Feeds

The ads will appear in only a few games at first as the firm assesses users' reactions.

BBC Click's Chris Fox explores what Huawei's new operating system can do.

China's biggest technology companies have been coming under increasing scrutiny both at home and abroad.

The Information Commissioner says the technology could be used "excessively or even recklessly".

Anyone flouting the rules faces having their drone confiscated, being fined or even imprisonment.

By sifting and analysing huge amounts of data, market researchers say they can find out what's on your mind.

More than 30 women say private or non-consensual videos of them were uploaded to the site.

The decision was aimed at the nation’s second Catholic president and exposed bitter divisions in American Catholicism.

Officials are still searching for two from a group of nine who trekked out to the Dan River around nightfall on Wednesday, the authorities said.

Researchers found that the total sexual assault risk to Army women at Fort Hood during 2018 was 8.4 percent, compared with a 5.8 percent risk for all women in the Army.

A man who finished last in his fantasy football league had to spend 24 hours at a Waffle House restaurant. For every waffle he ate, his sentence was reduced by an hour.

The Supreme Court’s latest ruling moved the country’s debate over health policy into a new phase. Tough questions await both parties.

For at least a century, spelling bees have been arenas of childhood triumphs and tragedies. What do you remember?

Ms. Hobbs, the Arizona secretary of state, saw her star rise as she fought Republicans’ election falsehoods. Senator Kyrsten Sinema, a fellow Democrat, insists on a bipartisan approach to voting rights.

The need for markets-focused competition watchdogs and consumer-centric privacy regulators to think outside their respective ‘legal silos’ and find creative ways to work together to tackle the challenge of big tech market power was the impetus for a couple of fascinating panel discussions organized by the Centre for Economic Policy Research (CEPR), which were livestreamed […]

Meet Mediflash, a new French startup that wants to improve temp staffing in healthcare facilities, such as nursing homes, clinics and mental health facilities. The company positions itself as an alternative to traditional temp staffing agencies. They claim to offer better terms for both caregivers and institutions. “It costs a small fortune to health facilities […]

Hello and welcome back to Equity, TechCrunch’s venture capital-focused podcast, where we unpack the numbers behind the headlines. This week, Natasha and Danny, otherwise known as your two new favorite Book influencers (inside joke, you’ll get if you listen to the show), hopped on the mics to take everyone through the news, with Grace and […]

Indian fintech startup BharatPe is in advanced stages of talks to raise about $250 million in a new financing round led by Tiger Global, two sources familiar with the matter told TechCrunch. The new round, a Series E, is giving the three-year-old New Delhi-headquartered firm a pre-money valuation of $2.5 billion, sources said, requesting anonymity […]

The UK’s chief data protection regulator has warned over reckless and inappropriate use of live facial recognition (LFR) in public places. Publishing an opinion today on the use of this biometric surveillance in public — to set out what is dubbed as the “rules of engagement” — the information commissioner, Elizabeth Denham, also noted that […]

Ford has two electric vehicles in the pipeline —  the E-Transit cargo van and F-150 Lighting Pro —aimed at commercial customers. Now, the automaker is rounding out its future EV commercial business with the acquisition of battery management and fleet monitoring software startup Electriphi. Terms of the acquisition weren’t disclosed. Ford is betting that the […]

On-demand goods, food and alcohol delivery service Gopuff has acquired fleet management platform rideOS for $115 million, sources familiar with the deal say. This acquisition comes just a few months after the Philadelphia-based startup announced a $1.15 billion funding round at a $8.9 billion valuation, up from $3.9 billion in October. Last fall, the company […]

In a previous article, I talk about this heuristic for using classes in Python: If you have functions that take the same set of arguments, consider using a class. Thing is, heuristics don't always work. To make the most out of them, it helps to know what the exceptions are. So, let's look at a few real-world examples where functions taking the same arguments don't necessarily make a class. Counter-example: two sets of arguments # Consider the following scenario: We have a feed reader web application. It shows a list of feeds and a list of entries (articles), filtered in various ways. Because we want to do the same thing from the command-line, we pull database-specific logic into functions in a separate module. The functions take a database connection and other arguments, query the database, and return the results. def get_entries(db, feed=None, read=None, important=None): ... def get_entry_counts(db, feed=None, read=None, important=None): ... def search_entries(db, query, feed=None, read=None, important=None): ... def get_feeds(db): ... The main usage pattern is: at the start of the program, connect to the database; depending on user input, repeatedly call the functions with the same connection, but different options. Taking the heuristic to the extreme, we end up with this: class Storage: def __init__(self, db, feed=None, read=None, important=None): self._db = db self._feed = feed self._read = read self._important = important def get_entries(self): ... def get_entry_counts(self): ... def search_entries(self, query): ... def get_feeds(self): ... This is not very useful: every time we change the options, we need to create a new Storage object (or worse, have a single one and change its attributes). Also, get_feeds() doesn't even use them – but somehow leaving it out seems just as bad. What's missing is a bit of nuance: there isn't one set of arguments, there are two, and one of them changes more often than the other. Let's take care of the obvious one first. The database connection changes least often, so it makes sense to keep it on the storage, and pass a storage object around: class Storage: def __init__(self, db): self._db = db def get_entries(self, feed=None, read=None, important=None): ... def get_entry_counts(self, feed=None, read=None, important=None): ... def search_entries(self, query, feed=None, read=None, important=None): ... def get_feeds(self): ... The most important benefit of this is that it abstracts the database from the code using it, allowing you to have more than one kind of storage. Want to store entries as files on disk? Write a FileStorage class that reads them from there. Want to test your application with various combinations of made-up entries? Write a MockStorage class that keeps the entries in in a list, in memory. Whoever calls get_entries() or search_entries() doesn't have to know or care where the entries are coming from or how the search is implemented. This is the data access object design pattern. In object-oriented programming terminology, a DAO provides an abstract interface that encapsulates a persistence mechanism. OK, the above looks just about right to me – I wouldn't really change anything else. Some arguments are still repeating, but it's useful repetition: once a user learns to filter entries with one method, they can do it with any of them. Also, people use different arguments at different times; from their perspective, it's not really repetition. And anyway, we're already using a class... Counter-example: data classes # Let's add more requirements. There's more functionality beyond storing things, and we have multiple users for that as well (web app, CLI, someone using our code as a library). So we leave Storage to do only storage, and wrap it in a Reader object that has a storage: class Reader: def __init__(self, storage): self._storage = storage def get_entries(self, feed=None, read=None, important=None): return self._storage.get_entries(feed=feed, read=read, important=important) ... def update_feeds(self): # calls various storage methods multiple times: # get feeds to be retrieved from storage, # store new/modified entries ... Now, the main caller of Storage.get_entries() is Reader.get_entries(). Furthermore, the filter arguments are rarely used directly by storage methods, most of the time they're passed to helper functions: class Storage: def get_entries(self, feed=None, read=None, important=None): query = make_get_entries_query(feed=feed, read=read, important=important) ... Problem: When we add a new entry filter option, we have to change the Reader methods, the Storage methods, and the helpers. And it's likely we'll do so in the future. Solution: Group the arguments in a class that contains only data. from typing import NamedTuple, Optional class EntryFilterOptions(NamedTuple): feed: Optional[str] = None read: Optional[bool] = None important: Optional[bool] = None class Storage: ... def get_entries(self, filter_options): query = make_get_entries_query(filter_options) ... def get_entry_counts(self, filter_options): ... def search_entries(self, query, filter_options): ... def get_feeds(self): ... Now, regardless of how much they're passed around, there are only two places where it matters what the options are: in a Reader method, which builds the EntryFilterOptions object where they get used, either a helper or a Storage method Note that while we're using the Python class syntax, EntryFilterOptions is not a class in the traditional object-oriented programming sense, since it has no behavior.1 Sometimes, these are known as "passive data structures" or "plain old data". A plain class or a dataclass would have been a decent choice as well; why I chose a named tuple is a discussion for another article. I used type hints because it's a cheap way of documenting the options, but you don't have to, not even for dataclasses. The example above is a simplified version of the code in my feed reader library. In the real world, EntryFilterOptions has more options (with more on the way), and the Reader and Storage get_entries() are a bit more complicated. Another real-world example of this pattern is Requests: get(), post() and so on end up calling Session.request() which packages the arguments into a Request and turns it into a PreparedRequest which is finally sent by an HTTPAdapter That's pretty much it for now – hang around for some extra stuff, though ;) I hope I managed add more nuance to the original article, and that you're now at least a little bit better equipped to use classes. Keep in mind that this is more an art than a science, and that you can always change your mind later. Learned something new today? Share this with others, it really helps! Bonus: other alternatives # Still here? Cool! Let's look at some of the other options I considered, and why I didn't go that way. Why not a dict? # Instead of defining a whole new class, we could've used a dict: {'feed': ..., 'read': ..., 'important': ...} But this has a number of drawbacks: Dicts are not type-checked. TypedDict helps, but doesn't prevent using the wrong keys at runtime. Dicts break code completion. TypedDict may help with smarter tools like PyCharm, but doesn't in interactive mode or IPython. Dicts are mutable. For our use case, immutability is a plus: the options don't have much reason to change, so it's useful to disallow it. Why not **kwargs? # Why not pass **kwargs directly to EntryFilterOptions? class Reader: def get_entries(self, **kwargs): return self._storage.get_entries(**kwargs) Because: It also breaks code completion. It makes the code less self-documenting: you don't know what arguments get_entries() takes, even if you read the source. Presumably, they're in the docstring, but not everybody writes one all the time. If we introduce another options object (say, for pagination), we still have to write code to split the kwargs between the two. Why not EntryFilterOptions? # Why not take an EntryFilterOptions directly, then? from reader import make_reader, EntryFilterOptions reader = make_reader(...) options = EntryFilterOptions(read=True) entries = reader.get_entries(options) Because it makes things verbose for the user: they have to import EntryFilterOptions, and build and pass one to get_entries() for every call. That's not very friendly. The Reader and Storage method signatures differ because they're used differently: Reader methods are mostly called by external users in many ways Storage methods are mostly called by internal users (Reader) in a few ways Ted Kaminski discusses this distinction in more detail in Data, objects, and how we're railroaded into poor design. [return]

Introduction Matplotlib is one of the most widely used data visualization libraries in Python. Much of Matplotlib's popularity comes from its customization options - you can tweak just about any element from its hierarchy of objects. In this tutorial, we'll take a look at how to turn off a Matplotlib plot's axis. That is to say - how to turn off individual elements, such as tick labels, grid, ticks and individual spines, but also how to turn them all off as a group, collectivelly referred to as the axis. Most of the approaches we'll be covering work the same for 2D and 3D plots - both the regular Axes and Axes3D classes have the same methods for this. For some, since 3D plots have more than two spines, they won't be the same. Note: For the remainder of this guide, we'll be using a 3D plot, as this is a more common case where you might want to turn off the spines and ticks - and let the data speak for itself, as well as a 2D plot on the same dataset. We'll specifically be using a recreation of the CP1919 pulsar's data, as discovered by Jocelyn Bell Burnell in 1967, visualized by by Harold D. Craft, Jr. in his PhD Thesis in 1970 and popularized by Peter Saville in 1979, when the English band Joy Division was on the lookout to release a new album - "Unknown Pleasures": import pandas as pd import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D df = pd.read_csv(r"https://raw.githubusercontent.com/StackAbuse/CP1919/master/data-raw/clean.csv") groups = df.groupby(['line']) plt.style.use('dark_background') fig = plt.figure(figsize=(6, 8)) ax = fig.add_subplot(111, projection='3d') ax.set_xlabel('Line Number') ax.set_ylabel('Time') ax.set_zlabel('Intensity') for group in groups: line = ax.plot(group[1]['line'], group[1]['x'], group[1]['y'], color='white') plt.show() Which results in: If you'd like to learn more about Ridge Plots (shown above), build a foundation in Matplotlib and explore advanced usage of the library, make sure to check out our Data Visualization in Python eBoook Bundle. This specific visualization is taken straight out of the book, from a longer section dedicated to this dataset and the history of the plot. Now, let's take a look at how to remove the spines, tick labels, ticks, grid and axis labels. Turning off the Axis with ax.axis('off') The easiest way to turn off everything axis-related is via a convenience function - axis('off') on your Axes or Axes3D instance: import pandas as pd import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # Load dataset in and set style... fig = plt.figure(figsize=(6, 8)) ax = fig.add_subplot(111, projection='3d') ax.set_xlabel('Line Number') ax.set_ylabel('Time') ax.set_zlabel('Intensity') ax.axis('off') for group in groups: line = ax.plot(group[1]['line'], group[1]['x'], group[1]['y'], color='white') plt.show() Note: You can also use the axis('off') function on the plt instance if you're plotting using the MATLAB-style API. Though, this approach is discouraged if you're plotting using the OOP API like we are currently. This change to the code results in a plot without the spines, axis lines, ticks, tick labels, axis labels and grid: The axis() function is a convennience function to access various properties. Turning off the Axis with ax.set_axis_off() Alternatively, you can use the ax.set_axis_off() function, in conjecture with the ax.set_axis_on() function, which reverses the former's effects. This is a very useful set of functions to use when updating plots, such as when animating them, or whenever you might want to turn the axis off and on, rather than just disabling it:\ import pandas as pd import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # Load dataset in and set style... fig = plt.figure(figsize=(6, 8)) ax = fig.add_subplot(111, projection='3d') ax.set_xlabel('Line Number') ax.set_ylabel('Time') ax.set_zlabel('Intensity') ax.set_axis_off() for group in groups: line = ax.plot(group[1]['line'], group[1]['x'], group[1]['y'], color='white') plt.show() This results in the exact same plot: Turning off Ticks and Tick Labels on Axes Instead of turning all of these elements off, and making them invisible, you can also turn them off individually if you'd like to keep some. This is where we'll be switching back to a 2D plot, since some of these functions don't work with 3D plots, given the fact that they've got more than just the X-axis and Y-axis. For example, you can turn off individual axes (ticks and tick labels). You can use the get_yaxis() or get_xaxis() to get the respective axes and turn them off by using set_visible(False): import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv(r"https://raw.githubusercontent.com/StackAbuse/CP1919/master/data-raw/clean.csv") groups = df.groupby(['line']) plt.style.use('dark_background') fig = plt.figure(figsize=(6, 8)) ax = fig.add_subplot(111) ax.set_xlabel('Time') ax.set_ylabel('Intensity') ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) for group in groups: line = ax.plot(group[1]['x'], group[1]['y'], color='white') plt.show() This turns off the tick labels and ticks themselves, but leaves the frame (spines) on: Turning off Individual Spines on an Axes To turn the spines off - you can access them via the ax.spines dictionary. Using their keys, top, bottom, left, and right, you can select each one, and using the set_visible() function, turn them off. Let's turn of the top and right spine: import pandas as pd import matplotlib.pyplot as plt # Load data and stylize fig = plt.figure(figsize=(6, 8)) ax = fig.add_subplot(111) ax.set_xlabel('Time') ax.set_ylabel('Intensity') ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) # To turn off the bottom or left #ax.spines['bottom'].set_visible(False) #ax.spines['left'].set_visible(False) for group in groups: line = ax.plot(group[1]['x'], group[1]['y'], color='white') plt.show() This turns off the top and right spine, but leaves the bottom and left intact - as well as the ticks and their labels: You can also iterate through them, and turn them all off: for key, spine in ax.spines.items(): spine.set_visible(False) Conclusion In this tutorial, we've gone over several ways to turn off the axis, as well as individual axis compoents in Matplotlib. If you're interested in Data Visualization and don't know where to start, make sure to check out our bundle of books on Data Visualization in Python: Data Visualization in Python Become dangerous with Data Visualization ✅ 30-day no-question money-back guarantee ✅ Beginner to Advanced ✅ Updated regularly for free (latest update in April 2021) ✅ Updated with bonus resources and guides Data Visualization in Python with Matplotlib and Pandas is a book designed to take absolute beginners to Pandas and Matplotlib, with basic Python knowledge, and allow them to build a strong foundation for advanced work with theses libraries - from simple plots to animated 3D plots with interactive buttons. It serves as an in-depth, guide that'll teach you everything you need to know about Pandas and Matplotlib, including how to construct plot types that aren't built into the library itself. Data Visualization in Python, a book for beginner to intermediate Python developers, guides you through simple data manipulation with Pandas, cover core plotting libraries like Matplotlib and Seaborn, and show you how to take advantage of declarative and experimental libraries like Altair. More specifically, over the span of 11 chapters this book covers 9 Python libraries: Pandas, Matplotlib, Seaborn, Bokeh, Altair, Plotly, GGPlot, GeoPandas, and VisPy. It serves as a unique, practical guide to Data Visualization, in a plethora of tools you might use in your career.

The popularity of Python is continuing to grow Developers across the globe are embracing the language. How is Python being used in all of these different countries? How does an organization like the Python Software Foundation (PSF) work toward the goals in its mission statement for supporting and growing this international community? This week on the show, we have Marlene Mhangami, a PSF board member and part of the Diversity and Inclusion Work Group. [ Improve Your Python With ? Python Tricks ? – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

This article looks at how to add basic and full-text search to a Django app with Postgres.

Andy Warhol is a well-known artist who created a famous image where there are multiple copies of the same face but with different background colors. You can do a similar trick with software using Python and the Pillow package. You will also need to have NumPy installed to make this work. Let’s find out how this all works! Getting Started The first thing you need to do is make sure you have Pillow and NumPy installed. If you use pip, you can try running the following command: python3 -m pip install numpy Pillow This will install NumPy and Pillow if you don’t already have them installed. If you are running Anaconda instead, then both of these packages should already be installed. Now you’re ready to create some art! Creating a Triptych Creating a Triptych with Python doesn’t take a lot of code. All you need is a little know-how and some experimentation. The first step is to learn why you need NumPy. NumPy isn’t so much a replacement for Pillow as it is a way to enhance Pillow’s capabilities. You can use NumPy to do some of the things that Pillow does natively. For the examples in this section, you will use this photo of the author: Michael Driscoll To get a feel for how you might use NumPy with Pillow, you will create a Python program that concatenates several images together. This will create your Triptych image! Open up your Python editor and create a new file named concatenating.py. Then enter this code in it: # concatenating.py import numpy as np from PIL import Image def concatenate(input_image_path, output_path): image = np.array(Image.open(input_image_path)) red = image.copy() red[:, :, (1, 2)] = 0 green = image.copy() green[:, :, (0, 2)] = 0 blue = image.copy() blue[:, :, (0, 1)] = 0 rgb = np.concatenate((red, green, blue), axis=1) output = Image.fromarray(rgb) output.save(output_path) if __name__ == "__main__": concatenate("author.jpg", "stacked.jpg") This code will open up the image using Pillow. However, rather than saving that image off as an Image object, you pass that object into a Numpy array(). Then you create three copies of the array and use some matrix math to zero out the other color channels. For example, for red, you zero out the green and blue channels, leaving the red channel alone. When you do this, it will create three tinted versions of the original image. You will now have a red, green, and blue version of the photo. Then you use NumPy to concatenate the three images together into one. To save this new image, you use Pillow’s Image.fromarray() method to transform the NumPy array back into a Pillow Image object. After running this code, you will see the following result: hat’s a neat effect! NumPy can do other things that Pillow doesn’t do easily, like Binarization or denoising. You can do even more when you combine NumPy with other scientific Python packages, such as SciPy or Pandas. Wrapping Up Python is powerful. You can do many things with Python, Pillow and NumPy. You should try doing this with your own image(s) and see what you can come up with. For example, instead of lining the images up left-to-right, you could create four images and put them together in a square! Related Reading StackOverflow – How to change color of image using Python? Pillow: Image Processing with Python The post Creating a Warhol Triptych Image with Pillow and Python appeared first on Mouse Vs Python.

A Solution To A Hackerrank Challenge

Summer is almost here (at least in half of the planet) and Python 3.10 is finishing baking in the oven. For those of you that want to taste it before is finally ready (and if you are a library developer, you certainly do!) you can have the second-to-last beta now, but be careful as is very hot https://www.python.org/downloads/release/python-3100b3/This is a beta preview of Python 3.10Python 3.10 is still in development. 3.10.0b3 is the third of four planned beta release previews. Beta release previews are intended to give the wider community the opportunity to test new features and bug fixes and to prepare their projects to support the new feature release.We **strongly encourage** maintainers of third-party Python projects to **test with 3.10** during the beta phase and report issues found to [the Python bug tracker](https://bugs.python.org/) as soon as possible. While the release is planned to be feature complete entering the beta phase, it is possible that features may be modified or, in rare cases, deleted up until the start of the release candidate phase (Monday, 2021-08-02). Our goal is to have no ABI changes after beta 4 and as few code changes as possible after 3.10.0rc1, the first release candidate. To achieve that, it will be **extremely important** to get as much exposure for 3.10 as possible during the beta phase.Please keep in mind that this is a preview release and its use is **not** recommended for production environments.Major new features of the 3.10 series, compared to 3.9Many new features for Python 3.10 are still being planned and written. Among the new major new features and changes so far:PEP 623 – Deprecate and prepare for the removal of the wstr member in PyUnicodeObject.PEP 604 – Allow writing union types as X | YPEP 612 – Parameter Specification VariablesPEP 626 – Precise line numbers for debugging and other tools.PEP 618 – Add Optional Length-Checking To zip.bpo-12782: Parenthesized context managers are now officially allowed.PEP 632 – Deprecate distutils module.PEP 613 – Explicit Type AliasesPEP 634 – Structural Pattern Matching: SpecificationPEP 635 – Structural Pattern Matching: Motivation and RationalePEP 636 – Structural Pattern Matching: TutorialPEP 644 – Require OpenSSL 1.1.1 or newerPEP 624 – Remove Py_UNICODE encoder APIsPEP 597 – Add optional EncodingWarning(Hey, fellow core developer, if a feature you find important is missing from this list, let Pablo know.)The next pre-release of Python 3.10 will be 3.10.0b4, currently scheduled for Saturday, 2021-07-10.bpo-38605: from __future__ import annotations (PEP 563) used to be on this list in previous pre-releases but it has been postponed to Python 3.11 due to some compatibility concerns. You can read the Steering Council communication about it here to learn more.More resourcesOnline DocumentationPEP 619, 3.10 Release ScheduleReport bugs at https://bugs.python.org.Help fund Python and its community.And now for something completely differentThere are no green stars. Why? In general, objects don't emit a single wavelength of light when they shine. Instead, they emit photons in a range of wavelengths. If you were to use some sort of detector that is sensitive to the wavelengths of light emitted by an object, and then plotted the number of them versus wavelength, you get a lopsided plot called a blackbody curve. For an object as hot as the Sun, that curve peaks at blue-green, so it emits most of its photons there. But it still emits some that are bluer, and some that are redder. When we look at the Sun, we see all these colors blended together. Our eyes mix them up to produce one color: white. A warmer star will put out more blue, and a cooler one redder, but no matter what, our eyes just won't see that as green. Due to how we perceive color, the only way to see a star as being green is for it to be only emitting green light. But as starts always emit radiation following the blackbody curve, that's pretty much impossible.We hope you enjoy those new releases!Thanks to all of the many volunteers who help make Python Development and these releases possible! Please consider supporting our efforts by volunteering yourself or through organization contributions to the Python Software Foundation.Your friendly release team,Pablo Galindo @pablogsalNed Deily @nadSteve Dower @steve.dower

2021-2027 Global and Regional Hardware Wallet Industry Status and Prospects Professional Market Research Report Standard Version – The Courier  The Courier

We’re hiring! PCGamesN is looking for a full-time Hardware Writer  PCGamesN

National hardware store chain opening Geneseo location June 29  The Livingston County News

Vigilante malware versus piracy? Phishing with hardware. Commercial data breaches. A crypter is convicted.  The CyberWire

Windows 11 Seems To Give Intel Hybrid CPU Architectures A Notable Performance Boost  Hot Hardware

IT Leaders Must Look to Network Modernization to Meet Shifting Business Needs. Here's Why.  CIO

RobotLAB and SoftBank Robotics America Announce Exclusive Channel Partnership to Offer Pepper and NAO in North America  PRNewswire

The Joomla! Project is pleased to announce the availability of Joomla! CMS 4.0 Release Candidate 2 and Joomla 3.10 Alpha 7.

The Joomla! Project is pleased to announce the availability of the first Joomla! CMS 4.0 Release Candidate and Joomla 3.10 Alpha 6.

Joomla 3.9.27 is now available. This is a security release for the 3.x series of Joomla which addresses 3 security vulnerabilities and contains more than 15 bug fixes and improvements.

Joomla 3.9.26 is now available. This is a security release for the 3.x series of Joomla which addresses 2 security vulnerabilities and contains more than 30 bug fixes and improvements.

Joomla 3.9.25 is now available. This is a security release for the 3.x series of Joomla which addresses 9 security vulnerabilities and contains more than 40 bug fixes and improvements.

Once again the Joomla Project is pleased to announce the availability of the Joomla 4.0 Beta 7 release, and Joomla 3.10 Alpha 5.

Joomla 3.9.24 is now available. This is a security release for the 3.x series of Joomla which addresses 3 security vulnerabilities and contains more than 35 bug fixes and improvements.

The PHP team is pleased to announce the first testing release of PHP 8.1.0, Alpha 1. This starts the PHP 8.1 release cycle, the rough outline of which is specified in the PHP Wiki.For source downloads of PHP 8.1.0 Alpha 1 please visit the download page.Please carefully test this version and report any issues found in the bug reporting system.Please DO NOT use this version in production, it is an early test version.For more information on the new features and other changes, you can read the NEWS file, or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive.The next release will be Alpha 2, planned for 24 Jun 2021.The signatures for the release can be found in the manifest or on the QA site.Thank you for helping us make PHP better.

As announced on the php.internals mailing list, a pair of malicious commits were made in the PHP source code repository over the weekend. These commits were immediately noticed and reverted, and thus never reached end users. The investigation into the root cause and exact scope of the compromise is still ongoing, therefore releases will be put on hold for two weeks assuming no further issues are discovered. Thank you for bearing with us while we endeavor to ensure that PHP is a stable and reliable platform for web development.

The PHP team is pleased to announce the eleventh testing release of PHP 8.0.0, Release Candidate 5. This is an extra unplanned release, but we're not planning to adjust the GA date, however, this may change during the course of the RC cycle. The updated release schedule can, as always, be found on the PHP Wiki page about the PHP 8.0. For source downloads of PHP 8.0.0 Release Candidate 5 please visit the download page.Please carefully test this version and report any issues found in the bug reporting system.Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file, or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release will be the General Availability, planned for Nov 26 2020.The signatures for the release can be found in the manifest or on the QA site.Thank you for helping us make PHP better.

The PHP team is pleased to announce the tenth testing release of PHP 8.0.0, Release Candidate 4. At this time, we're not planning to adjust the GA date, however this may change during the course of the RC cycle. The updated release schedule can, as always, be found on the PHP Wiki page about the PHP 8.0. For source downloads of PHP 8.0.0 Release Candidate 4 please visit the download page.Please carefully test this version and report any issues found in the bug reporting system.Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file, or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release will be the General Availability, planned for Nov 26 2020.The signatures for the release can be found in the manifest or on the QA site.Thank you for helping us make PHP better.

The PHP team is pleased to announce the tenth testing release of PHP 8.0.0, Release Candidate 3. At this time, we're not planning to adjust the GA date, however this may change during the course of the RC cycle. The updated release schedule can, as always, be found on the PHP Wiki page about the PHP 8.0. For source downloads of PHP 8.0.0 Release Candidate 3 please visit the download page.Please carefully test this version and report any issues found in the bug reporting system.Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file, or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release will be the Release Candidate 4, planned for Nov 12 2020.The signatures for the release can be found in the manifest or on the QA site.Thank you for helping us make PHP better.

The PHP team is pleased to announce the ninth testing release of PHP 8.0.0, Release Candidate 2. At this time, we're not planning to adjust the GA date, however this may change during the course of the RC cycle. The updated release schedule can, as always, be found on the PHP Wiki page about the PHP 8.0. For source downloads of PHP 8.0.0 Release Candidate 2 please visit the download page.Please carefully test this version and report any issues found in the bug reporting system.Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file, or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release will be the Release Candidate 3, planned for Oct 29 2020.The signatures for the release can be found in the manifest or on the QA site.Thank you for helping us make PHP better.

The PHP team is pleased to announce the eighth testing release of PHP 8.0.0, Release Candidate 1. At this time, we're not planning to adjust the GA date, however this may change during the course of the RC cycle. The updated release schedule can, as always, be found on the PHP Wiki page about the PHP 8.0. For source downloads of PHP 8.0.0 Release Candidate 1 please visit the download page.Please carefully test this version and report any issues found in the bug reporting system.Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file, or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release will be the Release Candidate 2, planned for Oct 15 2020.The signatures for the release can be found in the manifest or on the QA site.Thank you for helping us make PHP better.

PSH highlights and indents source code using highlight configuration files which are similar to Ultraedit highlighting files As such it currently supports C C Java Perl PHP Pascal Lisp Mumps The post PHP Syntax Highlighter appeared first on PHPBuilder.

404 redirect not found The post Redirecting on Page Not Found (404) appeared first on PHPBuilder.

This page will validate the structure of a string to your specifications This script will verify a password to make sure it contains a lower and upper case alpha character The post Password Structure Validation appeared first on PHPBuilder.

The Zend Framework's Zend_Cache component is an easy way to implement PHP caching, a sure site performance The post Boost PHP Site Performance with Zend Framework Data Caching appeared first on PHPBuilder.

By Ying Zhang on July 30, 2000 This document describes how to display safely formatted output from user input. We will discuss the dangers of displaying unfiltered output and then provide a safe means of displaying formatted output. Download the attachment and extract it into your web documents directory. Dangers... The post Displaying Formatted User Input appeared first on PHPBuilder.

This script will display a progress bar. It's beneficial when you are performing many tasks on the back end that take a while to complete. In these The post Displaying Dynamic Progress Bars appeared first on PHPBuilder.

All web developers will at some point need to program their own form. Whether it is for database submission, The post PHP Form Validation System: An Object-Oriented Approach appeared first on PHPBuilder.

Rank Performance Graph OS Outagehh:mm:ss FailedReq% DNS Connect Firstbyte Total 1 Rackspace Linux 0:00:00 0.000 0.489 0.010 0.022 0.022 2 New York Internet (NYI) FreeBSD 0:00:00 0.000 0.558 0.055 0.110 0.110 3 Bigstep Linux 0:00:00 0.000 0.213 0.074 0.148 0.148 4 CWCS Managed Hosting Linux 0:00:00 0.000 0.281 0.075 0.150 0.150 5 Webair Linux 0:00:00 0.000 0.331 0.078 0.157 0.157 6 ServerStack Linux 0:00:00 0.000 0.226 0.094 0.189 0.189 7 www.choopa.com Linux 0:00:00 0.006 0.256 0.004 0.023 0.023 8 Aruba Linux 0:00:00 0.006 0.449 0.005 0.027 0.028 9 www.dinahosting.com Linux 0:00:00 0.006 0.216 0.080 0.161 0.161 10 Hyve Managed Hosting Linux 0:00:00 0.006 0.149 0.083 0.165 0.165 See full table Rackspace was the most reliable hosting company site in May 2021, taking the top spot for the third time in the past 12 months, and appearing in the top 10 for 10 of the last 12 months. The top six hosting companies all responded to every request from Netcraft, and are ranked by the average connection time. Rackspace had the lowest average connection time of the six, earning it the title of most reliable hosting company site in May 2021. Rackspace offers a variety of cloud hosting solutions from 40 data centres across five different continents in the Americas, Europe, Asia and Australia.

In the May 2021 survey we received responses from 1,218,423,991 sites across 259,596,021 unique domains and 11,051,830 web-facing computers. This reflects a gain of 6.28 million sites and 112,000 computers, but a loss of 4.87 million domains. nginx gained the largest number of hostnames, active sites, and computers this month; but also suffered the largest loss of 4.73 million domains. Its most notable gain was of 78,900 computers (+2.03%), which increased its leading share to 36.0%. It also continues to lead in the hostnames and domains metrics, while Apache is top in active sites. Apache also maintains its lead amongst the top million websites, with a 25.4% share compared to nginx's 22.9%. Cloudflare's share of the top million sites is now up to 17.0% after increasing its presence by a further 3,090 sites, and Microsoft added 1,840 sites to bring its share up to 6.85%. OpenResty saw the largest decrease of 8.10 million hostnames (-9.88%), which has taken its market share down to 6.06% (-0.7 pp). Microsoft also suffered a large loss of 6.92 million sites (-10.3%), which took its share down to 4.95% (-0.6 pp). One of OpenResty's most prominent users is Automattic, which uses it to serve millions of Tumblr microblogging websites that can be found under the tumblr.com domain – for example, icontherecord.tumblr.com. Automattic is also responsible for the popular WordPress.com blogging service, where it instead uses nginx to serve millions of blogs. These WordPress-powered sites can either use custom domain names, or free blogs can be created directly under the wordpress.com domain – for example, catsbeingcats.wordpress.com. The underlying WordPress blogging software reached its 18th birthday this month. Automattic continues to contribute to this open source project, and the software is freely available from wordpress.org, allowing anyone to download and install the software on other compatible web server platforms. Although Apache and nginx are recommended, any server that supports PHP and MySQL ought to be capable of running a WordPress site. Such is the popularity of WordPress, some hosting providers also provide one-click installers and other tools that make it easy to manage WordPress sites. Other vendor and hosting news nginx 1.20.1 stable and nginx 1.21.0 mainline were released on 25 May. Both versions include a fix for a 1-byte memory overwrite vulnerability. NGINX Unit 1.24.0 was released on 27 May. This adds several new features, including SSL/TLS configuration commands, static file chrooting with symlink and mount resolution controls, static file filtering by MIME type, and compatibility with Ruby 3.0. It also includes some bugfixes. Caddy 2.4.0 was released on 10 May, with over 110 patches to resolve bugs and add features likes remote API access, automated identity management and dynamic config loading. Apache Tomcat 10.0.6, 9.0.46 and 8.5.66 were released on 12 May. Version 1.0.0 of the Apache Tomcat Migration Tool for Jakarta EE was released a few days earlier on 7 May – this is the tool that allows Tomcat web applications written in Java EE 8 to be automatically converted to run on Tomcat 10 which implements Jakarta EE 9. To showcase its new architectural paradigm of Workers, WebSockets and Durable Objects, Cloudflare has released a multiplayer Doom port which can be played at https://silentspacemarine.com. This game demonstrates how application code can be run on Cloudflare edge nodes rather than only on a client or server. DeveloperApril 2021PercentMay 2021PercentChange nginx432,167,30235.65%440,997,33636.19%0.54 Apache313,948,74125.90%314,774,49225.83%-0.07 OpenResty81,935,3916.76%73,839,9706.06%-0.70 Microsoft67,182,7405.54%60,265,1184.95%-0.60

Rank Performance Graph OS Outagehh:mm:ss FailedReq% DNS Connect Firstbyte Total 1 www.choopa.com Linux 0:00:00 0.000 0.255 0.003 0.024 0.025 2 Rackspace Linux 0:00:00 0.000 0.479 0.006 0.014 0.015 3 www.dinahosting.com Linux 0:00:00 0.000 0.211 0.081 0.162 0.162 4 Swishmail Linux 0:00:00 0.000 0.227 0.093 0.185 0.186 5 www.flexential.com Linux 0:00:00 0.000 0.258 0.099 0.198 0.198 6 New York Internet (NYI) FreeBSD 0:00:00 0.006 0.557 0.054 0.109 0.109 7 Bigstep Linux 0:00:00 0.006 0.200 0.076 0.152 0.152 8 Webair Linux 0:00:00 0.006 0.326 0.079 0.158 0.158 9 krystal.uk Linux 0:00:00 0.006 0.194 0.083 0.167 0.167 10 Hyve Managed Hosting Linux 0:00:00 0.006 0.141 0.083 0.167 0.167 See full table Choopa.com had the most reliable hosting company site in April 2021, heading the table for the fifth time in the past 12 months. The top five sites each responded to all of Netcraft’s requests, but Choopa.com’s average connection time of just 3ms wins it the top spot. The host offers a range of services including cloud hosting, dedicated hosting and colocation in its own primary facility in Piscataway, New Jersey as well as other facilities in Los Angeles, Amsterdam, and Tokyo.

In the April 2021 survey we received responses from 1,212,139,815 sites across 264,469,666 unique domains and 10,939,637 web facing computers. This is an increase of 24,611,866 sites, 1,114,050 domains and 91,955 computers. nginx gained the largest number of sites this month increasing by 12.5 million sites to 432,167,302. This also increases its market share to 35.65%, up 0.32 percentage points. Microsoft last month lost its place as third largest web server developer to OpenResty, this month it continued to lose sites (-3.6M) and market share, dropping 0.42pp to a market share of 5.54%. OpenResty gained 4.1 million sites and 0.21pp market share, while Apache gained 5.4 million sites but lost 0.08pp market share. Looking at domains Apache and OpenResty gained the largest amount, with LiteSpeed and Oracle also seeing increases. OpenResty increased by 467k unique domains (+1.2%), this growth is primarily fueled by its increased use at both Google Cloud and Amazon AWS. Apache gained a similar amount with 426k domains (+0.6%), LiteSpeed gained 52k (+1.0%), and Oracle gained 27k (+3.6%). In contrast, nginx, Microsoft and Google each lost domains, 483k (-0.6%), 331k (-2.2%) and 233k (-9.2%) respectively. In terms of web facing computers nginx gained the largest number with an increase of 59.6k (+1.6%) extending its market lead over Apache to 3.32 percentage points. Apache saw a smaller increase in the number of web facing computers making use of it (8k, +0.2%), this resulted in Apache losing 0.20pp market share. Microsoft lost both absolute numbers of computers, -15.3k (-1.1%), and market share, -0.25pp. Focusing in on the million busiest sites Cloudflare gained 2,721 sites, the only significant gain this month, it is now used by 16.70% of the million busiest sites. nginx and Microsoft saw the largest losses, 1,978 and 1,806 sites respectively, with Apache dropping 608. Other vendor and hosting news nginx version 1.20.0 was released on April 20th; this incorporates the features from the last year of development on the 1.19 mainline branch into a stable release that will not receive further feature updates. Prior to the release of version 1.20.0 the mainline 1.19 branch received a bug fix update on March 30th, and a minor update with features related to keepalives on April 13th. Apache Tomcat major versions 8, 9 and 10 were all updated on April 6th to pick up binaries built with OpenSSL 1.1.1k. Version 7 received a bug fix release on April 26th. DeveloperMarch 2021PercentApril 2021PercentChange nginx419,637,92335.34%432,167,30235.65%0.32 Apache308,509,04225.98%313,948,74125.90%-0.08 OpenResty77,819,4906.55%81,935,3916.76%0.21 Microsoft70,826,3425.96%67,182,7405.54%-0.42

Rank Performance Graph OS Outagehh:mm:ss FailedReq% DNS Connect Firstbyte Total 1 GoDaddy.com Inc Linux 0:00:00 0.000 0.348 0.006 0.031 0.033 2 Rackspace Linux 0:00:00 0.000 0.421 0.007 0.017 0.017 3 CWCS Managed Hosting Linux 0:00:00 0.000 0.285 0.081 0.161 0.161 4 Swishmail Linux 0:00:00 0.006 0.212 0.094 0.188 0.188 5 New York Internet (NYI) FreeBSD 0:00:00 0.011 0.519 0.055 0.110 0.110 6 Hyve Managed Hosting Linux 0:00:00 0.011 0.124 0.076 0.151 0.151 7 ServerStack Linux 0:00:00 0.011 0.205 0.094 0.188 0.188 8 Pair Networks Linux 0:00:00 0.017 0.336 0.110 0.219 0.219 9 Multacom Linux 0:00:00 0.017 0.381 0.124 0.250 0.250 10 www.flexential.com Linux 0:00:00 0.023 0.230 0.099 0.199 0.199 See full table In March 2021 GoDaddy had the most reliable hosting company site, with no failed requests and the fastest average connection time amongst the top 10 of 6ms. GoDaddy provides services that allow customers to build their own web presence, which include hosting solutions, domain registration, and a website builder focused on ease of use. In February, GoDaddy acquired Poynt to accelerate its strategy to provide a complete suite of commerce and payment services. The top three sites each had no failed requests and were separated by average connection time. Rackspace came in second place with an average connection time of 7ms, just 1ms slower than GoDaddy. Rackspace provides a wide variety of cloud services from its global network of over 50 locations in five continents. CWCS Managed Hosting wraps up the podium places, in third. CWCS supplies a wide range of hosting services from their ISO 27001, ISO 9001 accredited and Cyber Essentials certified UK data centres, powered by 100% renewable energy.

In the March 2021 survey we received responses from 1,187,527,949 sites across 263,355,616 unique domains and 10,847,682 web-facing computers. This reflects a loss of 16,724,462 sites, but a gain of 313,561 domains and 81,076 computers. nginx gained 3.7 million sites this month and holds 35.3% of the market with a total of 419.6 million sites. By contrast, Apache lost 8.5 million sites and accounts for just over a quarter of all sites with 308.5 million. Microsoft lost 9.6% (-7.5M) of its sites this month and ceded third place to OpenResty which in turn gained 1.2 million (+1.6%). OpenResty is a web platform based on nginx which integrates Lua-based modules and has been the third-largest server by domains for several months. Despite this, it trails the competition in terms of web-facing computers, with only 105,800 computers compared to Microsoft’s 1.4 million. nginx, Google, OpenResty, and LiteSpeed all acquired significant numbers of domains this month. nginx gained just over a million domains (+1.3%), while Google, OpenResty, and LiteSpeed gained 250,000 (+11.0%), 212,000 (+0.6%), and 68,600 (+1.3%). nginx’s domain growth came primarily from Freenom with 1.3 million domains using the server, while OpenResty’s growth came from its increased use on Google Cloud. Meanwhile, Apache and Microsoft lost -540,000 (-0.8%) and -585,000 (-3.7%) domains. nginx and Apache both gained web-facing computers this month with nginx gaining a substantial 74,000 additional computers and a gain of 0.4 percentage points of market share and Apache gaining 3,300 - though losing 0.2 percentage points of market share due to nginx’s comparative higher growth. Other vendors also saw market share losses, with Microsoft losing 24,200 computers (-0.3 pp) and OpenResty losing just over 200 computers (-0.01 pp) despite its gains in sites and domains. Looking at which web servers power the million busiest sites, only Cloudflare saw its count increase this month with a gain of 3,200 sites (+0.3 pp). Cloudflare’s growth came at the expense of nginx which lost the most with 1,570 fewer sites (-0.2 pp), along with Apache and Microsoft which both lost around 250 sites. The top spot remains hotly contested between Apache and nginx - Apache leads, but less than 2.5 percentage points separate the two. Other vendor and hosting news A major fire at OVH’s Strasbourg datacenters resulted in around 3.6 million websites across 464,000 domains being taken offline at the start of March. While this was not captured by this month’s Web Server Survey, additional investigation by Netcraft found that nearly 20% of the IP addresses attributed to OVH stopped responding during the incident. One of the four data centers at the site, SBG2, was completely destroyed, and OVH is now provisioning thousands of new servers to replace those lost. Windows Server 2022 is now in preview and will be made generally available later in 2021. The features added in this release focus on adding new layers of security, integrating more tightly with Microsoft’s Azure platform, and improving Windows Containers. The current major release, Windows Server 2019, was made generally available nearly two and a half years ago in October 2018. nginx version 1.19.8 and njs version 0.5.2 were released on the 9th March. Both updates add minor new features and bug fixes. OpenLiteSpeed, the open-source variant of LiteSpeed Enterprise, received several updates through February and March, with versions 1.5.12, 1.6.20, and 1.7.9 containing primarily security updates and bug fixes. Apache Tomcat was updated to versions 9.0.44 and 10.0.4. Both updates include a variety of fixes, including improvements to asynchronous error handling. DeveloperFebruary 2021PercentMarch 2021PercentChange nginx415,900,47934.54%419,637,92335.34%0.80 Apache316,992,63826.32%308,509,04225.98%-0.34 OpenResty76,623,4406.36%77,819,4906.55%0.19 Microsoft78,331,3796.50%70,826,3425.96%-0.54

Over 100,000 Outlook Web Access servers have been rebooted since Microsoft released security updates for the ProxyLogon remote code execution vulnerability. The subsequent flurry of reboot activity is likely indicative of many Microsoft Exchange servers being restarted after having security updates applied. Last reboot dates of Outlook Web Access servers as at 14 March 2021. Around half of all servers running Outlook Web Access (a service included with Microsoft Exchange Server) were rebooted in the five days after the emergency patch was released. Some of these have since been rebooted again, so will appear later in the above graph. Rebooted machines are likely to have been updated, but the absence of a reboot after 2 March does not necessarily indicate vulnerability. Anecdotally, most servers have requested a reboot after being updated, but some may only require services to be restarted – although administrators may have opted to reboot the servers anyway. Microsoft’s original fixes can only be applied to servers that already have the latest cumulative updates of Exchange Server already installed; however, amidst mass exploitation of the vulnerabilities, Microsoft also released a set of security updates that can be applied to older and unsupported Exchange servers that do not—or cannot—have the latest cumulative updates installed. The alternative security update path is intended as a temporary measure to protect vulnerable machines. Crucially, installing a later cumulative update that does not include the March 2021 security fixes will make the server vulnerable again, and any machine that uses the alternative security update path must be rebooted even if not prompted. In these cases, the servers will certainly not be protected until after the reboot. Some of the more recent reboots may have been prompted by Microsoft’s 9 March “Patch Tuesday” collection of software updates, which also includes fixes for the remote code execution vulnerabilities in Microsoft Exchange. On 6 March, four days after the original security updates were released, Netcraft found more than 99,000 Outlook Web Access servers were still running versions flagged as definitely vulnerable by Kevin Beaumont. However, applying Microsoft’s updates even in a timely fashion could have been like shutting the barn door after the horse had bolted, as more than 10% of all visited Outlook Web Access installations were already compromised with attackers’ web shells installed. These provide the criminal with continued administrative access to the compromised servers after the security updates had been applied.

We're showing you everything you need to know to clean up with this year's Prime Day vacuum deals.

Kaspersky has added new features and improvements to its password manager on both desktop and mobile.

As OnePlus and Oppo become even closer, we fantasize about the smartphone manufacturer mash-ups that could produce the perfect phone.

Early Amazon Prime Day deals are landing now, here are the best offers available ahead of the 2021 sale.

Here's everything we know about the Samsung Galaxy Z Fold 3, the next foldable we expect from Samsung.

Act quick and you can get the best broadband deal on the market right now. Vodafone's fibre plan ends tonight.

Here's everything you need to know about The Umbrella Academy season 3 on Netflix, including its recently announced episodic titles.