A repository is usually used to organize a single project. Repositories can contain folders and files, images, videos, spreadsheets, and data sets -- anything your project needs. Often, repositories include a README file, a file with information about your project. README files are written in the plain text Markdown language. You can use this cheat sheet to get started with Markdown syntax. GitHub lets you add a README file at the same time you create your new repository. GitHub also offers other common options such as a license file, but you do not have to select any of them now.
I would love to install gitx because I was using it extensively on my old computer. Unfortunately, it's no longer maintained! I have been experimenting with other git UIs but none of them are meeting my needs yet. More investigation is needed. If you have an older version of Mac, you might be able to get your hands on this original, extremely useful, tool. And if you can write natively, consider contributing!GitX is extremely useful for quickly reviewing changes in your current commit and editing those changes.
Software teams that do not use any form of version control often run into problems like not knowing which changes that have been made are available to users or the creation of incompatible changes between two unrelated pieces of work that must then be painstakingly untangled and reworked. If you're a developer who has never used version control you may have added versions to your files, perhaps with suffixes like "final" or "latest" and then had to later deal with a new final version. Perhaps you've commented out code blocks because you want to disable certain functionality without deleting the code, fearing that there may be a use for it later. Version control is a way out of these problems.
Version control allows you to keep track of your work and helps you to easily explore the changes you have made, be it data, coding scripts, notes, etc. You are probably already doing some type of version control, if you save multiple files, such as Dissertation_script_25thFeb.R, Dissertation_script_26thFeb.R, etc. This approach will leave you with tens or hundreds of similar files, making it rather cumbersome to directly compare different versions, and is not easy to share among collaborators. With version control software such as Git, version control is much smoother and easier to implement. Using an online platform like Github to store your files means that you have an online back up of your work, which is beneficial for both you and your collaborators.
The files you put on GitHub will be public (i.e. everyone can see them & suggest changes, but only the people with access to the repository can directly edit and add/remove files). You can also have private repositories on GitHub, which means that only you can see the files. GitHub now offers free private repositories as standard with up to three collaborators per repository. They also offer a free education package, with access to software and other perks, you can apply for one using this link.
Note how the dictionary keys have become column headers running along the top, and as with the Series, an index number has been automatically generated. The columns are also in the order we specified.
And thereafter, we could access the most commonly used features of Matplotlib with plt as shorthand. Note that this import statement is at the submodule level. We are not importing the full matplotlib module, but a subset of it called pyplot. Pyplot contains the most useful features of Matplotlib with an interface that makes interactive-style plotting easier. Submodule imports have the form import module.submodule and you will see them used in other Python libraries too sometimes.
This is the general idea behind a genetic algorithm. A genetic algorithm is a type of artificial intelligence, modeled after biological evolution, that begins with no knowledge of the subject, aside from available tools and valid instructions. The AI picks a series of instructions at random (to serve as a piece of DNA) and checks the fitness of the result. It does this with a large population size, of say 100 programs. Surely, some of the programs are better than others. Those that have the best fitness are mated together to produce offspring. Each generation gets a bit of extra diversity from evolutionary techniques such as roulette selection, crossover, and mutation. The process is repeated with each child generation, hopefully producing better and better results, until a target solution is found. Genetic algorithms are programmatic implementations of survival of the fittest. They can also be classified as artificially intelligent search algorithms, with regard to how they search an immense problem space for a specific solution.
Once you have committed the changes for a complete, logical unit of work,you should share those changes with your colleagues as soon as possible (bydoing git push or hg push). So long as your changes donot destabilize the system, do not hold the changes locally while you makeunrelated changes. The reason is the same as the reason forincorporating others' changes frequently.
If you plan to make significant changes to (a part of) a file that othersmay be editing, coordinate with them so that one of you can finish work(commit and push it) before the other gets started. This is the best wayto avoid conflicts.A special case of this is any change that touches many files (or parts ofthem), which requires you to coordinate with all your teammates.
Never refill/rejustify paragraphs. Doing so changes every line of theparagraph. This makes it hard to determine, later, what part of thecontent changed in a given commit. It also makes it hard for others todetermine which commits affected given content (as opposed to justreformatting it). If you follow this advice and do not refill/rejustifythe text, then the LaTeX/HTML source might look a little bit funny, withsome short lines in the middle of paragraphs. But, no one sees that exceptwhen editing the source, and the version control information is moreimportant.
A low-tech solution is to revert your changes with hg revert orthe analogous command for other version control systems, asdescribed above.Now, you can git pull or hg fetch, but you will have tomanually re-do the changes that you moved aside. There are other, moresophisticated ways to do this as well (for Git, use git stash;for Mecurial, see theMercurial FAQ).
Open Lab also provides sharing functionality for individual tasks. Like studies, tasks can be private, shared, or public. Public tasks can be shared in two ways: as a reference and as a full copy. Referencing is done via the Open Lab interface by adding a public task to the study. If the author later updates the task, it will also be updated in all the studies using that task. Furthermore, any task can be customized with study-specific parameters. Sharing the task by reference is useful if several research groups are working on a project in which the same version of the task is tested in different laboratories, such as the Many Labs project (Aarts et al., 2015). On the other hand, a full copy of the task (available via the lab.js builder) allows a researcher to add an independent copy of the task to a different project. Therefore, the ability to create an independent copy of the task can be used for task adaptation and more extensive customizations, which can be done by editing the code in the lab.js experiment builder.
Open Lab also supports between-subject study designs via a randomization of both tasks and parameter values. Those features are explained in more detail in the website documentation. A researcher can customize task parameters without needing to edit the original lab.js experiment script in the builder. To enable this, a sequence component has to be included in the lab.js builder as a parent for all the experiment components. Parameters that are shared across the experiment, such as the number of trials or the stimulus presentation time, must be defined for this parent component. Once the task has been uploaded, these parameters are available for editing via the Open Lab interface.
User authentication can leverage several benefits: participants can take part in many studies and have access to their results; furthermore, researchers can interact with participants by giving them feedback, conducting debriefings, inviting them to other studies, or running multiple experiments separated in time.
After authentication, the participant sees a task flow displayed on a dashboard showing which tasks have been accomplished and which task comes next. Users cannot participate in the same task twice if they have already completed it (in cases where the researcher activates this feature). Once all the tasks in a particular study have been finished, users see a confirmation code that both they and researchers can use to verify their participation. Participants can take part in different studies by switching between them on a page with the list of active studies. This list is a good place for researchers to promote new studies. 2b1af7f3a8