More transcript fixes.

Author: mikeckennedy

transcripts/003-Pyramid-Web Framework-Chris-McDonough.txt

@@ -1264,7 +1264,7 @@
 
 00:43:38 You know, I've had work to do and I took some time off.
 
-00:43:41 And I've I've I sort of didn't do as much on on pyramid or any of any of the other pylons
+00:43:41 And I've I've I sort of didn't do as much on pyramid or any of any of the other pylons
 
 00:43:47 projects projects.
 
@@ -1274,7 +1274,7 @@
 
 00:43:55 I should say this.
 
-00:43:56 There is a there is a pyramid IRC channel that if you're interested in pyramid on on free
+00:43:56 There is a there is a pyramid IRC channel that if you're interested in pyramid on free
 
 00:44:00 know that you should probably get into.
 

transcripts/003-Pyramid-Web Framework-Chris-McDonough.vtt

@@ -1906,7 +1906,7 @@ But over the last year or so, I have had to go and work.
 You know, I've had work to do and I took some time off.
 
 00:43:41.180 --> 00:43:47.920
-And I've I've I sort of didn't do as much on on pyramid or any of any of the other pylons
+And I've I've I sort of didn't do as much on pyramid or any of any of the other pylons
 
 00:43:47.920 --> 00:43:49.560
 projects projects.
@@ -1921,7 +1921,7 @@ Michael miracle is he's on.
 I should say this.
 
 00:43:56.420 --> 00:44:00.600
-There is a there is a pyramid IRC channel that if you're interested in pyramid on on free
+There is a there is a pyramid IRC channel that if you're interested in pyramid on free
 
 00:44:00.600 --> 00:44:01.620
 know that you should probably get into.

transcripts/012_Deep_dive_into_Modules_and_Packages.txt

@@ -1308,7 +1308,7 @@
 
 00:36:19 So if you had a if you had a like a package directory spam and then you had two files in there, you know, like foo and bar dot foo dot pi bar dot pi.
 
-00:36:30 In Python two, you can one of those files like like bar dot pi can just say import foo and it will find it in the same directory.
+00:36:30 In Python two, you can one of those files like bar dot pi can just say import foo and it will find it in the same directory.
 
 00:36:38 That does not work in Python three.
 

transcripts/012_Deep_dive_into_Modules_and_Packages.vtt

@@ -1981,7 +1981,7 @@ You know, one thing that might impact people is you can't do a relative import w
 So if you had a if you had a like a package directory spam and then you had two files in there, you know, like foo and bar dot foo dot pi bar dot pi.
 
 00:36:30.260 --> 00:36:38.920
-In Python two, you can one of those files like like bar dot pi can just say import foo and it will find it in the same directory.
+In Python two, you can one of those files like bar dot pi can just say import foo and it will find it in the same directory.
 
 00:36:38.920 --> 00:36:41.660
 That does not work in Python three.

transcripts/022_cpython.txt

@@ -1228,7 +1228,7 @@
 
 00:46:56 And there's no way they can help everybody at once, obviously.
 
-00:47:00 So what I've done is I built a dashboard that shows a tutor or a teacher in real time what a lot of students are doing at the same time on on a website like the Python tutor.
+00:47:00 So what I've done is I built a dashboard that shows a tutor or a teacher in real time what a lot of students are doing at the same time on a website like the Python tutor.
 
 00:47:11 So this dashboard can show up to dozens of people and each person's actions are just in a little tile.
 

transcripts/022_cpython.vtt

@@ -1849,7 +1849,7 @@ There may be a thousand students for every TA on the course.
 And there's no way they can help everybody at once, obviously.
 
 00:47:00.340 --> 00:47:11.960
-So what I've done is I built a dashboard that shows a tutor or a teacher in real time what a lot of students are doing at the same time on on a website like the Python tutor.
+So what I've done is I built a dashboard that shows a tutor or a teacher in real time what a lot of students are doing at the same time on a website like the Python tutor.
 
 00:47:11.960 --> 00:47:18.040
 So this dashboard can show up to dozens of people and each person's actions are just in a little tile.

transcripts/028_profiling.txt

@@ -582,7 +582,7 @@
 
 00:36:26 something right and it was doing crazy math like wavelet decomposition and all sorts of stuff okay this is
 
-00:36:33 like like like i was saying earlier it's the verge of understanding what we're doing right yeah and it
+00:36:33 like like i was saying earlier it's the verge of understanding what we're doing right yeah and it
 
 00:36:39 was too slow the first time we ran like oh no please don't make me try to optimize a wavelet
 
@@ -798,7 +798,7 @@
 
 00:47:01 to give a shout out to all my amazing colleagues at the imageo yeah that's awesome you guys are doing
 
-00:47:07 cool stuff so i'm glad to shine a light on on that so final two questions before i let you out of here
+00:47:07 cool stuff so i'm glad to shine a light on that so final two questions before i let you out of here
 
 00:47:13 favorite editor what do you what do you use to write python these days i'll say that for all my open
 

transcripts/028_profiling.vtt

@@ -1111,7 +1111,7 @@ it couldn't keep up well then you can't write a real time system or you need som
 something right and it was doing crazy math like wavelet decomposition and all sorts of stuff okay this is
 
 00:36:33.740 --> 00:36:39.160
-like like like i was saying earlier it's the verge of understanding what we're doing right yeah and it
+like like i was saying earlier it's the verge of understanding what we're doing right yeah and it
 
 00:36:39.160 --> 00:36:43.620
 was too slow the first time we ran like oh no please don't make me try to optimize a wavelet
@@ -1435,7 +1435,7 @@ davis uh no i was just uh i'm i'm just really excited to be given this opportuni
 to give a shout out to all my amazing colleagues at the imageo yeah that's awesome you guys are doing
 
 00:47:07.060 --> 00:47:13.040
-cool stuff so i'm glad to shine a light on on that so final two questions before i let you out of here
+cool stuff so i'm glad to shine a light on that so final two questions before i let you out of here
 
 00:47:13.040 --> 00:47:18.280
 favorite editor what do you what do you use to write python these days i'll say that for all my open

transcripts/050-web-scrapying-at-scale-with-scrapy-and-scrapinghub.txt

@@ -1310,7 +1310,7 @@
 
 00:48:12 That's a perfect example.
 
-00:48:14 And in fact, we didn't focus early on on just pushing a button and getting the data automatically.
+00:48:14 And in fact, we didn't focus early on just pushing a button and getting the data automatically.
 
 00:48:21 We focused more on the provides engineer hours and consulting and training for our tools.
 

transcripts/050-web-scrapying-at-scale-with-scrapy-and-scrapinghub.vtt

@@ -1969,7 +1969,7 @@ Is that kind of the thinking you guys went through?
 That's a perfect example.
 
 00:48:14.160 --> 00:48:21.580
-And in fact, we didn't focus early on on just pushing a button and getting the data automatically.
+And in fact, we didn't focus early on just pushing a button and getting the data automatically.
 
 00:48:21.580 --> 00:48:28.060
 We focused more on the provides engineer hours and consulting and training for our tools.

transcripts/060_scaling_to_1000s_of_cores.txt

@@ -642,7 +642,7 @@
 
 00:29:23 If the market price goes above your level, then you don't get the machine, but if it goes below, then you do.
 
-00:29:28 And it is usually around 10% of the regular cost of buying a machine, which means that I can get something like like a thousand cores for something like 10 bucks, which is a pretty insane price for that level of hardware.
+00:29:28 And it is usually around 10% of the regular cost of buying a machine, which means that I can get something like a thousand cores for something like 10 bucks, which is a pretty insane price for that level of hardware.
 
 00:29:42 That's great.
 
@@ -1384,7 +1384,7 @@
 
 01:03:20 You'll end up with like a reference to that list.
 
-01:03:22 It's like like a proxy object.
+01:03:22 It's like a proxy object.
 
 01:03:24 You can't do anything with it locally because you obviously can't bring a terabyte of strings back into your local Python process without crashing it or waiting for hours and hours.
 

transcripts/060_scaling_to_1000s_of_cores.vtt

@@ -967,7 +967,7 @@ If you're not familiar with that, it allows you to basically bid for the market
 If the market price goes above your level, then you don't get the machine, but if it goes below, then you do.
 
 00:29:28.080 --> 00:29:42.200
-And it is usually around 10% of the regular cost of buying a machine, which means that I can get something like like a thousand cores for something like 10 bucks, which is a pretty insane price for that level of hardware.
+And it is usually around 10% of the regular cost of buying a machine, which means that I can get something like a thousand cores for something like 10 bucks, which is a pretty insane price for that level of hardware.
 
 00:29:42.640 --> 00:29:43.080
 That's great.
@@ -2080,7 +2080,7 @@ That list of a billion strings, you can do whatever you want in your local Pytho
 You'll end up with like a reference to that list.
 
 01:03:22.900 --> 01:03:24.740
-It's like like a proxy object.
+It's like a proxy object.
 
 01:03:24.740 --> 01:03:33.340
 You can't do anything with it locally because you obviously can't bring a terabyte of strings back into your local Python process without crashing it or waiting for hours and hours.

transcripts/068_crossing_the_streams_with_Podcast__init__.mp3.txt

@@ -108,7 +108,7 @@
 
 00:04:39 attachment that never really materialized. And I really just got to the point where I was like,
 
-00:04:44 these are great. These games are great, but I want to control what's going on on the screen.
+00:04:44 these are great. These games are great, but I want to control what's going on the screen.
 
 00:04:49 And so I saved all my pennies and paper route money and my parents pitched in and we bought an Atari
 

transcripts/068_crossing_the_streams_with_Podcast__init__.mp3.vtt

@@ -166,7 +166,7 @@ I had always been into video games. I had Mattel and television and I gave up wa
 attachment that never really materialized. And I really just got to the point where I was like,
 
 00:04:44.560 --> 00:04:49.240
-these are great. These games are great, but I want to control what's going on on the screen.
+these are great. These games are great, but I want to control what's going on the screen.
 
 00:04:49.240 --> 00:04:54.580
 And so I saved all my pennies and paper route money and my parents pitched in and we bought an Atari

transcripts/074_ironpython.txt

@@ -186,7 +186,7 @@
 
 00:05:11 And I don't know if other Python interpreters call it this, but CPython, which is the main one developed.
 
-00:05:17 You know, you can go to python.org and download the installation or it comes on if you're running like Linux or something, it usually comes on on Linux.
+00:05:17 You know, you can go to python.org and download the installation or it comes on if you're running like Linux or something, it usually comes on Linux.
 
 00:05:25 We call that CPython because it's based on the C language.
 

transcripts/074_ironpython.vtt

@@ -286,7 +286,7 @@ There's also what we call it from Iron Python.
 And I don't know if other Python interpreters call it this, but CPython, which is the main one developed.
 
 00:05:17.740 --> 00:05:25.540
-You know, you can go to python.org and download the installation or it comes on if you're running like Linux or something, it usually comes on on Linux.
+You know, you can go to python.org and download the installation or it comes on if you're running like Linux or something, it usually comes on Linux.
 
 00:05:25.540 --> 00:05:28.920
 We call that CPython because it's based on the C language.

transcripts/076_renwable_python.txt

@@ -496,7 +496,7 @@
 
 00:14:59 Yeah.
 
-00:15:00 So for both of those kinds of problems, how our model works right now is that we get open data about what's going on on the power grid.
+00:15:00 So for both of those kinds of problems, how our model works right now is that we get open data about what's going on the power grid.
 
 00:15:08 So there's entities called balancing authorities that make sure that supply and demand are matched in real time for what's on the power grid.
 

transcripts/076_renwable_python.vtt

@@ -751,7 +751,7 @@ Yeah.
 Yeah.
 
 00:15:00.320 --> 00:15:08.420
-So for both of those kinds of problems, how our model works right now is that we get open data about what's going on on the power grid.
+So for both of those kinds of problems, how our model works right now is that we get open data about what's going on the power grid.
 
 00:15:08.700 --> 00:15:15.540
 So there's entities called balancing authorities that make sure that supply and demand are matched in real time for what's on the power grid.

transcripts/089-a-conversation-with-the-chief-data-scientist-of-the-united-states.txt

@@ -198,7 +198,7 @@
 
 00:07:54 But then you've also you also talked about this kind of general capacity building inside government.
 
-00:07:58 I mean, this administration has used data or kind of embedded data into different agencies like like no other administration before.
+00:07:58 I mean, this administration has used data or kind of embedded data into different agencies like no other administration before.
 
 00:08:06 And can you talk a little bit about the play between those two things, both kind of building capacity, looking at big issues like government transparency and accountability and how data informs that.
 

transcripts/089-a-conversation-with-the-chief-data-scientist-of-the-united-states.vtt

@@ -307,7 +307,7 @@ I mean, there's these sort of specific programs and initiatives that you talked
 But then you've also you also talked about this kind of general capacity building inside government.
 
 00:07:58.920 --> 00:08:06.560
-I mean, this administration has used data or kind of embedded data into different agencies like like no other administration before.
+I mean, this administration has used data or kind of embedded data into different agencies like no other administration before.
 
 00:08:06.560 --> 00:08:16.420
 And can you talk a little bit about the play between those two things, both kind of building capacity, looking at big issues like government transparency and accountability and how data informs that.

transcripts/122-home-assistant-pythonic-home-automation.txt

@@ -254,7 +254,7 @@
 
 00:11:13 Yeah, I think this is that example really highlights some of the huge possibility and benefits, but also some of the challenges, maybe even apprehension for some people that we have this possibility of having this thing listening all the time.
 
-00:11:28 You can just speak to it like like like on Star Trek, like computer, dim the lights, please.
+00:11:28 You can just speak to it like like on Star Trek, like computer, dim the lights, please.
 
 00:11:32 It's amazing, right?
 

transcripts/122-home-assistant-pythonic-home-automation.vtt

@@ -385,7 +385,7 @@ And it's very convenient.
 Yeah, I think this is that example really highlights some of the huge possibility and benefits, but also some of the challenges, maybe even apprehension for some people that we have this possibility of having this thing listening all the time.
 
 00:11:28.480 --> 00:11:32.700
-You can just speak to it like like like on Star Trek, like computer, dim the lights, please.
+You can just speak to it like like on Star Trek, like computer, dim the lights, please.
 
 00:11:32.700 --> 00:11:33.540
 It's amazing, right?

transcripts/124-python-for-ai-research.txt

@@ -26,7 +26,7 @@
 
 00:01:25 Yeah, I'm pretty excited to talk about AI, researching AI, and all the stuff that you guys
 
-00:01:31 are up to. It's something we haven't really touched that much on on the show, and it's
+00:01:31 are up to. It's something we haven't really touched that much on the show, and it's
 
 00:01:35 both interesting at a low-level, technical level, but also at a philosophical level, almost. So
 

transcripts/124-python-for-ai-research.vtt

@@ -43,7 +43,7 @@ Hi, thanks for having me.
 Yeah, I'm pretty excited to talk about AI, researching AI, and all the stuff that you guys
 
 00:01:31.540 --> 00:01:35.800
-are up to. It's something we haven't really touched that much on on the show, and it's
+are up to. It's something we haven't really touched that much on the show, and it's
 
 00:01:35.800 --> 00:01:42.120
 both interesting at a low-level, technical level, but also at a philosophical level, almost. So

transcripts/139-paths-into-a-data-science-career.txt

@@ -102,7 +102,7 @@
 
 00:05:00 So I started learning Python and and R to to do this. I learned online by online courses, a lot of web web resources.
 
-00:05:10 And in fact, you know, the open source community in Python and R really embracing. So any any questions I had, I could pick up on on the fly.
+00:05:10 And in fact, you know, the open source community in Python and R really embracing. So any any questions I had, I could pick up on the fly.
 
 00:05:19 Yeah, that's really cool. And I definitely see this working with scientists or in these types of areas. Very important.
 

transcripts/139-paths-into-a-data-science-career.vtt

@@ -157,7 +157,7 @@ Of course, to do that in to do that today, you need to be able to program. Right
 So I started learning Python and and R to to do this. I learned online by online courses, a lot of web web resources.
 
 00:05:10.340 --> 00:05:19.200
-And in fact, you know, the open source community in Python and R really embracing. So any any questions I had, I could pick up on on the fly.
+And in fact, you know, the open source community in Python and R really embracing. So any any questions I had, I could pick up on the fly.
 
 00:05:19.200 --> 00:05:27.760
 Yeah, that's really cool. And I definitely see this working with scientists or in these types of areas. Very important.

transcripts/146-building-alexa-skills-with-python-and-flask.txt

@@ -1884,7 +1884,7 @@
 
 00:58:21 It's a really cool on-your-own-network website.
 
-00:58:25 And yeah, I had the guys on on episode 122 a while back, so definitely cool.
+00:58:25 And yeah, I had the guys on episode 122 a while back, so definitely cool.
 
 00:58:30 Dave?
 

transcripts/146-building-alexa-skills-with-python-and-flask.vtt

@@ -2833,7 +2833,7 @@ It controls so many devices.
 It's a really cool on-your-own-network website.
 
 00:58:25.500 --> 00:58:30.740
-And yeah, I had the guys on on episode 122 a while back, so definitely cool.
+And yeah, I had the guys on episode 122 a while back, so definitely cool.
 
 00:58:30.740 --> 00:58:31.660
 Dave?

transcripts/150-technical-lessons-learned-from-pythonic-refactoring.txt

@@ -758,7 +758,7 @@
 
 00:28:58 Let's say I'm just going to completely make up a company that I don't really know whether they're technical or not.
 
-00:29:03 But let's say I work for like like a food production company that makes like cereal.
+00:29:03 But let's say I work for like a food production company that makes like cereal.
 
 00:29:08 Right.
 

transcripts/150-technical-lessons-learned-from-pythonic-refactoring.vtt

@@ -1141,7 +1141,7 @@ I feel like that will work well in a place like Yelp where it's a pretty technic
 Let's say I'm just going to completely make up a company that I don't really know whether they're technical or not.
 
 00:29:03.220 --> 00:29:08.320
-But let's say I work for like like a food production company that makes like cereal.
+But let's say I work for like a food production company that makes like cereal.
 
 00:29:08.320 --> 00:29:08.800
 Right.

transcripts/151-gradual-typing-of-production-applications.txt

@@ -1534,7 +1534,7 @@
 
 01:01:09 And particularly, I wrote the single dispatch like generic functions like in Python.
 
-01:01:15 And ever since, I was just poked by everybody to actually go full on on multiple generic dispatch.
+01:01:15 And ever since, I was just poked by everybody to actually go full on multiple generic dispatch.
 
 01:01:23 So I think like it's time for that.
 

transcripts/151-gradual-typing-of-production-applications.vtt

@@ -2305,7 +2305,7 @@ But just really quickly, what features would you like to see in these new versio
 And particularly, I wrote the single dispatch like generic functions like in Python.
 
 01:01:15.800 --> 01:01:23.280
-And ever since, I was just poked by everybody to actually go full on on multiple generic dispatch.
+And ever since, I was just poked by everybody to actually go full on multiple generic dispatch.
 
 01:01:23.280 --> 01:01:25.440
 So I think like it's time for that.

transcripts/157-the-journal-of-open-source-software.txt

@@ -1382,7 +1382,7 @@
 
 01:00:37 of context to then extract, extract the data. So we, but these log books are really cool. They've got,
 
-01:00:43 they've got basically stories about what's going on on the ship. We made a website where people could
+01:00:43 they've got basically stories about what's going on the ship. We made a website where people could
 
 01:00:47 transcribe them and people just got really into this and following, there was a guy, Lieutenant
 

transcripts/157-the-journal-of-open-source-software.vtt

@@ -2080,7 +2080,7 @@ And so you can do OCR on it, and try and get a machine to read it, but you still
 of context to then extract, extract the data. So we, but these log books are really cool. They've got,
 
 01:00:43.260 --> 01:00:47.920
-they've got basically stories about what's going on on the ship. We made a website where people could
+they've got basically stories about what's going on the ship. We made a website where people could
 
 01:00:47.920 --> 01:00:53.820
 transcribe them and people just got really into this and following, there was a guy, Lieutenant

transcripts/162-python-in-architecture.txt

@@ -956,7 +956,7 @@
 
 00:35:08 Like it's all kind of done.
 
-00:35:09 And, you know, obviously, like like I work for a professor that did a lot in solar research.
+00:35:09 And, you know, obviously, like I work for a professor that did a lot in solar research.
 
 00:35:14 And so, you know, by no means is that whole field done at all.