SOLID Coding in Python

SOLID Coding in Python by Mattia Cinelli.

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This was fun to read. It has some nice examples. 

I submit that the order of presentation (S, O, L, I, D) is misleading. The acronym is fun, but awkward.

My LinkedIn Learning course covers these in (what I think is) a more useful order.

1. Interface Segregation. I think this is the place to start: make your interfaces as small as possible.
2. Liskov Substitution. Where necessary, leverage inheritance.
3. Open/Closed. This is a good quality check to be sure you've followed the first two principles well.
4. Dependency Injection. This is often about test design and future expansion. In Python, where everything really happens at run time, we often fail to parameterize a type properly. We often figure that out a test time, and need to revisit the Open/Closed principle to get things right.
5. Single Responsibility is more of a summary of the previous principles than a distinct, new principle. I think it comes last and should be treated as a collection of good ideas, not a single idea.

I think time spent on the first three -- Interface Segregation, Liskov Substitution, and the Open/Closed principle -- pay off tremendously. The ILODS acronym, though, isn't as cool as SOLID.

The "Single Responsibility" suffers from an ambiguous context. At one level of abstraction, all classes have a single responsibility. As we dive into details, we uncover multiple responsibilities. The further we descend into implementation details the more responsibilities we uncover. I prefer to consider this a poetic summary, not the first step in reviewing a design.

How can people find inspiration at work? #CreateMeaning

What do I know about "inspiration" at work? I'm not sure I know much, but I think I may have some advice that could be useful.

I was in the high-tech write-software-every-day workplace since about 1976 or so. (The first two years were part-time while in college.) I use past perfect "was" because I'm old enough (and lucky enough) to be able to retire from daily work. I've switched from writing code to writing books about writing code.

For the math-impaired, my software career spanned 45 years.

Early in this career, the question of finding inspiration at work wasn't asked in the same stark way people discuss it nowadays. When I was younger, the idea of maintaining a work/life balance wasn't something we were asked or encouraged to consider. We did the best we could and tried to avoid getting replaced by someone who could do it better.

Which -- with the advantages of hind-sight -- was a terrible way to live and work. Simply awful. I was lucky enough to see hundreds of projects in my working career. I worked with scores of different organizations. There was a spectrum of bad behavior.

I did learn this: Fear is not Inspiring. I learned a few other things, but let's start with fear.

The Fear Factor

I want to dwell a bit on the fear factor in the workplace. I'm firmly convinced that some manager types suffer with a nagging background of essential fear for their own jobs. And they can project this fear onto folks around them.

Try these shoes on for a moment. The technology has moved on and you haven't kept up. You're trying to manage people, but you have a nagging suspicion your core managerial skills are weak enough that you could be replaced. Motivated by fear, you encourage "casual overtime" and "working weekends" and "meeting the committed schedule". Perhaps you feel it necessary to go so far as to demand these things.

Fear of getting fired creates an uninspiring place to work. It will be an incubator for burnout. 

Further, I suspect it can lead to worse situations than people quitting. I think the "work a little harder" folks plant seeds for various kinds of workplace abuses. 

I think there are a variety of fears. The fear of getting fired is at the bottom of Maslow's hierarchy of needs: we could get fired, and be unable to get another job. We're in the Physiological and Safety realm of the needs pyramid.

A fear of a project not working out means we'll tumble in the eyes of more senior management. This is somewhere in the higher level of Social Belonging and Esteem needs. The problem is that projects have a variety of metrics, and simply making the schedule is an easy metric and can seem to lead to immediate esteem.

What about higher level cognitive needs like Self-actualization and Transcendence? I strongly suspect fears related to these needs can color someone's workplace. I think these often show up as "Am I really going to be doing this for the Rest of My Life?" questions. This becomes an undercurrent of negativity stemming from fear of being trapped in unfulfilling work. 

We might see these fears in several places. We each harbor our own private fears. In any organization with a hierarchy, we'll have to deal with fears that trickle down to us from supervisors. In non-hierarchical organizations, we'll have to deal with fears of our peers and colleagues. We're surrounded by fears, and I think this can sap our inspiration.

What can we do to find inspiration in a work environment?

My Experience

I've worked on hundreds of projects. That means hundreds of jobs that came to an end. And when the project ended, I was no longer needed. 

In effect, I was fired hundreds of times.

This isn't a helpful thing. If Maslow's base physiological needs are met, then having a project end isn't too horrible. I was a contract programmer in the olden days when we were salaried, and the company would carry us from assignment to assignment. Being let go by a customer can be harsh, but getting paid in spite of being let go softens the blow.

I emphatically do not recommend this way of working as a source of inspiration. Some people like the constant changing gears and changing directions. Other people might find it terrifying: each project is a whole new group of people in a new organization. Potentially very unsettling.

I don't think the "get tougher" or "grow thicker skin" advice is good. I'm don't think it worked out for me. I think this kind of transience left me feeling isolated. I think it lead me to carry around a sense of superiority. So. Let's set aside any dumb-sounding advice based on a literal review of my experience. 

How Did I Cope?

Finding ways to cope, I think, is important, but it is also potentially misleading. The idea of coping with new projects, new organizations, travel, and getting fired all the time isn't inspiring. It's merely coping with an endless stream of loss and separation. 

Underpinning the idea of coping is a more foundational question. Where did I find the inspiration to keep on doing this contract programming thing for so many years? And the other question is how well my search for inspiration might apply to folks who aren't commuting computer programmers?

I think there's a first step that many people can take. It's this:

We can disentangle our self-worth from the work-place imposed sense of worth.

This may be overly glib. But. I think the things rewarded in the workplace aren't a good reflection of who we are and what we're capable of. While it's important to be confident in one's self, our confidence can be undermined by a toxic workplace. Having confidence can let us take our skills and abilities in a variety of directions. We might, for example, decide to find another workplace; one where our value is recognized. Or, we might decide to change our circumstances in the workplace we're currently inhabiting. In both cases, we're asserting our value. We're making a further claim about our value: we may not match the workplace's expectations of us. The workplace can change, or we can find another workplace.

We might see a mismatch in lousy performance reviews. These can can stem from many causes. Perhaps we're not suited for a job and need to find something else. Or, perhaps the person reviewing our work doesn't see what we could (or should) be doing. (They have their own fears, and they may not be willing to try to make the changes we'd like them to make.)

Looking back, I may have been doing this all along, without being clear or intentional about it. Perhaps I excelled at places that valued me, and failed to meet expectations at places that treated me poorly. Perhaps my job shifting was (in an indirect way) a search for a workplace that valued me, my unique experiences, and my distinctive voice.

I was not intentional about it. I stumbled from job to job, knowing the sales folks would find me a next assignment when the current assignment had run its course. I think a vague sense of self-worth is what lead me to locating inspiration in spite of a lot of change and disruption.

Finding Inspiration

When we think of inspiration, we think of a spiritual drive to do the work. This doesn't often parallel with working for pay to cover rent and expenses. 

A good manager, however, can create a cohesive team from a group of people. A group of peers can welcome a new colleague. This creates belonging and esteem: the middle levels of Maslow's hierarchy of needs. We may find that a team or a team's goal may be inspiring. This means that our own self-worth is recognized and valued by our co-workers. This can be a marvelous experience.

What about the bad manager or unhelpful group of colleagues? In this cases, we're forced to make the best of an awkward situation. I think we can do this:

We can search for inspiration at the margins of our work life. 

Can we find some side-bar aspect of the work that leads to some helpful insights? Perhaps there is a chronic problem we can take notes on and -- eventually -- fix. Perhaps someone is less helpful that others, and we can try to understand what would make them less toxic. Perhaps cleaning the break-room fridge is better than complaining about month-old food. (Yuck. But. If things are better, it may be worth it.)

For years, I had an aspiration to write about software development. To further this dream, I started taking more and more careful notes of projects I was on. In the era before the World Wide Web, publication was difficult, but not impossible. I wrote small articles for technical magazines; this effort was something that inspired me to work with customers who were inept and had horrible, horrible problems. I liked the awful customers because it provided me good examples of things that should not be done.

At the end of a horrible project, I'd have a good anecdote for what not to do.

I acknowledge my two ideas of self worth and inspiration isn't a dramatic, life-changing epiphany. I'm pretty sure the scales won't fall from anyone's eyes as they think about looking at sidebar topics as a source of inspiration.

Looking at the margins, edges, and corners of a job can help to reveal the whole job. The whole team. The whole goal. Finding this broader view might inspire us to look for a better team with better goals. In other cases, it might help us find the missing skills in the team we're on. In other cases, a better perspective might help us steer our supervisor toward doing something that's better than what they're doing right now.

There are very fine lines between toxic, poorly organized, poorly managed, confusing work places, and workplaces that are still trying to find a workable organization. Most places have a combination of good and bad, inept and well-done, confusing and sensible features. Indeed, these may all be different axes and an organization is really a multi-dimensional object with different kinds of overlaps and gaps. 

I believe the foundation for inspiration is a clear sense of self-worth. I think we create meaning in our workplace by knowing what we can contribute, what we want to contribute, and what the organization needs. Our unique contribution and what the organization needs may not overlap at all, or the organization may have always been searching for someone like us. Either way, our awareness of our skills, our experience, and our authentic voice is what lets us find inspiration.

What Books Should I Read? In What Order?

A fascinating question.

... I'm baffled by the amount of books you've published over the course of time. Currently Reddit suggests that I use Building Skills in Python under Beginner's section, but it looks quite outdated. So back and forth, I found your Building Skills in OO on GitHub Page and was quite happy with the read on the first 100 pages.

I searched for more info on the books you've published and wanted to know if you could sort them in ascending order of difficulty for me as I intend to purchase them slowly.

My main concern to learn Python is just to cross technical interviews and building applications that help with my workflow (they are in bash with around 200 functions, so I'm hoping to migrate them to something which is more robust). 

Currently the focus I intend to develop is on:

1) Strong Foundations of the Python Language.

2) Strong Foundations on the Basic Libraries for Data Structures and Algorithms (For example, bisect gives me insort(), calendar gives me isleap(), iter_tools gives me permutation(), etc).

3) Strong Foundations on the Design Patterns.

So could you please help me out and suggest your books?

This is challenging for a few reasons.

First, the "Building Skills" books have been reduced to only the Building Skills in OO Design. This can be found in GitHub. https://github.com/slott56/building-skills-oo-design-book.

That book is not really targeted to beginners, though. It presumes some core OO skills, and provides a (very) long series of exercises to build on those skills.

Second, I never really conceived of a beginner-to-expert sequence of books. From your letter, I see that I need to look at filling in some gaps in my sequence of books. I'll alert my editors at Packt, and we can consider this in the future.

Specific Needs

Let's look at your needs.

1. Foundations in the Python language.

This might be something you can learn from my Python Essentials. This isn't focused on complete n00bs. All of my books expect some programming background. Since you're an Android engineer and write code in C++ and Java, this may be helpful. This title is getting old, however, and needs a second edition.

For someone with core programming skills, I suspect that Mastering OO Python will be suitable. My Python 3 OO Programming (4th ed.) is similarly aimed at folks who can program and can learn a new language quickly.

A book like Martelli's Python in a Nutshell may provide a better foundation the way the language works than any of mine. Also Lutz's Learning Python is extremely popular.

2. Foundations in the Standard Library.

This is tricky. I touch on some of these topics in Functional Python Programming (2nd ed.) I also touch on some of these topics in the Modern Python Cookbook (2nd ed.)

I don't, however, cover very much of the library. I touch on a few really important modules. The library is vast. A book like Hellmann's The Python Standard Library by Example might be more suitable than one of mine.

3. Design Patterns.

This is central to Python 3 OO Programming (4th ed.) Dusty Phillips and I cover a number of popular design patterns in detail. 

There are -- of course -- a lot of very, very good books on Python. I'm honored you reached out to me.

Other Random Advice

Because Python is a relatively simple language (with a vast library) I have always suspected that language foundations don't really require a ton of explanation. Many languages (i.e., C++) are filled with odd details and weird features that are really unpleasantly complex. Many Java programmers get used to the distinction between the primitive int type and the Integer class type. While the Java and C++ approach can seem simple (after a while) it really isn't simple at all.

The standard library is vast, and it takes time to get used to how much is there. I would suggest having a browser tab open to https://docs.python.org/3/library/ at all times.

Design patterns, similarly, require some care. There are complex details around implementing the Singleton pattern in C++ and Java. Python class definitions and Python module definitions are Singletons, and using a class definition as a Singleton object is often far simpler than the commonly-used techniques for C++ and Java.

A Python Roadmap

An interesting tweet. The  roadmap has three sections. I'm not sure this is actually complete, or even grouped correctly. It is a very good list of topics.

	Pratham ⁦‪@PrasoonPratham‬⁩ Here's a complete roadmap of topics to master Python. 🧵 👇🏻   6/25/21, 10:42 AM

Foundations

I want start by quibbling about variables being first. I'm not sold on this.

I think that operators, expressions, and the built-in immutable types are foundational. int, float, str, and tuple are hugely important as core concepts in computing and Python.

I also think that "loops" is a sketchy notion and I kind of wish we wouldn't describe for and while statements as "loops". I think we should call them iterations. They implement two kinds of logical quantifiers "for all" and "there exists." I think we should talk about the final result of a for statement: all of the values in a range are processed. Similarly a for-if-break construct establishes a "for exists" that defines the first value in a range for which a condition is met. And yes, range objects will be central.

I think that a huge amount of programming can be covered with these topics. I'm not sure "basic" is the right term; foundations might be a better idea. 

The use of variables to manage state is part of this. But. Variables, assignment, and state change are a bit more advanced and maybe shouldn't be first.

I also think function definitions are foundational. Mathematics has been defining functions based on other functions. It's a way of providing a mental short-hand for complex concepts. I don't need to know all the details of how to compute a square root to make use of square root as a concept.

The wide varieties of assignment statements, including assignment to decompose collections aren't mentioned in the original post. This may be an important omission, causing me to quibble on "complete."

I agree that files and elements of File IO are part of this foundation. If we limit ourselves to reading and writing files, then they're essentially immutable structures. I think we can safely avoid update-in-place files because this is an application topic more than a language topic. Python offers the minimal level of support via seek and tell, but little more. And most modern application relies on a database for updatable files.

Data Structures

Moving from basic to intermediate. I prefer the term "data structures" which are built on the language foundations. I think that the mutable built-in data structures come next in the roadmap. My preference is to omit terms like Object-Oriented or Functional, and focus on list, dict, and set, and how the iteration works. This means comprehensions and generators are part of this essential data structure section.

No, comprehensions aren't and shouldn't be called "advanced." They're very much a core concept. Thinking about statements to implement a map/filter/reduce over a collection is the essence of a great deal of programming. We don't always learn it that way, but it needs to be presented in that framework even to beginners. A pile of for and if statements and a bunch of variables is a programmer's first step toward a simpler comprehension. In both cases, they're doing a mapping and it needs to be described as mapping one collection to another collection.

This is where the standard library collections module is introduced.  Yes it's part of the library. I think it's too central to be ignored. I think dataclasses belong here, too.

Talking about the mutable data structures means revisiting the for statement and using it on a variety of iterables. The way Python's concepts apply to a variety of data types is an important feature of the language. (In the olden days, they used to talk about "orthogonality" of data and processing; we don't need to dwell on it, but I think it helps to acknowledge it.)

Functional Programming

It appears to me that the functional programming topics can come next. The idea of functional composition via higher-order functions and decorators builds on the existing foundation. This is where map() and filter() belong. Because of the way sorted(), max(), and min() work on collections with a key= function, these are part of the functional programming roadmap. The inconsistency between map() and functions like max() is an important thing to note.

I also think itertools belongs here. We can make the case that it's in the standard library, but then, so is io. I think itertools and functools are as central to practical Python as the math module and collections.

I think typing.NamedTuple and dataclasses belong here, also. A frozen dataclass is stateless, and can be helpful when creating list comprehensions to perform a mapping from one collection to another collection.

Object-Oriented Programming

I think OO programming and related concepts build on the previous material. Class definitions and state management aren't simple, even though they're essential parts of Python.

To an extent, OO programming can be decomposed into two layers. While I hate to overuse "foundation", there seem to be two parts:

OO Foundations -- inheritance, composition, and different kinds of delegation. This tends to expose a number of common design patterns like Strategy, Decorator, and Facade.

OO Features -- this includes metaprogramming, decorators, ABC's, mixins, and the like. These topics are all designed to avoid copy-and-paste in sophisticated edge cases that cross class boundaries.

Concurrency

I'm not sure why concurrency and parallelism are separate topics in the original list. I've had folks try to split this hair a number of ways. The idea is to find a place where async lives that's "concurrency lite" or something.

The concepts here become blurry because threads and processes are OS features, not language features. The async/await language features, however, are clearly part of Python. It becomes particularly awful when working on something practical where asyncio doesn't provide the feature you need. Specifically, blocking file system I/O isn't part of asyncio and requires an explicit appeal to the underlying thread pool for the blocking operation. 

To an extent, async/await needs to be on the roadmap. It's tricky, though, to cover this without also digressing into threads as a way to deal with blocking operations.

Test, Integration, and Deployment

This is where tools show up. This is where pip, unittest, pytest, tox/nox, coverage, etc. live. Are these part of the language? Or are the part of the broader ecosystem?

I submit they're explicitly not part of the language. The roadmap ends just before this topic. The idea is that we should have a Python roadmap that uses the language and the standard library.

Once we've talked about the language (and some of the library) we can move on to pip and packaging. I don't think pip is and "intermediate" topic. I find that premature introduction of pip is a sign of trying to create useful interesting examples. Examples that don't use pip wind up being kind of boring. Everyone wants to play with pygame and pillow and other kinds of projects, but, those aren't foundational to the language. They're interesting and appealing and -- frankly -- a lot of fun.

tl;dr

I'm not a fan of the roadmap. I like some of it. I don't like some of it.

I am a fan of presenting the idea for discussion.

Letter to Mom -- What Is This "Computer Programming" Thing?

Happy birthday, mom. Glad to see you're still doing well, avoiding the complications of COVID-19.

You asked what it was I did for a living. Emphasis on the past tense, now that we're both fully retired old people. 

I have to confess that it's not easy describing high-tech work. There's a lot of jargon. Your varied range of careers included many things, one of which was being a school librarian. The world has had libraries and librarians for millennia. The job title is pretty well understood. The world hasn't had electronic computers for very long, making the job of programming them a relative novelty.

Aids to computation include slide rules and other mechanical devices. The idea of a mechanical computer dates from the 1830's. You can read about the first computer programmer, Ada Lovelace, here: https://www.computerhistory.org/babbage/adalovelace/. Proper electronic digital computes didn't arise until the 40's, when you were a child. ENIAC, for example dates from 1945.

While a lot has changed since the ENIAC, there are a few universal truths. I'm going to beat one of those truths like a dead horse because it's both essential and obscured by layers of technology.

This first and most fundamental truth is that a computer -- even something as sophisticated as a laptop with a dozen open browser tabs, zoom, and two different solitaire games -- is really a small device that is patiently waiting for you to type or click; the software works out some response and this is displayed on the screen or burbles out the speakers (or both.)

We can say that a general-purpose computer is "applied" to a specific problem. We shorthand this into creating "Application Software;" the software that applies the computer's hardware to a problem. And we shorthand this into "Apps" or "Applications" that do useful things on a general-purpose device.

The distinction between software (things you download and change) and hardware (the box on your desk) has become pretty common-place. The details of the software are what we need to put under the magnifying glass to look at closely.

To make your computer more useful, clever engineers have worked out a way to interleave activities from a variety of applications, all of which are using your computer concurrently. There's a set of rules to determine which application is in the "foreground"; this is the application software that has access to keyboard, mouse, display, and speakers. When you click on another window, you bring another application to the foreground. Access to the hardware switches and the display updates. It's very slick. They provide a number of visual cues to show you which application's "window" is in the foreground; all the others have different cues to show you they're in the background.

What's important about this foreground/background concept is that each application is -- from one point of view -- free to behave as if it is in total control of the entire computer. In reality, an application emphatically does not have unfettered control over the computing resources; there are a large number of gates and fences forcing applications into an orderly, and disciplined sharing and cooperation.

You taught at a nursery school. You know how important an orderly set of rules is. Applications are no different than unruly three- and four-year olds: they try to grab snacks out of order. They forget how pants work when they try to use the toilet. They need lessons in how to put their coats on to go outside in the winter.

These rules -- the set of policies and procedures that constrain applications -- is collectively called the "Operating System." (Don't ask why, the computer folks borrow terms from other disciplines and imbue them with new meanings. There's rarely a sensible etymology, just conventional usage.) The idea of a "system" of components is essential. There are a lot of layers of engineering in the OS.

The presence of an operating system lets multiple apps cooperate. But, it doesn't change the fundamental truth that originated with Babbage and Lovelace and continued on through Turing and Von Neumann and others and was handed down to me.

The general-purpose computer is applied ("programmed") to a problem; it's set up to respond to inputs by displaying outputs.

So that was boring. What did you do?

Good point. That was boring. But necessary, I think, to bracket the nuanced difference between "computer" as a collection and an individual application. The computer-as-collection includes a lot of software: an OS plus applications. This is distinct from each individual application that's part of the collection. It's all software, but the context shifts from everything the computer is doing to one specific solitaire game.

Above, I mentioned that the OS has layers. In a way it's like a quilt, there's a backing, batting in the middle, and a complex quilt top made from pieces. Most important is the quilting that holds the layers together. 

In a way, it's also like a library. There's the foundational problem of storing and loaning books. But there's a secondary problem of finding the damn things; leading to Dewey or LC codes for topics so we can keep related books together. And there's a third layer problem of having an accurate index or catalog of all the books. Using small cards (the card catalog) gives the library flexibility to make sure the catalog matches the stacks. And there are related problems of loaning them out with some reasonable promise to return them. 

I might even be able to work out an analogy with the Apple Orchard or the Arboretum or the Summer Camp. But, I think you get my drift here, that there are foundational elements that we can't really change, and we build on those foundations to make the whole slightly easier for people to use.

I get it, you built application software. What did you do?

What's important about concept of layers is how pervasive the layering idea is in all of computing.

Because of the potential complexity of a solution to a problem, we take the "layering" idea one step further than simply decreeing there should be layers.

What we found, starting in the 70's, was that the operating system tended to conceal many details of the underlying hardware. A modern programming language also divorced us from details of the hardware. Admiral Grace Murray Hopper's idea was to have an application that would transform a program written in some neutral language into the language of whatever hardware we had on hand. She pioneered the COBOL programming language; the language was utterly unlike any specific piece of hardware, and required a "compiler" application to translate COBOL statements into a form that the OS could run as an application.

We liked this idea: the underlying hardware became a kind of hazy abstraction. We knew it was there, but between our languages, libraries of pre-written software, our compilers, and the OS, we didn't really see the underlying hardware. This lets us decompose a complex problem into a number of smaller problems; giving us a lot of leverage.

The core idea of "abstraction" leads to the idea of layers of abstraction. Within our application software we can can also use this idea of layers to decompose our solution to a problem. An application layer can be quilted to a library layer that we bought or downloaded. The library is -- independently -- quilted to an OS layer. And the whole stack of layers is carefully stitched down to the underlying silicon chip. Maybe it was a Motorola chip, or an Intel chip, or an AMD chip. We didn't much know or care.

Well. We cared a little. Some of the AMD chips were faster than some of the Intel chips. So we would prefer to have our OS and our language focused on those chips because things were faster. Until Intel jumped ahead of AMD. The concept was to remain divorced from gritty details of how the little fleck of silicon with its millions of transistors actually worked. 

Recap

Application software configures the general-purpose computer to a specific task. Applications coexist via an operating system and reusable libraries.

Software (application, operating system, libraries) is created in layers and provide abstractions to hide the details of underlying layers.

My job?

Design the layers. Get other programmers to understand the design for the layers. Help them to create statements ("code") using the language of choice. (I'm a big fan of Python, but I've used many, many other languages.)

Note that I didn't (generally) design the visible quilt top in any detail. My job was to help the visual designers and the user experience (UX) designers create a top that delighted people using the software. I made sure that the top and the layers underneath it all fit together reasonably well for a sensible budget. Cutting and stitching all the blocks was a specialized skill that I tried to avoid.

I did more than design, however. When I say design of the structure, you can imagine an architect or civil engineer looking over drawings of girders and beams and making sure the floor would hold the weight of all those books in the new wing of the library.

While many software designers and architects do pour over drawings, I -- personally -- didn't like to leave it at the drawing stage. This was probably a career-limiting choice, but I liked to get my hands dirty actually digging holes and standing up cinderblocks in the foundation. The idea of swinging a hammer to build components told me -- directly -- how good (or bad) my design was.

There's a fork in the career path for programmers. Some software architects work best with Keynote presentations to developers and executives. They build understanding and consensus. They're trusted with larger projects and larger budgets. If things didn't work out, they could deflect blame to the folks writing the software. This distinction between design and realization can be used to avoid culpability. It worried me.

Other architects (me, specifically) work best with code. I still needed to build understanding and consensus. But I also built software so I could be *sure* things worked. I liked to provide concrete, tangible, "do it like this" code.

To higher-level executives -- people with budget authority -- I was only a low-level programmer. 

For decades, this meant a project would wind down after completion, and I would leave the customer's location, and move on to a new project. That's why I traveled a LOT.

A few clients would come to realize that I did offer significant value by being able to design the layers and abstractions while also helping folks actually build the software. This recognition was a rarity, which is why I call it a career-limiting choice. It happened a few times. There's a particularly memorable offer from a client in the 90's that -- in retrospect -- I should have taken. But, generally, I moved from work site to work site, designing, and building the application software for very, very large computers.

So, you went to meetings a lot?

Precisely.

At first, I needed to talk about the problem. What they want software to do. Why do they think new, custom-built, unique software will solve the problem they have? This means meeting with people to understand the problem in the first place. "What can't you do?" "Why can't you do it?" There's a lot of "Why?" questions that need to be asked to locate the obstacle that's easiest to remove. (Or the lowest-hanging fruit we can pick.)

Then, we need to talk about the solution. How will we solve the problem with computers and software? In some cases, they have departments that aren't talking. Or they have legal obstacles. Or they have a half-wit vice president in charge of being the owner's brother. Eventually, we wind up at "aha. They have software that acts as a kind of 'custodian' for their cloud-based resources, but the language of the rules for that custodian are opaque." 

(Seriously. A real problem. Very, very removed from reality: governance of rented "cloud" resources. Enterprise policies for use of cloud resources. Concrete rules for cleaning up the computers rented from a cloud vendor. Mathematical foundations for those rules. Very. Abstract. https://github.com/cloud-custodian/cel-python)

Once we've got the preferred solution, we need to decompose it into things we can download, and things we have to build. Ideally, we can download most (or all) of it and move on. Realistically, the problem domain is unique or something about the overall context and organization is unique, and means leads to customized software to reflect the unique situation.

Before too long, we have meetings to review some pictures: some contexts, some containers for application software, some components (or I've called them "layers" above). This will lead to people writing some code. (The 4 C's: Context, Container, Component, Code.)

(Side-bar. The "container" is a generalization of the idea of a computer. The OS lets multiple applications cooperate; what if we have multiple OS's cooperating? This idea of layers of abstraction is so compelling, we can apply it in a variety of places. This lets us to talk about abstract containers instead of concrete computers.)

We'll have daily meetings while we're building the code that populates the components that gets installed into the containers that fills out the context. These last 10 minutes. What we've done. What we're doing.

We'll have meetings every two weeks to look at components and containers and be sure they work. People will demo what they've done. It will be fun. We'll have donuts.

We'll have impromptu meetings to talk about how to write tests and do quality assurance on our code and components. The testing and quality checking became my obsession during the last five years of my career. Answering the question "Did you test everything?" 

We'll have meetings to talk about managing the containers to be sure they're working. And how to integrate and deploy the components into the containers. 

In and among the meetings, I wrote code. For the last ten years, it was always in Python. Before that it was in other languages.

So, that's what I did for a living. I went to meetings. I wrote code.

SQL linting — this sounds cool

 https://www.pythonpodcast.com/sqlfluff-sql-linter-episode-318/

Architectural Boundaries: Which Package/Module/Class Owns That Responsibility?

 The SOLID design principles beat the design boundary issue to death. Here are the principles in my preferred order. (See https://www.linkedin.com/learning/learning-s-o-l-i-d-programming-principles) 

1. Interface Segregation -- minimize the boundaries. Do this first.
2. Liskov Substitution -- keep the boundaries consistent. Do this for hierarchies.
3. Open/Closed -- keep the boundaries stable and allow subclasses. 
4. Dependency [Inversion] Injection -- keep the implementation separate from the design.
5. Single Responsibility -- This is essentially a summary of the above four principles.

The point here is that these principles are pleasantly poetic, but there are those edgy cases where an interface can go either way.

Specifically, here's an Edgy Case that can go either way.

We're reading GPX (GPS Exchange) data. See https://www.topografix.com/GPX/1/1/. 

Associated with this is what's known as the Lowrance USR file format. A lot of devices include the same (or similar) underlying software, and can exchange waypoint and route information in USR format.

We have this as part of the underlying model.

• The underlying Angle as an abstraction. This has two subclasses:
• Latitude. An angle with "N" and "S" for its sign, conventionally shown as a two-digit number of degrees: 25°42.925′N
• Longitude. An angle with "E" and "W" for its sign, conventionally shown as a three-digit number of degrees: 080°13.617′W
• A Point (or LatLon) is a two-tuple, tuple[Lat, Lon].

A waypoint includes name, description, a time-of-last-update (TOLU), and display symbol to be used. It may also include a GUID to track name changes and assure uniqueness in spite of repeated names.

So far, so good. Nothing too edgy there. "Where's the problem?" you ask.

The problem is representation.

In GPX files, latitude and longitude are float values in degrees. You'll see this: <wpt lon="-80.22695124" lat="25.7154147">...</wpt>.

To do any useful computation, they need to be radians. Or a geocode that supports proximity comparisons, like OLC.

And. If you work with a CSV export from a tool like OpenCPN, then you get strings. This can be any combination of degrees and minutes or degrees, minutes, and seconds. And, depending on the software, there may be either ° or º for the degrees. Can't tell the apart? One is U+00B0, the DEGREE SIGN. The other is U+00BA, the MASCULINE ORDINAL INDICATOR. Plus, of course, everyone uses apostrophe (') and quote (") where they should have used prime (′) and double prime (″). These are easy regular expression problems to solve.

This leads to a class like the following:

class Angle(float):
    @classmethod
    def fromdegrees(cls, deg: float, hemisphere: Optional[str] = None) -> "Angle":
        ...
    @classmethod
    def fromstring(cls, value: str) -> "Angle":
        ...

This Angle class converts numbers or strings into useful values; in radians internally. Formatted in degrees externally.  (And yes, this gets a warning from Python 3.9 that we can't usefully extend float like this.)

The problem is USR files. 

In USR files, they use millimeter mercator numbers for latitude and longitude. These are distances from the equator or the prime meridian. Because they're in millimeters, an integer will do nicely. A little computation is done to extract degrees (or radians) from these values.

SEMIMINOR_B = 6_356_752.3142

lon = round(math.degrees(mm_lon / SEMIMINOR_B), 8)
lat = round(
        math.degrees(2 * math.atan(math.exp(mm_lat / SEMIMINOR_B)) - math.pi / 2), 8
    )

These aren't too bad. But.

Here's the question.

Where does this belong? Is it part of the underlying Angle class? It is separate?

Where does Millimeter Mercator representation belong?

This raises a secondary question: Where does ANY representation belong?

Do we separate the essential object (an angle in radians, a float) from all representation questions? If so, how do we properly bind value and representation at run time? 

Is our app full of complex mixins to bind the float with representation choices?  class Latitude(float, DMS, MM, etc.): pass. This seems potentially annoyingly complex: we have to make sure names don't collide, when defining all these aspects separately.

I think the representation for latitudes and longitudes *is* the essential problem here. The math (i.e. computing the loxodromic distance between points) is trivially separated from all of these representation concerns. 

If we buy into the centrality of representation issues, then, we're down to the following argument.

Resolution: millimeter mercator belongs in the Angle class.

Affirmative: it's yet another representation of an angle's value. 

Negative: it's not used outside USR files and belongs in the USR file parser module.

Affirmative Rebuttal: None of the other representations in Angle are tied specifically to a file format.

Negative Rebuttal: Because the other formats (float, string) are intermixed in CSV files and text displays, making them "widely used." While float is used consistently in GPX, this encoding is a pleasant exception that relies on widely-used encodings.

Okay. We seem to have conflicting goals here. Some representation is a generic thing that crosses file formats and some representation is localized to a specific file format and not reused.

The SOLID design principles don't help chose between these designs. Instead, they provide post-hoc justification for the design we chose.

We can exploit the SOLID principles in a variety of ways. Some Examples.

• We could claim that LatitudeMM is a subclass of Latitude with the MM conversions mixed in. Open/Closed. Liskov Substitution. 
• We could claim that Latitude has several load/dump strategies available, including Load from MM. Open/Closed. Dependency is Injected at run-time.

Sigh.

Prior Art

Methods like __str__() and __repr__() are generally considered part of the essential class. That means the most common string representations need to be provided. The parsing of a string, similarly, is the constructor for  an instance of the float class.

So. Some representations are part of the class. Clearly, however, not all representations are part of the class. Representation codecs like pickle, struct, or ctype are kept separate.

I'm going to make the case that there's a very, very fine line between unique and non-unique-but-not-widely-used aspects of a class of objects. And, in this specific case, the millimeter mercator should be kept separate.

I'm going to rely on other representations like PlusCode (also called OLC) as yet another obscure representation and insist these aren't essential to the class. Indeed, I'm going to suggest that proximity-friendly geocoding is clearly separate because it's a hack to replace complex distance computations with substring comparisons.