Feeds

 

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Someone blogged about a particular problem:

From: https://theweeklychallenge.org/blog/perl-weekly-challenge-294/#TASK1
Given an unsorted array of integers, `ints`Write a script to return the length of the longest consecutive elements sequence.Return -1 if none found. *The algorithm must run in O(n) time.*

The solution they blogged used a sort which meant it could not be O(n) in time, but the problem looked good so I gave it some thought.

Sets! sets are O(1) in Python and are good for looking things up.

What if when looking at the inputted numbers, one at a time, you also looked for other ints in the input that would extend the int you have to form a longer  range?  Keep tab of the longest range so far and if you remove ints from the pool as they form ranges, when the pool is empty, you should know the longest range.

I added the printout of the longest range too.

My codedef consec_seq(ints) -> tuple[int, int, int]:    "Extract longest_seq_length, its_min, its_max"    pool = set(ints)    longest, longest_mn, longest_mx = 0, 1, 0    while pool:        this = start = pool.pop()        ln = 1        # check down        while (this:=(this - 1)) in pool:            ln += 1            pool.remove(this)        mn = this + 1        # check up        this = start        while (this:=(this + 1)) in pool:            ln += 1            pool.remove(this)        mx = this - 1        # check longest        if ln > longest:            longest, longest_mn, longest_mx = ln, mn, mx
    return longest,longest_mn,longest_mx
def _test():    for ints in[(),            (69,),            (-20, 78, 79, 1, 100),            (10, 4, 20, 1, 3, 2),            (0, 6, 1, 8, 5, 2, 4, 3, 0, 7),            (10, 30, 20),            (2,4,3,1,0, 10,12,11,8,9),  # two runs of five            (10,12,11,8,9, 2,4,3,1,0),  # two runs of five - reversed            (2,4,3,1,0,-1, 10,12,11,8,9),  # runs of 6 and 5            (2,4,3,1,0, 10,12,11,8,9,7),   # runs of 5 and 6            ]:        print(f"Input {ints = }")        longest, longest_mn, longest_mx = consec_seq(ints)
        if longest <2:            print("  -1")        else:            print(f"  The/A longest sequence has {longest} elements {longest_mn}..{longest_mx}")

# %%if __name__ == '__main__':    _test()

Sample outputInput ints = ()  -1Input ints = (69,)  -1Input ints = (-20, 78, 79, 1, 100)  The/A longest sequence has 2 elements 78..79Input ints = (10, 4, 20, 1, 3, 2)  The/A longest sequence has 4 elements 1..4Input ints = (0, 6, 1, 8, 5, 2, 4, 3, 0, 7)  The/A longest sequence has 9 elements 0..8Input ints = (10, 30, 20)  -1Input ints = (2, 4, 3, 1, 0, 10, 12, 11, 8, 9)  The/A longest sequence has 5 elements 0..4Input ints = (10, 12, 11, 8, 9, 2, 4, 3, 1, 0)  The/A longest sequence has 5 elements 0..4Input ints = (2, 4, 3, 1, 0, -1, 10, 12, 11, 8, 9)  The/A longest sequence has 6 elements -1..4Input ints = (2, 4, 3, 1, 0, 10, 12, 11, 8, 9, 7)  The/A longest sequence has 6 elements 7..12Another Algorithm

What if, you kept and extended ranges untill you amassed all ranges then chose the longest? I need to keep the hash lookup. dict key lookup should also be O(1).  What to look up? Look up ints that would extend a range!

If you have an existing (integer) range, say 1..3 inclusive of end points then finding 0 would extend the range to 0..3 or finding one more than the range maximum, 4 would extend the original range to 1..4 

So if you have ranges then they could be extended by finding rangemin - 1 or rangemax +1. I call then extends

If you do find that the next int from the input ints is also an extends value then you need to find the range that it extends, (by lookup), so you can modify that range. - use a dict to map extends to their range and checking if an int is in the extends dict keys should also take O(1) time.

I took that sketch of an algorithm and started to code. It took two evenings to finally get something that worked and I had to work out several details that were trying. The main problem was what about coalescing ranges? if you have ranges 1..2 and 4..5 what happens when you see a 3? the resultant is the single range 1..5. It took particular test cases and extensive debugging to work out that the extends2range mapping should map to potentially more than one range and that you need to combine ranges if two of them are present for any extend value being hit.

So for 1..2 the extends being looked for are 0 and 3. For 4..5 the extends being looked for are 3, again, and 6. The extends2ranges data structure for just this should look like:

{0: [[1, 2]], 3: [[1, 2], [4, 5]], 6: [[4, 5]]}

 The Code #2from collections import defaultdict

def combine_ranges(min1, max1, min2, max2):    "Combine two overlapping ranges return the new range as [min, max], and a set of limits unused in the result"    assert (min1 <= max1 and min2 <= max2          # Well formed            and ( min1 <= max2 and min2 <= max1 )) # and ranges touch or overlap    range_limits = set([min1, max1, min2, max2])    new_mnmx = [min(range_limits), max(range_limits)]    unused_limits = range_limits - set(new_mnmx)
    return new_mnmx, unused_limits
def consec_seq2(ints) -> tuple[int, int, int]:    "Extract longest_seq_length, its_min, its_max"    if not ints:        return -1, 1, -1    seen = set()  # numbers seen so far    extends2ranges = defaultdict(list)  # map extends to its ranges    for this in ints:        if this in seen:            continue        else:            seen.add(this)
        if this not in extends2ranges:            # Start new range            mnmx = [this, this]    # Range of one int            # add in the extend points            extends2ranges[this + 1].append(mnmx)            extends2ranges[this - 1].append(mnmx)        else:            # Extend an existing range            ranges = extends2ranges[this]  # The range(s) that could be extended by this            if len(ranges) == 2:                # this joins the two ranges                extend_and_join_ranges(extends2ranges, this, ranges)            else:                # extend one range, copied                extend_and_join_ranges(extends2ranges, this, [ranges[0], ranges[0].copy()])
    all_ranges = sum(extends2ranges.values(), start=[])    longest_mn, longest_mx = max(all_ranges, key=lambda mnmx: mnmx[1] - mnmx[0])
    return (longest_mx - longest_mn + 1), longest_mn, longest_mx
def extend_and_join_ranges(extends2ranges, this, ranges):    mnmx, mnmx2 = ranges    mnmx_orig, mnmx2_orig = mnmx.copy(), mnmx2.copy() # keep copy of originals    mn, mx = mnmx    mn2, mx2 = mnmx2    if this == mn - 1:        mnmx[0] = mn = this  # Extend lower limit of the range    if this == mn2 - 1:        mnmx2[0] = mn2 = this  # Extend lower limit of the range    if this == mx + 1:        mnmx[1] = mx = this  # Extend upper limit of the range    if this == mx2 + 1:        mnmx2[1] = mx2 = this  # Extend lower limit of the range    new_mnmx, _unused_limits = combine_ranges(mn, mx, mn2, mx2)
    remove_merged_from_extends(extends2ranges, this, mnmx, mnmx2)    add_combined_range_to_extends(extends2ranges, new_mnmx)

def add_combined_range_to_extends(extends2ranges, new_mnmx):    "Add in the combined of two ranges's extends"    new_mn, new_mx = new_mnmx    for extend in (new_mn - 1, new_mx + 1):        r = extends2ranges[extend]  # ranges at new limit extension        if new_mnmx not in r:            r.append(new_mnmx)
def remove_merged_from_extends(extends2ranges, this, mnmx, mnmx2):    "Remove original ranges that were merged from extends"    for lohi in (mnmx, mnmx2):        lo, hi = lohi        for extend in (lo - 1, hi + 1):            if extend in extends2ranges:                r = extends2ranges[extend]                for r_old in (mnmx, mnmx2):                    if r_old in r:                        r.remove(r_old)                if not r:                    del extends2ranges[extend]    # remove joining extend, this    del extends2ranges[this]

def _test():    for ints in[            (),            (69,),            (-20, 78, 79, 1, 100),            (4, 1, 3, 2),            (10, 4, 20, 1, 3, 2),            (0, 6, 1, 8, 5, 2, 4, 3, 0, 7),            (10, 30, 20),            (2,4,3,1,0, 10,12,11,8,9),  # two runs of five            (10,12,11,8,9, 2,4,3,1,0),  # two runs of five - reversed            (2,4,3,1,0,-1, 10,12,11,8,9),  # runs of 6 and 5            (2,4,3,1,0, 10,12,11,8,9,7),   # runs of 5 and 6            ]:        print(f"Input {ints = }")        longest, longest_mn, longest_mx = consec_seq2(ints)
        if longest <2:            print("  -1")        else:            print(f"  The/A longest sequence has {longest} elements {longest_mn}..{longest_mx}")

# %%if __name__ == '__main__':    _test()

Its Output
 Input ints = ()  -1Input ints = (69,)  -1Input ints = (-20, 78, 79, 1, 100)  The/A longest sequence has 2 elements 78..79Input ints = (4, 1, 3, 2)  The/A longest sequence has 4 elements 1..4Input ints = (10, 4, 20, 1, 3, 2)  The/A longest sequence has 4 elements 1..4Input ints = (0, 6, 1, 8, 5, 2, 4, 3, 0, 7)  The/A longest sequence has 9 elements 0..8Input ints = (10, 30, 20)  -1Input ints = (2, 4, 3, 1, 0, 10, 12, 11, 8, 9)  The/A longest sequence has 5 elements 0..4Input ints = (10, 12, 11, 8, 9, 2, 4, 3, 1, 0)  The/A longest sequence has 5 elements 8..12Input ints = (2, 4, 3, 1, 0, -1, 10, 12, 11, 8, 9)  The/A longest sequence has 6 elements -1..4Input ints = (2, 4, 3, 1, 0, 10, 12, 11, 8, 9, 7)  The/A longest sequence has 6 elements 7..12

This second algorithm gives correct results but is harder to develop and explain. It's a testament to my stubbornness as I thought their was a solution there, and debugging was me flexing my skills to keep them honed.

END.

My thoughts on my election as a member of the Django Software Foundation (DSF) board of directors.

Python’s zip() function combines elements from multiple iterables. Calling zip() generates an iterator that yields tuples, each containing elements from the input iterables. This function is essential for tasks like parallel iteration and dictionary creation, offering an efficient way to handle multiple sequences in Python programming.

By the end of this tutorial, you’ll understand that:

• zip() in Python aggregates elements from multiple iterables into tuples, facilitating parallel iteration.
• dict(zip()) creates dictionaries by pairing keys and values from two sequences.
• zip() is lazy in Python, meaning it returns an iterator instead of a list.
• There’s no unzip() function in Python, but the same zip() function can reverse the process using the unpacking operator *.
• Alternatives to zip() include itertools.zip_longest() for handling iterables of unequal lengths.

In this tutorial, you’ll explore how to use zip() for parallel iteration. You’ll also learn how to handle iterables of unequal lengths and discover the convenience of using zip() with dictionaries. Whether you’re working with lists, tuples, or other data structures, understanding zip() will enhance your coding skills and streamline your Python projects.

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Understanding the Python zip() Function

zip() is available in the built-in namespace. If you use dir() to inspect __builtins__, then you’ll see zip() at the end of the list:

Python >>> dir(__builtins__) ['ArithmeticError', 'AssertionError', 'AttributeError', ..., 'zip'] Copied!

You can see that 'zip' is the last entry in the list of available objects.

According to the official documentation, Python’s zip() function behaves as follows:

Returns an iterator of tuples, where the i-th tuple contains the i-th element from each of the argument sequences or iterables. The iterator stops when the shortest input iterable is exhausted. With a single iterable argument, it returns an iterator of 1-tuples. With no arguments, it returns an empty iterator. (Source)

You’ll unpack this definition throughout the rest of the tutorial. As you work through the code examples, you’ll see that Python zip operations work just like the physical zipper on a bag or pair of jeans. Interlocking pairs of teeth on both sides of the zipper are pulled together to close an opening. In fact, this visual analogy is perfect for understanding zip(), since the function was named after physical zippers!

Using zip() in Python

The signature of Python’s zip() function is zip(*iterables, strict=False). You’ll learn more about strict later. The function takes in iterables as arguments and returns an iterator. This iterator generates a series of tuples containing elements from each iterable. zip() can accept any type of iterable, such as files, lists, tuples, dictionaries, sets, and so on.

Passing n Arguments

If you use zip() with n arguments, then the function will return an iterator that generates tuples of length n. To see this in action, take a look at the following code block:

Python >>> numbers = [1, 2, 3] >>> letters = ["a", "b", "c"] >>> zipped = zip(numbers, letters) >>> zipped # Holds an iterator object <zip object at 0x7fa4831153c8> >>> type(zipped) <class 'zip'> >>> list(zipped) [(1, 'a'), (2, 'b'), (3, 'c')] Copied!

Here, you use zip(numbers, letters) to create an iterator that produces tuples of the form (x, y). In this case, the x values are taken from numbers and the y values are taken from letters. Notice how the Python zip() function returns an iterator. To retrieve the final list object, you need to use list() to consume the iterator.

If you’re working with sequences like lists, tuples, or strings, then your iterables are guaranteed to be evaluated from left to right. This means that the resulting list of tuples will take the form [(numbers[0], letters[0]), (numbers[1], letters[1]),..., (numbers[n], letters[n])]. However, for other types of iterables (like sets), you might see some weird results:

Python >>> s1 = {2, 3, 1} >>> s2 = {"b", "a", "c"} >>> list(zip(s1, s2)) [(1, 'a'), (2, 'c'), (3, 'b')] Copied!

In this example, s1 and s2 are set objects, which don’t keep their elements in any particular order. This means that the tuples returned by zip() will have elements that are paired up randomly. If you’re going to use the Python zip() function with unordered iterables like sets, then this is something to keep in mind.

Passing No Arguments

You can call zip() with no arguments as well. In this case, you’ll simply get an empty iterator:

Python >>> zipped = zip() >>> zipped <zip object at 0x7f196294a488> >>> list(zipped) [] Copied!

Here, you call zip() with no arguments, so your zipped variable holds an empty iterator. If you consume the iterator with list(), then you’ll see an empty list as well.

Read the full article at https://realpython.com/python-zip-function/ »

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Review: Dark Deeds, by Michelle Diener

Series: Class 5 #2 Publisher: Eclipse Copyright: January 2016 ISBN: 0-6454658-4-4 Format: Kindle Pages: 340

Dark Deeds is the second book of the self-published Class 5 science fiction romance series. It is a sequel to Dark Horse and will spoil the plot of that book, but it follows the romance series convention of switching to a new protagonist in the same universe and telling a loosely-connected story.

Fiona, like Rose in the previous book, was kidnapped by the Tecran in one of their Class 5 ships, although that's not entirely obvious at the start of the story. The book opens with her working as a slave on a Garmman trading ship while its captain works up the nerve to have her killed. She's spared this fate when the ship is raided by Krik pirates. Some brave fast-talking, and a touch of honor among thieves, lets her survive the raid and be rescued by a pursuing Grih battleship, with a useful electronic gadget as a bonus.

The author uses the nickname "Fee" for Fiona throughout this book and it was like nails on a chalkboard every time. I had to complain about that before getting into the review.

If you've read Dark Horse, you know the formula: lone kidnapped human woman, major violations of the laws against mistreatment of sentient beings that have the Grih furious on her behalf, hunky Grih starship captain who looks like a space elf, all the Grih are fascinated by her musical voice, she makes friends with a secret AI... Diener found a formula that worked well enough that she tried it again, and it would not surprise me if the formula repeated through the series. You should not go into this book expecting to be surprised.

That said, the formula did work the first time, and it largely does work again. I thoroughly enjoyed Dark Horse and wanted more, and this is more, delivered on cue. There are worse things, particularly if you're a Kindle Unlimited reader (I am not) and are therefore getting new installments for free. The Tecran fascination with kidnapping human women is explained sufficiently in Fiona's case, but I am mildly curious how Diener will keep justifying it through the rest of the series. (Maybe the formula will change, but I doubt it.)

To give Diener credit, this is not a straight repeat of the first book. Fiona is similar to Rose but not identical; Rose had an unshakable ethical calm, and Fiona is more of a scrapper. The Grih are not stupid and, given the amount of chaos Rose unleashed in the previous book, treat the sudden appearance of another human woman with a great deal more caution and suspicion. Unfortunately, this also means far less of my favorite plot element of the first book: the Grih being constantly scandalized and furious at behavior the protagonist finds sadly unsurprising.

Instead, this book has quite a bit more action. Dark Horse was mostly character interactions and tense negotiations, with most of the action saved for the end. Dark Deeds replaces a lot of the character work with political plots and infiltrating secret military bases and enemy ships. The AI (named Eazi this time) doesn't show up until well into the book and isn't as much of a presence as Sazo. Instead, there's a lot more of Fiona being drafted into other people's fights, which is entertaining enough while it's happening but which wasn't as delightful or memorable as Rose's story.

The writing continues to be serviceable but not great. It's a bit cliched and a bit awkward.

Also, Diener uses paragraph breaks for emphasis.

It's hard to stop noticing it once you see it.

Thankfully, once the story gets going and there's more dialogue, she tones that down, or perhaps I stopped noticing. It's that kind of book (and that kind of series): it's a bit rough to get started, but then there's always something happening, the characters involve a whole lot of wish-fulfillment but are still people I like reading about, and it's the sort of unapologetic "good guys win" type of light science fiction that is just the thing when one simply wants to be entertained. Once I get into the book, it's easy to overlook its shortcomings.

I spent Dark Horse knowing roughly what would happen but wondering about the details. I spent Dark Deeds fairly sure of the details and wondering when they would happen. This wasn't as fun of an experience, but the details were still enjoyable and I don't regret reading it. I am hoping that the next book will be more of a twist, or will have a character more like Rose (or at least a character with a better nickname). Sort of recommended if you liked Dark Horse and really want more of the same.

Followed by Dark Minds, which I have already purchased.

Rating: 6 out of 10

Taking notes well can help to listen better, remember things, show respect, be more accountable, free up mind space to solve problems.

This episode discusses

• the benefits of writing things down
• preparing for a meeting
• taking notes in meetings
• reviewing notes for action items, todo items, things to follow up on, etc.
• taking notes to allow for better focus
• writing well structured emails
• writing blog posts and books

 Learn pytest

• pytest is the number one test framework for Python.
• Learn the basics super fast with Hello, pytest!
• Then later you can become a pytest expert with The Complete pytest Course
• Both courses are at courses.pythontest.com

<p>Taking notes well can help to listen better, remember things, show respect, be more accountable, free up mind space to solve problems.</p><p>This episode discusses</p><ul><li>the benefits of writing things down</li><li>preparing for a meeting</li><li>taking notes in meetings</li><li>reviewing notes for action items, todo items, things to follow up on, etc.</li><li>taking notes to allow for better focus</li><li>writing well structured emails</li><li>writing blog posts and books</li></ul> <br><p><strong> Learn pytest</strong></p><ul><li>pytest is the number one test framework for Python.</li><li>Learn the basics super fast with <a href="https://courses.pythontest.com/hello-pytest">Hello, pytest!</a></li><li>Then later you can become a pytest expert with <a href="https://courses.pythontest.com/the-complete-pytest-course">The Complete pytest Course</a></li><li>Both courses are at <a href="https://courses.pythontest.com/">courses.pythontest.com</a></li></ul>

The len() function in Python is a powerful and efficient tool used to determine the number of items in objects, such as sequences or collections. You can use len() with various data types, including strings, lists, dictionaries, and third-party types like NumPy arrays and pandas DataFrames. Understanding how len() works with different data types helps you write more efficient and concise Python code.

Using len() in Python is straightforward for built-in types, but you can extend it to your custom classes by implementing the .__len__() method. This allows you to customize what length means for your objects. For example, with pandas DataFrames, len() returns the number of rows. Mastering len() not only enhances your grasp of Python’s data structures but also empowers you to craft more robust and adaptable programs.

By the end of this tutorial, you’ll understand that:

• The len() function in Python returns the number of items in an object, such as strings, lists, or dictionaries.
• To get the length of a string in Python, you use len() with the string as an argument, like len("example").
• To find the length of a list in Python, you pass the list to len(), like len([1, 2, 3]).
• The len() function operates in constant time, O(1), as it accesses a length attribute in most cases.

In this tutorial, you’ll learn when to use the len() Python function and how to use it effectively. You’ll discover which built-in data types are valid arguments for len() and which ones you can’t use. You’ll also learn how to use len() with third-party types like ndarray in NumPy and DataFrame in pandas, and with your own classes.

Free Bonus: Click here to get a Python Cheat Sheet and learn the basics of Python 3, like working with data types, dictionaries, lists, and Python functions.

Getting Started With Python’s len()

The function len() is one of Python’s built-in functions. It returns the length of an object. For example, it can return the number of items in a list. You can use the function with many different data types. However, not all data types are valid arguments for len().

You can start by looking at the help for this function:

Python >>> help(len) Help on built-in function len in module builtins: len(obj, /) Return the number of items in a container. Copied!

The function takes an object as an argument and returns the length of that object. The documentation for len() goes a bit further:

Return the length (the number of items) of an object. The argument may be a sequence (such as a string, bytes, tuple, list, or range) or a collection (such as a dictionary, set, or frozen set). (Source)

When you use built-in data types and many third-party types with len(), the function doesn’t need to iterate through the data structure. The length of a container object is stored as an attribute of the object. The value of this attribute is modified each time items are added to or removed from the data structure, and len() returns the value of the length attribute. This ensures that len() works efficiently.

In the following sections, you’ll learn about how to use len() with sequences and collections. You’ll also learn about some data types that you cannot use as arguments for the len() Python function.

Using len() With Built-in Sequences

A sequence is a container with ordered items. Lists, tuples, and strings are three of the basic built-in sequences in Python. You can find the length of a sequence by calling len():

Python >>> greeting = "Good Day!" >>> len(greeting) 9 >>> office_days = ["Tuesday", "Thursday", "Friday"] >>> len(office_days) 3 >>> london_coordinates = (51.50722, -0.1275) >>> len(london_coordinates) 2 Copied!

When finding the length of the string greeting, the list office_days, and the tuple london_coordinates, you use len() in the same manner. All three data types are valid arguments for len().

The function len() always returns an integer as it’s counting the number of items in the object that you pass to it. The function returns 0 if the argument is an empty sequence:

Python >>> len("") 0 >>> len([]) 0 >>> len(()) 0 Copied!

In the examples above, you find the length of an empty string, an empty list, and an empty tuple. The function returns 0 in each case.

A range object is also a sequence that you can create using range(). A range object doesn’t store all the values but generates them when they’re needed. However, you can still find the length of a range object using len():

Python >>> len(range(1, 20, 2)) 10 Copied!

This range of numbers includes the integers from 1 to 19 with increments of 2. The length of a range object can be determined from the start, stop, and step values.

In this section, you’ve used the len() Python function with strings, lists, tuples, and range objects. However, you can also use the function with any other built-in sequence.

Read the full article at https://realpython.com/len-python-function/ »

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When working on an application it’s not uncommon to be testing with your own configuration and data, and often more in a power-user setup of that application. While that has advantages it’s easy to lose sight of how the application looks and behaves when first opened in a clean environment.

Testing in a clean environment

Testing the first use experience is technically easy, you just have to delete the entire application state and configuration, or create a new user account. However that’s very cumbersome and thus wont be done regularly.

Fortunately there are more convenient and less invasive shortcuts.

Isolated XDG environment

For many applications we get very far already by separating the XDG directories. That includes configuration files, application data and state as well as cached data.

This means creating four new directories and pointing the following environment variables to one of those:

• XDG_CACHE_HOME (cached data)
• XDG_CONFIG_HOME (configuration files)
• XDG_DATA_HOME (application data)
• XDG_STATE_HOME (application state)

Running an application in such an environment will make it not see any of its existing state and configuration (without destroying that). That is, as long as the entire state and configuration is actually stored in those locations.

A somewhat common exception is credential storage in a platform service like Secret Service or KWallet. Those wont be isolated and depending on the application you might not get a clean first use state or you might be risking damaging the existing state.

Multi-instance Akonadi

Other services used by an applications might need special attention as well. A particularly complex one in this context is Akonadi, as it contains a lot of configuration, state and data.

Fortunately Akonadi has built-in support for running multiple isolated instances for exactly that reason. All we need is setting the AKOANDI_INSTANCE environment variable to a unique name and we get our own separated instance.

Automation

Given the above building blocks we can create a little wrapper script that launches a given application in a clean ephemeral environment:

import os import subprocess import sys import tempfile xdgHome = tempfile.TemporaryDirectory(prefix='testing-') for d in ['CACHE', 'CONFIG', 'DATA', 'STATE']: os.mkdir(os.path.join(xdgHome.name, d)) os.environ[f"XDG_{d}_HOME"] = os.path.join(xdgHome.name, d) os.environ['AKONADI_INSTANCE'] = 'testing' subprocess.call(sys.argv[1:]) subprocess.call(['akonadictl', '--instance', 'testing', 'stop', '--wait']) xdgHome.cleanup()

I’ve been using this on Itinerary since some time, and it became additionally useful with the introduction of Appium-based UI tests, as those run in a similarly isolated environment.

If you need something slightly longer living, launching a shell with this wrapper is also possible. In that you then can launch your application multiple times, e.g. for testing whether changes are persisted correctly.

Limitations

There’s one unsolved issue with how this isolates applications though: D-Bus. Applications claiming a unique D-Bus service name wont be able to run alongside a second instance this way, so you will have to shut down an already running instance during testing. In most cases that’s not a big deal, but quite inconvenient when working on one of your main communication apps.

I looked at two possible ways to isolate D-Bus (both relatively easy to integrate in a wrapper script):

• xdg-dbus-proxy: This can limit access to certain host services, but has no way of having a second isolated instance of a service.
• Running a separate D-Bus session bus: Having a second instance of a service is no problem then, but we have no way to access host services anymore (which means also no credential storage service etc).

Neither of those help with the applications I work on, but they might nevertheless be viable in other scenarios.

Overall, the more entangled an application is in platform state, the harder it becomes to achieve this kind of isolation, and the more you’ll need to customize how to do this. It quickly pays off though, an easy and always available way to quickly test things in a clean state has been super helpful.

This week no major new features were merged, so we focused on polishing up what we already have and fixing bugs. That's right, Phoronix readers; we do in fact regularly do this! And let me also remind folks about our ongoing 2024 fundraiser: in it, you can adopt a KDE app to have your name displayed as an official supporter of that app. If you love KDE or its apps, this is a great way to show your appreciation.

We're almost halfway to our year-end goal with 6 weeks to go. That's not bad, but I know we can get there quickly and unlock the stretch goals. So check it out! And after that, check out this stuff too:

Notable UI Improvements

When using a color scheme with Header colors such as Breeze Light and Breeze Dark, the color scheme editor no longer confusingly offers the opportunity to edit the Titlebar colors, which aren't used for such color schemes. Instead, you need to edit the Header colors. (Akseli Lahtinen, 6.2.4. Link)

The System Tray no longer shows tooltips for items in the hidden/expanded view that would be identical to the visible text of the item being hovered with the pointer. (Nate Graham, 6.2.4. Link)

The first time you use Plasma to create a network hotspot, it gets assigned a random password by default, rather than no password. (Albert Astals Cid, 6.3.0. Link)

In KRunner-powered searches, you can now jump between categories using the Page Up/Page Down and Ctrl+Up/Ctrl+Down. (Alexander Lohnau, 6.3.0. Link 1 and link 2)

Implemented support for the "Highlight changed settings" feature for most of System Settings' Drawing Tablet page. (Joshua Goins, 6.3.0. Link)

Discover now shows installation progress more accurately when downloading an app that also requires downloading any new Flatpak runtimes. (Harald Sitter, 6.3.0. Link)

When you have multiple Brightness and Color widgets, adjusting the screen brightness in one of them now mirrors this change to all of them, so they stay in sync. (Jakob Petsovits, 6.2.4. Link)

Added a new symbolic icon for WINE, which allows the category that WINE creates in Kickoff to use a symbolic icon that matches all the others. Also improved the existing colorful icon to better match the upstream branding. (Andy Betts, Frameworks 6.9. Link)

Notable Bug Fixes

Speaking of WINE, we fixed a recent regression that caused WINE windows to display black artifacts around them. (Vlad Zahorodnii, 6.2.4. Link)

The feature to save a customized Plasma System Monitor widget as a new preset once again works. And we added an autotest to make sure it doesn't break again! (Arjen Hiemstra, 6.2.4. Link)

Fixed an extremely strange issue that could cause an actively focused XWayland window to lose the ability to receive keyboard and pointer input when the screen was locked using the Meta+L keyboard shortcut. (Adam Nydahl, 6.2.4. Link)

Fixed a recent regression that caused System Monitor to stop gathering statistics for some ARM-based CPUs. (Hector Martin, 6.2.4. Link)

Discover once again allows you to update update-able add-ons acquired using the "Get New [thing]" windows, which had gotten broken in the initial release of Plasma 6. (Harald Sitter, 6.3.0. Link 1 and link 2)

Fixed a case where the real session restoration feature in the X11 session wouldn't restore everything correctly. (David Edmundson, 6.3.0. Link)

Fixed a visual glitch affecting Kirigami's SwipeListItem component which would give it the wrong background color when using Breeze Dark and other similar color schemes, and could be prominently seen on Discover's Settings page. (Marco Martin, Frameworks 6.9. Link)

Fixed a major Qt regression that caused the lock and login screens to become non-functional under various circumstances. (Olivier De Cannière, Qt 6.8.1, but distros will be back-porting it to their Qt 6.8.0 packages soon, if they haven't already. Link)

Fixed a Qt regression that caused the error dialog on "Get New [Thing]" windows to be visually broken until the window was resized. (David Edmundson, Qt 6.8.1. Link)

Fixed another Qt regression that caused clicking on a virtual desktop to switch to it in KWin's overview effect to stop working after you use the Desktop Cube at least once. (David Edmundson, Qt 6.8.1. Link)

Other bug information of note:

• 2 Very high priority Plasma bugs (down from 4 last week). Current list of bugs
• 36 15-minute Plasma bugs (down from 37 last week). Current list of bugs
• 119 KDE bugs of all kinds fixed over the last week. Full list of bugs

Notable in Performance & Technical

We've re-enabled the ability to turn on HDR mode when using version 565.57.1 or later of the NVIDIA driver for NVIDIA GPU users, or version 6.11 or later of the Linux kernel for Intel GPU users. These are the versions of those pieces of software that have fixed the worst bugs affecting HDR on those GPUs. (Xaver Hugl, 6.2.4. Link 1 and link 2)

Fixed a performance issue that affected users of multi-monitor setups while using a VR headset. (Xaver Hugl, 6.2.4. Link)

Reduced the slowness and lag that you could experience when drag-selecting over a hundred items on the desktop. (Akseli Lahtinen, 6.3.0. Link)

Implemented support for the xdg_toplevel_icon Wayland protocol in KWin. (David Edmundson, 6.3.0. Link)

How You Can Help

KDE has become important in the world, and your time and contributions have helped us get there. As we grow, we need your support to keep KDE sustainable.

You can help KDE by becoming an active community member and getting involved somehow. Each contributor makes a huge difference in KDE — you are not a number or a cog in a machine!

You don’t have to be a programmer, either. Many other opportunities exist:

• Filter and confirm bug reports, maybe even identify their root cause
• Contribute designs for wallpapers, icons, and app interfaces
• Design and maintain websites
• Translate user interface text items into your own language
• Promote KDE in your local community
• …And a ton more things!

You can also help us by donating to our yearly fundraiser! Any monetary contribution — however small — will help us cover operational costs, salaries, travel expenses for contributors, and in general just keep KDE bringing Free Software to the world.

To get a new Plasma feature or a bugfix mentioned here, feel free to push a commit to the relevant merge request on invent.kde.org.

Before my remote talk for the Drupal London meetup, I'm updating the usage statistics for the Override Node Options module - one of the modules I maintain on Drupal.org.

In my slides for DrupalCamp Belgium, I showed the usage figures from October 2023, which showed 38,096 installations and it being the 173rd most installed module.

This week, the number of installations has slightly increased to 38,223.

What's interesting is that whilst the number of installations has been consistent, there are a lot less Drupal 7 websites using the module and a lot more Drupal 8+ sites using it.

October 2023

• 5.x-1.x: 1
• 6.x-1.x: 297
• 7.x-1.x: 13,717
• 8.x-2.x: 24,081
• Total: 38,096

November 2024

• 5.x-1.x: 4
• 6.x-1.x: 202
• 7.x-1.x: 10,429
• 8.x-2.x: 27,588
• Total: 38,223

Assuming these numbers are correct, this makes me feel very positive and happy about the adoption of newer versions of Drupal and that people are upgrading their D7 websites to Drupal 10 or 11.

Dear digiKam fans and users,

After five months of active maintenance and many weeks triaging bugs, the digiKam team is proud to present version 8.5.0 of its open source digital photo manager.

Generalities

More than 160 bugs have been fixed and we spent a lot of time contacting users to validate changes in pre-release versions to confirm fixes before deploying the program to production.

Check out the important work our volunteers accomplished at today's Free Software Directory (FSD) IRC meeting.

Check out the important work our volunteers accomplished at today's Free Software Directory (FSD) IRC meeting.

Let’s go for my web review for the week 2024-46.

No GPS required: our app can now locate underground trains

Tags: tech, mobile, sensors, gps, transportation

Now this is definitely a smart trick to estimate position in tunnels.

https://blog.transitapp.com/go-underground/

OpenAI, Google and Anthropic Are Struggling to Build More Advanced AI - Bloomberg

Tags: tech, ai, machine-learning, gpt

More signs of the generative AI companies hitting a plateau…

https://www.bloomberg.com/news/articles/2024-11-13/openai-google-and-anthropic-are-struggling-to-build-more-advanced-ai

Releasing the largest multilingual open pretraining dataset

Tags: tech, ai, machine-learning, gpt, data, copyright, licensing

It shouldn’t be, but it is a big deal. Having such training corpus openly available is one of the big missing pieces to build models.

https://simonwillison.net/2024/Nov/14/releasing-the-largest-multilingual-open-pretraining-dataset/#atom-blogmarks

Everything I’ve learned so far about running local LLMs

Tags: tech, ai, machine-learning, gpt, foss

This is an interesting and balanced view. Also nice to see that local inference is really getting closer. This is mostly a UI problem now.

https://nullprogram.com/blog/2024/11/10/

When Machine Learning Tells the Wrong Story

Tags: tech, cpu, hardware, security, privacy, research

Fascinating research about side-channel attacks. Learned a lot about them and website fingerprinting here. Also interesting the explanations of how the use of machine learning models can actually get in the way of proper understanding of the side-channel really used by an attack which can prevent developing actually useful counter-measures.

https://jackcook.com/2024/11/09/bigger-fish.html

Abusing Ubuntu 24.04 features for root privilege escalation

Tags: tech, linux, security

Nice chain of attacks. This shows more than one vulnerability needs to be leveraged to lead to root access. This provides valuable lessons.

https://snyk.io/blog/abusing-ubuntu-root-privilege-escalation/

Way too many ways to wait on a child process with a timeout

Tags: tech, unix, linux, system

The title says it all. This is very fragmented and there are several options to fulfill the task. Knowing the tradeoffs can be handy.

https://gaultier.github.io/blog/way_too_many_ways_to_wait_for_a_child_process_with_a_timeout.html

The CVM Algorithm

Tags: tech, databases, algorithm

This is a nice view into how a query planner roughly works and a nice algorithm which can be used internally to properly estimate the number of distinct values in a column.

https://buttondown.com/jaffray/archive/the-cvm-algorithm/

Mergiraf

Tags: tech, version-control, git, tools, conflict

Looks like a nice way to improve handling of merge conflicts. I’ll test this one out.

https://mergiraf.org/

Opposite of Cloud Native is?

Tags: tech, cloud, complexity, vendor-lockin, self-hosting

Definitely a good post. No you don’t have to go all in with cloud providers and signing with your blood. It’s often much more expensive for little gain but much more complexity and vendor lock in.

https://mkennedy.codes/posts/opposite-of-cloud-native-is-stack-native/

Booleans Are a Trap

Tags: tech, design, type-systems

Avoiding boolean parameters in library APIs should be a well known advice by now. Still they should probably be avoided when modeling domain types as well.

https://katafrakt.me/2024/11/09/booleans-are-a-trap/

Complex for Whom?

Tags: tech, design, complexity

Good musing about complexity. Very often we need to move it around, the important question is where should it appear. For sure you don’t want it scattered everywhere.

https://notes.billmill.org/link_blog/2024/11/Complex_forWhom.html

What makes concurrency so hard?

Tags: tech, distributed, complexity

Interesting reasoning about what is hard in systems with concurrency. It’s definitely about the state space of the system and the structure of that space.

https://buttondown.com/hillelwayne/archive/what-makes-concurrency-so-hard/

Algorithms we develop software by

Tags: tech, programming, craftsmanship, engineering, problem-solving

Interesting musing on the heuristics we use when solving problems. There are good advices in there to make progress and become a better developer.

https://grantslatton.com/software-pathfinding

Bye for now!

KDE Gear is our release service for many apps such as mail and calendaring supremo Kontact, geographers dream Marble, social media influencing Kdenlive and dozens of others. KDE needs you to test that your favourite feature has been added and your worst bug has been squished.

You can do this with KDE neon Testing edition, built from the Git branches which get used to make releases from. You can download the ISO and try it on spare hardware or on a virtual machine to test them out.

But maybe you don’t want the faff of installing a distro. Containers give an easier way to test thanks to Distrobox.

Install Distrobox on your normal computer. Make sure Docker or podman are working.

Download the container with

distrobox create -i invent-registry.kde.org/neon/docker-images/plasma:testing-all

Then start it with
distrobox enter all-testing
And voila it will mount the necessary bits to get Wayland connections working and keep your home directory available and you can run say

kontact

and test the beta for the mail app.

Recently, in one of our projects with Drupal 10, we faced an interesting challenge: implementing two-level "local tasks" for a specific functionality of our module. Despite the number of documentation related to local tasks in Drupal, setting up two levels of these tasks proved challenging, as we couldn't get them to display in the way we needed. However, after exhaustive research, we found an example in an existing module that helped us solve the problem.

Exploring the Problem

The need was to add a main "local task" and three associated subtasks that would show up when viewing or editing a node. Initially, the main obstacle was finding the right way to implement two levels of local tasks.

The Solution: Inspiration from Contributed Modules

During our search among existing contributed modules, we found…

10 years after DrupalCity Berlin 2014 the community kicked-off another DrupalCamp in the heart of Europe uniting the global Drupal community. Learn what's behind this triumphant return.

How do you build a sustainable open-source project and community? What lessons can be learned from Python's history and the current mess that the WordPress community is going through? This week on the show, we speak with Paul Everitt from JetBrains about navigating open-source funding and the start of the Python Software Foundation.

[ 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 ]

In this article, I’ll demonstrate how to integrate Drupal with a Lightweight Directory Access Protocol (LDAP) server, using JumpCloud as an example. With this guide, you’ll be able to quickly and securely manage users on your website. I encourage you to read the blog post or watch the video in the “Nowoczesny Drupal” series.