Planet Python
Test and Code: 219: Building Django Apps & SaaS Pegasus - Cory Zue
I'm starting a SaaS project using Django, and there are tons of decisions right out of the gate.
To help me navigate these decisions, I've brought on Cory Zue.
Cory is the creator of SaaS Pegasus, and has tons of experience with Django.
Some of the topics discussed:
- Building Django applications
- SaaS Pegasus
- placecard.me
- What boilerplate projects are
- Django cookiecutter
- Cookiecutter
- Which database to use, probably PostgreSQL
- Authentication choises, probably Allauth
- Docker, Docker for development, Docker for deployment
- Deployment targets / hosting services. Render, Heroku, Fly.io, for PaaS options.
- Front end frameworks. Bootstrap, Tailwind, DaisyUI, TailwindUI
- HTMX vs React vs straight Django templates
- Rockets
- Font Awesome
- and of course, SaaS Pegasus
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PyCoder’s Weekly: Issue #626 (April 23, 2024)
#626 – APRIL 23, 2024
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When the user presses CTRL-C on the keyboard, the OS raises an interrupt signal to your program. When writing concurrent code this can get complicated as the signal goes to the process. This article shows you how to handle capturing CTRL-C elegantly when using asyncio.
JASON BROWNLEE
In this video course, you’ll learn how to raise exceptions in Python, which will improve your ability to efficiently handle errors and exceptional situations in your code. This way, you’ll write more reliable, robust, and maintainable code.
REAL PYTHON course
Join this fireside chat featuring Dustin Ingram, Fellow at the Python Software Foundation (PSF), as we discuss trust and security for PyPI and other repositories in light of recent attacks, and initiatives such as Trusted Publishing - Tune in this Thursday! →
ACTIVESTATE sponsor
Ruff has changed from a generated parser to a hand-written recursive descent parser in the latest release. This has resulted in >2x speedup in its linting performance. This article covers what has changed and why they did it.
DHRUV MANILAWALA
OpenStreetMaps (OSM) is an an open source mapping project that allows people to browse the world map and to plan routes. Not only does it have the expected web interface, but it also has an API known as Overpass. This article shows you two ways to use Python to query Overpass.
JUDITH VOCKENROTH
The Python strip, lstrip, and rstrip functions can have unexpected behavior: they strip a list of characters, not strings. Even though this is documented, it can lead to unexpected results. This post explains what happens and shows you two functions to use instead.
ANDREW WEGNER
What are the benefits of using a decoupled data processing system? How do you write reusable queries for a variety of backend data platforms? This week on the show, Phillip Cloud, the lead maintainer of Ibis, will discuss this portable Python dataframe library.
REAL PYTHON podcast
Wasmer, a company that builds Wasm tools, has announced py2wasm which converts your Python programs to WebAssembly, running at 3x faster speeds than direct Wasm. This post introduces you to the tool and shows how they took advantage of the Nuitka library.
SYRUS AKBARY
In this tutorial, you’ll learn how to use Python format specifiers within an f-string to allow you to neatly format a float to your required precision. You’ll also learn how to extract digits from existing strings and format them as well.
REAL PYTHON
The mpmetrics library contains tools for determining runtime metrics on your code. To do this it requires some esoteric memory management techniques normally not undergone in Python. This post describe what they’ve done and why.
TRENDS BLOG
Big O notation describes the relationship between an algorithm, how much data it is using, and the time it takes to run. This article covers the time complexities of a variety of Python data structures.
TREY HUNNER
Unit is a web server developed by the NGINX folks with pluggable support for Python using WSGI and ASGI. Aidas tried it out and this post talks about how he got some Django code working on this server.
AIDAS BENDORAITIS
Daksh asked 100 developers why they aren’t shipping faster and this blog post shares what he learned. Problems include dependency bugs, overly complicated code bases, waiting on requirements, and more.
DAKSH GUPTA
This quick blog post shows you how to use Python type checking to ensure the dimensions of matrices being multiplied together. Typing is no longer just “is it an integer”.
JAMES FISHER
Open source is free, and although that is a good thing, it also means companies get to take without giving back. This opinion piece talks about how that should change.
GLYPH LEFKOWITZ
GITHUB.COM/POMPONCHIK • Shared by Evgeniy Blinov
Events Weekly Real Python Office Hours Q&A (Virtual) April 24, 2024
REALPYTHON.COM
April 25, 2024
MEETUP.COM
April 26 to April 28, 2024
DJANGOGIRLS.ORG
April 27, 2024
MEETUP.COM
April 27, 2024
PYTHON.ORG.BR
April 28 to April 29, 2024
FB.ME
Happy Pythoning!
This was PyCoder’s Weekly Issue #626.
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Real Python: Python Basics Exercises: Scopes
On your Python journey, you’ve worked with functions and loops. To fully understand functions and loops in Python, you need to be familiar with the issue of scope.
In this Python Basics Exercises video course, you’ll practice:
- Identifying the scope of objects
- Working with the global keyword
- Exploring the LEGB rule for scope resolution
- Using the return statement in a function
Scope can be one of the more difficult programming concepts to understand, so in this video course, you’ll get additional practice working with it.
This video course is part of the Python Basics series, which accompanies Python Basics: A Practical Introduction to Python 3. You can also check out the other Python Basics courses.
Note that you’ll be using IDLE to interact with Python throughout this course. If you’re just getting started, then you might want to check out Python Basics: Setting Up Python before diving into this course.
[ 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 ]
Django Weblog: DjangoCon US 2024 CFP Last Call
Hey Django enthusiasts!
Have you submitted your talk or tutorial for DjangoCon US 2024, in beautiful Durham, North Carolina, USA?
Time flies, and we're fast approaching the deadline (April 24, 2024 at 12 PM EDT) for DjangoCon US 2024 Call for Proposals (CFP). If you've been pondering sharing your Django journey, showcasing your innovative projects, or imparting your expertise to fellow developers, now is the perfect moment to seize the opportunity!
Here's why you should take action and submit your proposals before the clock runs out:
- Share Your Knowledge: DjangoCon US isn't just a conference; it's a platform for sharing insights, discoveries, and lessons learned. Your unique experiences could be just what someone else needs to overcome a hurdle or spark a new idea.
- Join the Community: DjangoCon US isn't just about talks; it's about building connections. By presenting at the conference, you become an integral part of the vibrant Django community, exchanging ideas, and forging new friendships with like-minded developers.
- Boost Your Profile: Whether you're a seasoned speaker or a first-time presenter, DjangoCon US offers a valuable opportunity to elevate your profile in the tech industry. Showcase your expertise, gain visibility, and enhance your professional credibility among peers and potential employers.
- Contribute to Diversity: DjangoCon US values diversity and inclusion. Your perspective matters, and by sharing your voice, you contribute to creating a more inclusive and representative tech community.
Submitting a proposal is easy! Just head over to the DjangoCon US website, fill out the submission form, and share your compelling idea with us. Whether it's a deep dive into a technical topic, a case study of your latest project, or a discussion on the future of Django, we want to hear from you.
Remember, the deadline for CFP submissions is fast approaching, so don't wait until the last minute. Take this opportunity to inspire, educate, and connect with your fellow Django enthusiasts at DjangoCon US 2024!
See you at the conference!
If you have questions feel free to contact us.
PyPy: PyPy v7.3.16 release
The PyPy team is proud to release version 7.3.16 of PyPy.
This release includes security fixes from upstream CPython, and bugfixes to the garbage collector, described in a gc bug-hunt blog post.
The release includes three different interpreters:
PyPy2.7, which is an interpreter supporting the syntax and the features of Python 2.7 including the stdlib for CPython 2.7.18+ (the + is for backported security updates)
PyPy3.9, which is an interpreter supporting the syntax and the features of Python 3.9, including the stdlib for CPython 3.9.19.
PyPy3.10, which is an interpreter supporting the syntax and the features of Python 3.10, including the stdlib for CPython 3.10.14.
The interpreters are based on much the same codebase, thus the multiple release. This is a micro release, all APIs are compatible with the other 7.3 releases. It follows after 7.3.15 release on Jan 15, 2024
We recommend updating. You can find links to download the v7.3.16 releases here:
https://pypy.org/download.html
We would like to thank our donors for the continued support of the PyPy project. If PyPy is not quite good enough for your needs, we are available for direct consulting work. If PyPy is helping you out, we would love to hear about it and encourage submissions to our blog via a pull request to https://github.com/pypy/pypy.org
We would also like to thank our contributors and encourage new people to join the project. PyPy has many layers and we need help with all of them: bug fixes, PyPy and RPython documentation improvements, or general help with making RPython's JIT even better.
If you are a python library maintainer and use C-extensions, please consider making a HPy / CFFI / cppyy version of your library that would be performant on PyPy. In any case, both cibuildwheel and the multibuild system support building wheels for PyPy.
What is PyPy?PyPy is a Python interpreter, a drop-in replacement for CPython It's fast (PyPy and CPython 3.7.4 performance comparison) due to its integrated tracing JIT compiler.
We also welcome developers of other dynamic languages to see what RPython can do for them.
We provide binary builds for:
x86 machines on most common operating systems (Linux 32/64 bits, Mac OS 64 bits, Windows 64 bits)
64-bit ARM machines running Linux (aarch64).
Apple M1 arm64 machines (macos_arm64).
s390x running Linux
PyPy support Windows 32-bit, Linux PPC64 big- and little-endian, and Linux ARM 32 bit, but does not release binaries. Please reach out to us if you wish to sponsor binary releases for those platforms. Downstream packagers provide binary builds for debian, Fedora, conda, OpenBSD, FreeBSD, Gentoo, and more.
What else is new?For more information about the 7.3.16 release, see the full changelog.
Please update, and continue to help us make pypy better.
Cheers, The PyPy Team
Real Python: Python Sequences: A Comprehensive Guide
In this quiz, you’ll test your understanding of Python sequences.
By working through this quiz, you’ll revisit the basic characteristics of a sequence, operations common to most sequences, special methods associated with sequences, and how to create user-defined mutable and immutable sequences.
[ 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 ]
Programiz: Getting Started with Python
Python Bytes: #380 Debugging with your eyes
Django Weblog: An open letter regarding the DjangoCon Europe CfP
As many of you are not doubt aware there have recently been some community conversation regarding a license agreement that was published as part of the DjangoCon Europe Call for Proposals for organizers. I believe it is fair to characterize the reaction as negative, with expressions of anger, frustration, and confusion towards the DSF for placing burdensome and one sided requirements for hosting a DjangoCon Europe.
I want to state first and foremost that I added the licensing agreement to the CfP. My deepest apologies for everyone who felt betrayed by it. I hold our conference organizers in the highest regard, for the equity, effort, blood, sweat, and tears they put into creating amazing community events that bring us all together. I considered resigning from the board for the pain and confusion that I have caused. I have not because I want to be part of finding a way forward and keeping DjangoCon Europe the calendar event that so many of us eagerly await. It will be up to all of you if I can be part of that journey back.
The most difficulty accusation to face is that of a lack of transparency and surprising former and prospective organizers with the license. It is difficult for me because I honestly believed I was increasing the level of transparency to organizers. This licensing agreement is not a new document, though I now understand that it is new to, well, everyone. The license has existed in the DSF legal documents repository since at least 2016 and has been signed for at least one DjangoCon Europe. In my years on the board prior to my presidency on several occasions there were todos to have selected organizers "sign the organizer contract". In addition I know that DEFNA is party to an almost identical licensing contract with the DSF. It was my intention to provide to potential organizers with the contract prior to making a proposal so that when a proposal was selected the organizers would not be taken aback when being presented with the license.
I was mortified when I heard, first in private and then publicly, that no DjangoCon Europe organizers of current memory had ever signed the license. The group of some of our community's most dedicated volunteers felt like with the click of a keyboard had turned into an arms length transaction among lawyers. At the same time I was mortified at the potential legal jeopardy the DSF had unknowingly been in by not having license agreements in place for all DjangoCons.
I'd like to touch on several points brought up in the discussion that has come from this.
The first regards the time window of DjangoCon Europe as laid out in the CfP. The DSF has long standing agreements with the PSF, DEFNA, and EuroPython to not adversely impact each other's conferences by scheduling them too close together. The agreement specifies that no two conferences should be within a month of each other. The sentiment of the agreement is well placed, but I believe it is time to revisit it. With new conferences coming into the fold (it was rightly pointed out the DjangoCon Africa is not mentioned in the license), the ability for conferences to have meaningful remote participation, and a calendar that due to weather often has the four stated conferences vying for a 5-7 month slot the one month gap is difficult to plan around. I am publicly proposing here that the interested parties to this agreement modify it such that the one month window apply to either region or theme, but not both. That would have, for instance, DjangoCon US avoiding Pycon and DjangoCons Europe and Africa but not EuroPython and vice versa.
The second point is in regards to the requirement to not schedule against major religious and cultural holidays. While I did not write that, I embody it. My first DjangoCon was DjangoCon Europe. I was unable to attend DjangoCon US in Chicago while I was living in Chicago as it had been partly scheduled against Rosh Hashanah. So I convinced my boss to send me to France instead and fell in love with the Django community.
Finally to the comments that the DSF does not care about organizers and has no interest in helping them put on a conference. Unfortunately DSF does not have inherent knowledge of running conferences, no legal entity outside the US to bring to bear, or the finances to backstop a DjangoCon. We ask for your help every year to run DjangoCon Europe because, quite simply, we can't. There was a meeting in Edinburgh of former organizers to discuss how organizing a DjangoCon Europe coule be improved. That meeting was the start of the European Organizers Support working group. It did take time to get started. We have it now. It is the sincerest hope of all of us that it is the first step in improving the experience of European organizers, and hopefully others down the line. It is a slow process, but one I dearly hope bears fruit.
I sincerely hope that we can all together still work to make DjangoCon Europe 2025 a reality. I will do all that I can to make that happen, if you will continue to have me. I do not participate in social media, but have started a thread on the Django Forum to continue this discussion. For most of the next week I will be unavailable due to the Passover holiday but it was imperative to me that I get this message to you all as soon as I could.
Thank you for your time, and for letting me serve you, Chaim Kirby President, Django Software Foundation
Real Python: Write Unit Tests for Your Python Code With ChatGPT
Having a good battery of tests for your code may be a requirement for many Python projects. In practice, writing unit tests is hard and can take a lot of time and effort. Therefore, some developers don’t like to write them. However, with large language models (LLMs) and tools like ChatGPT, you can quickly create robust and complete sets of tests for your Python code.
In Python, you can use multiple different tools for writing tests. The most commonly used tools include doctest, unittest, and pytest. ChatGPT can be of great help in writing tests with any of these tools.
In this tutorial, you’ll:
- Prompt ChatGPT to create tests using doctest
- Use ChatGPT to write unittest tests, fixtures, and suites
- Craft ChatGPT prompts to write pytest tests and fixtures
- Use alternative prompts for cases where the code isn’t available
To get the most out of this tutorial, you should set up a ChatGPT account and know the basics of interacting with this tool using prompt engineering. You should also know the basics of how to test code in Python.
Get Your Code: Click here to download the free sample code you’ll use to write unit tests for your Python code using ChatGPT.
Benefits of Using ChatGPT for Testing Python CodeHaving good and up-to-date unit tests for your code is a must for any Python project. Poorly tested code or code without tests may end up being unreliable and weak. With automated tests, you can ensure and show that your code works correctly in different scenarios. So, having tests is important from the technical and commercial point of view.
Writing good tests is hard and can take a lot of time. That’s why some developers don’t like to write them at all. Using large language models (LLM) like ChatGPT can be a viable alternative for providing your projects and code with proper tests.
Note: You can use ChatGPT and other LLMs in many useful ways in the context of Python development. Check out the following resources for other use cases and useful insights:
- ChatGPT: Your Personal Python Coding Mentor
- Episode 174: Considering ChatGPT’s Technical Review of a Programming Book
- Document Your Python Code and Projects With ChatGPT
- Prompt Engineering: A Practical Example
- Build an LLM RAG Chatbot With LangChain
Some of the benefits of using ChatGPT to write tests for your Python code include the following:
- Efficiency and speed: It can generate unit tests based on specifications or code snippets. This possibility significantly reduces the time that you need to spend writing tests. So you can focus on writing application logic.
- Coverage improvement: It can suggest tests for edge cases or scenarios that developers might not immediately consider. This way, you can improve your code’s test coverage.
- Error reduction: It can reduce human error in writing repetitive or boilerplate test code.
- Learning and onboarding: It can serve as an educational tool for developers who are new to testing frameworks or unfamiliar with best practices in unit testing. The generated tests can help developers learn about testing patterns, assertions, and ways to effectively write tests.
With ChatGPT, you can generate unit tests for your Python code in almost no time. However, you must note that even though the generated tests can look good, you should still review and possibly refine the generated tests.
Note: For this tutorial, you’ll use a free ChatGPT account that’s powered by the GPT-3.5 LLM. You can use this model for free with the default web interface by OpenAI. If you use a paid account with GPT-4, then your results might be more accurate and faster.
Additionally, you’ll use an individual chat thread for each major section in the tutorial. So you’ll use different threads for the doctest, unittest, and pytest sections.
Using a single chat thread to run all the examples in this tutorial may cause the chat to generate significantly different responses because of the past context, which includes previous prompts and responses.
In the following sections, you’ll learn the basics of using ChatGPT as an assistant for creating coherent unit tests for your Python projects using different test frameworks and libraries.
Writing doctest Tests With ChatGPTPython’s doctest module provides a lightweight testing framework that you can use for test automation. It can read the test cases from your project’s documentation and your code’s docstrings. This framework comes with the Python interpreter, so you’ll have it at your disposal with any Python installation, which is great.
Note: To dive deeper into how to use the doctest module for testing your code, check out Python’s doctest: Document and Test Your Code at Once.
With doctest, you can write code that provides examples of how your code works, simulating REPL sessions. These examples work as documentation and also as test cases or unit tests. Each doctest test needs a statement or expression and an expected result.
You can use ChatGPT to help you out with writing doctest tests. First, you need some code to test. The following function tries to tackle the FizzBuzz challenge, where you return "fizz" for numbers divisible by three, "buzz" for those divisible by five, and "fizz buzz" for those divisible by both three and five.
Go ahead and create a fizzbuzz.py file and add the following code to it:
Python fizzbuzz.py def fizzbuzz(number): if number % 3 == 0: return "fizz" elif number % 5 == 0: return "buzz" elif number % 15 == 0: return "fizz buzz" else: return number Copied!This function works okay for numbers divisible by 3 or 5. However, there is a slight issue with numbers that are divisible by both. Even though you know this, go ahead and start a new chat thread. Then, prompt ChatGPT to write doctest test for the function:
You: Write doctest tests for the following function:
Python def fizzbuzz(number): if number % 3 == 0: return "fizz" elif number % 5 == 0: return "buzz" elif number % 15 == 0: return "fizz buzz" else: return number Copied!ChatGPT:
Certainly! The function you’ve provided implements a basic FizzBuzz algorithm but with a slight logical error. The check for divisibility by 15 should come before checks for 3 and 5, as every number divisible by 15 is also divisible by both 3 and 5, but your current order will never reach the "fizz buzz" case. Here’s a corrected version of your function with the logical order fixed, followed by doctest tests for it:
Python def fizzbuzz(number): """ Returns 'fizz' if number is divisible by 3, 'buzz' if number is divisible by 5, 'fizz buzz' if number is divisible by 15, and the number itself otherwise. >>> fizzbuzz(3) 'fizz' >>> fizzbuzz(5) 'buzz' >>> fizzbuzz(15) 'fizz buzz' >>> fizzbuzz(4) 4 >>> fizzbuzz(30) 'fizz buzz' >>> fizzbuzz(9) 'fizz' >>> fizzbuzz(10) 'buzz' >>> fizzbuzz(2) 2 """ if number % 15 == 0: return "fizz buzz" elif number % 3 == 0: return "fizz" elif number % 5 == 0: return "buzz" else: return number Copied!To use these doctest tests, you’ll need to include them in the docstring of your function as shown above. Then, you can run the tests with the doctest module by adding the following to your script:
Python if __name__ == "__main__": import doctest doctest.testmod() Copied!This code will automatically run the tests defined in the docstrings when you execute the Python file. Remember, the corrected order of conditions in the function ensures that numbers divisible by both 3 and 5 return "fizz buzz", which is crucial for the FizzBuzz logic.
Wow! That great! The chat not only wrote the doctest tests for you. It also fixed the issue, explained important concepts around doctest, and provided instructions on how to run the tests. Note that the tests cover both regular and edge cases, which is pretty convenient.
Read the full article at https://realpython.com/chatgpt-unit-tests-python/ »[ 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 ]
Zato Blog: Enterprise Python: Integrating with Salesforce
Salesforce connections are one of the newest additions to Zato 3.2, allowing you to look up and manage Salesforce records and other business data. To showcase it, the article will create a sample Salesforce marketing campaign in a way that does not require the usage of anything else except for basic REST APIs combined with plain Python objects, such as dicts.
If you have not done it already, you can download Zato here.
Basic workflowThe scope of our works will be:
- Creating Salesforce credentials for our integration project
- Defining a Salesforce connection in Zato
- Authoring a service that will map input data to the format that Salesforce expects
- Creating a Zato REST channel that will be invoked through curl during tests
- Testing the integration
To be able to create as connection to Salesforce in the next step, we need a few credentials. There is a full article about how to prepare them and this section is the gist of it.
In runtime, based on this information, Zato will obtain the necessary authentication and authorization tokens itself, which means that you will only focus on the business side of the integrations, not on the low-level aspects of it.
The process of obtaining the credentials needs to be coordinated with an administrator of your organization. To assist in that, the screenshots below explain where to find them.
The credentials are:
- Username and password
- Consumer key
- Consumer secret
The username and password are simply the same credentials that can be used to log in to Salesforce:
Consumer key and secret are properties of a connected app - this is a term that Salesforce uses for API clients that invoke its services. If you are already an experienced Salesforce REST API user, you may know the key and secret under their aliases of "client_id" and "client_secret" - these are the same objects.
Note that when a connected app already exists and you would like to retrieve the key and secret, they will be available under the "View" menu option for the app, not under "Edit" or "Manage".
Defining a Salesforce connection in ZatoWith all the credentials in place, we can create a new Salesforce connection in Zato Dashboard, as below.
Authoring an integration service in PythonAbove, we created a connection definition that lets Zato obtain session tokens and establish connections to Salesforce. Now, we can create an API service that will make use of such connections.
In the example below, we are using the POST REST method to invoke an endpoint that creates new Salesforce campaigns. In your own integrations, you can invoke any other Salesforce endpoint, using any REST method as needed, by following the same pattern, which is, create a model with input fields, build a Python dict for the request to Salesforce, invoke it and map all the required from the response from Salesforce to that which your own service returns to its own callers.
Note that we use a datamodel-based SimpleIO definition for the service. Among other things, although we are not going to do it here, this would let us offer definitions for this and other services.
# -*- coding: utf-8 -*- # stdlib from dataclasses import dataclass # Zato from zato.server.service import Model, Service # ########################################################################### if 0: from zato.server.connection.salesforce import SalesforceClient # ########################################################################### @dataclass(init=False) class CreateCampaignRequest(Model): name: str segment: str # ########################################################################### @dataclass(init=False) class CreateCampaignResponse(Model): campaign_id: str # ########################################################################### class CreateCampaign(Service): class SimpleIO: input = CreateCampaignRequest output = CreateCampaignResponse def handle(self): # This is our input data input = self.request.input # type: CreateCampaignRequest # Salesforce REST API endpoint to invoke - note that Zato # will add a prefix to it containing the API version. path = '/sobjects/Campaign/' # Build the request to Salesforce based on what we received request = { 'Name': input.name, 'Segment__c': input.segment, } # .. create a reference to our connection definition .. salesforce = self.cloud.salesforce['My Salesforce Connection'] # .. obtain a client to Salesforce .. with salesforce.conn.client() as client: # type: SalesforceClient # .. create the campaign now .. sf_response = client.post(path, request) # .. build our response object .. response = CreateCampaignResponse() response.campaign_id = sf_response['id'] # .. and return its ID to our caller. self.response.payload = response # ########################################################################### Creating a REST channelNote that we assign HTTP Basic Auth credentials to the channel. In this manner, it is possible for clients of this REST channel to authenticate using a method that they are already familiar which simplifies everyone's work - it is Zato that deals with how to authenticate against Salesforce whereas your API clients use the ubiquitous HTTP Basic Auth method.
Testing
The last step is to invoke the newly created channel:
$ curl http://api:password@localhost:17010/api/campaign/create -d '{"name":"Hello", "segment":"123"}' {"campaign_id":"8901Z3VHXDTebEJWs"} $That is everything - you have just integrated with Salesforce and exposed a REST channel for external applications to integrate with!
More blog posts➤Go Deh: Searching OEIS tables
A few months ago I submitted a series to OEIS* that was accepted; yes, but OEIS does not seem to leave my series searchable!
*OEIS is the Online Encyclopedia of Integer Series. I guess table is not in the name, but...
(best viewed on larger than a portrait phone)
The documentation for OEIS, explains that if you have a 2D triangle or table of values rather than a one dimensional strict series, then one should antidiagonalise the data and submit the series produced.
They give as an example A003987 . This gives this table:
Table begins 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, ... 1, 0, 3, 2, 5, 4, 7, 6, 9, 8, 11, 10, ... 2, 3, 0, 1, 6, 7, 4, 5, 10, 11, 8, ... 3, 2, 1, 0, 7, 6, 5, 4, 11, 10, ... 4, 5, 6, 7, 0, 1, 2, 3, 12, ... 5, 4, 7, 6, 1, 0, 3, 2, ... 6, 7, 4, 5, 2, 3, 0, ... 7, 6, 5, 4, 3, 2, ... 8, 9, 10, 11, 12, ... 9, 8, 11, 10, ... 10, 11, 8, ... 11, 10, ... 12, ... ...The above gets antidiagonalised to the series beginning:
0, 1, 1, 2, 0, 2, 3, 3, 3, 3, 4, 2, 0, 2, 4, 5, 5, 1, 1, 5, 5, 6, 4, 6, 0, 6, 4, 6, 7, 7, 7, 7, 7, 7, 7, 7, 8, 6, 4, 6, 0, 6, 4, 6, 8, 9, 9, 5, 5, 1, 1, 5, 5, 9, 9, 10, 8, 10, 4, 2, 0, 2, 4, 10, 8, 10, 11, 11, 11, 11, 3, 3, 3, 3, 11, 11, 11, 11, 12, 10, 8, 10, 12, 2, 0, 2, 12, 10, 8, 10, 12, 13, 13, 9, 9, 13, 13, 1, 1, 13, 13, 9, 9, 13, 13Searching...If we search for a sub-sequence of the antidiagonalised table, we can find the correct entry.
If, however, we search for a row of the values from the table, A003987 is not found!
The values chosen: to search for: 5,4,7,6,1,0,3 appear near the end of the table which shows that that row of numbers should be followed by a 2.
The table shows 13*13 / 2 ~ 85 values. OEIS has a list of 104 values, so it has the data to search through.
It seems to me that the most intuitive way to search a table of values is by row, left to right. There are other ways to search a table, (assuming an origin at top left and the table extends to the right and down):
- By row, L2R. →, R2L ←
- By Column Top2Bottom, ↓, B2T ↑
- By 45 degree diagonals, ↘, ↖, ↙, ↗
OEIS doesn't seem to do these searches on tabular data.
I did play around and created some code to recreate a table as a list of row-lists, in Python, given an OEIS B-file. The options handling is a work in progress, but the main part was being able to generate the table.
# -*- coding: utf-8 -*-# %%"""adia_to_table.py fileGenerate 2D table from OEIS type anti-diagonalised sequence held in file
Created on Thu Apr 4 18:16:07 2024
@author: paddy3118"""
from itertools import zip_longestimport math# from pprint import ppfrom antidiagonals import antidiag_triangle_indices
def read_bfile(bfname: str) -> tuple[int | None, # first index list[int]]: # anti-diag values """Read B-file bfname
## B-File format:* Ref: https://oeis.org/SubmitB.html* Blank lines ignored* Lines beginning with # ignored* Lines of two, space-separated integers <index> <indexed-value>
It is assumed the index increments by one on subsequent lines. """ first_index, values = None, [] with open(bfname) as bfile: for line in bfile: ln = line.strip() if not ln or ln.startswith('#'): continue index, value = [int(field) for field in ln.split()[:2]] if first_index is None: first_index = index values.append(value)
return first_index, values
def antidiag_to_table(sequence: list[int]) -> list[list[int]]: """ Convert anti-diagonalised sequence back to infinite 2D table.
Parameters ---------- sequence : list[int] Anti-diagonalised values from table.
Returns ------- list[list[int]] Table of rows of ints.
Table rows will fill in from successive sequence values like this:
1 2 4 7 11 16 ... 3 5 8 12 17 6 9 13 18 10 14 19 15 20 21 . . . """
# 1, 3, 6, 10, 15, 21, ... rows*(rows+1) / 2
# min columns in triangular table generation. ~= min rows size = len(sequence) # = rows*(rows+1)/2 rows = math.ceil((-1 + math.sqrt(4 * 2 * size)) / 2) # solve for rows # cols = rows # last row may be deleted if last anti-diag is part filled. # print(f"{(size, cols) = }")
# Empty (triangular) table of None's table = [[None] * (rows - i) for i in range(rows)]
indices = antidiag_triangle_indices() col = 0 # for if sequence is empty for val, (row, col) in zip(sequence, indices): table[row][col] = val
# Remove unfilled part of last anti-diag of table while col > 0: row, col = next(indices) table[row].pop(-1) # remove last row if present and empty if table and not table[-1]: table.pop(-1)
return table
def pp_table(table: list[list[int]]) -> None: "Pretty-print table of differring row lengths" if not table: return col_width = max(max(len(str(val)) for val in row) for row in table) for row in table: print(''.join(f"{val:{col_width}}" for val in row))
def transpose(table: list[list[int]]) -> list[list[int]]: "Table of rows to x<>y transposed table of new rows" fv = math.nan tr = [list(new_row) for new_row in zip_longest(*table, fillvalue=fv)] # remove fillvalues in triangular transposition for row in tr: try: row[row.index(fv):] = [] except ValueError: continue
return tr
if __name__ == "__main__": print("# TEST FILL BY ANTI-DIAGONAL\n") for n in range(0, 8): print(f"{n = }:\n") ans = antidiag_to_table(list(range(1, n+1))) pp_table(ans) print()
fname, m = 'b365096.txt', 505 print(f"\n\n# Data from {fname}, first {m} values:\n") ad = read_bfile(fname) ans = antidiag_to_table(ad[1][:m]) pp_table(ans) print("\n## Transposed:\n") pp_table((tr:=transpose(ans)))
And antidiagonals.py is this:
# -*- coding: utf-8 -*-"""Anti-diagonals:0,0 0,1 0,2 0,31,0 1,1 1,2 1,32,0 2,1 2,2 2,33,0 3,1 3,2 3,3
Of Square:0,0 0,1 1,0 0,2 1,1 2,0 0,3 1,2 2,1 3,0 1,3 2,2 3,1 2,3 3,2 3,3
of Infinite table:0,0 0,1 1,0 0,2 1,1 2,0 0,3 1,2 2,1 3,0 0,4 1,3 2,2 3,1 4,0 ...
Created on Mon Aug 21 13:36:31 2023
@author: paddy
"""# %% Triangles
from itertools import islice
def antidiag_triangle_indices() -> tuple[int, int]: x = y = 0 while True: yield (x, y) x, y = (x+1, y-1) if y else (0, x+1)
list(islice(antidiag_triangle_indices(), 15))
# %% Rectangles
from itertools import islice
def antidiag_rectangle_indices(sizex: int=4, sizey: int=4) -> tuple[int, int]: x = y = 0 while True: yield (x, y) if (x, y) == (sizex - 1, sizey - 1): break x, y = (x+1, y-1) if x == sizex or y < 0: u = x + y + 1 x, y = (0, u) if u < sizey else (u - sizey + 1, sizey - 1)
list(antidiag_rectangle_indices(3, 4))
END.
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- How to comment multiple lines
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[ 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 ]
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To read this article in full, please click hereThis post appeared first on ListenDataListenData: Complete Guide to Massively Multilingual Speech (MMS) Model
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To read this article in full, please click hereThis post appeared first on ListenDataPages
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