WEBVTT 00:00:00.001 --> 00:00:04.760 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:04.760 --> 00:00:10.980 This is episode 216, recorded January 13th, 2021. 00:00:10.980 --> 00:00:12.500 I'm Michael Kennedy. 00:00:12.500 --> 00:00:13.480 I'm Brian Okken. 00:00:13.480 --> 00:00:15.420 And Brian, we have a special guest, Yousef. 00:00:15.420 --> 00:00:16.000 Welcome. 00:00:16.000 --> 00:00:16.580 Hi. 00:00:16.580 --> 00:00:17.640 Great to have you here. 00:00:17.640 --> 00:00:22.200 You want to just take a quick moment and tell folks about yourself, maybe about your podcast real quick? 00:00:22.200 --> 00:00:22.800 Yeah, sure. 00:00:22.800 --> 00:00:26.340 Thanks for even being able to participate in this podcast. 00:00:26.340 --> 00:00:27.740 So my name is Yousef. 00:00:28.300 --> 00:00:31.340 I might not be well known as you guys are, for sure. 00:00:31.340 --> 00:00:33.140 I'm a mechanical engineer from Germany. 00:00:33.140 --> 00:00:34.640 I'm based in Germany as well. 00:00:34.640 --> 00:00:40.260 And I'm working for an IT company called SimScale, who are providing cloud-based simulation technology. 00:00:40.260 --> 00:00:43.060 On the site, I'm hosting a podcast called Engineered Mind. 00:00:43.060 --> 00:00:45.240 And I'm working on a bunch of other stuff. 00:00:45.240 --> 00:00:49.980 For example, my thesis, which we'll extensively cover, let's see, in this podcast. 00:00:49.980 --> 00:00:50.740 Yeah, yeah. 00:00:50.740 --> 00:00:55.380 You used a couple of cool libraries and stuff over there, which we'll feature here. 00:00:55.380 --> 00:00:56.000 All right. 00:00:56.000 --> 00:01:04.620 Well, the very first item, Brian, let's talk about maybe doing a pip search a lot. 00:01:04.620 --> 00:01:05.280 Yeah. 00:01:05.280 --> 00:01:12.540 Well, I kind of forgot pip search was the thing, because when I'm looking for PyPI stuff, I go to pypi.org. 00:01:12.540 --> 00:01:13.760 Yeah, so do I. 00:01:13.760 --> 00:01:14.780 Yeah, exactly. 00:01:14.780 --> 00:01:16.280 It's really fast there. 00:01:16.660 --> 00:01:20.300 Yeah, but there's a feature called pip. 00:01:20.300 --> 00:01:30.560 You can do a pip search, which the documentation says it's supposed to search for PyPI packages whose name or summary contains whatever query. 00:01:30.740 --> 00:01:39.680 So I can say pip search pytest, for instance, and it should show me if pytest is a package on PyPI. 00:01:39.680 --> 00:01:43.900 But right now, if you do that, it comes back with a big traceback. 00:01:43.900 --> 00:01:48.580 And it says, fault, what, minus 32500? 00:01:48.580 --> 00:01:49.620 Runtime error. 00:01:50.360 --> 00:01:52.180 The PyPI's XML. 00:01:52.180 --> 00:01:53.880 Anyway, the API is broken. 00:01:53.880 --> 00:01:56.000 So this is on purpose. 00:01:56.000 --> 00:02:03.960 What happened is, actually, I don't know really what's happened, but the service is getting swamped. 00:02:03.960 --> 00:02:07.380 The search endpoint is getting hit extremely hard. 00:02:07.800 --> 00:02:15.820 Yeah, I saw some message or some tweet that was to the effect of, is somebody out there running an insane number of searches against this endpoint? 00:02:15.820 --> 00:02:17.080 Please don't. 00:02:17.080 --> 00:02:18.820 Yeah, I don't know what's going on. 00:02:18.820 --> 00:02:20.320 So there's some guesses. 00:02:20.320 --> 00:02:26.900 Maybe it's a rogue continuous integration server or something weird's going on. 00:02:26.900 --> 00:02:36.700 But in the meantime, right now, we're going to link to a Python infrastructure status page, which has an update on this. 00:02:36.700 --> 00:02:40.280 So if anybody wants to follow, you can check that out. 00:02:40.280 --> 00:02:46.180 It says that the search endpoint remains disabled due to ongoing request volume. 00:02:46.180 --> 00:02:51.360 And I think this really started becoming a problem mid-December. 00:02:51.360 --> 00:02:54.080 And so I'm not sure what happened then. 00:02:55.140 --> 00:03:00.840 And then there's a related issue on GitHub for pip. 00:03:00.840 --> 00:03:05.100 So there's an issue open saying remove the pip search command. 00:03:05.100 --> 00:03:13.060 So I think the end result is, and even the error message says the search endpoint will be deprecated in the near future. 00:03:13.060 --> 00:03:18.080 So I think that this way to do pip search is just going to go away. 00:03:18.080 --> 00:03:24.380 That's actually a little surprising because usually a lot of these things are so backwards compatible. 00:03:24.880 --> 00:03:25.160 Yeah. 00:03:25.160 --> 00:03:29.260 And there's quite a discussion on the issue thread. 00:03:29.660 --> 00:03:36.720 But the gist of it is the current architecture is never designed to handle the volume it's getting right now. 00:03:36.720 --> 00:03:49.860 So there's a comment at the end of the thread that says, if you've got an idea for how to do this algorithm better or a way to do it scaled, go ahead and discuss it. 00:03:49.940 --> 00:03:50.940 But there's a link to it. 00:03:50.940 --> 00:03:51.580 But there's a link to it. 00:03:51.580 --> 00:03:53.220 We're not going to put that link in the show notes. 00:03:53.220 --> 00:04:00.300 But in the IPI thread or the GitHub thread, there's a link to it if you want to comment on that. 00:04:00.300 --> 00:04:02.400 But basically, we're bringing this up. 00:04:02.400 --> 00:04:07.160 You may have figured out it might be a fluke or whatever, but it's really going on. 00:04:07.500 --> 00:04:11.700 And a plea to look at your continuous integration scripts. 00:04:11.700 --> 00:04:13.940 And if you're doing a pip search in there, take those out. 00:04:13.940 --> 00:04:15.040 It ain't going to work anyway. 00:04:15.380 --> 00:04:21.200 It's got to be some kind of bot, some automatic thing like this, because it's already given the error message. 00:04:21.200 --> 00:04:25.360 Like, people would stop, you know, if it wasn't. 00:04:25.360 --> 00:04:30.000 Maybe somebody's trying, like, constantly trying to scrape all of the PIPI data out. 00:04:30.000 --> 00:04:30.620 I don't know. 00:04:30.620 --> 00:04:31.980 Yeah, why do a search? 00:04:31.980 --> 00:04:33.020 That's just weird. 00:04:33.020 --> 00:04:33.800 Yeah, exactly. 00:04:33.800 --> 00:04:35.300 I don't know what's going on here. 00:04:35.300 --> 00:04:37.540 But I guess don't do it. 00:04:37.540 --> 00:04:38.240 Don't do it. 00:04:38.240 --> 00:04:40.200 Doctor, it hurts when I do this. 00:04:40.200 --> 00:04:40.840 Stop doing that. 00:04:40.840 --> 00:04:52.200 So the next one I want to talk about is QPython, not QTPython or Qt or anything like that, but QPython, which is a way to do Python on Android. 00:04:52.200 --> 00:04:55.740 So we've talked about a couple of interesting applications. 00:04:55.740 --> 00:04:57.740 We've talked about Carnets or Carnet. 00:04:57.740 --> 00:05:00.500 I think it's French pronunciation, I've been told. 00:05:00.500 --> 00:05:03.480 And that's a really cool way to do Jupyter on iPad. 00:05:03.480 --> 00:05:04.400 So local. 00:05:04.400 --> 00:05:07.540 All these are local, obviously, not just running in the browser. 00:05:07.540 --> 00:05:10.780 There's Pythonista, which is really interesting. 00:05:11.280 --> 00:05:14.460 And QPython is also an interesting one for a couple reasons. 00:05:14.460 --> 00:05:20.120 Because you get an SDK and a REPL for your Android device, which is pretty interesting. 00:05:20.120 --> 00:05:23.460 But the reason I'm covering it, I think it's interesting. 00:05:23.460 --> 00:05:25.480 Somebody, I think somebody sent this over. 00:05:25.480 --> 00:05:26.940 No, I ran across this myself. 00:05:26.940 --> 00:05:35.080 Anyway, is it allows you just to integrate with the underlying Android APIs and features for automation. 00:05:35.080 --> 00:05:36.460 Yeah, cool, right? 00:05:36.460 --> 00:05:41.240 So you can do things like check the system. 00:05:41.240 --> 00:05:43.380 You can send out toast notifications. 00:05:43.380 --> 00:05:45.340 You can interact with applications. 00:05:45.340 --> 00:05:46.880 You can mess with the clipboard. 00:05:46.880 --> 00:05:57.820 You can do barcode scanning, speech recognition, send emails, like all those kinds of things around even, you know, screen brightness or checking your battery or whatever. 00:05:58.180 --> 00:06:05.760 So if you want to get access and automate your Android things, Python, well, here's a cool little app to do it. 00:06:05.760 --> 00:06:06.320 Okay. 00:06:06.320 --> 00:06:07.720 Wait a second. 00:06:07.720 --> 00:06:10.000 So I'm not an Android user that much. 00:06:10.000 --> 00:06:11.600 I've got like one Android tablet. 00:06:11.600 --> 00:06:13.380 But I didn't know it can make toast. 00:06:13.380 --> 00:06:14.240 Yeah. 00:06:14.400 --> 00:06:21.840 Well, it really prefers sourdough, but it will go even as far as rye if you have to. 00:06:21.840 --> 00:06:23.240 No, what's toast? 00:06:23.240 --> 00:06:24.140 Do you know what toast is? 00:06:24.140 --> 00:06:25.960 It's like a pop-up notification, I think. 00:06:25.960 --> 00:06:26.720 Oh, okay. 00:06:26.720 --> 00:06:32.880 Yosef, are you an Android person or an iPhone person? 00:06:32.880 --> 00:06:35.120 I have to confess I'm an iPhone person. 00:06:35.440 --> 00:06:40.680 I used to be completely against iPhone, but once you're in the ecosystem, you never get out. 00:06:40.680 --> 00:06:42.120 It's like the godfather. 00:06:42.120 --> 00:06:43.940 They just keep pulling you back in, man. 00:06:43.940 --> 00:06:44.900 Yeah. 00:06:44.900 --> 00:06:49.140 I just recently got a new iPhone as well, and I'm general about it. 00:06:49.140 --> 00:06:56.840 But because we have our mobile apps, the training for the courses, I've got an Android tablet and I've got an Android phone and so on. 00:06:56.840 --> 00:07:00.140 Oh, also, we've got a comment here on YouTube. 00:07:00.580 --> 00:07:05.580 So is it an own framework or can you use it in Android, Kotlin, and Java? 00:07:05.580 --> 00:07:11.520 I believe it's more like an app that you run, and then within that, you can do little jobs and stuff. 00:07:11.520 --> 00:07:12.620 So way to aesthetic. 00:07:12.620 --> 00:07:19.600 It's not something you can bring in that I'm aware of because you install it from Google Play, for example, to get started and so on. 00:07:19.600 --> 00:07:21.120 But maybe you can plug it in. 00:07:21.120 --> 00:07:27.720 They do talk about having an SDK, so possibly, but I got the sense that it's more for writing code outside than getting it on your device. 00:07:27.720 --> 00:07:29.260 But yeah, pretty cool. 00:07:29.400 --> 00:07:34.100 So if you're into Android and you want to do Python automation on it, this is pretty cool. 00:07:34.100 --> 00:07:34.780 It's free. 00:07:34.780 --> 00:07:35.960 Get it on the Android store. 00:07:35.960 --> 00:07:38.360 Apparently, it has ads, but it's also open source. 00:07:38.360 --> 00:07:39.640 So go with that. 00:07:39.640 --> 00:07:41.860 Do you know if there's a counterpart for iOS? 00:07:41.860 --> 00:07:44.400 I don't know about the automation side. 00:07:44.400 --> 00:07:48.080 There's a thing called Carnets, which is really cool. 00:07:48.080 --> 00:07:49.780 Let's see if I can find that. 00:07:49.780 --> 00:07:51.520 Carnets app. 00:07:51.520 --> 00:07:52.920 I believe that's how you spell it. 00:07:52.920 --> 00:07:53.440 Yes. 00:07:53.440 --> 00:07:55.140 That's Jupyter on the App Store. 00:07:55.540 --> 00:07:59.400 And that thing, I don't really want to open the App Store, but apparently I have to. 00:07:59.400 --> 00:08:00.620 Well, so much for that. 00:08:00.620 --> 00:08:02.540 But Carnets, it's here. 00:08:02.540 --> 00:08:06.140 Oh, and it's also on Google Play. 00:08:06.140 --> 00:08:07.020 Is that the same thing? 00:08:07.020 --> 00:08:08.360 No, that's a totally different thing. 00:08:08.360 --> 00:08:13.520 But Carnets or Carnet is a very cool app that lets you do something similar. 00:08:13.520 --> 00:08:14.840 There's also Pythonista. 00:08:14.840 --> 00:08:16.420 Those are the two I know for iOS. 00:08:16.840 --> 00:08:17.400 All right. 00:08:17.400 --> 00:08:26.020 So moving along, Yosef, maybe tell us a little bit about your research and then one of the libraries you've been working with here. 00:08:26.020 --> 00:08:27.020 Yeah, sure. 00:08:27.020 --> 00:08:29.940 So Open3D is one of the possibilities to visualize. 00:08:29.940 --> 00:08:30.620 There you go. 00:08:30.620 --> 00:08:31.480 3D. 00:08:31.480 --> 00:08:32.260 I had it out of order. 00:08:32.260 --> 00:08:32.680 Yeah, yeah. 00:08:32.680 --> 00:08:34.340 Let's talk about PyTorch first. 00:08:34.340 --> 00:08:34.560 Sorry. 00:08:34.560 --> 00:08:35.420 That's fine. 00:08:35.420 --> 00:08:37.340 So PyTorch 3D is basically an option. 00:08:37.340 --> 00:08:39.120 Let's say if you work with meshes. 00:08:39.780 --> 00:08:46.780 Let's say a mesh consists of edges and points, for example, and these edges connect all the points and what you get at the end is a mesh. 00:08:46.780 --> 00:08:57.220 So PyTorch, which is for Facebook, Facebook AI research, and they created this framework, so to speak, to be able to work efficiently with 3D data. 00:08:57.220 --> 00:08:59.640 So unfortunately, I'm using point cloud data. 00:08:59.640 --> 00:09:17.400 But the beautiful thing is that if you use PyTorch native application, which you produce for your 3D geometry, it runs, I wouldn't say significantly, but roughly 10 times slower than this PyTorch 3D, which is implemented especially for 3D problems. 00:09:17.400 --> 00:09:17.660 Oh, wow. 00:09:17.660 --> 00:09:18.240 Okay. 00:09:18.240 --> 00:09:20.500 So what kind of problem do people solve? 00:09:20.500 --> 00:09:23.280 What problem are you solving when you're working with this? 00:09:23.280 --> 00:09:23.700 Yeah. 00:09:23.700 --> 00:09:26.640 So in the beginning, it was like I was doing some kind of research. 00:09:26.740 --> 00:09:35.180 Unfortunately, they are coming out paper like every day and not too many, actually, in the field of deep learning, especially when it comes to point cloud or like geometric data. 00:09:35.180 --> 00:09:45.040 And the goal, just to inform the audience a bit, is my goal is basically to use deep learning and use some kind of or create an assistant system for engineers and designers. 00:09:45.040 --> 00:09:48.780 That means, let's say you're an engineer and we have this CAD model. 00:09:48.780 --> 00:09:51.160 So CAD, which stands for Computer Aided Design. 00:09:51.160 --> 00:09:54.800 So you would create a model, for example, of a gear, and then you would have that gear. 00:09:54.940 --> 00:09:59.120 But sometimes we have this differentiation between implicit knowledge and explicit knowledge. 00:09:59.120 --> 00:10:02.840 Explicit knowledge means this is existing knowledge, which we already know about. 00:10:02.840 --> 00:10:05.320 Let's say this knowledge can sit in a database. 00:10:05.320 --> 00:10:08.340 And sometimes we are not making use out of it. 00:10:08.340 --> 00:10:09.740 And then we have this implicit knowledge. 00:10:09.740 --> 00:10:14.340 Let's say an engineer comes into a company, is completely new, and he brings knowledge with him to the company. 00:10:14.340 --> 00:10:21.340 Now, the problem I want to tackle is because we're having so many data and we're accumulating geometric data in a company, we have to make use of that. 00:10:21.660 --> 00:10:35.800 And my approach is, hopefully, when I'm at the end of the thesis, which is like in roughly two months, is that I have a system or web application as a front end where the engineer or designer picks or starts a design or picks a point cloud or a design. 00:10:35.800 --> 00:10:41.760 And then it would suggest the engineer or designer with a probability of what they want to model. 00:10:41.760 --> 00:10:42.840 Let's say he picks a gear. 00:10:42.840 --> 00:10:48.660 Or maybe you want to have like an arrangement of gears or any specific big component. 00:10:48.660 --> 00:10:50.120 Or let's say you take a wheel. 00:10:50.120 --> 00:10:51.520 Okay, for example, what would you... 00:10:51.520 --> 00:10:52.600 Like for a transmission or something? 00:10:52.980 --> 00:10:57.940 Exactly, for a transmission or they pick a wheel and it could be a Tesla or it could be any other car. 00:10:57.940 --> 00:10:59.420 And then it would give you a probability. 00:10:59.420 --> 00:11:01.220 Okay, this wheel is maybe from a Tesla. 00:11:01.220 --> 00:11:05.820 And then it would suggest you Tesla with a, for example, 89% probability. 00:11:05.820 --> 00:11:07.780 And then you would click on the web application. 00:11:07.780 --> 00:11:08.740 This is the idea. 00:11:08.740 --> 00:11:12.940 And then it would pop the geometry into the web browser in the front end. 00:11:12.940 --> 00:11:14.080 Oh, that's pretty cool. 00:11:14.080 --> 00:11:21.820 So it basically, it's like image recognition, but instead of for pictures, it's image recognition for 3D CAD outlines. 00:11:21.820 --> 00:11:22.580 Exactly. 00:11:22.580 --> 00:11:29.840 It's so cool that you mentioned it because there's a big difference between doing a convolutional neural networks or deep learning for images because images are 2D. 00:11:29.840 --> 00:11:31.140 It's like a 2D matrix. 00:11:31.140 --> 00:11:34.740 But if you have a point cloud, then you have a tensor of higher dimensionality. 00:11:34.740 --> 00:11:39.040 And then you are kind of forced to use, for example, NumPy and all these kind of things. 00:11:39.680 --> 00:11:45.980 And if you're lucky, you could use something like PyTorch, PyTorch 3D, which you can also use CUDA on to be way more efficient. 00:11:45.980 --> 00:11:46.520 Yeah. 00:11:46.520 --> 00:11:47.000 Yeah. 00:11:47.000 --> 00:11:47.400 Wow. 00:11:47.400 --> 00:11:47.940 That's really cool. 00:11:47.940 --> 00:11:49.560 So it looks like a neat thing. 00:11:49.560 --> 00:11:55.420 This is, you know, I haven't done any 3D work for a while, but yeah, it looks, looks pretty cool. 00:11:55.420 --> 00:11:58.100 I would love to see, I don't know, some pictures and stuff. 00:11:58.100 --> 00:11:59.540 It would not be neat, but yeah. 00:11:59.540 --> 00:12:08.860 They have a very good, like if someone is interested in seeing what PyTorch 3D can do, Facebook AI Research has an own YouTube channel and they pitched PyTorch 3D on that channel. 00:12:09.200 --> 00:12:11.840 And they really do a nice, they show you what you can do with it. 00:12:11.840 --> 00:12:13.000 So it's really interesting. 00:12:13.000 --> 00:12:13.480 Yeah. 00:12:13.480 --> 00:12:14.240 Oh, awesome. 00:12:14.240 --> 00:12:21.160 Well, I guess I'd never really thought about applying, you know, AI, ML stuff to 3D meshes, but it makes perfect sense. 00:12:21.160 --> 00:12:23.060 And I can see it's totally different than images. 00:12:23.060 --> 00:12:23.620 Yeah. 00:12:23.620 --> 00:12:24.080 Yeah. 00:12:24.080 --> 00:12:24.620 Very cool. 00:12:24.620 --> 00:12:27.420 Brian, do you guys do any, you don't do any CAD stuff with your devices, do you? 00:12:27.420 --> 00:12:29.220 Well, I mean, yeah, some people do. 00:12:29.220 --> 00:12:30.620 Not me either. 00:12:30.620 --> 00:12:33.820 There's a lot of CAD that goes on in the ASIC design and stuff. 00:12:33.820 --> 00:12:35.220 Yeah, I can imagine. 00:12:35.220 --> 00:12:35.700 Yeah. 00:12:35.700 --> 00:12:36.000 Cool. 00:12:36.560 --> 00:12:36.820 All right. 00:12:36.820 --> 00:12:40.080 Now, before we get to the next one, I want to get something sorted out, Brian. 00:12:40.080 --> 00:12:40.580 Okay. 00:12:40.580 --> 00:12:42.220 I want to talk about Datadog. 00:12:42.220 --> 00:12:43.680 So they're back to support the show. 00:12:43.680 --> 00:12:44.480 Thank you, Datadog. 00:12:44.480 --> 00:12:44.980 Yay. 00:12:44.980 --> 00:12:52.940 And so they're really about helping you troubleshoot latency, CPU, memory bottlenecks in your apps. 00:12:52.940 --> 00:13:03.520 And if you don't know where it's coming from, Datadog will seamlessly correlate the logs and the traces at the level of individual requests, cross systems, allowing you to quickly troubleshoot your Python app. 00:13:03.520 --> 00:13:12.660 And they have a continuous profiler that allows you to find the most resource consuming parts of your app in production, just running all the time at any scale. 00:13:12.660 --> 00:13:14.160 And it has very little overhead. 00:13:14.160 --> 00:13:14.980 So that's pretty cool. 00:13:14.980 --> 00:13:20.480 Instead of trying to debug it and then deploy it and hope that kind of translates to production, just turn it on and watch. 00:13:20.480 --> 00:13:20.940 Yeah. 00:13:20.940 --> 00:13:21.940 So, yeah, that's cool. 00:13:21.940 --> 00:13:25.480 So be the hero that got that app back on track at your company. 00:13:25.480 --> 00:13:30.540 Get started with a free trial and support the podcast at pythonbytes.fm/Datadog. 00:13:30.540 --> 00:13:33.240 Or just click the link in your podcast player show notes. 00:13:33.240 --> 00:13:35.020 Now that that's sorted out, Brian. 00:13:35.500 --> 00:13:35.900 Yeah. 00:13:35.900 --> 00:13:36.980 Yeah. 00:13:36.980 --> 00:13:37.720 So sorting. 00:13:37.720 --> 00:13:39.800 Sorting's a thing. 00:13:39.800 --> 00:13:43.560 And the default Python containers are not sorted. 00:13:43.560 --> 00:13:47.540 And there's reasons behind that. 00:13:47.540 --> 00:13:48.980 But sometimes you need to sort stuff. 00:13:48.980 --> 00:13:54.240 So there's a Python library or a package called sorted containers. 00:13:54.240 --> 00:13:55.680 I like it. 00:13:55.680 --> 00:13:57.460 It's a very, I mean, I like the name at least. 00:13:57.460 --> 00:13:59.700 It's a very easy to remember sort of thing. 00:13:59.700 --> 00:14:02.800 But this is amazing. 00:14:02.800 --> 00:14:03.940 I looked into this. 00:14:04.020 --> 00:14:09.280 So this was recommended by Fanch and Bao recently for us to take a look at. 00:14:09.280 --> 00:14:14.580 And it's a pure Python based sorted collections library. 00:14:14.580 --> 00:14:20.340 And it's as fast as other packages that are built using C extensions. 00:14:20.340 --> 00:14:21.100 Wow. 00:14:21.100 --> 00:14:22.960 That's the impressive part. 00:14:22.960 --> 00:14:26.540 It's also fairly memory safe. 00:14:26.540 --> 00:14:29.740 But the documentation is pretty cool. 00:14:29.740 --> 00:14:32.380 There's a whole bunch of different benchmarks. 00:14:32.600 --> 00:14:36.300 So you can take a look at how it deals with large, large things. 00:14:36.300 --> 00:14:37.940 But it's really pretty zippy. 00:14:37.940 --> 00:14:39.720 It was pretty cool. 00:14:39.720 --> 00:14:43.160 The right on the front page, there's some, there's some example. 00:14:43.160 --> 00:14:45.200 And we're going to throw this in the show notes too. 00:14:45.200 --> 00:14:49.860 Of just, there's, you've got, it handles a handful of different data types. 00:14:49.860 --> 00:14:54.320 It shows sorted lists, sorted dictionaries, and sorted set. 00:14:54.320 --> 00:14:57.460 There's also a sorted key list. 00:14:57.460 --> 00:15:00.020 And I had to look that up to figure out what that was. 00:15:00.020 --> 00:15:06.700 So sorted, the sorted function within Python allows you to pass in a key, 00:15:06.800 --> 00:15:12.980 which the key really is a function to use to create a key for sorting. 00:15:12.980 --> 00:15:14.060 And. 00:15:14.060 --> 00:15:14.480 Right. 00:15:14.480 --> 00:15:17.360 Because the things in there might not have a natural sort, right? 00:15:17.360 --> 00:15:21.180 Like if you put a bunch of order objects in there, well, how do you sort those? 00:15:21.180 --> 00:15:22.440 Do you sort them by price? 00:15:22.440 --> 00:15:24.240 Do you sort them by date? 00:15:24.240 --> 00:15:24.820 Right. 00:15:24.820 --> 00:15:25.020 Right. 00:15:25.020 --> 00:15:26.100 So you select out that element. 00:15:26.100 --> 00:15:26.300 Yeah. 00:15:26.400 --> 00:15:26.600 Yeah. 00:15:26.600 --> 00:15:31.620 You should have it selected out or you can, you can do something like they might be sortable 00:15:31.620 --> 00:15:35.480 by default, but you want it to be like a reverse sort or something like that. 00:15:35.480 --> 00:15:35.840 Right. 00:15:35.840 --> 00:15:40.260 So, so, and there's some, some caveats listed so that you have to make sure that your, 00:15:40.260 --> 00:15:46.140 the key that you pass in, it follows some conventions like two identical items should be, 00:15:46.140 --> 00:15:48.700 should have the same key, stuff like that. 00:15:48.700 --> 00:15:54.940 It's all reasonable things, but it's a, it's a fairly easy, easy and complete package to just 00:15:54.940 --> 00:15:55.780 use. 00:15:55.780 --> 00:16:02.020 It looks, it acts just sort of like a normal, the normal thing, containers like lists and 00:16:02.020 --> 00:16:02.880 dictionaries and sets. 00:16:02.880 --> 00:16:05.640 It just, it just remains sorted all the time. 00:16:05.640 --> 00:16:07.160 And this is pretty incredible. 00:16:07.160 --> 00:16:07.620 So. 00:16:07.620 --> 00:16:08.140 Yeah. 00:16:08.140 --> 00:16:13.000 I can totally see bugs get into your code because you're like, well, we put stuff into this list 00:16:13.000 --> 00:16:14.380 and oh, I want the latest one. 00:16:14.380 --> 00:16:18.600 So it's the last one, but maybe, you know, you forgot to sort it before you did that or 00:16:18.600 --> 00:16:21.200 the first one's the last cause you reversed it or whatever. 00:16:21.200 --> 00:16:24.660 So what are the things that confused me when I first look at the, at this, I was 00:16:24.660 --> 00:16:25.960 scratching my head for a second. 00:16:25.960 --> 00:16:31.700 Cause it looks like a fairly simple set of like examples with just like a small set of 00:16:31.700 --> 00:16:32.380 elements in it. 00:16:32.380 --> 00:16:39.760 So like the first one is a, a list of like AB, E, A, C, D, B, just, you know, a few characters. 00:16:39.760 --> 00:16:43.420 And, you know, it's a whole bunch of these examples with just a little small amounts. 00:16:43.420 --> 00:16:49.440 And it says, underneath, this is all, all of the demo listed above, takes 00:16:49.440 --> 00:16:50.420 a gigabyte of memory. 00:16:50.420 --> 00:16:52.180 And I'm like, what the heck? 00:16:52.180 --> 00:16:54.300 Why is it taking so much memory? 00:16:55.020 --> 00:16:56.920 It's only five things. 00:16:56.920 --> 00:16:57.320 Come on. 00:16:57.320 --> 00:16:57.460 Yeah. 00:16:57.460 --> 00:16:58.420 I mean, like why? 00:16:58.420 --> 00:16:58.940 It's cheap. 00:16:58.940 --> 00:16:59.580 Don't worry about it. 00:16:59.580 --> 00:17:01.260 But there's hidden in there. 00:17:01.260 --> 00:17:09.000 There's an example of, that five character list sorted list that gets multiplied, 00:17:09.000 --> 00:17:11.080 by 10, 10 million. 00:17:11.320 --> 00:17:17.180 So it's a 10, like a 50 million, a million characters in a list that got sorted. 00:17:17.180 --> 00:17:17.480 Right. 00:17:17.480 --> 00:17:17.980 Yeah. 00:17:17.980 --> 00:17:22.300 So, and then like things like, and then all the operations like count. 00:17:22.300 --> 00:17:24.860 So you can say, count all the C's in there. 00:17:24.860 --> 00:17:26.220 It'll tell you how many there are. 00:17:26.220 --> 00:17:31.380 and all of, a lot of these operations like counting stuff with, with a sorted set take, 00:17:31.380 --> 00:17:33.300 uh, you know, less than linear time. 00:17:33.300 --> 00:17:34.580 So, yeah. 00:17:34.580 --> 00:17:38.960 So there's, there's, there's times you need sort and this is a cool one to check out. 00:17:38.960 --> 00:17:39.200 Yeah. 00:17:39.200 --> 00:17:39.500 It's cool. 00:17:39.500 --> 00:17:40.780 It's nice that it's pure Python. 00:17:40.960 --> 00:17:42.580 super easy to install. 00:17:42.580 --> 00:17:42.860 Right. 00:17:42.860 --> 00:17:45.300 And it's not going to have any like weirdness around that. 00:17:45.300 --> 00:17:49.480 Like if you say got an M1 computer and thing won't compile or whatever. 00:17:49.480 --> 00:17:51.780 No, this looks really cool. 00:17:51.780 --> 00:17:52.620 Yosef, what do you think? 00:17:52.620 --> 00:17:53.720 Yeah, this looks amazing. 00:17:53.720 --> 00:17:59.300 I'm also, I'm in touch with my brother, on the side and he's also watching our podcast 00:17:59.300 --> 00:17:59.820 at the moment. 00:17:59.820 --> 00:18:03.620 And he's also saying because of the one gigabyte memory for sorting is, it's incredible. 00:18:03.620 --> 00:18:04.540 It's crazy. 00:18:04.540 --> 00:18:06.020 Yeah. 00:18:06.020 --> 00:18:07.040 That's, that's pretty awesome. 00:18:07.040 --> 00:18:07.480 Yeah. 00:18:07.480 --> 00:18:10.220 I guess it's just showing like you can have a ton and it's all nice. 00:18:10.600 --> 00:18:14.840 So it's, I mean, it seems really straightforward, but having these things sorted, we just got 00:18:14.840 --> 00:18:16.980 dictionaries that would stay put. 00:18:16.980 --> 00:18:19.780 So, having sort of dictionaries is also cool. 00:18:19.780 --> 00:18:21.220 Yeah. 00:18:21.220 --> 00:18:22.160 Right. 00:18:22.220 --> 00:18:26.360 It used to be that they sort of, if they had the same keys and stuff or they wouldn't 00:18:26.360 --> 00:18:29.700 necessarily retain their order, of the things you added, but now they do. 00:18:29.700 --> 00:18:30.160 Right. 00:18:30.160 --> 00:18:33.400 So if people are confused and think, well, aren't dictionaries already sorted? 00:18:33.400 --> 00:18:34.200 No, they're there. 00:18:34.200 --> 00:18:36.700 They just stay in the order that they were created. 00:18:36.700 --> 00:18:37.900 Exactly. 00:18:38.300 --> 00:18:39.320 So yeah. 00:18:39.320 --> 00:18:39.360 Yeah. 00:18:39.360 --> 00:18:41.900 Similar, but not exactly the same thing. 00:18:41.900 --> 00:18:42.940 Yeah. 00:18:42.940 --> 00:18:43.520 All right. 00:18:43.520 --> 00:18:48.320 so I want to, this next one, I want to riff a little bit on typing and I want to do 00:18:48.320 --> 00:18:54.940 that around a tweet, which I think I've got to put into a different type of, hold on. 00:18:54.940 --> 00:18:59.940 For some reason, Twitter has stopped showing me like the entire conversation of things. 00:19:00.180 --> 00:19:02.660 I don't know why, but I guess it doesn't really matter. 00:19:02.660 --> 00:19:07.100 So Lucas Lenga responded to a tweet that went out there. 00:19:07.100 --> 00:19:09.720 You know, Lucas is obviously he's core developer. 00:19:09.720 --> 00:19:13.340 He's been doing really important stuff, but one of the main focuses that he's been working 00:19:13.340 --> 00:19:17.300 on is around type hints and typing with things like my pie. 00:19:17.300 --> 00:19:23.440 He was instrumental in bringing typing to Facebook and the Instagram code bases and things like 00:19:23.440 --> 00:19:23.600 that. 00:19:23.600 --> 00:19:30.020 So, there's a tweet that says controversial take types in a Python code base are a net negative. 00:19:30.220 --> 00:19:31.540 That's not Lucas. 00:19:31.540 --> 00:19:34.820 This is his, he's about to have a whole long conversation about this that I'm going to 00:19:34.820 --> 00:19:35.520 talk about. 00:19:35.520 --> 00:19:37.300 But, Brian, what do you think? 00:19:37.300 --> 00:19:39.060 You retweeted this. 00:19:39.060 --> 00:19:40.580 Them's fighting words. 00:19:40.580 --> 00:19:42.300 Them's is fighting words. 00:19:42.300 --> 00:19:43.780 Yeah. 00:19:43.780 --> 00:19:45.500 So yeah, what do you think? 00:19:45.500 --> 00:19:48.400 I think that, I think that they're good too. 00:19:48.400 --> 00:19:49.840 Yeah, I do too. 00:19:49.840 --> 00:19:54.000 I think when I first saw them, I was a little concerned, like, oh my goodness, this is going 00:19:54.000 --> 00:19:58.780 to potentially, you know, turn Python into something like TypeScript. 00:19:59.020 --> 00:20:05.040 And while I appreciate what TypeScript does to make JavaScript much better, I almost always 00:20:05.040 --> 00:20:08.780 walk away from working with TypeScript with a feeling of like, ah, that kind of hurt and 00:20:08.780 --> 00:20:09.240 was painful. 00:20:09.240 --> 00:20:14.200 I wonder why it had to go that way, you know, because the TypeScript requires, it's like, 00:20:14.200 --> 00:20:16.000 it's like C# or C++. 00:20:16.000 --> 00:20:18.520 The types have to match and they have to be there. 00:20:18.520 --> 00:20:21.580 And if they don't match at all, then it just won't work. 00:20:21.580 --> 00:20:21.980 Right. 00:20:21.980 --> 00:20:22.860 It's super frustrating. 00:20:22.860 --> 00:20:24.340 Oh, this thing is not defined. 00:20:24.480 --> 00:20:27.460 And, you know, there's, cause there's libraries that might not have types and then how do 00:20:27.460 --> 00:20:28.060 you work with them? 00:20:28.060 --> 00:20:30.820 And it's just, if I find it, there's always some little edge case. 00:20:30.820 --> 00:20:33.780 It's like, ah, this is frustrating, but I never feel that way with Python. 00:20:33.780 --> 00:20:36.660 And I really have come to love Python's type hints. 00:20:37.120 --> 00:20:42.480 And obviously Lucas starts out his conversation saying, this is easily disproven. 00:20:42.480 --> 00:20:48.240 If you ever use PyCharm or VS Code, the code completion in there is based on type annotations. 00:20:48.240 --> 00:20:53.500 If you've ever seen your editor highlight a function and squiggly say, this expects something 00:20:53.500 --> 00:20:57.500 else than what you're giving it, you know, besides the number of variables, but like you're 00:20:57.500 --> 00:21:00.640 giving it a string and it wants a number or something like that. 00:21:00.640 --> 00:21:05.080 You're using type annotations and you can enhance your code by doing that. 00:21:05.140 --> 00:21:08.140 Right. So I was actually talking to Yusuf about this yesterday. 00:21:08.140 --> 00:21:12.480 My philosophy, or maybe my, my rule of thumb is you don't have to always do it this way, 00:21:12.480 --> 00:21:18.460 but you know, if you're working in your editor and you have to type more than say three characters 00:21:18.460 --> 00:21:21.640 to get some kind of symbol to come up, you're probably doing it wrong. 00:21:21.640 --> 00:21:27.140 So like if you have email service and you want to have email service, send account email, 00:21:27.140 --> 00:21:30.940 you should be able to say dot S A E S A E, right. 00:21:31.020 --> 00:21:35.440 Send account email and it should know the type that's been returned, what an email service 00:21:35.440 --> 00:21:38.920 is, that it has this property and just write it for you. 00:21:38.920 --> 00:21:43.200 Right. So to me, a lot of the typing stuff, I know, I mean, this comment is somewhat about 00:21:43.200 --> 00:21:45.800 bugs. Like I never found a good bug because of this. 00:21:45.800 --> 00:21:48.140 To me, that's almost like a side benefit. 00:21:48.300 --> 00:21:53.000 It's about quickly generating code without stopping to go look at the code definition, 00:21:53.000 --> 00:21:56.960 without going over to the documentation to see what I could have typed over here. 00:21:56.960 --> 00:22:00.140 You know, it's for example, AWS people. 00:22:00.580 --> 00:22:05.880 This is insanely frustrating to work with AWS because you get these like weird, create this 00:22:05.880 --> 00:22:10.000 service and you give it a name and then you get an S3 service back, but it has no idea that it's 00:22:10.000 --> 00:22:16.000 an S3 service. So you get zero help on what anything, even I think go to definition doesn't 00:22:16.000 --> 00:22:20.220 quite work because it's, you know, use some factory method to reach down some weird place 00:22:20.220 --> 00:22:25.960 and get the thing. So I think really driving the code generation experience without being in 00:22:25.960 --> 00:22:30.680 documentation, without jumping around and reading all the source, just go forward. I think it's 00:22:30.680 --> 00:22:34.680 super nice. So to me, like that is the biggest win of all of this stuff. 00:22:34.680 --> 00:22:38.600 So let me give you the entire thread is very interesting. So yeah. 00:22:38.600 --> 00:22:42.940 Yeah. So let me touch on a couple of the points of the thread because I can't get it to come up in 00:22:42.940 --> 00:22:48.260 the screen share, but that's fine. I took notes luckily. So some of the things he pointed on, he's 00:22:48.260 --> 00:22:54.200 like, here's tweet one of 10. So number one, put enough annotations and then the tooling will 00:22:54.200 --> 00:22:59.340 connect the dots and make plenty of errors evident as well as like heighten this code 00:22:59.340 --> 00:23:05.500 generation auto magic, right? That's one. The most common types of errors though, that'll creep in 00:23:05.500 --> 00:23:10.920 is if, if none is being used where you expect a concrete, concrete type and things like my pie 00:23:10.920 --> 00:23:16.080 will say you're using a type that is an optional of something, but you're not checking to see if it's 00:23:16.080 --> 00:23:20.500 none before you dereference it. You're probably going to end up at some point with an attribute. 00:23:21.260 --> 00:23:25.880 None type does not contain attribute, whatever you tried to do, you know, upper or whatever, 00:23:25.880 --> 00:23:26.220 right? 00:23:26.220 --> 00:23:29.240 None is, none is not scriptable or something. 00:23:29.240 --> 00:23:35.240 Yes. So yeah. Something like that, right. Or callable or any of the things. Also another 00:23:35.240 --> 00:23:42.280 common bug is the return case. So if you've got a function, you can, you know, maybe check 00:23:42.280 --> 00:23:47.480 something and return one value, check something else, return another value. But if you forget at the 00:23:47.480 --> 00:23:51.960 end and you fall through and you don't put up some kind of concrete return type, Python 00:23:51.960 --> 00:23:56.680 functions just return none. Like this actually blew me away when I learned Python and I learned 00:23:56.680 --> 00:24:01.020 about functions that they always, always, always return something. There's no such thing as a 00:24:01.020 --> 00:24:02.140 void function in Python. 00:24:02.140 --> 00:24:02.600 Yeah. 00:24:02.600 --> 00:24:06.800 As a C++ person, that probably surprised you too, right? Like with your C++ background. 00:24:06.800 --> 00:24:09.560 I did a bunch of other Perl and stuff like that. 00:24:09.700 --> 00:24:16.820 But the return type is actually one of the greatest documentation features as well, because 00:24:16.820 --> 00:24:21.840 sometimes you can try to, you can kind of figure out what the parameters are going to look, 00:24:21.840 --> 00:24:26.800 think, you know, you can guess, but what's the return type? Is it going to be a list? Is it going 00:24:26.800 --> 00:24:31.260 to be a tuple? Is it going to be a single element? What if there's more than one element? Yeah. 00:24:31.260 --> 00:24:35.420 Having type hints around the return types is a great feature. 00:24:35.420 --> 00:24:40.620 Yeah, absolutely. All right. Let me touch on a couple more and I see some listener comments in 00:24:40.620 --> 00:24:45.400 the stream as well. Squiggly lines in your editor. Anyone like I just got this the other day. I, 00:24:45.400 --> 00:24:51.280 I thought I was supposed to pass an object ID, the primary key manga, but we'd overridden it. And 00:24:51.280 --> 00:24:55.320 it's actually a string. It said, you're passing a object ID when you expect a string. I'm like, 00:24:55.320 --> 00:24:59.860 Oh yeah, I guess I am. All right. Well change that. Right. That's really nice. Instead of that 00:24:59.860 --> 00:25:06.820 being a runtime error. And he talks about the work with TypeScript and Anders Hausberg and what he did 00:25:06.820 --> 00:25:11.260 to help build that. And TypeScript, like I said, is pretty neat. But he also points out that, you know, 00:25:11.260 --> 00:25:16.840 the same company, Microsoft is developing powerful type checking and code completion for Python with 00:25:16.840 --> 00:25:22.020 VS Code. And they're, you know, they have one of the Python steering council folks working on there. 00:25:22.020 --> 00:25:28.760 And maybe that's Brett. And also possibly the Python creator himself, Weido. So do you think those two 00:25:28.760 --> 00:25:32.860 people would be working on something that just provides the illusion of productivity? Probably 00:25:32.860 --> 00:25:38.720 not. So let's see a couple of comments. Chris May. Hey, Chris, happy to see you out there. He says, 00:25:38.720 --> 00:25:42.980 code completion is such a confidence builder too. I think it's so awesome because for me, 00:25:42.980 --> 00:25:47.640 it's both amazing for beginners because they can type dot and go now what? And for experts, 00:25:47.640 --> 00:25:52.760 they can just blast out code so quickly because you just type dot the few things and you know, 00:25:52.760 --> 00:25:54.720 like you said, with confidence, you just keep going. 00:25:55.220 --> 00:26:03.900 A lot of these features, you get them if everybody around you writing the code that you're using is 00:26:03.900 --> 00:26:09.940 using type hints. You don't necessarily need to use type hints yourself, but then you're being a bad 00:26:09.940 --> 00:26:14.960 citizen and not helping the people out that you're sharing code with. So if you don't share code at all, 00:26:14.960 --> 00:26:19.100 and you're only working on projects with yourself, then, you know, go ahead. Don't use type hints. 00:26:19.260 --> 00:26:24.080 It's up to you, right? Yeah, absolutely. Yusuf, what do you think? Do you guys use 00:26:24.080 --> 00:26:25.580 type hints on your project? 00:26:25.580 --> 00:26:30.840 No, not really. Like it's not something that's in our conscious mind, I would say. I'm not sure if 00:26:30.840 --> 00:26:35.000 that's also something really because you're an engineer. I wouldn't want to generalize, but 00:26:35.000 --> 00:26:39.940 engineers are usually bothered with the problem itself rather than digging down on the types, 00:26:39.940 --> 00:26:42.560 for example. It depends. It depends on what language we use. 00:26:42.940 --> 00:26:48.720 Yeah, it's a bit of a computer science-y topic, I can see. But yeah, I just, like I said, I love how 00:26:48.720 --> 00:26:56.460 it generates the content so much easier. Magnuson also commented, I love, for example, Pydantic, 00:26:56.460 --> 00:27:01.220 but I agree with Romalo, Luciano Romalo, who was in this thread. Hopefully it won't be required 00:27:01.220 --> 00:27:06.180 in Python to help people get started. Yeah. So I think the typing stuff is really interesting. Like 00:27:06.180 --> 00:27:10.820 Pydantic, we've talked about a bunch. It's a super interesting example of really using typing 00:27:10.820 --> 00:27:17.280 to generate cool data ingestion and processing. Like if you say I've got a Pydantic model and one 00:27:17.280 --> 00:27:21.900 of its fields is a list of integers, but you give it a list and the things in the list happen to be 00:27:21.900 --> 00:27:26.200 strings that could be integers, it'll automatically convert it and stuff like that is really fantastic. 00:27:26.200 --> 00:27:29.680 Yeah, I think that's always going to be an add-on type of thing. 00:27:29.680 --> 00:27:35.600 Yeah, even though I'm a fan of typants, I don't use them all the time and I would be very opposed 00:27:35.600 --> 00:27:37.080 to having them be required. 00:27:37.500 --> 00:27:41.620 Yes, I would too. I would too. I don't think they need to be on the whole code base. I mean, 00:27:41.620 --> 00:27:47.620 it depends if your goal is to say, I want to use them for mypy or mypyc and like completely generate 00:27:47.620 --> 00:27:52.320 stuff. But if your goal really is to get a little bit of help with editors, just having it on the 00:27:52.320 --> 00:27:56.820 boundaries, like here's the data access layer, the things that come out of there return whatever, 00:27:56.960 --> 00:28:00.420 like you don't have to do anything else and the editors will pick it up and run. 00:28:00.420 --> 00:28:00.840 Yeah. 00:28:00.840 --> 00:28:01.260 Yeah. 00:28:01.260 --> 00:28:06.080 All right. One quick question. What is a function return? If there's no return, it returns none. 00:28:06.080 --> 00:28:09.860 It returns none. So that's why you don't have to just say whether there's a return type. It always 00:28:09.860 --> 00:28:15.160 returns none. All right. Next up, I guess we got the one I tried to open with there, 00:28:15.160 --> 00:28:18.440 Yousef, is Open3D. That looks fun. 00:28:18.840 --> 00:28:23.100 Yeah. This is basically a library which you could use in Jupyter, which I tried to use, 00:28:23.100 --> 00:28:28.920 but somehow they at the moment have problems using Open3D. So what you can do is you call Open3D in 00:28:28.920 --> 00:28:33.520 your Jupyter notebook and then have the point cloud visualized. However, there are some ways around it, 00:28:33.520 --> 00:28:38.240 but Open3D, I think if I would start all over again, I would probably use Open3D to visualize my 00:28:38.240 --> 00:28:43.020 point cloud, which I'm actually working with in my Jupyter notebook. I'm not sure if using a Jupyter 00:28:43.020 --> 00:28:46.740 notebook is also something you would recommend personally, maybe Brian and Michael, 00:28:47.040 --> 00:28:50.020 if you're a fan of Jupyter notebooks. I think it depends on the application, right? 00:28:50.020 --> 00:28:55.840 Yeah. I think it depends as well. And to me, it really depends on what I'm trying to do 00:28:55.840 --> 00:29:01.160 and the kind of code. Am I trying to explore data and does it have a really strong visualization 00:29:01.160 --> 00:29:07.940 component or is it like a utility type thing? So for example, one of the things that I wrote 00:29:07.940 --> 00:29:12.600 recently that I would never put into a Jupyter notebook, but I find really helpful is we've got 00:29:12.600 --> 00:29:19.780 literally thousands of video files, MP4s and whatnot for the online courses. And in order to 00:29:19.780 --> 00:29:25.360 import them, one of the things I have to tell the database is how many, how long in seconds is each 00:29:25.360 --> 00:29:29.960 file and where does it live and stuff. So I've got a little script and I just say, go, go to this 00:29:29.960 --> 00:29:36.460 directory and generate a little JSON output for all of the files and by and parse them and tell me how 00:29:36.460 --> 00:29:40.400 long they are like that kind of app doesn't belong there. Right. It's just, that's a command line type 00:29:40.400 --> 00:29:45.680 of utility type of thing. But if I want to visualize something like this, I think it may well be really 00:29:45.680 --> 00:29:47.620 good for it actually. So I think it varies. 00:29:48.400 --> 00:29:55.580 Yeah. There's a lot of application parts of my work that I think using a Jupyter notebook actually might be 00:29:55.580 --> 00:30:02.860 more beneficial. So I'm often taking big, huge trace data and stuff for like a spectrum traces and 00:30:02.860 --> 00:30:09.660 there, those could easily be driven from a Jupyter notebook and with the visualization stuff would be good. 00:30:09.660 --> 00:30:19.880 Yeah. Yeah. Cool. So this thing is a set of both C++ and Python libraries for basically working with 3D meshes, right? 00:30:19.880 --> 00:30:26.700 Mostly 3D data. For example, if you use a LIDAR, so when you work with a laser and this looks, for example, great. 00:30:26.700 --> 00:30:32.400 I never watched the video, but if you scan objects in your surroundings, usually what you get is a point cloud 00:30:32.400 --> 00:30:38.180 and which you can then visualize using Open3D. And the big disadvantage with point clouds is that 00:30:38.180 --> 00:30:43.460 they're kind of unstructured. So you could have one matrix representing one point cloud and you could 00:30:43.460 --> 00:30:48.500 have the same matrix switching two points, but the matrix would be different. This is also a problem 00:30:48.500 --> 00:30:53.020 that a lot of papers try to tackle and make sure that, you know, get around the spot. 00:30:53.020 --> 00:31:02.400 Yeah. Nice. Yeah. The example video here is using Open3D for 3D object detection, which is, that's pretty wild. 00:31:02.400 --> 00:31:03.500 Yeah. Nice. 00:31:03.500 --> 00:31:04.920 The things people do these days. 00:31:04.920 --> 00:31:10.940 I know. I think it's really interesting, all this image processing and analysis stuff. Yeah. 00:31:10.940 --> 00:31:15.400 Good question, Brian, by the way. This is what I ask myself when I listen to Python bytes. As an engineer, 00:31:15.400 --> 00:31:17.040 what are you guys doing? It's great. 00:31:17.040 --> 00:31:25.060 Absolutely. Cool. All right. Well, that's it for all of our main items. Brian, you got anything extra 00:31:25.060 --> 00:31:25.880 that you want to throw out there? 00:31:26.380 --> 00:31:32.980 Yeah. I just wanted a couple of things. One, 2021 has been exhausting so far. Yeah. I don't know if 00:31:32.980 --> 00:31:38.880 anybody else has got the same experience, but wow. And also I've got a lot of extra projects, 00:31:38.880 --> 00:31:43.080 side projects that I'm working on right now. Python bytes is one of them, but there's other stuff going 00:31:43.080 --> 00:31:50.160 on as well, trying to do more writing. And because of that testing code is, has shifted to an every other 00:31:50.160 --> 00:31:57.160 week cadence. So it's not going away. I know a lot of, oddly enough, I've had a lot of feedback in the 00:31:57.160 --> 00:32:02.580 last couple of months of people saying, thank you for the podcast. I've learned so much. So I do not, 00:32:02.580 --> 00:32:06.220 I don't want to shut it down. I want to keep it going and there's no plans on shutting it down. 00:32:06.220 --> 00:32:11.040 It's just slowing down so that I have room in my life for other, other projects as well. So just 00:32:11.040 --> 00:32:12.080 wanted to let people know that. 00:32:12.160 --> 00:32:17.820 Yeah. Yeah. I try to, for Talk Python, batch it up and do a whole bunch, just to say this week, I'm just 00:32:17.820 --> 00:32:22.360 going to get nothing done, but I'll do a ton of recording and then just roll them out. I had three 00:32:22.360 --> 00:32:26.940 months of stuff done in like a week and a half. I was, I really needed a break after that, but then I, 00:32:26.940 --> 00:32:27.520 I was good. 00:32:27.520 --> 00:32:28.100 Yeah, cool. 00:32:28.100 --> 00:32:32.880 Yeah. Cool. Well, thanks for the update. Yusef, anything you want to share on the way, 00:32:32.880 --> 00:32:34.080 way out into the show? 00:32:34.080 --> 00:32:39.960 I just want to say thank you for letting me or being able to participate in this quick and brief podcast. 00:32:40.420 --> 00:32:44.400 Keep doing what you do guys. I follow you both on Twitter and what you share and what you do is really 00:32:44.400 --> 00:32:48.720 amazing. So it's really inspiring for an engineer who wants to delve into the field of Python and 00:32:48.720 --> 00:32:53.560 all fancy kind of things to, to listen to your podcast, taking your courses or following you 00:32:53.560 --> 00:32:58.360 on social media. It's really great. You learn a lot and I actually have to learn more to be honest 00:32:58.360 --> 00:32:58.920 myself. 00:32:58.920 --> 00:33:00.860 You know, I have to learn more. 00:33:00.860 --> 00:33:01.260 Yeah. 00:33:01.260 --> 00:33:03.780 It never stops. It never stops. 00:33:03.780 --> 00:33:06.440 Yeah. But that's awesome. Thank you so much. Really appreciate that. 00:33:06.440 --> 00:33:07.360 How about you, Michael? 00:33:07.840 --> 00:33:14.260 I have one quick thing, driven by yours, a comment you had last time. So Francisco Silva 00:33:14.260 --> 00:33:22.020 pointed out, we had talked about some of the num pic, Pythonic, the idiomatic numpy stuff that you 00:33:22.020 --> 00:33:27.400 might do and how like, instead of looping over stuff, you can just like add say like two numpy arrays 00:33:27.400 --> 00:33:32.380 and it'll add them or you can, you know, dot product them and whatnot. Right. So one of the things you can do, 00:33:32.380 --> 00:33:37.300 uh, I guess we also talked about like ones and zeros to generate a prebuilt list of those. 00:33:37.300 --> 00:33:43.260 So one of the things he talked about is the all close method. So if you've got floating point numbers, 00:33:43.260 --> 00:33:47.920 one of the things that's really frustrating is like, are these equal? Well, does it mean floating point 00:33:47.920 --> 00:33:53.820 numbers equal, right? Like they could be so nearly the same, but not the same, right? They could be within 00:33:53.820 --> 00:34:00.100 an insane amount of closeness, right? Like 10 decimal places and then a one, right? So all close is like, 00:34:00.100 --> 00:34:05.000 well, if they're within, you know, one, one thousandth of each other call, consider them the same. 00:34:05.000 --> 00:34:10.220 Well, all close takes a bunch of parameters that you can, you can specify the tolerance though. So yeah. 00:34:10.220 --> 00:34:11.380 Anyway, I thought that was cool. 00:34:11.380 --> 00:34:17.500 Yeah. Hey, while we're on the topic, I may as well throw out, I've got it. I've got it. So I tried to use this method 00:34:17.500 --> 00:34:22.600 of using NumPy and, I ran into a problem. So I'm hoping some data science people can help me 00:34:22.600 --> 00:34:27.600 figure out how to solve it. So my problem is just the simple thing. If I've got two, two arrays, 00:34:27.600 --> 00:34:32.960 I want to see if all of the elements are element wise, less than or equal to the other element in the 00:34:32.960 --> 00:34:39.600 other array. Okay. I can do that with NumPy, but what I can't, that assumes that all of the elements are 00:34:39.600 --> 00:34:46.160 the same data type, like comparable. If, if there are strings thrown in there, it doesn't work. So 00:34:46.160 --> 00:34:51.820 obviously, I don't know if it's obvious, but so I gotta, I had to do some cleanup at a time, 00:34:51.820 --> 00:34:55.960 but I don't know what the most, the best way is. So reach out to me if you've got an answer. 00:34:55.960 --> 00:34:59.980 Awesome. Yeah. I don't have an answer, but I'm sure people do. And, quick, quick comment, 00:34:59.980 --> 00:35:05.940 uh, here, this is the one on my show. Magnus Carlson, says tip. I found out about 00:35:05.940 --> 00:35:12.380 copier and alternative to cookie cutter that can be run later as well to update the project to a newer 00:35:12.380 --> 00:35:18.720 template. That's pretty cool. I hadn't heard of that. And, also Toml spec has reached 1.0. 00:35:18.720 --> 00:35:22.880 parser might be added, added to the standard lib. Also haven't covered that, but that's cool news. 00:35:22.880 --> 00:35:28.160 Thanks for sharing you guys. Yeah. And, I guess thanks for being here. Yousef, thanks for joining 00:35:28.160 --> 00:35:32.680 us and Brian. Thank you as always, man. Thank you. Thank you so much, guys. Bye everyone. Bye.