array or linked list, with few other helper operations. The following will demonstrate both implementations, using pseudocode.
Some languages, such as Perl, LISP, JavaScript and Python, make the stack operations push and pop available on their standard list/array types. Some languages, notably those in the Forth family (including PostScript), are designed around language-defined stacks that are directly visible to and manipulated by the programmer.
Stacks are often visualized growing from the bottom up (like real-world stacks). They may also be visualized growing from left to right, so that \"topmost\" becomes \"rightmost\", or even growing from top to bottom. The important feature is that the bottom of the stack is in a fixed position. The illustration in this section is an example of a top-to-bottom growth visualization: the top (28) is the stack \"bottom\", since the stack \"top\" (9) is where items are pushed or popped from.
Many CISC-type CPU designs, including the x86, Z80 and 6502, have a dedicated register for use as the call stack stack pointer with dedicated call, return, push, and pop instructions that implicitly update the dedicated register, thus increasing code density. Some CISC processors, like the PDP-11 and the 68000, also have special addressing modes for implementation of stacks, typically with a semi-dedicated stack pointer as well (such as A7 in the 68000). In contrast, most RISC CPU designs do not have dedicated stack instructions and therefore most, if not all, registers may be used as stack pointers as needed. 59ce067264