WEBVTT 00:00:00.001 --> 00:00:07.680 I love to bring you stories of Python being used in amazing places outside the traditional tech silos of pure web development and data science. 00:00:07.680 --> 00:00:14.600 On this episode, you'll meet Robert Kane Repligal, who works on simulation and test software at Richard Childress Racing, 00:00:14.600 --> 00:00:18.780 the NASCAR team that just finished number one and two at the Texas Motor Speedway. 00:00:18.780 --> 00:00:25.820 You'll hear how Python is allowing them to model car behavior, airflow, and much more, faster than others using outdated tools. 00:00:26.480 --> 00:00:31.920 This is Talk Python To Me, episode 281, recorded July 22nd, 2020. 00:00:31.920 --> 00:00:37.600 Wait, wait, wait, wait. Normally, I'd play a little guitar riff right here to kick off the episode. 00:00:37.600 --> 00:00:44.380 But this episode is about racing and race cars, so let's kick it off with a different kind of instrument and note. 00:00:44.380 --> 00:00:51.720 Let's jump into a 2017 Indy Pro Mazda, a furlap around the storied Brands Hatch circuit, and let the engine notes be the music. 00:00:51.720 --> 00:01:21.700 This episode is brought to you by the music. 00:01:21.700 --> 00:01:50.000 Welcome to Talk Python To Me, a weekly podcast on Python, the language, the libraries, the ecosystem, and the personalities. 00:01:50.300 --> 00:01:54.060 This is your host, Michael Kennedy. Follow me on Twitter, where I'm @mkennedy. 00:01:54.060 --> 00:02:00.240 Keep up with the show and listen to past episodes at talkpython.fm, and follow the show on Twitter via at Talk Python. 00:02:00.240 --> 00:02:05.780 This episode is brought to you by Sonar Cube and Linode. Please check out what they're offering during their segments. 00:02:05.780 --> 00:02:09.160 This is a really fun episode. You're going to enjoy Kane's story. 00:02:09.160 --> 00:02:15.860 And if you're interested, you can find the full five-minute video that Drive Around Brands Hatch at talkpython.fm/hatch. 00:02:15.860 --> 00:02:17.900 Kane, welcome to Talk Python To Me. 00:02:17.900 --> 00:02:19.080 Thanks. Glad to be here. 00:02:19.080 --> 00:02:25.020 Yeah, it's awesome to have you here. I get to bring two of the things that I'm really passionate about together, racing and Python. 00:02:25.020 --> 00:02:25.860 All right. 00:02:25.860 --> 00:02:31.260 And you've got some really cool stories to tell, I could tell already. So I'm looking forward to having you on the show. 00:02:31.260 --> 00:02:33.260 Thanks for having me. I'm excited about it. 00:02:33.260 --> 00:02:39.200 Yeah, it's going to be a lot of fun. And I guess, as usual, let's just start at the beginning. How do you get into programming in Python? 00:02:39.600 --> 00:02:50.280 Well, it's a pretty long story. But I started off with, do you remember the basic game, Gorillaz? It was like Windows 3.1 and you threw the bananas that blew up. 00:02:50.280 --> 00:02:50.860 Yeah. 00:02:50.860 --> 00:02:51.280 Yeah. 00:02:51.780 --> 00:03:12.060 That's where I started. A friend of mine showed me how to get in there and hack the explosion side so I could never lose to my brother. So that was the intro. It at least got me started on looking at logic and reading code. And then my next project was actually programming my HP calculator. I don't know if you ever used one of those. 00:03:12.300 --> 00:03:16.700 You know, I got to admit, I was always a TI sort of person. 00:03:16.700 --> 00:03:17.140 Yeah. 00:03:17.140 --> 00:03:25.380 I didn't have the HP when I had the TI. Even I had one of those big widescreen TI-93s that had a Cordy keyboard in it. It was crazy. 00:03:25.380 --> 00:03:25.980 Oh, yeah. 00:03:25.980 --> 00:03:27.100 But yeah, I remember. 00:03:27.100 --> 00:03:37.600 No, this one goes back to the reverse Polish notation and everything else that everybody hated. But you were shamed if you had a TI in mechanical engineering. So everybody had HPs. 00:03:37.600 --> 00:03:47.100 But yeah, so I did a couple of scripts with that stuff, just mostly playing around. At Auburn, we studied Fortran 77. This was back in 96. 00:03:47.100 --> 00:03:48.680 You were in engineering? 00:03:48.680 --> 00:03:49.300 Yes. 00:03:49.300 --> 00:03:50.140 In mechanical engineering? 00:03:50.140 --> 00:03:51.600 I studied mechanical engineering in Auburn. 00:03:51.600 --> 00:04:00.240 Yeah. I was in chemical engineering for a year and I wanted to take C++ and they told me, no, no, no, you have to take Fortran. It's going to be the most important language you ever learned. 00:04:00.240 --> 00:04:07.200 Yeah. Yeah. And the older generation of mechanical engineers are still diehard Fortran. 00:04:07.200 --> 00:04:07.880 Interesting. 00:04:07.880 --> 00:04:18.920 One of the teams I worked with a few years ago, their simulation was actually written mostly in Fortran. And I mean, you can't argue with the processing speed of numerical calculations, which is all the sim really is. 00:04:19.100 --> 00:04:20.580 So yeah, it smokes. 00:04:20.580 --> 00:04:33.560 By the time I was like a sophomore year, I had moved on to MATLAB and most people were using MATLAB for their coursework and projects and things like that. So yeah, I was a big fan of MATLAB for a long time. 00:04:33.660 --> 00:04:56.240 And then all my graduate work, I did my master's at Purdue with a focus on vehicle dynamics and all my master's work was done in MATLAB. So got a pretty good amount of experience in that. I really like the scripting languages. I've done some Objective-C stuff and felt out of place, you know, with compiled code and not being able to really dig into it after using MATLAB for so long. 00:04:56.340 --> 00:05:00.620 So yeah, you can't just open up those APIs and have a look at them, right? They're binary. 00:05:01.020 --> 00:05:18.580 Right, right. Yeah. And so a lot of the problems that we work on in this industry, you have to explore the data a ton before you know what you're even going to do with the project. So the scripting is good for me. My coworkers that are the C-sharp and C++ guys shame me and tell me I'm not a real programmer, you know, so. 00:05:18.700 --> 00:05:38.680 But it's all in good fun. And I continued using MATLAB as I started my career. Ford Motor Company had pretty much all their stuff in MATLAB at the time. When I left Ford and went to Hendrick Motorsports to be the race engineer for the 24 car with Jeff Gordon, I was sadly, sadly depressed to not be given a license for MATLAB. 00:05:38.680 --> 00:05:44.040 So, I mean, they were doing everything in Excel and they were the ones who SIM runs in Fortran. 00:05:44.040 --> 00:05:44.420 Right. 00:05:44.420 --> 00:06:00.100 So it was the simulation, they used Excel as a GUI and then had this backend of Fortran piped in to run it. So it was pretty interesting because having the power of Excel where I can just click here and do some calculations was pretty useful. 00:06:00.100 --> 00:06:09.820 Yeah. Excel is really powerful like that, right? Except it's hard to follow the logic of it if you've got this formula that leads to that, which is back over there. And then there's a conditional, which jumps here. 00:06:09.820 --> 00:06:11.920 Oh, it's impossible. It's impossible. 00:06:11.920 --> 00:06:12.160 Yeah. 00:06:12.160 --> 00:06:24.940 I cringe when somebody sends me a big Excel sheet with all kinds of formulas all over the place. The logic of the structure of it is what makes sense. And I am not a fan of VBA, so I don't like getting in the backend of that either. 00:06:24.940 --> 00:06:34.640 But yet moving into the, you know, racing stuff, we'll spend a million dollars to try to make five horsepower, but we can't spend $2,000 on a MATLAB license to solve analytical problems. 00:06:34.640 --> 00:06:46.560 Yeah. I think that's the, I mean, that's the racing take on that story, but you hear and see that story all over the place. Like companies spending millions of dollars or there's millions of dollars of efficiencies to be gained, but like, no, no, no. 00:06:46.560 --> 00:06:56.560 You're going to use this crappy old computer that doesn't work that well. It's all locked down or you're going to not get a license to this thing or that thing because for whatever reason, it's right. 00:06:56.560 --> 00:07:01.780 It blows my mind how some of these, these sort of organizations can be like Pennywise pound foolish. 00:07:02.300 --> 00:07:24.940 Yeah. Yeah. And it's like, I had just finished simplified vehicle simulation optimization program as my masters. So I see the power of not even having to figure out what you want the car to do. You give the simulation, these limits of things that you want for the balance and for the travels and things. And you just say, go. And it finds the setups that will get you those vehicle outputs. 00:07:25.260 --> 00:07:32.020 And it's like, wow, that's still not really being done in the same way as what I was doing way back when. 00:07:32.020 --> 00:07:39.280 So when you're talking about simulations, is this like tire pressure, ride height, cambers, like the mechanical setup or something else? 00:07:39.280 --> 00:07:53.540 No, it's a full model, physical model of a vehicle. So the big modeling package right now is Demola, which is built on Modelica and it handles all the dynamics and vehicle parts, but there's still a lot more to it. 00:07:53.540 --> 00:08:06.120 When you want to have a nice GUI interface, you want to be able to output files. There's a whole realm of difficulty in putting this stuff together. It's pretty impressive that a lot of the teams develop their own in-house. 00:08:06.120 --> 00:08:36.100 Yeah. 00:08:36.100 --> 00:08:51.000 Get out of racing, actually, after I left Hendrick. Didn't really know what I wanted to do. You know, the schedule is just brutal in NASCAR. It's 38 events. And my last year on the road with a 24, we had 22 tests. So I was in town 31 hours a week. 00:08:51.000 --> 00:08:51.480 Wow. 00:08:51.780 --> 00:09:13.080 And I was in the shop working. And I mean, I was not having a good relationship. We were having problems. And it just got to the point where I got moved into the R&D group where I did only about half as much travel and got to work on some pretty cool projects doing experimental, I guess I should say, exhibition races. 00:09:13.780 --> 00:09:21.240 So, like, Dale Jr. Decides he's going to run the three car in Xfinity with his dad's old Wrangler paint scheme. 00:09:21.240 --> 00:09:21.600 Right. 00:09:21.740 --> 00:09:37.040 And they would work out a deal with Hendrick to make that race happen. And so I'd still get to do some race engineering, but I was mostly doing beta testing and breaking things because I had a reputation for being able to break any sim release. 00:09:37.040 --> 00:09:48.480 So they all went through me before it got released to the guys, you know, that were going to the track. So, but I still wasn't programming much. But through that, I got a lot of experience with user experience part of it. 00:09:48.480 --> 00:10:15.520 Like, just making suggestions to the simulation group of, hey, can we add this little calculator thing over here? And can we add this would be really useful for the race engineers at the track. And maybe I'm lazy, but I like software to work very easily and fluidly. And I hate doing a lot of clicking. And if it takes 75 clicks for me to get the sim to run, I am not a happy guy, you know? 00:10:15.520 --> 00:10:16.360 Yeah, exactly. 00:10:16.360 --> 00:10:43.860 But then moving on after that, I kind of did an about face and started doing lighting design for concerts. And I wasn't happy with the software that was available for that. So this is where I delved into the Objective-C part using Xcode with Course Composer and doing some video projection stuff and pretty fun stage lighting, motion lights, had some pretty cool little gimmicks. I always got frustrated at shows where they had lights and video, but the color schemes clashed. 00:10:43.860 --> 00:10:52.180 So I programmed mine where the projection mapping would follow the complementary color set of the lighting design. 00:10:52.180 --> 00:10:53.520 Oh, that's clever. That's cool. 00:10:53.860 --> 00:11:06.080 Yeah. And then I ran into the problem where not, you know, sometimes I would just do the projection stuff and there would be another lighting designer, but they wouldn't have an output that they could give me easily to receive what their color scheme was. 00:11:06.440 --> 00:11:18.500 So I started using a digital camera and I had an app that I had built for my iPad where I could pick from the video image and it would decode what that color of those pixels were. 00:11:18.720 --> 00:11:22.300 And then I had a little color matching deal that would match it up. 00:11:22.300 --> 00:11:27.160 So even if I didn't have that data coming in, really, I was solving problems. That's what I do. Right? 00:11:27.300 --> 00:11:28.200 Yeah, that's awesome. 00:11:28.200 --> 00:11:33.420 This portion of Talk Python To Me is brought to you by SonarCube. 00:11:33.420 --> 00:11:35.100 Here's something I get asked a lot. 00:11:35.100 --> 00:11:39.560 Once you've mastered the basics of Python, how do you keep building your skill of the language? 00:11:39.560 --> 00:11:47.900 Not everyone is lucky enough to have a well-versed mentor who can take the time to look over new commits and guide them in mastering the more subtle aspects of Python. 00:11:48.420 --> 00:11:51.100 That's where SonarCube static analysis comes in. 00:11:51.100 --> 00:11:56.220 SonarCube finds bugs, vulnerabilities, code smells, and security hotspots in your code. 00:11:56.220 --> 00:12:04.420 Every issue is presented in the context of the code with a detailed description of why it's a problem and code examples to guide you in making the fix. 00:12:04.420 --> 00:12:07.800 It's powerful, fast, and accurate out of the box. 00:12:07.800 --> 00:12:09.420 Oh yeah, and it's free. 00:12:09.420 --> 00:12:15.320 So learn more and download SonarCube today at talkpython.fm/sonar. 00:12:15.440 --> 00:12:19.080 That's talkpython.fm/sonar, S-O-N-A-R. 00:12:19.080 --> 00:12:22.840 Yeah, so I got back into racing. 00:12:22.840 --> 00:12:27.700 Obviously, the pay is pretty good compared to normal engineer's pay. 00:12:27.700 --> 00:12:29.540 So they drug me back in. 00:12:29.540 --> 00:12:35.020 I did a technical director role at a small team and then was still doing the lighting on the side. 00:12:35.020 --> 00:12:44.640 But then I moved on to, I got invited while I was still racing, I got invited to be an associate professor at UNC Charlotte, University of North Carolina, Charlotte. 00:12:44.640 --> 00:12:45.340 Oh, awesome. 00:12:45.340 --> 00:12:46.760 In mechanical engineering? 00:12:46.760 --> 00:12:51.020 It was in, they actually have a mechanical engineering motorsports division. 00:12:51.020 --> 00:12:52.320 Oh wow, that's pretty specific. 00:12:52.320 --> 00:12:52.840 That's awesome. 00:12:52.840 --> 00:12:53.280 Yeah. 00:12:53.280 --> 00:13:02.700 And so they try to prepare the students for getting out into the actual race industry, which is pretty much centered here in Charlotte, especially for NASCAR. 00:13:02.980 --> 00:13:10.160 Yeah, that's sort of the world headquarters for NASCAR, like Indianapolis is for Indy and maybe UK is for Formula One. 00:13:10.160 --> 00:13:10.500 Yeah. 00:13:10.500 --> 00:13:19.300 And I mean, it helps physically for all the teams to be close together because there's a lot of turnover between different teams and all. 00:13:19.300 --> 00:13:23.780 And, you know, as you go, not have to move all over the country to go to different places. 00:13:24.080 --> 00:13:27.440 It's just the length of my commute changes for what team I'm working with. 00:13:27.440 --> 00:13:28.640 Funny. 00:13:28.640 --> 00:13:29.360 Yeah. 00:13:29.580 --> 00:13:43.500 But so I started thinking a lot on designing this new vehicle dynamics class and how what I was seeing in other industries with this data science movement and machine learning stuff is that it's going to be the way of the future. 00:13:43.500 --> 00:13:45.680 I mean, there is no doubt about it. 00:13:45.680 --> 00:13:55.220 The gains that we're able to make by applying machine learning algorithms to this data is just leaps and bounds of what anybody could do in an Excel spreadsheet. 00:13:55.220 --> 00:13:58.160 And it's fast and it's fast to develop. 00:13:58.160 --> 00:14:13.580 And somebody can have a super great idea, but the three other engineers that are going to be using this tool, they want some small aspects of it changed, like how you break up the track, where you're looking at cornering forces and things. 00:14:13.580 --> 00:14:20.500 And so I felt like I wanted to make the class a lot like the vehicle dynamics class I took in my master's program. 00:14:20.500 --> 00:14:23.000 And it was heavily numerical based. 00:14:23.000 --> 00:14:23.860 It was all MATLAB. 00:14:24.000 --> 00:14:29.040 And so I just said, well, it's been a few years since I used MATLAB, but I'm sure it can't be hard to relearn. 00:14:29.040 --> 00:14:40.820 So while I was on a full travel schedule again, I was staying up till one and two in the morning, just making toy projects and doing tutorials and trying to get my class together. 00:14:40.820 --> 00:14:48.140 Cause I only had two months to have not only skills enough to teach the material, but I had to write the whole course. 00:14:48.140 --> 00:14:50.000 I had to write all the homework assignments. 00:14:50.000 --> 00:14:51.360 I had to write all the tests. 00:14:51.360 --> 00:14:51.720 Wow. 00:14:51.880 --> 00:14:55.780 And I think I was getting like four hours of sleep there for that two months period. 00:14:55.780 --> 00:15:02.520 And then when I finally had the class done, I slept for like 24 hours straight and it doesn't help. 00:15:02.520 --> 00:15:04.480 You still just wake up tired, you know? 00:15:04.480 --> 00:15:05.020 Wow. 00:15:05.020 --> 00:15:06.440 But I had a lot of great feedback. 00:15:06.440 --> 00:15:09.520 You know, they had never even had a MATLAB class. 00:15:09.520 --> 00:15:12.120 Any of the students, they don't teach that in the department. 00:15:12.440 --> 00:15:14.520 And so they were all learning from scratch. 00:15:14.520 --> 00:15:18.800 And one of my TAs, he loved it so much. 00:15:18.800 --> 00:15:23.100 He took my class the first semester and then was my TA the second semester. 00:15:23.100 --> 00:15:26.920 And he came to me like halfway through his semester. 00:15:26.920 --> 00:15:38.560 And he's like, I almost had my whole thesis research work redone in MATLAB because he was trying to use Excel to process engine data that was recording at like a thousand hertz. 00:15:38.560 --> 00:15:41.240 You know, 50 channels at a thousand hertz. 00:15:41.240 --> 00:15:44.760 He had these spreadsheets that were just massive. 00:15:45.260 --> 00:15:47.280 And I mean, Excel can't handle that. 00:15:47.280 --> 00:15:47.720 Yeah. 00:15:47.720 --> 00:15:49.640 The document too large or whatever. 00:15:49.640 --> 00:15:51.240 Like it only does five minutes at a time. 00:15:51.240 --> 00:15:51.700 Who knows? 00:15:51.700 --> 00:15:52.180 Yeah. 00:15:52.180 --> 00:15:55.120 And he redid it and he was just so proud of himself. 00:15:55.120 --> 00:16:02.360 And I mean, I was proud for him because that is a big thing to take the semester you're about to graduate and just start at ground zero. 00:16:02.360 --> 00:16:02.920 Yeah. 00:16:02.920 --> 00:16:07.000 But to have the skill to be able to do that and make it happen is pretty good. 00:16:07.000 --> 00:16:13.500 He actually ended up taking a job at Caterpillar designing huge diesel engines for the earth moving equipment. 00:16:13.500 --> 00:16:14.160 Okay. 00:16:14.160 --> 00:16:14.660 Yeah. 00:16:14.720 --> 00:16:14.840 Yeah. 00:16:14.840 --> 00:16:16.560 Caterpillar is doing a bunch of stuff with Python. 00:16:16.560 --> 00:16:17.900 Oh, yeah. 00:16:17.900 --> 00:16:18.300 Yeah. 00:16:18.300 --> 00:16:20.760 Actually, Caterpillar is one of our sponsors too. 00:16:20.760 --> 00:16:22.500 I don't know if you knew that for our team. 00:16:22.500 --> 00:16:23.460 No, I didn't know that. 00:16:23.460 --> 00:16:23.960 Yeah. 00:16:23.960 --> 00:16:26.220 They were telling us, I met with some of the guys. 00:16:26.220 --> 00:16:34.500 We were comparing machine learning notes and he was telling us about their division that does all the reliability testing and things. 00:16:34.500 --> 00:16:37.220 So they have all their trucks instrumented. 00:16:37.660 --> 00:16:49.580 And as they're going out, they're beaming the data up to a satellite and beaming it back to their data science department and processing it and trying to figure out when these engines are going to fail in a certain way. 00:16:49.580 --> 00:16:50.020 Wow. 00:16:50.340 --> 00:16:50.580 Yeah. 00:16:50.580 --> 00:16:56.800 It's pretty impressive how industry has really latched onto the data science, but racing still lagging behind, I feel. 00:16:57.200 --> 00:16:58.980 But we still haven't got to my Python. 00:16:58.980 --> 00:17:00.440 Well, you know, one of the things... 00:17:00.440 --> 00:17:01.240 That's right. 00:17:01.240 --> 00:17:02.700 Bo, just one quick comment. 00:17:02.800 --> 00:17:06.280 I think the type of software that you're talking about is really interesting challenges. 00:17:06.280 --> 00:17:10.840 I got to work in this for a while as well, where it's event-based, like human event-based. 00:17:10.840 --> 00:17:11.400 Yeah. 00:17:11.400 --> 00:17:11.800 Right. 00:17:11.800 --> 00:17:20.440 We're going to go to the track and on Saturday, the software has to work because we're going to run the simulation in the morning and apply it in the afternoon. 00:17:20.440 --> 00:17:24.920 And if it doesn't work, it's not like, well, we'll fix the bug next week or we'll push that to the next sprint. 00:17:24.920 --> 00:17:28.200 Like, yeah, that was the Daytona 500 and it didn't work. 00:17:28.200 --> 00:17:28.840 So you missed it. 00:17:28.840 --> 00:17:29.120 Oh, yeah. 00:17:29.120 --> 00:17:29.780 Tell me about it. 00:17:29.780 --> 00:17:31.660 I mean, and it's even worse than that. 00:17:31.660 --> 00:17:40.540 What I always tell all the young engineers that I've trained is there's really two things you need to do to be successful in the racing industry as an engineer. 00:17:40.540 --> 00:17:48.500 The first thing and foremost is you need to remember that the decisions you're making on this car are going to potentially affect somebody's life. 00:17:48.500 --> 00:17:50.740 I mean, much less you could crash the car. 00:17:50.740 --> 00:17:52.340 You could crash the car and hurt the driver. 00:17:52.340 --> 00:17:53.360 You could kill the driver. 00:17:53.360 --> 00:17:59.700 You could crash the car so bad that parts go through the catch fence and hurt fans, which has happened before. 00:17:59.700 --> 00:18:04.840 And so I always try to make sure that they understand the impact that they could have. 00:18:04.840 --> 00:18:13.300 But when it comes to the key talent that you need, it's measuring disappointment because it doesn't matter how hard you try. 00:18:13.560 --> 00:18:23.080 You can show up to the racetrack with the fastest car and be the fastest in every practice and qualify on the pole and lead the most laps of the race. 00:18:23.200 --> 00:18:28.660 And then by no fault of your own, cut a tire, plow into the fence and your whole weekend. 00:18:28.660 --> 00:18:33.200 Now, no matter how good it was to that point, you're a failure now. 00:18:33.200 --> 00:18:35.180 You know, and yeah, absolutely. 00:18:35.180 --> 00:18:35.800 Yeah. 00:18:35.800 --> 00:18:39.780 I see a lot of the young people really have a problem battling with depression. 00:18:39.780 --> 00:18:44.400 And I mean, I tell this story as kind of a joke in a way, but it's serious. 00:18:44.540 --> 00:18:50.240 And it's easy to get disgruntled and like think of yourself as a failure in this industry. 00:18:50.240 --> 00:18:50.680 Yeah. 00:18:50.680 --> 00:18:51.740 I can imagine. 00:18:51.740 --> 00:18:54.300 I mean, you have to set your expectations right. 00:18:54.300 --> 00:18:55.300 Stuff's going to happen. 00:18:55.300 --> 00:18:56.660 Things are going to go wrong. 00:18:56.660 --> 00:18:57.040 Right. 00:18:57.040 --> 00:18:58.540 Sometimes your fault, sometimes not. 00:18:58.540 --> 00:19:04.980 Sometimes finishing in the middle is something you've got to be proud of and not like seen as a failure or whatever. 00:19:04.980 --> 00:19:05.200 Right. 00:19:05.200 --> 00:19:12.980 Like it just I think it does take that mental sort of not necessarily toughness, but just setting the expectations in a real way. 00:19:12.980 --> 00:19:13.680 Absolutely. 00:19:14.000 --> 00:19:15.280 It seems really frustrating. 00:19:15.280 --> 00:19:16.300 Like you spend the whole weekend. 00:19:16.300 --> 00:19:18.020 Someone takes you out in the first corner of the race. 00:19:18.020 --> 00:19:19.800 It doesn't matter how good your simulation was. 00:19:19.800 --> 00:19:20.280 Nope. 00:19:20.280 --> 00:19:22.600 You you're packing it up and you're going back home. 00:19:22.600 --> 00:19:23.120 Yeah. 00:19:23.120 --> 00:19:23.460 Yeah. 00:19:23.460 --> 00:19:27.500 And there's a few other aspects of it, too, where I mean, we're a team. 00:19:27.500 --> 00:19:28.060 Right. 00:19:28.060 --> 00:19:41.200 So if one guy gets down on the team and you're riding back to the hotel in your your minivan and he makes some comments about how bad practice was that day or something that affects everybody around you. 00:19:41.620 --> 00:19:46.560 And so having that positive mental attitude is the only way to be successful. 00:19:46.780 --> 00:19:51.640 And I mean, I've definitely learned a lot through my career, which is why I try to share that. 00:19:51.640 --> 00:19:53.540 But in my earlier days, it was rough. 00:19:53.540 --> 00:20:00.020 I was not a good teammate and would say harsh things and I would have harsh things said to me. 00:20:00.020 --> 00:20:00.280 Yeah. 00:20:00.440 --> 00:20:03.580 But back on the Python track, since we still haven't made it there. 00:20:03.580 --> 00:20:09.460 So after I had my son, I couldn't do working on a race team full time and teaching classes. 00:20:09.460 --> 00:20:14.780 So I had to quit the UNC Charlotte job and I loved it and love my time with MATLAB. 00:20:14.780 --> 00:20:17.920 But then I lost my license because I was on an educational license. 00:20:18.380 --> 00:20:23.660 So I had to look at the landscape of like, what do I want to do at Richard Children's Racing? 00:20:23.660 --> 00:20:34.780 We had some legacy licenses at the time, but they were so old that going back and relearning what the syntax changes were was not really something that I had any interest in. 00:20:35.020 --> 00:20:38.740 And now we've actually upgraded and we have plenty of MATLAB licenses as well. 00:20:38.740 --> 00:20:42.900 But I've put so much effort into my Python experience and I just love it. 00:20:42.900 --> 00:20:43.780 I mean, it's clean. 00:20:43.780 --> 00:20:44.120 Yeah. 00:20:44.120 --> 00:20:45.280 My code is beautiful. 00:20:45.280 --> 00:20:46.600 I love PEP 8. 00:20:46.600 --> 00:20:47.920 I love black. 00:20:47.920 --> 00:20:48.340 Oh, yeah. 00:20:48.340 --> 00:20:53.720 And just not even having to think about the white space and how I do my continuation lines. 00:20:53.720 --> 00:20:56.300 And I mean, just every aspect of it. 00:20:56.300 --> 00:21:05.500 I really, the most surprising thing to me is that Python didn't show up on my radar during my entire career up until three years ago. 00:21:05.500 --> 00:21:06.560 Yeah, that is pretty interesting. 00:21:06.560 --> 00:21:06.860 Yeah. 00:21:06.860 --> 00:21:09.120 Because just nobody in my industry used it. 00:21:09.120 --> 00:21:19.480 But yeah, so I've kind of gone full tilt on the Python kick, probably done 20 or 25 online courses and everything that's on the Kaggle page. 00:21:19.480 --> 00:21:21.660 Been working on the fast AI. 00:21:22.420 --> 00:21:28.340 I think you interviewed Jeremy Howard, who is just an amazing teacher, has an amazing teaching style. 00:21:28.340 --> 00:21:29.820 You can't not like the guy. 00:21:29.820 --> 00:21:30.480 Yeah, awesome. 00:21:30.480 --> 00:21:31.880 Yeah, I don't think I have interviewed him. 00:21:31.880 --> 00:21:32.300 I should. 00:21:32.300 --> 00:21:34.300 But yeah, fast AI is cool. 00:21:34.300 --> 00:21:34.760 Yeah. 00:21:34.760 --> 00:21:35.620 And clean. 00:21:35.620 --> 00:21:39.680 And I was already using PyTorch and it works on top of PyTorch now. 00:21:39.680 --> 00:21:40.640 Okay. 00:21:40.640 --> 00:21:43.980 So maybe give me a perspective now that you're three years in. 00:21:43.980 --> 00:21:47.000 You're not new to the Python world at all anymore. 00:21:47.000 --> 00:21:49.260 Some of the stuff we're going to talk about that you've been doing, it's incredible. 00:21:49.260 --> 00:21:51.500 But it's still pretty fresh in your mind, I suspect. 00:21:51.980 --> 00:22:03.460 Coming from this commercial MATLAB, every little aspect of a library, every toolkit I want to use is like another $1,000 in a negotiation with a purchasing side of my company. 00:22:03.460 --> 00:22:11.160 Versus if you can find it on GitHub or on PyPI and it works decent, now that's another thing you can do. 00:22:11.160 --> 00:22:15.040 It feels like the world is more open to just doing what you want to solve your problem. 00:22:15.040 --> 00:22:23.820 Well, and I think to put that even further, I would say that the competition level is really interesting with open source. 00:22:23.820 --> 00:22:31.000 Because as you see some new idea come out, you might see a package start and then another flower pops up somewhere else. 00:22:31.480 --> 00:22:35.980 And the contributors look at each other's work and they're like, okay, I'm going to come work on yours now. 00:22:35.980 --> 00:22:36.260 Yeah. 00:22:36.260 --> 00:22:42.040 You know, and just like abandon that space if somebody comes out with like a better approach to solving some issue. 00:22:42.040 --> 00:22:47.880 And I think that that is a powerful thing that these commercial softwares can't compete with. 00:22:47.880 --> 00:22:48.600 Right, exactly. 00:22:48.600 --> 00:22:53.060 And they definitely can't compete with all the free help that people give. 00:22:53.060 --> 00:22:53.500 Yep. 00:22:53.500 --> 00:22:54.120 Absolutely. 00:22:54.120 --> 00:22:55.560 That's really, really interesting. 00:22:55.760 --> 00:22:59.540 So let's talk really quickly just about like some high level stuff in racing. 00:22:59.540 --> 00:23:04.820 I want to keep it a little bit focused just so we can spend a lot of time on the Python that you're riding, the simulations you're doing. 00:23:04.820 --> 00:23:08.980 But just give us a sense of what type of data you're collecting. 00:23:08.980 --> 00:23:17.820 You know, is this during the race you're tracking like tire wear or is it speeds or are you trying to simulate when you should pit? 00:23:17.820 --> 00:23:19.660 Like what kind of stuff are you guys doing? 00:23:19.660 --> 00:23:21.520 Yeah, pretty much most of that. 00:23:21.520 --> 00:23:23.540 We're limited during the race. 00:23:23.880 --> 00:23:30.600 NASCAR has, they issue us a data logger and it is nothing that you can really mess with. 00:23:30.600 --> 00:23:32.320 And it only has certain channels. 00:23:32.320 --> 00:23:36.480 So we're only allowed to have a few channels during the race, you know, GPS location. 00:23:36.480 --> 00:23:39.560 So lat, long, elevation type stuff. 00:23:39.560 --> 00:23:41.380 A few basic driver things. 00:23:41.380 --> 00:23:45.160 So you'll have brake pressure, throttle position, and steering wheel angle. 00:23:45.160 --> 00:23:47.760 Everything he's doing, he controls with those three things. 00:23:47.760 --> 00:23:53.340 So, I mean, other than shifting gears, but most of the time we don't even shift gears on track because you get to fourth gear. 00:23:53.340 --> 00:23:55.340 And that's where you stay for the whole lap. 00:23:55.340 --> 00:23:56.580 Unless you're on a road course, yeah. 00:23:56.580 --> 00:23:57.160 Yeah. 00:23:57.160 --> 00:24:08.120 But during tests, we might have a small test where we just have like 50 instruments where we're actually tracking damper travels for the suspension, all kinds of engine things. 00:24:08.120 --> 00:24:10.120 Air flows through the radiators. 00:24:10.120 --> 00:24:14.620 Air flows through brake ducts, you know, if we're running out of braking track. 00:24:15.040 --> 00:24:19.220 We have these things called wheel force transducers that are like the top of the line. 00:24:19.220 --> 00:24:20.940 They're the most expensive sensor. 00:24:20.940 --> 00:24:27.520 So you typically actually detune your car when you go test with this because you don't want to risk hitting the wall. 00:24:28.240 --> 00:24:34.240 But it measures the three directions of force, three directions of torque, tire pressure, tire temperature. 00:24:34.240 --> 00:24:37.440 And so there's quite a few sensors on just that one tire. 00:24:37.440 --> 00:24:43.800 You know, we'll use pitot tubes mounted to the roof to track airflow across the top of the roof. 00:24:43.800 --> 00:24:45.600 So we can look at the pressure difference there. 00:24:45.600 --> 00:24:49.680 There's inside the engine, there's just a hundred things that you could do. 00:24:49.900 --> 00:24:54.020 So the data system, the data person job is pretty demanding as well. 00:24:54.020 --> 00:24:55.380 Yeah, it sounds like it. 00:24:55.380 --> 00:24:59.240 But the system we use for the races is pretty small. 00:24:59.240 --> 00:24:59.940 It's pretty lightweight. 00:24:59.940 --> 00:25:00.900 So it's not a big deal. 00:25:00.900 --> 00:25:01.200 Yeah. 00:25:01.200 --> 00:25:04.120 And then, I mean, not to mention we go to the wind tunnel. 00:25:04.120 --> 00:25:09.340 I don't know if you've seen the wind shear wind tunnel, which is what we use most of the time, but it's a... 00:25:09.340 --> 00:25:09.900 Yeah, I don't think so. 00:25:09.900 --> 00:25:11.200 Maybe tell people about it. 00:25:11.200 --> 00:25:11.400 Yeah. 00:25:11.400 --> 00:25:11.840 Yeah. 00:25:11.840 --> 00:25:15.580 It's a 180 mile an hour rolling road wind tunnel. 00:25:15.580 --> 00:25:18.440 Like a treadmill, but that goes 180 miles an hour. 00:25:18.620 --> 00:25:22.320 It's a treadmill with a $300,000 stainless steel belt. 00:25:22.320 --> 00:25:23.120 Oh my goodness. 00:25:23.120 --> 00:25:23.500 Okay. 00:25:23.500 --> 00:25:30.160 You know, and so there's force pads under the belt to measure the weight on the tires. 00:25:30.160 --> 00:25:36.200 And you're also trying to capture the wind speed through the radiator and that kind of test too. 00:25:36.200 --> 00:25:39.600 And you don't want to mess with that belt because it's very expensive. 00:25:39.600 --> 00:25:42.600 But 180 miles an hour is the... 00:25:42.600 --> 00:25:44.400 I believe it's still the fastest wind tunnel in the world. 00:25:44.400 --> 00:25:44.900 Wow. 00:25:44.900 --> 00:25:46.320 That's pretty awesome. 00:25:46.620 --> 00:25:50.680 And I love when we do a wind tunnel test because I only live about three miles from 00:25:50.680 --> 00:25:53.160 the wind tunnel and I live 40 miles from the shop. 00:25:53.160 --> 00:25:58.800 So it's much nicer commute, but then I don't get to listen to as much podcast. 00:25:58.800 --> 00:25:59.840 Yeah, that's true. 00:25:59.840 --> 00:26:00.980 That's definitely a drawback. 00:26:00.980 --> 00:26:02.980 But yeah, a three mile commute is pretty sweet. 00:26:02.980 --> 00:26:03.320 Yeah. 00:26:03.460 --> 00:26:07.780 So one of the things that you actually have been spending a lot of time lately is applying 00:26:07.780 --> 00:26:12.280 Python and scikit-learn to this wind tunnel data, right? 00:26:12.280 --> 00:26:12.800 Oh yeah. 00:26:12.800 --> 00:26:22.000 We had an aerodynamicist leave at the end of 2018 who managed the wrangling of all the data 00:26:22.000 --> 00:26:29.400 from the raw wind tunnel data into what our sim needs to ingest to do all of its calculations. 00:26:29.400 --> 00:26:32.840 And he kind of left all of a sudden. 00:26:33.100 --> 00:26:35.180 And so I wasn't that busy. 00:26:35.180 --> 00:26:37.120 And so I was just like, hey, pick me. 00:26:37.120 --> 00:26:38.840 And so I took it over. 00:26:38.840 --> 00:26:43.920 And I have to say that the system that they were using for all this wind tunnel data in 00:26:43.920 --> 00:26:44.900 Excel was a nightmare. 00:26:45.560 --> 00:26:51.860 And so our manufacturer, General Motors, had been working with a company to develop a new 00:26:51.860 --> 00:26:53.760 fitting algorithm to fit this data. 00:26:53.760 --> 00:26:56.940 You have to imagine this data is four dimensional. 00:26:56.940 --> 00:27:03.380 So you have two travel things on the body that tell you the attitude of your car. 00:27:03.380 --> 00:27:06.740 And then you have the yaw angle that your car is to the wind. 00:27:06.740 --> 00:27:07.940 And then you have roll. 00:27:07.940 --> 00:27:08.480 Right. 00:27:08.480 --> 00:27:08.820 Okay. 00:27:08.820 --> 00:27:10.820 So it's a 4D fit problem. 00:27:10.820 --> 00:27:14.680 So they're kind of struggling with getting the accuracy that we were trying to demand. 00:27:15.200 --> 00:27:21.560 And their system, the workflow was pretty hairy and a lot of manual cutting and pasting and 00:27:21.560 --> 00:27:23.120 putting it into a form that you needed. 00:27:23.120 --> 00:27:28.200 And I got so fed up with it, with this wind tunnel a few months ago that I said, you know 00:27:28.200 --> 00:27:28.420 what? 00:27:28.420 --> 00:27:30.740 There's another guy who's helping with this now. 00:27:30.740 --> 00:27:35.160 Hey, you start with their stuff and try to do it, mash it through. 00:27:35.160 --> 00:27:39.820 And I'm just going to write a new script in Python and let's see who finishes and what 00:27:39.820 --> 00:27:40.540 the results are. 00:27:40.540 --> 00:27:44.840 And so, you know, at first I was really nervous because it was like the first time I was 00:27:44.840 --> 00:27:49.100 the first time I had tried to fit a four dimensional surface like that in Python. 00:27:49.100 --> 00:27:55.920 And at the end of the second day, I had improved the accuracy of the fit by five times. 00:27:55.920 --> 00:27:56.480 Wow. 00:27:56.480 --> 00:27:57.900 In some parts of the map. 00:27:57.900 --> 00:27:58.840 And just... 00:27:58.840 --> 00:28:00.320 500% improvement. 00:28:00.320 --> 00:28:00.880 That's insane. 00:28:00.880 --> 00:28:01.340 Yeah. 00:28:01.340 --> 00:28:05.080 I'm like, you know, but where it missed, it missed really bad. 00:28:05.080 --> 00:28:06.500 It wasn't bad everywhere. 00:28:06.500 --> 00:28:06.860 Okay. 00:28:07.040 --> 00:28:08.940 But where it missed, it was really bad. 00:28:08.940 --> 00:28:14.100 So I might've had, we talked wind tunnel terms in terms of counts, which is the coefficient, 00:28:14.100 --> 00:28:18.880 but they might have an error at some point of the map of 80 counts. 00:28:19.240 --> 00:28:23.880 And I might have an error of one count or two counts at that same point. 00:28:23.880 --> 00:28:30.500 And so the support vector regression algorithm inside Git Learn is phenomenal and so easy to 00:28:30.500 --> 00:28:31.040 work with. 00:28:31.040 --> 00:28:33.900 And it fits super fast for the amount of data that we're using. 00:28:33.900 --> 00:28:39.180 But yeah, I think another interesting aspect of that project is the other aerodynamicists 00:28:39.180 --> 00:28:44.420 I was working with who kind of went on and got the headaches of the other package. 00:28:44.580 --> 00:28:45.600 You take the bumpy road. 00:28:45.600 --> 00:28:46.120 Yeah. 00:28:46.120 --> 00:28:50.640 When he came back, well, I had been doing it for almost three years at that point, 00:28:50.640 --> 00:28:51.680 two and a half years. 00:28:51.680 --> 00:28:56.960 So I had suffered enough, but yeah, he came back and I showed him what I was doing. 00:28:56.960 --> 00:29:02.740 And he was struggling with his sheet that he uses in the wind tunnel of his Excel sheet locking 00:29:02.740 --> 00:29:03.840 up and things like that. 00:29:03.840 --> 00:29:09.220 And so we had a few discussions back and forth about MATLAB versus Python and everything else. 00:29:09.600 --> 00:29:14.800 And I showed him the code for the aero map fitter that I built. 00:29:14.800 --> 00:29:16.160 And he was like, I'm sold. 00:29:16.160 --> 00:29:17.720 That looks so easy. 00:29:17.720 --> 00:29:19.360 That looks so easy. 00:29:19.360 --> 00:29:26.700 And so he actually jumped on board and has been working on this project to replace his thing. 00:29:26.700 --> 00:29:30.220 And he finished it last week and used it for the first time in the wind tunnel last week. 00:29:30.220 --> 00:29:32.120 So kudos to him. 00:29:32.120 --> 00:29:34.160 That's a pretty good advancement. 00:29:34.160 --> 00:29:35.140 Yeah, that's a big achievement. 00:29:35.140 --> 00:29:35.540 Yeah. 00:29:35.540 --> 00:29:37.440 In a two month span, you know. 00:29:37.440 --> 00:29:37.760 Yeah. 00:29:37.760 --> 00:29:38.500 Wow. 00:29:38.500 --> 00:29:42.960 But he also had a good teacher, I guess, since he calls me up. 00:29:42.960 --> 00:29:46.900 But one thing that you talk about on the show a lot is the imposter syndrome. 00:29:46.900 --> 00:29:52.560 And I have to say that that hits me right in the heart because every project I do, 00:29:52.560 --> 00:29:54.680 I've got to learn something new to finish the project. 00:29:54.680 --> 00:30:00.920 And it's like, when will I ever know enough to just sit down and not have to pull up the 00:30:00.920 --> 00:30:03.780 documentation on some package or something? 00:30:03.780 --> 00:30:07.080 And it kind of gets to you after a while. 00:30:07.080 --> 00:30:09.080 You get stuck on some pandas. 00:30:09.080 --> 00:30:10.640 I forget what it's called now. 00:30:10.640 --> 00:30:16.940 It's like, it's where pandas is trying to make, pandas thinks you're trying to make a copy of data. 00:30:16.940 --> 00:30:21.140 But what you're really doing is pointing to the same spot in memory. 00:30:21.140 --> 00:30:21.540 Right. 00:30:21.760 --> 00:30:27.380 So you're going to run into problems when you change the one thing and now the other thing changes. 00:30:27.380 --> 00:30:31.540 And that kind of stuff is hard to get your head around when you come from the MATLAB space. 00:30:31.540 --> 00:30:33.620 So he has lots of errors like that. 00:30:33.620 --> 00:30:39.960 But, you know, having my coworker come to me and show me what he's struggling with and me let, you know, just have that. 00:30:39.960 --> 00:30:40.580 Wow. 00:30:40.580 --> 00:30:42.020 I remember struggling with that. 00:30:42.020 --> 00:30:43.120 Here's how you fix it. 00:30:43.120 --> 00:30:43.560 Yeah. 00:30:43.700 --> 00:30:49.880 And that really takes away a lot of the imposter part when you can provide so much help so quickly without any effort. 00:30:49.880 --> 00:30:55.640 That's a great point because so often you're pushing the edge of the envelope, trying to do more and more with your current skill set. 00:30:55.640 --> 00:30:58.060 So you always are pushing against that edge and always. 00:30:58.060 --> 00:30:58.540 Right. 00:30:58.760 --> 00:31:00.800 You got to feel that like that struggle. 00:31:00.800 --> 00:31:03.820 But then you get a chance to look back and help others. 00:31:03.820 --> 00:31:04.780 You're like, oh, actually. 00:31:04.780 --> 00:31:05.740 This is easy. 00:31:05.740 --> 00:31:07.800 I clearly see what you need to do. 00:31:07.800 --> 00:31:10.660 I remember this was hard, but it doesn't feel hard anymore. 00:31:10.660 --> 00:31:11.220 Yeah. 00:31:11.220 --> 00:31:11.680 Great. 00:31:11.680 --> 00:31:17.560 Well, and, you know, in the racing industry, I mean, I have data that I'm processing that comes directly from NASCAR. 00:31:17.560 --> 00:31:20.500 I have the track data that's coming from multiple sources. 00:31:20.500 --> 00:31:23.560 I have the wind tunnel data that's coming from source. 00:31:23.940 --> 00:31:33.740 Your joke about, I think it was you or Brian said the joke about 80% of a data scientist's job is wrangling data and getting it into the right form. 00:31:33.740 --> 00:31:34.020 Yeah. 00:31:34.020 --> 00:31:38.480 And 20% is them complaining about spending so much time wrangling the data. 00:31:38.480 --> 00:31:42.920 I definitely have been accused of that because I will complain. 00:31:42.920 --> 00:31:46.360 But again, you have all these different sources that you don't control. 00:31:47.080 --> 00:32:00.440 And so when you have a sheet, a data file being delivered to you that changes size in all different aspects, every week, depending on the track, it has different widths and different lengths and all this stuff. 00:32:00.440 --> 00:32:04.220 And dealing with that by hand is just a nightmare. 00:32:04.220 --> 00:32:07.420 So Python's been a definite blessing with regards to that. 00:32:09.480 --> 00:32:12.340 This portion of Talk Python To Me is brought to you by Linode. 00:32:12.340 --> 00:32:21.280 Whether you're working on a personal project or managing your enterprise's infrastructure, Linode has the pricing, support, and scale that you need to take your project to the next level. 00:32:21.280 --> 00:32:35.940 With 11 data centers worldwide, including their newest data center in Sydney, Australia, enterprise-grade hardware, S3-compatible storage, and the next-generation network, Linode delivers the performance that you expect at a price that you don't. 00:32:36.080 --> 00:32:52.000 Get started on Linode today with a $20 credit and you get access to native SSD storage, a 40-gigabit network, industry-leading processors, their revamped cloud manager at cloud.linode.com, root access to your server, along with their newest API and a Python CLI. 00:32:52.000 --> 00:33:00.020 Just visit talkpython.fm/Linode when creating a new Linode account and you'll automatically get $20 credit for your next project. 00:33:00.020 --> 00:33:01.320 Oh, and one last thing. 00:33:01.320 --> 00:33:02.100 They're hiring. 00:33:02.100 --> 00:33:05.200 Go to linode.com slash careers to find out more. 00:33:05.680 --> 00:33:06.520 Let them know that we sent you. 00:33:06.520 --> 00:33:09.420 I can imagine. 00:33:09.420 --> 00:33:13.020 And, you know, the version of the software changes or you go to the track the next time. 00:33:13.020 --> 00:33:17.940 They're like, oh, we've changed the format of this or we've added a column or taken away a column. 00:33:17.940 --> 00:33:19.040 And you're just like, oh, great. 00:33:19.040 --> 00:33:20.100 Here we go. 00:33:20.100 --> 00:33:20.960 Yeah, constantly. 00:33:20.960 --> 00:33:23.080 Oh, and they always would add the column. 00:33:23.080 --> 00:33:24.460 They don't add it at the end. 00:33:24.700 --> 00:33:31.980 So, in Excel, where you had all your formulas related to that cell, now that cell is not the same data anymore. 00:33:31.980 --> 00:33:34.340 And you got to go through and change all your formulas again. 00:33:34.340 --> 00:33:35.540 And, oh, man, it's just... 00:33:35.540 --> 00:33:38.060 Like halfway through the session, it's changed or something. 00:33:38.060 --> 00:33:38.340 Yeah. 00:33:38.340 --> 00:33:41.940 Or, you know, it's a data stream. 00:33:42.160 --> 00:33:44.260 So, maybe the data is just not coming in anymore. 00:33:44.260 --> 00:33:45.280 Like, it just... 00:33:45.280 --> 00:33:45.980 You missed a packet. 00:33:45.980 --> 00:33:46.380 Yeah. 00:33:46.380 --> 00:33:47.440 Like, what do you do then? 00:33:47.440 --> 00:33:50.660 And, you know, or there's a big section of blank. 00:33:50.660 --> 00:33:50.920 Yeah. 00:33:50.920 --> 00:33:58.080 So, dealing with that with pandas is just a dream come true because it has like the drop NAs that just gets rid of all the NAs. 00:33:58.080 --> 00:34:03.400 Or, you can replace all the NAs with a zero or whatever kind of makes sense for whatever that entity is. 00:34:03.400 --> 00:34:10.820 And especially for the amount of data that we're processing from a race event where the data is all being recorded at 20 hertz. 00:34:10.820 --> 00:34:11.240 Right. 00:34:11.240 --> 00:34:11.920 For hours? 00:34:11.920 --> 00:34:16.180 It's a four-hour event with 10 channels at 20 hertz. 00:34:16.180 --> 00:34:19.640 Oh, and NASCAR releases all 40 cars data. 00:34:19.640 --> 00:34:21.200 So, everybody had... 00:34:21.200 --> 00:34:23.600 So, you don't just have your two cars that are in the race. 00:34:23.600 --> 00:34:25.820 You've got the 40 cars that are in the race. 00:34:25.820 --> 00:34:26.720 That's interesting. 00:34:26.720 --> 00:34:29.200 And do you do a lot of analysis on the competitors' cars? 00:34:29.200 --> 00:34:29.940 Yes. 00:34:29.940 --> 00:34:30.700 I can imagine. 00:34:30.700 --> 00:34:31.220 Yes. 00:34:31.220 --> 00:34:32.300 Quite a bit. 00:34:32.300 --> 00:34:33.520 We're actually... 00:34:33.520 --> 00:34:41.520 Right now, what I'm gearing up to do is do a full suite of dashboards to try to get the hands into... 00:34:41.520 --> 00:34:45.520 Or the data in a usable format into the hands of the less technical people. 00:34:45.520 --> 00:34:49.000 Or even the technical people who just don't have Python experience. 00:34:49.000 --> 00:34:53.020 It's been hard for me to provide them with it. 00:34:53.020 --> 00:34:58.820 It's mostly them sending me work orders where it's like, hey, can you run this on these files? 00:34:58.820 --> 00:35:00.420 I need this report. 00:35:00.420 --> 00:35:00.720 Yeah. 00:35:00.720 --> 00:35:01.460 Give me the report. 00:35:01.460 --> 00:35:02.040 Yeah. 00:35:02.040 --> 00:35:03.920 And so, you had done... 00:35:03.920 --> 00:35:05.020 I guess it's been a couple months ago. 00:35:05.020 --> 00:35:07.380 You had the guy from Streamlit on. 00:35:07.380 --> 00:35:07.680 Yeah. 00:35:07.680 --> 00:35:09.000 Adrian on from Streamlit. 00:35:09.000 --> 00:35:09.240 Yeah. 00:35:09.240 --> 00:35:09.720 Adrian. 00:35:09.720 --> 00:35:10.000 Yeah. 00:35:10.400 --> 00:35:13.000 And I had been working with Plotly and Dash. 00:35:13.000 --> 00:35:19.580 And I think for the end polish product, maybe you can make it look nicer with Dash still. 00:35:19.580 --> 00:35:22.080 But you have to do all the CSS programming. 00:35:22.080 --> 00:35:24.200 I mean, who really wants to do all that stuff? 00:35:24.200 --> 00:35:31.080 So, I actually downloaded Streamlit today for the first time and started working with this afternoon. 00:35:31.580 --> 00:35:35.720 And I've already got a dashboard built that is functioning with the lap data. 00:35:35.720 --> 00:35:36.500 Oh, that's cool. 00:35:36.500 --> 00:35:37.680 Plotting out lap times. 00:35:37.680 --> 00:35:39.240 And I mean, it is just... 00:35:39.240 --> 00:35:40.000 It is... 00:35:40.000 --> 00:35:40.300 Wow. 00:35:40.300 --> 00:35:41.320 What a simple thing. 00:35:41.320 --> 00:35:41.800 Yeah. 00:35:41.980 --> 00:35:46.260 So, for those folks who haven't heard about Streamlit or didn't listen to that episode, 00:35:46.260 --> 00:35:47.680 I'll give my take on it. 00:35:47.680 --> 00:35:48.740 You can give your take on it. 00:35:48.740 --> 00:35:53.720 So, the idea is you take something that would be just a top-to-bottom regular script. 00:35:53.720 --> 00:35:54.840 Maybe it has functions. 00:35:54.840 --> 00:35:56.000 Maybe it doesn't. 00:35:56.000 --> 00:35:59.020 And you want to turn that into an interactive website. 00:35:59.020 --> 00:36:01.240 That sounds like a very far bridge to cross. 00:36:01.240 --> 00:36:07.080 But the way Streamlit works is it does a bunch of magic to give you stuff that you just put into your script. 00:36:07.300 --> 00:36:14.060 And then it turns that into interactive dashboards with sliders and drop-downs and search fields and all kinds of stuff. 00:36:14.060 --> 00:36:17.060 And integrates with Plotly and other tools as well. 00:36:17.060 --> 00:36:17.280 Yeah. 00:36:17.280 --> 00:36:18.260 It's pretty slick, right? 00:36:18.260 --> 00:36:19.460 It's really slick. 00:36:19.460 --> 00:36:25.200 And one of the things that I'm surprised Adrian didn't really touch on, or maybe he did and I just didn't recall, 00:36:25.200 --> 00:36:27.660 it works similar to... 00:36:27.660 --> 00:36:29.500 I do a lot of stuff in Jupyter Notebook. 00:36:29.500 --> 00:36:31.120 So, I'm always wanting to review my data. 00:36:31.120 --> 00:36:38.020 So, I create a data frame and then it's df.head so I can see and make sure that it's in the right form and stuff. 00:36:38.020 --> 00:36:41.080 And Streamlit does that and it puts it on your webpage. 00:36:41.080 --> 00:36:45.740 So, you don't even have to tell it st.table or whatever. 00:36:45.740 --> 00:36:48.580 You just put the data frame, you know, just data frame. 00:36:48.580 --> 00:36:50.340 And then, bam, it's on your page. 00:36:50.340 --> 00:36:50.760 Nice. 00:36:51.020 --> 00:36:58.760 Anything that is a variable that is on a line by itself, it just assumes you want it to put that output to the HTML. 00:36:58.760 --> 00:37:00.780 And, I mean, how awesome is that? 00:37:00.780 --> 00:37:04.700 I mean, this is like the best, laziest package I've ever seen. 00:37:04.700 --> 00:37:08.020 Productivity, productive laziness or something like that. 00:37:08.020 --> 00:37:09.300 There's got to be a term for it. 00:37:09.300 --> 00:37:09.800 Yeah. 00:37:09.800 --> 00:37:16.460 And working with it for the few hours that I did and to already have a dashboard that doesn't look great, 00:37:16.460 --> 00:37:19.780 but is functioning, I think is pretty impressive. 00:37:20.440 --> 00:37:21.640 Yeah, yeah, it definitely is. 00:37:21.640 --> 00:37:29.420 So, this is something you could publish for people to interact with more real-time data or so they don't have to bother you. 00:37:29.420 --> 00:37:34.000 They can go do the slider themselves or select the thing themselves and they get the answer, right? 00:37:34.000 --> 00:37:35.240 Yeah, exactly. 00:37:35.240 --> 00:37:42.860 And sometimes too many opinions is too much, definitely in racing, because everybody's making decisions from partial information. 00:37:43.000 --> 00:37:57.020 But I still think getting tools like that into as many hands in front of as many eyeballs as possible has got to be an improvement of somebody noticing something that the other people haven't noticed yet. 00:37:57.160 --> 00:37:58.340 And hey, what's with this? 00:37:58.340 --> 00:38:00.160 Or hey, look at this correlation. 00:38:00.160 --> 00:38:11.740 You know, if you do this output metric versus your finishing position and you see a trend of spring rates and things, having all those people poke around at this stuff, because we do have, 00:38:12.100 --> 00:38:18.700 I think our simulation has 634 user inputs right now. 00:38:18.700 --> 00:38:24.500 So, and some of those aren't, some of those are not stuff that you touch all the time. 00:38:24.500 --> 00:38:25.360 A lot of them aren't. 00:38:25.560 --> 00:38:30.760 But there's about 275 things that you set in the model to actually be able to simulate a car. 00:38:30.760 --> 00:38:34.300 And it gets worse every day because things keep getting added to it, right? 00:38:34.300 --> 00:38:35.060 So- 00:38:35.060 --> 00:38:35.360 Right, right. 00:38:35.360 --> 00:38:38.060 Different knobs you can turn, different adjustments you can make. 00:38:38.060 --> 00:38:38.580 Yeah. 00:38:38.580 --> 00:38:40.060 And everything's interactive. 00:38:40.060 --> 00:38:41.760 It's a dynamic system. 00:38:41.760 --> 00:38:43.140 It's not a dynamic part. 00:38:43.140 --> 00:38:49.180 You can't change this spring and change, you can't change one thing and get any benefit. 00:38:49.180 --> 00:38:51.500 You have to change a system of parts. 00:38:51.500 --> 00:38:51.920 Right. 00:38:52.020 --> 00:38:59.220 You change the springs to make them softer, that changes the ride height, which changes the aerodynamics, which changes the wind pressure or whatever, right? 00:38:59.220 --> 00:39:00.020 Like it's all- 00:39:00.020 --> 00:39:00.480 Exactly. 00:39:00.480 --> 00:39:01.620 It's like a whack-a-mole. 00:39:01.620 --> 00:39:03.680 You change one thing, it's going to change a bunch of others. 00:39:03.680 --> 00:39:04.080 Yeah. 00:39:04.080 --> 00:39:05.080 Yep, exactly. 00:39:05.080 --> 00:39:18.260 And you'll have crew chiefs that'll come to you, you know, in eight minutes left in practice and want to change these five things and expect you to know how to set the other things to keep your car from crashing into the ground. 00:39:18.260 --> 00:39:20.080 So it better run quick. 00:39:20.080 --> 00:39:20.560 Yeah. 00:39:20.560 --> 00:39:20.660 Yeah. 00:39:20.820 --> 00:39:22.120 So how do you run these things? 00:39:22.120 --> 00:39:26.380 Is it computationally slow or do you just have a lot of hardware or is it not actually that bad? 00:39:26.380 --> 00:39:28.060 Yes. 00:39:28.060 --> 00:39:32.320 So it depends. 00:39:32.320 --> 00:39:33.280 There's a different aspect. 00:39:33.280 --> 00:39:38.340 There's the full dynamic model and that needs to solve at a thousand hertz. 00:39:38.900 --> 00:39:39.900 So that's a lot of data. 00:39:39.900 --> 00:39:49.140 So that's a lot of data, a lot of decisions being made for how all these parts are moving on the suspension and everything, how the car is sliding with the tires across the pavement. 00:39:49.600 --> 00:40:08.160 That I won't say is slow, but it's way slower than a steady state model, which is getting rid of all the dynamics, basically using a set of data inputs that are super simplified and only solving at 10 hertz. 00:40:08.720 --> 00:40:10.020 So you don't get the same answer. 00:40:10.020 --> 00:40:11.700 So you don't get the same answer. 00:40:11.700 --> 00:40:12.200 Right. 00:40:12.200 --> 00:40:17.640 But like in a case where you need to make a decision really quick and like right now, that is a great tool to have as well. 00:40:17.640 --> 00:40:18.060 Right. 00:40:18.160 --> 00:40:30.700 If you can get an answer that you're 90% sure of in the next 14 seconds, that is way better than getting an answer that you're 95% sure of in 42 minutes. 00:40:30.700 --> 00:40:31.080 Yeah. 00:40:31.080 --> 00:40:32.020 It's exponentially. 00:40:32.020 --> 00:40:40.240 As you get your accuracy, you pay with money, you pay with computational time, you pay with losing the engineer's focus. 00:40:40.240 --> 00:40:47.160 The longer he has to sit there twiddling his thumbs, like he's daydreaming about what he's going to do when the track closes or whatever, you know? 00:40:47.160 --> 00:40:47.480 Right. 00:40:47.480 --> 00:40:51.320 It's easy to lose yourself when you're running 200 sims a week. 00:40:51.320 --> 00:40:52.220 Yeah, I can imagine. 00:40:52.220 --> 00:40:52.520 Wow. 00:40:52.520 --> 00:41:01.420 So one thing I've been thinking about as we're talking is all these different inputs, trying to notice what correlations there might be. 00:41:01.420 --> 00:41:04.180 You have access to a lot of the different cars and whatnot. 00:41:04.180 --> 00:41:09.460 It seems like there's probably a lot of machine learning stuff that you could potentially apply here. 00:41:09.540 --> 00:41:12.140 Like what's the story of machine learning in your world? 00:41:12.140 --> 00:41:13.880 I think it is the future. 00:41:13.880 --> 00:41:23.340 So when I say we have a lot of data, most of our projects, it's not what most people like big data guys would think is big data. 00:41:23.340 --> 00:41:32.940 We don't have a lot of places where we can do like deep learning applications because we just don't have enough of the data that fits in this box. 00:41:32.940 --> 00:41:35.460 We're trying to get very specific answers. 00:41:36.100 --> 00:41:44.100 And even though you have a race with four hours of data, there's a ton of data in there that's completely worthless because the caution came out. 00:41:44.100 --> 00:41:44.800 Right. 00:41:44.800 --> 00:41:53.080 You can't feed that to a training system when over two different races where one part you did that stint with no yellow flag. 00:41:53.160 --> 00:41:56.480 Obviously, higher pressures and temperatures will change. 00:41:56.480 --> 00:41:58.020 The amount of fuel used has changed. 00:41:58.020 --> 00:42:00.100 All those things are just like they're not comparable, right? 00:42:00.100 --> 00:42:00.580 Yeah. 00:42:00.580 --> 00:42:03.980 There's a few places that we've used some deep learning. 00:42:03.980 --> 00:42:04.460 Okay. 00:42:04.460 --> 00:42:05.840 Some photo stuff. 00:42:06.200 --> 00:42:09.120 General Motors provides a photographer to the track. 00:42:09.120 --> 00:42:11.060 Well, during the pandemic, they don't always. 00:42:11.060 --> 00:42:17.500 But they provide a professional photographer to the track to take pictures of all your other cars, all the cars on track. 00:42:17.500 --> 00:42:24.040 So you can say, well, here's their attitude on track and here's our attitude and we're doing it wrong because they're faster than us or whatever. 00:42:24.840 --> 00:42:30.140 But over a weekend, you have about 7,000 photos taken and to go. 00:42:30.140 --> 00:42:41.500 And of course, it comes in with the file name of whatever the camera is set up to produce, which is pretty much meaningless when you want to find all the pictures of the 18 car. 00:42:41.500 --> 00:42:42.280 Right. 00:42:42.280 --> 00:42:49.760 And so you have to look at skim through all these thumbnails of these mixed cars because everybody's on track at the same time. 00:42:49.760 --> 00:42:51.660 So it's not like they're grouped together in any way. 00:42:52.260 --> 00:43:07.700 So one good application is to use NeuralNet to, yeah, I guess I should say convolutional NeuralNet to look at these photos and write new file names and organize them into folders and just take the manual drudgery out of that kind of thing. 00:43:07.700 --> 00:43:08.220 All right. 00:43:08.220 --> 00:43:18.720 Maybe even create a dashboard that says, I want to look at this car and I need to look at it from the side so that I can understand how much it's like tilting forward or whatever, right? 00:43:18.720 --> 00:43:19.120 The rake. 00:43:19.120 --> 00:43:20.840 So show me that. 00:43:20.840 --> 00:43:21.960 Show me that for this car. 00:43:22.120 --> 00:43:23.460 You could probably build that with Streamlight. 00:43:23.460 --> 00:43:24.420 That's a great idea. 00:43:24.420 --> 00:43:25.480 I think I'll build that next week. 00:43:25.480 --> 00:43:26.820 There you go. 00:43:26.820 --> 00:43:30.320 What's crazy is like, I mean, you're joking. 00:43:30.320 --> 00:43:41.660 It theoretically could be a couple of weeks, whereas like 10 years ago, that was like, we're going to need 50 years and a million dollars of research and like a bunch of PhDs. 00:43:41.660 --> 00:43:43.340 And now it's like, maybe we'll do that in this little app. 00:43:43.340 --> 00:43:43.760 Yeah. 00:43:43.760 --> 00:43:44.680 Our cute little app. 00:43:45.160 --> 00:43:45.600 Yeah. 00:43:45.600 --> 00:43:50.480 So a lot of the performance stuff, we have enough data during the race to use some deep learning. 00:43:50.480 --> 00:44:00.100 But I really think the simpler just regression analysis and classification analysis types of things where you're looking at data. 00:44:00.100 --> 00:44:06.380 You take all the metrics of output of these different cars and you split it up around the track. 00:44:06.380 --> 00:44:11.720 And then you train a fairly simplified model on what do the fast cars have in common? 00:44:11.720 --> 00:44:13.480 What do the slow cars have in common? 00:44:13.480 --> 00:44:18.020 And let's not be like the slow cars because there is so much data. 00:44:18.020 --> 00:44:20.300 It's hard to say what knob does matter. 00:44:20.300 --> 00:44:22.820 What knob is the crucial thing? 00:44:22.820 --> 00:44:24.700 So that's the direction we're going. 00:44:24.700 --> 00:44:26.200 And I'm excited about it. 00:44:26.200 --> 00:44:33.800 Building some new skills and being given the opportunity to apply them in so many different aspects of the job is just huge. 00:44:33.800 --> 00:44:34.380 Keep it. 00:44:34.380 --> 00:44:35.140 Keep me motivated. 00:44:35.140 --> 00:45:05.120 Keep me motivated. 00:45:05.120 --> 00:45:12.440 Or ditch that unused subscription for our course bundles, which include all the courses and you pay about the same price as a subscription once. 00:45:12.440 --> 00:45:19.740 For details, visit training. talkpython.fm/business or just email sales at talkpython.fm. 00:45:21.680 --> 00:45:22.820 Yeah, I can imagine. 00:45:22.820 --> 00:45:29.220 And racing is one of those things that's pretty interesting because there's a lot of bespoke engineering that happens. 00:45:29.220 --> 00:45:31.820 Like one-off custom engineering. 00:45:31.820 --> 00:45:32.420 Yeah. 00:45:32.420 --> 00:45:35.560 There's a team of people that are building stuff and adjusting stuff. 00:45:35.560 --> 00:45:38.860 It's not like, well, these are the three things I can get from the parts store. 00:45:38.860 --> 00:45:39.820 I'm going to stick on here. 00:45:39.820 --> 00:45:42.820 Like, you guys are like, well, it would be better if it was like this. 00:45:42.920 --> 00:45:44.540 Let's go build that and try it. 00:45:44.540 --> 00:45:44.900 Right. 00:45:44.900 --> 00:45:45.020 Right. 00:45:45.020 --> 00:45:45.600 Yeah. 00:45:45.600 --> 00:45:49.200 And our hands have been tied quite a bit recently with the rules package. 00:45:49.640 --> 00:45:51.660 And we can't do a lot of that anymore. 00:45:51.660 --> 00:45:55.720 All the parts have to be submitted to NASCAR ahead of time. 00:45:55.720 --> 00:45:58.060 And then they judge whether they like it or not. 00:45:58.060 --> 00:46:02.680 And if it's too different from anybody else, they won't approve it. 00:46:03.140 --> 00:46:06.840 So it turns into a bit of a cat and mouse game where you're trying to sneak stuff around. 00:46:06.840 --> 00:46:14.760 And every once in a while, you'll see somebody get caught with something, some unapproved part, and they get a $100,000 fine and their hands slapped. 00:46:14.760 --> 00:46:15.320 Yeah. 00:46:15.320 --> 00:46:16.660 You know, but that's a real deal. 00:46:16.660 --> 00:46:17.860 Yeah, I can imagine. 00:46:17.860 --> 00:46:19.540 Every little bit is an edge. 00:46:19.540 --> 00:46:21.620 And, you know, you got to push it to the limit. 00:46:21.620 --> 00:46:22.560 Yeah, for sure. 00:46:22.560 --> 00:46:35.240 But I think a lot of people take for granted how much tech is in NASCAR because they look at the somewhat antiquated cars with a rear suspension from a 1956 Chevrolet pickup, and they can't understand. 00:46:35.240 --> 00:46:49.300 But the science behind trying to make a 3,500-pound car with a truck suspension go fast is actually pretty impressive because, I mean, the Formula One car is absolutely designed to be unbelievable race cars. 00:46:49.300 --> 00:46:50.800 But we're not allowed to do that. 00:46:51.200 --> 00:46:58.380 So you have to use a lot of weird tricks and things to get these things to go fast and handle well. 00:46:58.380 --> 00:47:00.000 And so it's a little bit different. 00:47:00.000 --> 00:47:04.880 I think from an engineering standpoint, the jobs in the different series is quite a bit different. 00:47:04.880 --> 00:47:07.140 I think NASCAR and IndyCar are pretty similar. 00:47:07.140 --> 00:47:10.320 You see people going back and forth between those series. 00:47:10.320 --> 00:47:12.080 You don't see a lot of Formula One guys. 00:47:12.080 --> 00:47:17.680 Yeah, both of those series have the somewhat spec aspect to it, right? 00:47:17.680 --> 00:47:20.660 Like a lot of rules about we're kind of going to agree on this part. 00:47:20.660 --> 00:47:26.200 IndyCar goes here or IndyCar is the same chassis as all Dallara chassis or something like that, right? 00:47:26.200 --> 00:47:26.820 Right. 00:47:26.820 --> 00:47:27.240 Yeah. 00:47:27.240 --> 00:47:30.080 But I think the jobs are quite a bit different too. 00:47:30.080 --> 00:47:36.340 If a race team has 300 employees, they might only have 25 engineers in NASCAR. 00:47:36.340 --> 00:47:44.960 In Formula One, if they have 300 employees, my understanding is that almost 300 of them have an engineering degree. 00:47:45.120 --> 00:47:45.500 Exactly. 00:47:45.500 --> 00:47:50.400 And they're back there building carbon-carbon adapters or for who knows what, right? 00:47:50.400 --> 00:47:51.120 Yeah. 00:47:51.120 --> 00:47:56.260 And I think that we had a few Formula One people come in and work at Richard Childress a few years ago. 00:47:56.760 --> 00:48:02.960 And the parts that they had worked on, they were incredibly talented at that aspect. 00:48:02.960 --> 00:48:09.480 But like in NASCAR, because you have fewer people, you can't just be a tire guy, really. 00:48:09.480 --> 00:48:18.940 Like as a race engineer, you need to be over the full spectrum of working on every aspect of this car to even hope the thing is going to go fast. 00:48:18.940 --> 00:48:19.280 Yeah. 00:48:19.280 --> 00:48:25.560 And if you had 30 or 40 people all working on the little details, it'd be better, but our budgets just aren't there. 00:48:25.560 --> 00:48:26.560 Right, right. 00:48:26.560 --> 00:48:28.900 I don't know what the NASCAR price is. 00:48:28.900 --> 00:48:29.640 Maybe you tell me. 00:48:29.640 --> 00:48:34.360 To run an IndyCar, I think, is about $15 million per year. 00:48:34.360 --> 00:48:35.920 Run a Formula One car. 00:48:35.920 --> 00:48:42.060 You know, Ferrari and Mercedes are spending like $300, $400 million a year. 00:48:42.060 --> 00:48:42.920 Yeah. 00:48:44.120 --> 00:48:46.600 That's a 10X or 20X difference. 00:48:46.600 --> 00:48:50.360 That means a really different constraints if you can operate with those two numbers. 00:48:50.360 --> 00:48:50.960 Yeah. 00:48:50.960 --> 00:48:57.180 There's nobody spending $100 million that I'm aware of running, you know. 00:48:57.180 --> 00:49:02.940 You have to keep in mind, too, that the Formula One cars, they're only running two cars. 00:49:02.940 --> 00:49:03.480 Yeah. 00:49:03.480 --> 00:49:04.000 Right? 00:49:04.000 --> 00:49:07.820 And so they have all that resource and they're still only running two cars. 00:49:07.820 --> 00:49:09.180 So they're only paying two drivers. 00:49:09.180 --> 00:49:13.400 You look at a Hendrick Motorsports or something and they have four drivers that you're paying. 00:49:13.480 --> 00:49:15.740 The driver's the most expensive part, for sure. 00:49:15.740 --> 00:49:19.760 Besides maybe that adapter that measures the wheel that you don't want to crash. 00:49:19.760 --> 00:49:20.320 Yeah. 00:49:20.320 --> 00:49:21.980 No, the driver. 00:49:21.980 --> 00:49:25.340 Most drivers are still way more expensive than that. 00:49:25.340 --> 00:49:27.540 But when they win, it's worth it, right? 00:49:27.540 --> 00:49:28.060 Right. 00:49:28.060 --> 00:49:28.560 Yeah. 00:49:28.560 --> 00:49:31.600 I haven't looked at much of the budget stuff in a long time. 00:49:31.600 --> 00:49:36.280 What I'll say is there's a huge gap in what kind of team you're running. 00:49:36.280 --> 00:49:41.020 The guys that are out there running 35th to 40th are doing it pretty cheap. 00:49:41.400 --> 00:49:47.040 The cars themselves aren't ridiculously expensive because it's old tech and there's, you know, 00:49:47.040 --> 00:49:48.540 we can't do carbon bodies. 00:49:48.540 --> 00:49:53.060 I mean, we have some carbon parts, but teams don't have autoclays where they're making a 00:49:53.060 --> 00:49:54.060 monocoque, right? 00:49:54.240 --> 00:50:01.380 But I think that going on the budget, I think the last number I heard, it was about $150,000 00:50:01.380 --> 00:50:03.600 a week, you know, an event. 00:50:03.600 --> 00:50:08.500 So if you do, as good as I am at math and science, I'm terrible at basic math. 00:50:08.500 --> 00:50:15.220 So whatever 150,000 times 38 is, is a pretty decent number. 00:50:15.220 --> 00:50:15.780 Yeah. 00:50:15.880 --> 00:50:16.800 5.7 million. 00:50:16.800 --> 00:50:17.420 Yeah. 00:50:17.420 --> 00:50:17.780 Nice. 00:50:17.780 --> 00:50:18.360 Yeah. 00:50:18.360 --> 00:50:20.180 So there's a big difference there. 00:50:20.180 --> 00:50:20.640 Yeah. 00:50:20.640 --> 00:50:20.980 Yeah. 00:50:20.980 --> 00:50:21.340 For sure. 00:50:21.340 --> 00:50:24.220 You know, but there's a lot of benefits to keeping your costs down too. 00:50:24.220 --> 00:50:24.520 Yeah. 00:50:24.520 --> 00:50:24.940 Oh yeah. 00:50:24.940 --> 00:50:28.760 Because when you're looking at the sponsorship and you're trying to make the sponsor dollar 00:50:28.760 --> 00:50:33.040 go as far as possible, you know, being much cheaper is better. 00:50:33.040 --> 00:50:33.640 Yeah. 00:50:34.020 --> 00:50:37.460 Well, and it means that the sport could be more vibrant, right? 00:50:37.460 --> 00:50:39.980 Like if one team drops out, another can come in. 00:50:39.980 --> 00:50:42.380 It's not like they have to come up with half a billion dollars. 00:50:42.380 --> 00:50:42.780 Right. 00:50:42.780 --> 00:50:43.360 Right. 00:50:43.360 --> 00:50:44.540 Or something insane, right? 00:50:44.540 --> 00:50:47.620 And an engineering factory and all that kind of stuff. 00:50:47.620 --> 00:50:49.860 So yeah, it's definitely an interesting angle. 00:50:49.860 --> 00:50:53.340 So let's maybe just wrap up our show on this, which has been really, really fun. 00:50:53.540 --> 00:51:01.000 Just to maybe get your thoughts on this idea of open source and Python, both for ramping 00:51:01.000 --> 00:51:05.480 up the capabilities, but also opening up the possibilities, right? 00:51:05.480 --> 00:51:09.120 You don't have to buy expensive licenses to custom modeling software and whatnot. 00:51:09.120 --> 00:51:14.960 How do you see that affecting the advantage of your team or the industry in general? 00:51:14.960 --> 00:51:15.320 Yeah. 00:51:15.320 --> 00:51:16.900 I think it's going to be the way of the future. 00:51:16.900 --> 00:51:20.180 I mean, I don't see why you would buy a MATLAB license now. 00:51:20.180 --> 00:51:20.520 Yeah. 00:51:20.880 --> 00:51:26.980 Definitely, if you're having young engineers coming in that haven't ever used MATLAB and 00:51:26.980 --> 00:51:31.440 to have that be your standard, I feel like the learning curve of MATLAB is just much more 00:51:31.440 --> 00:51:31.780 difficult. 00:51:31.780 --> 00:51:37.680 Getting it all right, getting things to work, especially the file input, output aspects that 00:51:37.680 --> 00:51:38.680 I always struggle. 00:51:38.680 --> 00:51:44.020 I had a ton of experience with MATLAB and I still struggled with, oh, I've got an XML I've 00:51:44.020 --> 00:51:44.440 got to get. 00:51:44.440 --> 00:51:46.140 Okay, well, how do I even do that? 00:51:46.140 --> 00:51:48.140 And, you know, or JSON or something. 00:51:48.660 --> 00:51:53.840 And with pandas, you just, it's one line and it interprets what you, what it thinks you 00:51:53.840 --> 00:51:54.760 mean it to be. 00:51:54.760 --> 00:51:57.200 And it, I think it's going to be the wave of the future. 00:51:57.200 --> 00:51:59.820 And for race teams, free is always better. 00:51:59.820 --> 00:52:02.380 Cheap is not always good, but free is better. 00:52:02.380 --> 00:52:02.760 Right. 00:52:02.880 --> 00:52:09.220 And the other angle is you can bring in people who are not potentially trained, you know, 00:52:09.220 --> 00:52:14.540 brought up through the engineering side or the math and MATLAB side of things, right? 00:52:14.540 --> 00:52:19.300 You could find somebody who was working at a startup, but is passionate about this and they're 00:52:19.300 --> 00:52:21.340 really good at Python and data science. 00:52:21.340 --> 00:52:24.520 They could maybe make that transition over potentially. 00:52:24.920 --> 00:52:25.540 Yeah, absolutely. 00:52:25.540 --> 00:52:30.940 I mean, that's what I was saying before, the science of what you do, it doesn't matter if 00:52:30.940 --> 00:52:37.020 you're, if you're working at Caterpillar or a race team or, you know, a toilet paper factory, 00:52:37.020 --> 00:52:42.900 the way that you're studying the data, all the data science stuff, it applies to every field in the, 00:52:42.900 --> 00:52:43.900 in the universe. 00:52:43.900 --> 00:52:49.360 So that's absolutely true that you could bring in some excellent programmers. 00:52:49.360 --> 00:52:53.580 The one thing I will say, and we have some excellent programmers that are the ones who 00:52:53.580 --> 00:52:56.260 actually do our simulation department and everything. 00:52:56.260 --> 00:52:59.560 And, but they don't necessarily come from a racing background. 00:52:59.560 --> 00:53:03.920 And the one thing I'll say, like I said, I was passionate about UX. 00:53:03.920 --> 00:53:09.340 The biggest thing we fight with them is trying to get them to do it the way we want. 00:53:09.340 --> 00:53:09.780 Right. 00:53:09.780 --> 00:53:11.660 Not the easiest way to code. 00:53:11.660 --> 00:53:12.160 Yeah. 00:53:12.720 --> 00:53:14.960 Because I need flexible tools. 00:53:14.960 --> 00:53:19.260 I need a lot of buttons and things to be able to manipulate this the way I want. 00:53:19.260 --> 00:53:24.800 And it's like one of our programmers, he was doing a job for me and he delivered it to me. 00:53:24.800 --> 00:53:26.280 And I'm like, what is this? 00:53:26.280 --> 00:53:28.700 This is nothing like what we discussed. 00:53:28.700 --> 00:53:30.540 Like, where is this part of it? 00:53:30.540 --> 00:53:31.480 And where is this part? 00:53:31.480 --> 00:53:33.040 How did you not include this? 00:53:33.040 --> 00:53:35.140 And he's a Spanish guy. 00:53:35.140 --> 00:53:37.020 I won't try to mimic his accent. 00:53:37.020 --> 00:53:38.560 He's an ex-Formula 1 guy too. 00:53:38.560 --> 00:53:39.700 Brilliant programmer. 00:53:39.700 --> 00:53:41.420 But he explained it to me. 00:53:41.780 --> 00:53:46.480 He said, well, when we talked about the project, you explained a lot of things and I didn't see why they were needed. 00:53:46.480 --> 00:53:48.580 Here's what you need to understand. 00:53:48.580 --> 00:53:54.680 If you hand me a banana and tell me to eat it and don't tell me to peel it, I will eat it unpeeled. 00:53:55.240 --> 00:53:59.300 But no, he's a great guy. 00:53:59.300 --> 00:54:01.300 And we're doing a lot of good work right now. 00:54:01.300 --> 00:54:08.920 He's building a data store to be a server-based data store for all these dashboards to be able to work flawlessly across. 00:54:08.920 --> 00:54:10.200 We're all over the country, right? 00:54:10.200 --> 00:54:15.200 So you need to have a good data system and you need to keep that data synced. 00:54:15.440 --> 00:54:19.980 So if somebody does an analysis and posts it up, it needs to change on everybody's computer. 00:54:19.980 --> 00:54:20.480 Right. 00:54:20.680 --> 00:54:31.980 But the synchronization part is tough problems still with if you have a database and there's 18 people across the company editing the same setup, like how do you merge all that stuff? 00:54:31.980 --> 00:54:35.740 And so that has been the bane of my existence for a long time. 00:54:36.020 --> 00:54:40.520 Yeah, I'm sure to keep all that data in sync and you're going to run a report and expect an answer out of it. 00:54:40.520 --> 00:54:45.860 You want to run it against the same data because otherwise you want to run it on sale data and all that kind of stuff, right? 00:54:45.860 --> 00:54:46.360 Oh, yeah. 00:54:46.360 --> 00:54:47.360 Yeah, absolutely. 00:54:47.360 --> 00:54:53.600 Well, thank you so much for sharing your story about how you're using Python and over at the race team. 00:54:53.600 --> 00:54:55.060 That's just fantastic. 00:54:55.060 --> 00:54:57.960 Now, before we get out of here, though, I've got to ask you the final two questions. 00:54:57.960 --> 00:54:58.600 Oh, yeah. 00:54:58.600 --> 00:55:02.600 If you're going to write some Python code, what editor do you use? 00:55:02.600 --> 00:55:07.520 If it's an analysis project, I almost always start with Jupyter Notebook. 00:55:07.520 --> 00:55:08.000 Okay. 00:55:08.000 --> 00:55:09.660 Jupyter or Jupyter Lab. 00:55:09.660 --> 00:55:11.100 Which one are you on right now? 00:55:11.100 --> 00:55:11.700 Jupyter. 00:55:11.700 --> 00:55:12.180 Okay. 00:55:12.180 --> 00:55:18.380 And that lets me do my old MATLAB, like searching around where I can print out the data. 00:55:18.380 --> 00:55:23.860 And just it's a visual thing for me to understand how the flow of this analysis needs to go. 00:55:23.860 --> 00:55:35.140 And then generally what I'll do is once I get done, I'll merge that all into one cell and put it in VS Code for when it's ready to actually be used on a regular basis. 00:55:35.140 --> 00:55:41.100 That way I don't have to go through and click all the cells and, well, this thing was out of order and this thing was, you know. 00:55:41.100 --> 00:55:41.420 Yeah. 00:55:41.420 --> 00:55:43.820 The Jupyter is a gift and also a curse. 00:55:43.820 --> 00:55:44.220 Yeah. 00:55:44.220 --> 00:55:54.900 It's a little bit like Excel in that regard that it has like these implicit go-tos, not because the control flow, but because you could go back three cells, run that thing and then jump down five cells and run the next one. 00:55:54.900 --> 00:55:59.380 And it looks like in order, like they happened in order, but no, you've got to read that little execution number. 00:55:59.380 --> 00:56:04.820 Then there's missing numbers that you don't really know what they maybe did, but there's no more evidence of them. 00:56:04.820 --> 00:56:06.580 It's very tricky in that regard. 00:56:06.580 --> 00:56:10.140 So I think productizing straight Python files makes a lot of sense. 00:56:10.140 --> 00:56:10.560 Yeah. 00:56:10.560 --> 00:56:11.240 Yeah. 00:56:11.240 --> 00:56:13.380 So that's my typical workflow. 00:56:13.380 --> 00:56:14.140 Yeah. 00:56:14.140 --> 00:56:14.840 That sounds good. 00:56:14.840 --> 00:56:16.380 And then notable PyPI package. 00:56:16.380 --> 00:56:26.120 I know everybody's heard about Black, but for anybody who uses Jupyter, there's another package called Black Cell Magic that lets you apply Black's formatting inside a Jupyter notebook. 00:56:26.120 --> 00:56:27.220 And so. 00:56:27.220 --> 00:56:28.100 Oh my goodness. 00:56:28.100 --> 00:56:34.460 So that keeps even like my workbook, things that never make it to production, at least formatted right and looking nice. 00:56:34.460 --> 00:56:40.680 And then I can't say enough about OpenPixel, which is an Excel, a Python Excel interface. 00:56:40.680 --> 00:56:46.140 It provides all the functionality of cell colors and borders. 00:56:46.140 --> 00:56:54.000 You could program it all just by typing it out instead of clicking through 8,000 Excel pop-up windows and things. 00:56:54.000 --> 00:56:54.600 That's cool. 00:56:54.600 --> 00:57:00.460 And this gives you, this is like your bridge to all the people who have legacy Excel stuff laying around or they want to work in Excel and they're like Excel. 00:57:00.460 --> 00:57:01.240 And they're like, here you go. 00:57:01.240 --> 00:57:03.040 You're like, ah, I'm not doing VBA. 00:57:03.040 --> 00:57:03.500 Hold on. 00:57:03.580 --> 00:57:03.980 Yeah. 00:57:03.980 --> 00:57:04.400 No. 00:57:04.400 --> 00:57:09.780 And when a crew chief can only use Excel, you have to pass them an Excel sheet at the end of the day. 00:57:09.780 --> 00:57:10.140 Yeah. 00:57:10.140 --> 00:57:11.560 So that has been a blessing. 00:57:11.560 --> 00:57:19.680 And then the one thing I found out today when I went and downloaded, when I was going through the documentation on Streamlit is they have a new Streamlit for Teams coming out. 00:57:19.680 --> 00:57:20.680 I don't know if you've seen that. 00:57:20.680 --> 00:57:20.960 Yeah. 00:57:20.960 --> 00:57:24.360 That's going to have one click server deployment for your app. 00:57:24.360 --> 00:57:25.200 So you can work. 00:57:25.200 --> 00:57:25.920 That's going to be sweet. 00:57:25.920 --> 00:57:27.060 Work together and everything. 00:57:27.060 --> 00:57:29.380 So definitely check that out if you haven't. 00:57:29.380 --> 00:57:29.660 All right. 00:57:29.660 --> 00:57:30.000 Awesome. 00:57:30.000 --> 00:57:31.100 Very good recommendations. 00:57:31.100 --> 00:57:31.720 Okay. 00:57:31.720 --> 00:57:32.600 Final call to action. 00:57:32.600 --> 00:57:33.920 People are excited about this stuff. 00:57:33.920 --> 00:57:36.780 Maybe making Python a bigger part of their engineering story. 00:57:36.780 --> 00:57:37.460 What do you say? 00:57:37.460 --> 00:57:38.160 I like it. 00:57:38.160 --> 00:57:38.920 They got to do it. 00:57:38.920 --> 00:57:40.160 It's a friendly environment. 00:57:40.160 --> 00:57:42.500 Great places to learn all over the internet. 00:57:42.500 --> 00:57:43.820 Also mostly free. 00:57:44.260 --> 00:57:49.800 And I guess the other thing I'd like to say is just shout out to all my teammates in case anybody listens to this. 00:57:49.800 --> 00:57:53.600 They probably hear me talk about Python enough, but we won. 00:57:53.600 --> 00:57:56.240 We finished first and second in the race on Sunday. 00:57:56.240 --> 00:57:58.580 So shout out to Austin Dillon for. 00:57:58.580 --> 00:58:00.320 What event was that? 00:58:00.320 --> 00:58:01.140 It was Texas. 00:58:01.140 --> 00:58:02.200 Texas Motor Speedway. 00:58:02.200 --> 00:58:02.680 Yeah. 00:58:02.680 --> 00:58:02.860 Cool. 00:58:02.860 --> 00:58:03.420 All right. 00:58:03.580 --> 00:58:04.020 Congratulations. 00:58:04.020 --> 00:58:08.360 And I guess it's cool to know a little bit of Python was behind the scenes there, huh? 00:58:08.360 --> 00:58:08.960 Oh, yeah. 00:58:08.960 --> 00:58:09.380 Yeah. 00:58:09.380 --> 00:58:10.040 Winning races. 00:58:10.040 --> 00:58:10.700 Right on. 00:58:10.700 --> 00:58:15.800 And I will mention that there's another race tomorrow night, Thursday, on the NBC Streaming Network. 00:58:15.800 --> 00:58:16.260 Cool. 00:58:16.260 --> 00:58:17.400 In Kansas. 00:58:17.400 --> 00:58:19.160 So definitely check that out. 00:58:19.160 --> 00:58:19.620 Right on. 00:58:19.620 --> 00:58:20.060 Awesome. 00:58:20.060 --> 00:58:21.960 Well, Kane, thanks again for being on the show. 00:58:21.960 --> 00:58:23.780 It was really good to hear your story. 00:58:23.780 --> 00:58:25.420 And it's inspiring what you're doing. 00:58:25.420 --> 00:58:25.760 Yeah. 00:58:25.800 --> 00:58:26.940 Thanks so much for having me. 00:58:26.940 --> 00:58:28.840 I really enjoyed it and love the show. 00:58:28.840 --> 00:58:29.500 Yeah. 00:58:29.500 --> 00:58:30.060 See you later. 00:58:30.060 --> 00:58:30.480 All right. 00:58:30.480 --> 00:58:30.640 Bye. 00:58:30.640 --> 00:58:34.660 This has been another episode of Talk Python To Me. 00:58:34.660 --> 00:58:37.800 Our guest on this episode was Robert Kane Replical. 00:58:37.800 --> 00:58:40.820 And it's been brought to you by SonarCube and Linode. 00:58:40.820 --> 00:58:46.380 SonarCube finds bugs, vulnerabilities, code smells, and security hotspots in your code. 00:58:46.380 --> 00:58:47.440 Try it out today. 00:58:47.440 --> 00:58:48.480 It's free to use. 00:58:48.480 --> 00:58:52.920 Just visit talkpython.fm/sonar and click download to get started. 00:58:52.920 --> 00:58:54.920 That's 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like a subscription that never expires. 00:59:31.280 --> 00:59:34.920 Be sure to subscribe to the show. 00:59:34.920 --> 00:59:39.520 This is your host, Michael Kennedy. 00:59:39.520 --> 00:59:47.740 This is your host, Michael Kennedy. 00:59:47.740 --> 00:59:49.240 Thanks so much for listening. 00:59:49.240 --> 00:59:50.280 I really appreciate it. 00:59:50.280 --> 00:59:52.040 Now get out there and write some Python code. 00:59:52.040 --> 00:59:56.000 Thank you. 00:59:56.000 --> 01:00:25.980 Thank you.