estateclaims@my.usdirectexpress.com

1-650-649-1942

docs@my.usdirectexpress.com

every document could disappear 6 months after it's published unless whoever uploaded or created it says otherwise in an email the system sends them after six months, documents shouldn't live rent free

Reload the systemd manager configuration. This will rerun all generators (see systemd.generator(7)), reload all unit files, and recreate the entire dependency tree. While the daemon is being reloaded, all sockets systemd listens on behalf of user configuration will stay accessible.

we do need to have this somewhere

I'm afraid there's only so much the docs & tutorials can do about something like this actually. When you first read them, you don't get Svelte well enough (since you're reading a tutorial...) for this to make sense to you. Then you try something, encounter a behaviour, question it, understand better... That's learning.

Anyway, If this is an expected behaviour, we should probably add an asterisk to the docs, describing the pitfall, because I believe many will be bitten by this.

Would you mind adding it to the vendor README.md with a note to this commit so we remember?

More in-depth examples definitely sound like a good idea. I've seen cookbooks quite a few times already and they are always helpful.

Only peer dependencies: Svelte and Final Form

In an effort to rethink how documentation works, we recently introduced Articles, longer-form prose that can sit side by side with shorter Q&A.

I don't read comments as I think they are dangerous

I really have no idea how you came up with this solution but that reflects a major problem with many npm packages, i.e. 90 percent of the library documentation should be pulled from the Git issues or SO answers.

To add documentation on a Svelte component that will show up as a docstring in LSP-compatible editors, you can use an HTML comment with the @component tag:

All of the components should allow passing MUI configuration properties to them so that they can be easily customized. In the case of RFF and MUI components with deeply nested structures of multiple subcomponents, you can pass the properties in with sepecial top level properties. This is very hard to document fully without making a mess, so please refer to the source code and demos for examples.

If we find that GitLab doesn't work as people expect, the documentation should be updated so this is no longer a surprise. This applies whether we classify it as a feature request or a bug.

Sometimes, the best way to learn is to mimic others. Here are some great examples of projects that use documentation well:

“Code is more often read than written.” — Guido van Rossum

Documenting code is describing its use and functionality to your users. While it may be helpful in the development process, the main intended audience is the users.

Class method docstrings should contain the following: A brief description of what the method is and what it’s used for Any arguments (both required and optional) that are passed including keyword arguments Label any arguments that are considered optional or have a default value Any side effects that occur when executing the method Any exceptions that are raised Any restrictions on when the method can be called

Comments to your code should be kept brief and focused. Avoid using long comments when possible. Additionally, you should use the following four essential rules as suggested by Jeff Atwood:

From examining the type hinting, you can immediately tell that the function expects the input name to be of a type str, or string. You can also tell that the expected output of the function will be of a type str, or string, as well.

def hello_name(name: str) -> str:
    return(f"Hello {name}")

Docstrings can be further broken up into three major categories: Class Docstrings: Class and class methods Package and Module Docstrings: Package, modules, and functions Script Docstrings: Script and functions

According to PEP 8, comments should have a maximum length of 72 characters.

use `multiple line comment`

Docstring conventions are described within PEP 257. Their purpose is to provide your users with a brief overview of the object.

All multi-lined docstrings have the following parts: A one-line summary line A blank line proceeding the summary Any further elaboration for the docstring Another blank line

"""This is the summary line

This is the further elaboration of the docstring. Within this section,
you can elaborate further on details as appropriate for the situation.
Notice that the summary and the elaboration is separated by a blank new
line.

# Notice the blank line above. Code should continue on this line.

say_hello.__doc__ = "A simple function that says hello... Richie style"

def say_hello(name):
    print(f"Hello {name}, is it me you're looking for?")

say_hello.__doc__ = "A simple function that says hello... Richie style"

def say_hello(name):
    """A simple function that says hello... Richie style"""
    print(f"Hello {name}, is it me you're looking for?")

>>> help(say_hello)

Help on function say_hello in module __main__:

say_hello(name)
    A simple function that says hello... Richie style

class constructor parameters should be documented within the __init__ class method docstring

Scripts are considered to be single file executables run from the console. Docstrings for scripts are placed at the top of the file and should be documented well enough for users to be able to have a sufficient understanding of how to use the script.

Documenting your code, especially large projects, can be daunting. Thankfully there are some tools out and references to get you started

Commenting your code serves multiple purposes

In general, commenting is describing your code to/for developers. The intended main audience is the maintainers and developers of the Python code. In conjunction with well-written code, comments help to guide the reader to better understand your code and its purpose and design

Along with these tools, there are some additional tutorials, videos, and articles that can be useful when you are documenting your project

If you use argparse, then you can omit parameter-specific documentation, assuming it’s correctly been documented within the help parameter of the argparser.parser.add_argument function. It is recommended to use the __doc__ for the description parameter within argparse.ArgumentParser’s constructor.

There are specific docstrings formats that can be used to help docstring parsers and users have a familiar and known format.

Daniele Procida gave a wonderful PyCon 2017 talk and subsequent blog post about documenting Python projects. He mentions that all projects should have the following four major sections to help you focus your work:

Since everything in Python is an object, you can examine the directory of the object using the dir() command

Documenting your Python code is all centered on docstrings. These are built-in strings that, when configured correctly, can help your users and yourself with your project’s documentation.

Along with docstrings, Python also has the built-in function help() that prints out the objects docstring to the console.

The general layout of the project and its documentation should be as follows:

project_root/
│
├── project/  # Project source code
├── docs/
├── README
├── HOW_TO_CONTRIBUTE
├── CODE_OF_CONDUCT
├── examples.py

In all cases, the docstrings should use the triple-double quote (""") string format.

TSDoc is a way of writing TypeScript comments where they’re linked to a particular function, class or method (like Python docstrings).

Installation in Windows Compatibility: > OpenCV 2.0 Author: Bernát Gábor You will learn how to setup OpenCV in your Windows Operating System!

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 18 million. The library is used extensively in companies, research groups and by governmental bodies. Along with well-established companies like Google, Yahoo, Microsoft, Intel, IBM, Sony, Honda, Toyota that employ the library, there are many startups such as Applied Minds, VideoSurf, and Zeitera, that make extensive use of OpenCV. OpenCV’s deployed uses span the range from stitching streetview images together, detecting intrusions in surveillance video in Israel, monitoring mine equipment in China, helping robots navigate and pick up objects at Willow Garage, detection of swimming pool drowning accidents in Europe, running interactive art in Spain and New York, checking runways for debris in Turkey, inspecting labels on products in factories around the world on to rapid face detection in Japan. It has C++, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS. OpenCV leans mostly towards real-time vision applications and takes advantage of MMX and SSE instructions when available. A full-featured CUDAand OpenCL interfaces are being actively developed right now. There are over 500 algorithms and about 10 times as many functions that compose or support those algorithms. OpenCV is written natively in C++ and has a templated interface that works seamlessly with STL containers.

tessdoc Tesseract documentation

“The source code for Falcon is so good, I almost prefer it to documentation. It basically can’t be wrong.”

Used By

Write things down We document everything: in the handbook, in meeting notes, in issues. We do that because "the faintest pencil is better than the sharpest memory." It is far more efficient to read a document at your convenience than to have to ask and explain. Having something in version control also lets everyone contribute suggestions to improve it.

12

Required Peer Dependencies These libraries are not bundled with Reactstrap and required at runtime:

Aside from testing, we place high value in documenting code. It's our legacy for anyone who will be working with our codebase in the future. Fortunately, having a component library like Storybook allows us to document components visually and declaratively. Storybook provides a developer with all the information needed to understand the structure and state of a component.

React.cloneElement

Grid API

peerDependencies

No Match1 Match1+ MatchAwait?

Unit test coverage grants confidence that code logic is correct(and serves as great developer documentation!)

Peer Dependencies react-hooks-testing-library does not come bundled with a version of react or react-test-renderer to allow you to install the specific version you want to test against.

Tutorials and how-to guides are similar because they are both concerned with describing practical steps, while what how-to guides share with technical referenceis that they’re what we need when we are actually at work, coding. Reference guides and explanation are similar because they’re concerned with theoretical knowledge, and finally, what tutorials have in common with explanation is that they are most useful when we are studying, rather than actually working

"http://lobid.org/items/HT019025795:DE-Kn3:KMB%2FYG%20BERLINW%2033%20%202016%20A#!"

@angular-react/core

A beginner’s guide to writing

"classification"

"rs"

It is important to understand the interaction between aggregates and SQL's WHERE and HAVING clauses. The fundamental difference between WHERE and HAVING is this: WHERE selects input rows before groups and aggregates are computed (thus, it controls which rows go into the aggregate computation), whereas HAVING selects group rows after groups and aggregates are computed. Thus, the WHERE clause must not contain aggregate functions; it makes no sense to try to use an aggregate to determine which rows will be inputs to the aggregates. On the other hand, the HAVING clause always contains aggregate functions. (Strictly speaking, you are allowed to write a HAVING clause that doesn't use aggregates, but it's seldom useful. The same condition could be used more efficiently at the WHERE stage.)

"sameAs"

"license"

"dateModified"

"schemaVersion"

"serviceType"

"documentation"

"serviceUrl"

"type"

"addressLocality"

"postalCode"

"streetAddress"

"addressCountry"

"type"

"type"

"address"

"latitude"

"longitude"

"geo"

"location"

"type"

"email"

"type"

"name"

"provider"

"availableChannel"

"startDate"

"isAccessibleForFree"

"about"

"audience"

"serviceType"

id

"name"

"description"

"inLanguage"

"image"

"logo"

"@context"

"type"

after the terminal operation of the stream pipeline commences.

Ce qui est commun aux formes de documentation est la fixation, dans le sens collecte avec indexation, d'informations et de processus stockés menant à une réalisation reproductible et diffusable.

"changeDate"

"label": "Report of the trial of George Ryan : before the Superior Court at Charlestown, N.H., in the county of Cheshire, May term 1811, for highway robbery."