Freedom! Today I'm gonna give you a tip that I don't think anyone's given ever about YouTube. I could be wrong, however it's not one that I've ever really heard. The biggest tip that I can give any YouTuber that I haven't already given is simply make friends. Have fun, but make friends. Friends on YouTube are the difference between being able to accomplish things and not having doors open for you. And I'll give you some examples. If it weren't for a friend of mine called TheColeBox, that's his YouTube name, I would have never even gotten into YouTube. However, he kind of gave me a crazy idea about two and a half years ago. He told me that you can play video games on the internet and people will watch it and you can actually get a little bit of money to pay for more games. At the time, that sounded insane! But I had no life, so I figured I would try it. And lo and behold, all that time later, here I am. Another friend of mine several months later told me that I should join a network. And he recommended a network for me to join. That network at the time was George's previous network. And I did a little bit of research, I watched a few videos, and I joined. Though in the long run, that network wasn't for me. It did set in motion a set of events that would have made anything that you're watching right now impossible. My gaming channel, which is now dying because I'm helping build freedom and I just don't have the time anymore, was the cornerstone that I built everything that I've done on YouTube upon. However, the cornerstone of that channel was a game called Minecraft. And I would never have discovered Minecraft if it weren't for a friend of mine and my brother saying, Check out this game, it's kind of like Legos on steroids in a video game. Instantly I was hooked. And without friends, that would have never happened. While I was with George's former network, I met a recruiter. This recruiter put me into a Skype group where I met multiple people who later turned into the people that helped me build Game Mode 5. Game Mode 5 was the network I ran with freedom, which led to my getting hired with freedom. Without that friend, without that recruiter, none of this would have been possible. In the end, almost every single good thing that I can think of came from relationships I built with people. Whether it be friendships or acquaintances, building contacts, building my social network, if you will. It's through those experiences that I accomplished everything that I've accomplished so far, and hopefully they will be the key to helping me accomplish everything that I have planned going forward. So I know that this is kind of a roundabout way of saying it, but friends are awesome. And you should definitely make some, whether it be on the freedom forums, in the comments of YouTube videos, whether it be on Twitter, using the hashtag Freedom Family. It never hurts to reach out to somebody whose content you like. It never hurts to put out a friendly face and talk to somebody. Remember, this is the Freedom Family. We're a community first and it's really built to help build friendships and connections. I hope that this video has been helpful for you. If it has, let us know in the comment section down below. Until next time, I'm Anthony with Freedom Life. Subscribe, comment, please. Let's discover freedom. Click the first video to learn what is freedom and how it helps you grow. Click the second video to learn about sponsorships for all freedom partners, even if you have just 10 subscribers. And click the other videos like How Do I Leave Freedom to learn more. Click Get More Views to learn how to build your audience faster on YouTube. Click Get More CPM to learn how to make more money from your channel. And click Get More Music to learn where to get more music to spice up your videos. Click the first playlist to watch The George Show on Freedom. That's my show. Or watch The Anthony Show or The Alex Show also on Freedom.


Welcome to the session on Earth Synchronous Counter in Digital Design. At the end of this session, students will be able to design an Earth Synchronous Counter. Let us see what is counter. Counter are circuits that cycle through a specified number of states. A counter is a device which can count any particular event on the basis of how many times the particular event is occurred. In a digital logic system or computer, the counter can count and store the number of times any particular event or process have occurred, depending on clock signal. For example, in up counter, counter increases count value as per the rising edge of the clock pulse. Counter can follow the certain sequence based on our design like 0, 1, 2, 3, etc. They can also be designed with the help of flip-flops. The types of counters are synchronous counters and Earth Synchronous counters. Now Earth Synchronous counters, whereas the flip-flops do not change state at exactly the same time as they do not have a common clock pulse. Earth Synchronous stands for the absence of synchronization, something that is not existing or occurring at the same time. In computing string, Earth Synchronous stands for controlling the operation timing by sending a pulse only when the previous operation is completed rather than sending it in regular interval. It is also known as a ripple counter as the input clock pulse ripples through the counter and cumulative delay is a drawback. Those flip-flops are serially connected together and the clock pulse ripples through the counter and Earth Synchronous counter can count 2 to the power n minus 1 possible counting states. For example, 2-bit ripple binary counter, a ripple counter is an Earth Synchronous counter where only the first flip-flop is clocked by an external clock and the circuit shown here 2 jk flip-flops to implement a 2-bit ripple binary counter and it counts towards up and the number of states are 2 to the power 2 is 4. Now the operation of Earth Synchronous counter here, the clock input is only connected for the first stage or we can say the first flip-flop. The clock is applied to the clock input of only first flip-flop which is always the least significant bit. The second stage is triggered by the output of the first stage because the propagation delay of flip-flop. The second flip-flop is triggered by the output of the first flip-flop because of the inherent propagation delay time through a flip-flop and the transition of the input clock pulse and the transition of the output of first flip-flop can never occur exactly the same time. In next step, the transition of input clock pulse and given output will never occur simultaneously. Therefore, the 2 flip-flops are never simultaneously triggered. So the counter operation is Earth Synchronous. Now here we will check it out the state table of 2-bit Earth Synchronous counter and it will count from minimum that is 0 to maximum 3 that is it adds as a up counter. Note that for simplicity the transition of Q1, Q2 and clock in the timing diagram which we will see in the next slide this is an Earth Synchronous counter. Actually there is some small delay between the clock, Q1 and Q2 transition. Usually all clear inputs are connected together so that a single pulse can clear all the flip-flops before counting starts. The clock pulse fed into flip-flop 1 is rippled through the other counters after propagation delay and the 2-bit ripple counter circuits above has 4 different states each one corresponds to a count value. Similarly a counter with n flip-flops can have 2 to the power of n states and the number of states in a counter is known as its mod number thus a 2-bit counter is a mod 4 counter. Here in the waveform diagram or from the timing diagram we can observe that Q1 changes state only during the negative edge of the applied clock initially the flip-flop is at state 0, flip-flop stays in the state until the applied clock goes from 1 to 0. As the jk value r1 the flip-flop should toggle so it changes state from 0 to 1. The process continues for all pulses of the clock. Coming to the second flip-flop here the waveform generated by flip-flop 1 is given as a clock pulse. As we can see the timing diagram when Q1 goes transition from 1 to 0 the state Q2 changes. Now in the applications of counters very first one is digital clock the digital circuit which is used to count the number of pulses are formally called the counter or we can say the timer counting means incrementing or decrementing the values of an operator with respect to its previous state value. Second frequency counter digital counter function as frequency divider since the divide the input control clock frequency by the modulus of the counter the binary counter ok the next one is binary counter the number of output bits of a particular binary counter is equal to the flip-flop stages of the counter a mod 2 n counter requires n stages or flip-flops in order to produce a count sequence of the desired length and the first stage of counter is the LSB that is least significant bit and the last stage of the counter is the most significant bit design of 3 bit of asynchronous counter so please pause the video and write your answer here in answer in the 3 bit triple counter 3 flip-flops are used in the circuit as n values is 3 the counter can count up to 2 to the power 3 is equals to 8 values and the circuit diagram is shown here and the 3 bit mod 8 or synchronous counter consist of 3 jk flip-flops or all propagation delay time is the sum of individual delays initially all flip-flops are reset to produce 0 and the output condition is q 3 q 2 q 1 equals to 0 0 0 when the first clock pulse is applied the ff 1 changes state on its negative edge therefore q 3 q 2 q 1 equals to 0 0 1 on the negative edge of the second clock pulse flip-flop ff 1 toggles its output changes from 1 to 0 this being negative change ff 2 changes state therefore q 2 q 3 q 2 q 1 equals to 0 1 0 similarly the output of the flip-flop 3 changes only when there is a negative transition as its input when 4th clock pulse is applied count sequence is the output of the flip-flop is a binary number equivalent to the number of clock pulses received here and the output conditions are as shown in the truth table also in the waveform diagram here the output waveform of q 1 is given as clock pulse to the flip-flop of first jk when q 1 goes from 1 to 0 transition the state of q 2 is changed also when q 2 goes from 1 to 0 transition the state of q 2 is changed and the output of q 3 is the MSB note that output value of q 1 are considered as LSB and q 3 are considered as MSB counter is a device which stores the number of times a particular event or process has occurred a ripple counter is an asynchronous counter where only the first flip-flop is clocked by an external clock all subsequent flip-flops are clocked by the output of the preceding flip-flop asynchronous counters are also called ripple counters because of the way the clock pulse ripples it way through the flip-flops these are my references which I used thank you.


As one of the original 13 colonies in America, Pennsylvania has a long history of firsts for our nation. For example, many folks know both the Declaration of Independence and the Articles of Confederation were signed in our Commonwealth, and the American Constitution of 1787 was drafted here. However, what many Pennsylvanians may not realize is that Thanksgiving was first proclaimed in my hometown of York, Pennsylvania. Today, as Americans and Pennsylvanians, we continue to hold our independence as one of our proudest values. And we continue to work towards peace in our homes, communities, schools, workplaces, state, and nation. It is as true today as it was in 1777 that we are grateful for these American principles. This year, Francis and I are thankful for the opportunity granted to us by Pennsylvania's voters to lead our state into a bright, successful future. My administration remains committed to upholding the principles for which our founding fathers fought and said thanks in our commonwealth almost 250 years ago. Today, I'd like to wish all the people of Pennsylvania a very happy and healthy Thanksgiving. Thank you.


And good morning, everybody. Maybe what I'm unsure about is whether or not my microphone volume level is all right. But so give us feedback somewhere on one of these chats. But let's start with an introduction. So my name is Kada Balbast. I'm super excited that I will teach data visualization here with Johan. We will talk about data visualization and map plot lip, two topics I really like to talk about. I work in Norway, in Tromsø. I do research of engineering. And I like to help researchers with improving their code. And I like to teach Python and different things. And with me is Johan. Yeah, good morning, everyone. My name is Johan Helsvik, I work at the PDC Center for High Performance Computing in Stockholm. But right now I'm sitting here from Uppsala. And I realize that I should take the screen and arrange it. Let me do that. Just a sec. He, you know, that's not right. Almost I will arrange. And while I open the sharing window, I will close my physical window. All right. Good. Good. We will talk about data visualization. And let me first tell you how the best way to participate So what to expect? It's good if you have your Jupyter lab open. And I understand you have that already if you were following the Pandas episode. But if you joined later, it's a good moment to open up your Jupyter lab. We will be using it. So we will be doing data visualization inside Jupyter. And we will also explain why that is such a good fit. And we also, you can open up this episode data visualization with Mapotlib, which is also linked in our collaborative notes. And Johan and me, we will look at these notes. So that's a good place to ask us questions. And in a different window on my screen, I have them open. And we will try to react to questions live. So please keep the questions coming. We plan to do a bit of introduction for the first 15 minutes. We will then do an exercise block. So you will get 20 minutes to try this on your own. We will then do a break. And after the break, we will do something. We will talk about how to make plots even prettier. And then we will have another exercise block. And now the question is, should we show you how this works? In the notebook, you can either try that with us. So if I add something to the notebook, you can do it as well. If that is cognitively a bit too much to listen and also go into a different window and type something, it's also totally fine to just watch what we do. And you will have enough time in the exercise box to try it yourself. Good. So let's first motivate why we do this and why we chose MatVotLip and why we do that in a Jupyter notebook.  And let me also zoom in here a bit, data visualization with MatVotLip. So our goal is not becoming experts and knowing everything that can be done with MatVotLip. But we want to give you a really good overview of how to find help, how do we use it, and what are good starting points, and how does it connect to the previous episodes, like pandas. So that's our goal. In total, we will spend one and a half hours on this topic. So not only being able to start, we also want to show you how can you tweak, how can you improve plots without remembering all the options and the commands, because we don't remember them either. The interface of MatVotLip is really big. And it's also so that... So, so Radavani is MatVotLip the only Python packet for visualization, or are there more? No, there are many. Here are the ones that we know, so they are probably even more. I have tested most of them and I use them in different contexts. So there is mapplotlib, this is the one that we will show you, there is also, and it is probably the most popular one, but there is Seaborn, which builds on top of mapplotlib, there is Vega out there, plotlib, ggplot, and then there are different libraries for more special use cases. I would consider the top four here or the top five really general. So there are many libraries and also Python is not the only language that can do plotting, so why do we do mapplotlib? So I would say one very good thing with mapplotlib is that, I mean as Python is a completely free ecosystem, it is transferable, so if you write the plotting scripts in mapplotlib, you can share them with your colleagues and also with the community at large. This can be a little bit in contrast with some commercial packages for plotting, where you might have a lock-in effect that you prepare your scripting and then you share it and others cannot, perhaps they can read the script file, but they cannot make use it because they do not have the program. And it may even be the same person or you in a different job in the future and so the fact that it is free, that both Python is free and open source and mapplotlib is free and open source is really important and it's really important for reusability and reproducibility. And I like to, so when I talk about data visualization, I like to start with this quote, which I took from this fantastic book by Klaus Wilke, Fundamentals of Data Visualization, which you can browse, it is, the book is online, and it is this quote here, that one thing I have learned over the years is that automation is our friend. I think figures should be auto-generated as part of a data analysis pipeline and they should come out of the pipeline ready to be sent to the printer with, and here we are paraphrased with minimal post-processing needed. And this is because when we generate plots for posters or publications or thesis, at least in my experience, I never do it only once. I do it at least twice. I do it once and then I do it again one day before the deadline because something changed. I want to modify the color or I get new data or I realize that there was a tiny mistake and I need to change the figure. And if this is not automated, it's hard. Might need different versions of your figures. So the precise size and shape of a figure that you have in a manuscript might be different from what you then need to go on a poster or on slides for presentation. Then if you have that inscript form, you can then tune to the appropriate format and then reuse your work. Yeah. Our new data comes in and you want to update the figure. And we will see that if we use tools like Matbotlib in combination with tools like Jupyter Notebook, this becomes a really nice combination. So we will now focus on Matbotlib, but you can browse of course all the other ones as well. At the end of the session, we might also have a look at one of the other libraries. Why did we start with Matbotlib? We motivated a little bit. It's also, so it is the most popular library. It is, if you come from Matlab, it will feel familiar because it takes a lot of inspiration from how plotting is in Matlab. Even if you choose to not use Matbotlib, maybe you prefer Seaborn. Many of these libraries build on top of Matbotlib. And then if you want to tweak it, improve it, it helps to have an understanding of Matbotlib to be able to improve your plots. But it is relatively low level. Low level in terms of we can really modify everything. So in terms of abstraction. It doesn't provide statistical functions. Some of the other libraries do. But the advantage of Matbotlib is that you can adjust everything. You can really make things publication ready. Everything can be configured and modified. Speaking of statistics, which is I'm not containing Matbotlib, you can naturally combine the Matbotlib scripting that you do with other Python packages, such as NumPy, SciPy, and then also with Pandas that we have been talking about earlier. And I'm having just a quick look on the questions. Thanks for coming, raising them. For instance, question nine, when should I use X and when should I use BLT? This is something that confused me a lot when learning and using Matbotlib. And we will comment on that. So we will clarify that. This is something I realized maybe 10 years into using it. That there were these different interfaces. So we will comment on it. Also, another really good question is, how is Matbotlib built on top, built using? So how does it connect to Pandas? And also, this is something we will discuss. Some of the plotting libraries interface with Pandas in a more nice way or less. But also with Matbotlib, it is possible to use Pandas data frames. We will come back to that. But I think we are ready to open up the notebook and start creating our first plot. At this moment, you can either do it as well if you have enough screen space and enough cognitive management. But you can also watch what we do. You will have the chance to test it out in the exercise block. I will open up a new notebook. So let's start with a new notebook. Let's not continue from one from before. I will open up a new one. And also, a good first reflex is to rename it. I don't want to have my notebooks untitled, untitled one, untitled two. I want to give it a good name. I will rename it. Right-click, rename notebook. Let's call it plotting. And back to the material. I will copy-paste the code here from this block. I will run it in a notebook, but let's also explain then what is happening. Let me copy. So I copy the whole block. Let's see whether this works at all. Yeah, you are the Python for Psycho. Yes. Or Anaconda environment. Yes, yes. So at this moment, all we import is a library called Matpotlib, which is part of the Python for Psycho. It is also part of an Anaconda-based environment. So if you get here an error that Matpotlib not found, then you are probably in a different environment. And I ran this code. I got a first plot, which shows some dots. I have an x-axis. I have a y-axis and a title. They are not very concrete yet. And now let's inspect the corresponding code. What did we do? We imported the functionality. I defined two lists of numbers, x-values, y-values. And these values, they are part of the so-called ans-com-squatted, which is a really important data set because it is used to motivate why we even do data visualization because it's four data sets, which really look very different when we plot them. But when you look at the statistical values, like the mean, the sample variance, the correlation, the regression, the statistical values are the same. So if I didn't plot these, if I would only look at the numbers at the table, I would maybe have less insight. But back to the example, what else can we discuss here? This is the important part, these two lines. We set up a figure and we set up axes. These are objects, which we then can use to, for instance, do a scatter plot. And here I sent the data x into, I said, these are the x-values, these are the y-values, and I define a color, which in a really weird format here, but I will later comment on why we do this. Instead I could also use a named color, I could say red. And if I run that, then the dots will be red. And this is self-descriptive here. Just having a look at the questions. I import it using just import nutlotlib instead of nutlotlib.pyplot. So why I do this here? Because there is more in nutlotlib than the byplot. Byplot is one of the interfaces that nutlotlib provides. I could also import nutlotlib. And then here I would have nutlotlib.byplot.plt. I could do that as well, and it would also work. It wouldn't be on any noticeable penalty, maybe a little bit more typing. I chose this way because this is often what people do, and this is often what you find when you look for examples on the internet, or if you ask one of these AI chat solutions. Just looking at the other. So question 15, is it better to use this way of doing it rather than the PLT? We will comment on that. So we will come to this. We recommend to do it this way. So we show you the more robust way, but we need to then also explain why this is possibly more robust. I think we will do that after the exercise. To go into the first exercise, so should we perhaps present it? Yes. So now you have all the tools ready to do the first exercise block. Your goal, I mean here it says 15 minutes, but we really want to give you 20 until five minutes past the hour, but I need to explain also clearly. Your goal will be to do what we did here with Johan. Open a notebook, copy the block, get it to run. But once you get the image that we got here, you are asked to extend it. You should add a second data set and then yet another data set, which is this one multiplied by two. And here we wanted to show you also that this is a way to multiply all numbers. This is one of the many ways in Python to multiply a list by a factor. Then you will get a plot that looks like this. And another thing that you can try to do is browse the documentation, find out, so you can have a look at the quick start guide and try to find out how can you get a legend into the plot that we can then link to the data values. And at the end, it should look like this. You can also experiment changing, modifying the colors. And if you get stuck, there is a solution here. So if I would open this up, whoop, but I will do it only very quickly because we don't want to have a spoiler, you find a solution for this exercise. And then we can come back five minutes after the hour. And after that, we will send you into a break. And if you are curious about why did we choose these particular colors, here is an explanation. Do we have everything we need for the exercise block? So your goal is this exercise number, Matpotlib 1. And I will add instructions into the document. And we will be back five minutes after the hour. All right, good luck and see you in a bit. Bye. And welcome back from the break. We will continue with Matpotlib. I wanted to also show you the result of the exercise session, so hopefully you get the result that looks like this. You also find this in the solution. So here we have plotted three sets of X and Y values with different colors. I want to save the notebook. I just wanted to remind a really good practice that I find very useful is that before I save a notebook and before I share a notebook with other people, I like to run all cells from top to bottom. It's not too long, I would recommend to do the restart kernel. Yeah. This is even better because this will reset and run everything from top to bottom because this is exactly what the next person will do. Because the next person opening the notebook will not have anything in memory. It will table run the notebook from top to bottom and I want to make sure that it still produces the results that I wanted. This will prevent me from having to run the notebook in a very particular order that nobody will remember and now I can save. Super. And before I hand over to Johan and before we talk about now how can we improve a plot, how can we customize it, I wanted to comment on the question that we got a couple of times, which is which of the two possible ways that one can use Matpotlib should we use and why. And I admit, sorry I need to zoom in here, I admit that although I was using Matpotlib for quite a while, it wasn't clear to me that there were actually two different interfaces and I got really confused because every time I was asking the internet for how do I do something in Matpotlib, I saw an answer but it always looked different than what I remembered. And I was doubting myself for a long while until I learned that there are actually two ways to run Matpotlib. One way is the so called object oriented way or also called the explicit interface. And in this explicit interface we create these objects and then we use them. This is the method that we use in this lesson. This is also the method that we recommend. There is another method which is the so called pipe plot way of doing things or the so called implicit interface, which looks shorter so there is less to type. I don't have to create the figure object, I don't have to create the axis object, I can do this directly. So it looks easier. The downside is that now that we start customizing, so if I now change the line width and the format and colors and settings, I will affect the settings for all my plots that come later in the notebook or in my Python code. And sometimes this is not what you want. So this will be more practical if you, once you start putting this into a function because then when you change settings, you change settings only for the function that you want and for the plots that you wanted instead of changing it implicitly for everything. So that's why we recommend this way. But we show you both ways because so that again, you know, if you then search Stack Overflow or you ask GPD for how do I do something with my plot lip, you might get, you might see this kind of an answer and then you know why this is different than what we have just learned. Here's also an explanation why we even spent now a couple of minutes emphasizing this. And with this, Johan will now take over and guide us through styling and customizing plots and we continue watching the collaborative nodes and please continue asking questions. We really appreciate it. We will now touch upon the topic of styling and customization plots and a starting notion here is that this is also an aspect of reproducibility because I used myself earlier in the days to often do plots with, often with us with Matlab and I would then get a certain feel and look of the figures and perhaps do something which needed to be tuned a little bit and I might then do it in a drawing program. It's fine if you do it once or twice, but if you need to do it for 10 figures, it has a lot of extra work. So Matlab and other libraries, they allow to customize almost every aspect of a plot and it's going to be good to know what are the different Matlab partial figures so that we know what we can search for when to customize things. So we open up this and this is here for a two-dimensional figure. So you can see here that in the obj oriented modality, we have all of these variables. So you have the axis set minor locator, you have a major tick label, you have the markers that you can change and you have a legend. And one thing that can be very convenient, which you can show later in the notebook, is that in order to see what are the different properties that are available, you can use the help command. Yes and it's also nice to know how is this even called, like if I want to web search for something, I really like this figure because then I know that I need to search for something called minor tick or legend. There's also, yeah, below here on the same web page, you also have an extensive listing on what are these properties in web format. So from this paragraph here, we also have a resource here, which is, this is to a Github repository with Matlab sheet reads. So this is something that you can explore later. There is also a number of predefined style sheets that you can activate with the use command. You can show some of these. So these are with different collections of colors and marker styles and line styles that you then can use from and these are designed so that they have a good collection of colors, which is something that is very important because it is not uncommon that a reader or a viewer of your figures might have a limited capability to, that you can be color blind, red, green color blind or other vision impairments. And then it's good to have a color palette that works also when you are working in a gray scale. So that you have lighter, you have that the color ranges from lighter up until darker. Yeah, like during my PhD, one recommendation was always to print your plot in black and white on a printer to see how it looks then if somebody later prints the paper on a black and white printer. Then later I thought, well, that doesn't make any sense anymore because I think nobody's reading papers anymore in paper. People read it on the computer, but now again, I know that it does make sense because it can help us identify any sort of color problems for color vision deficiencies. Yes, that's a very good point. So we will now have an hands-on example of exercises and styling and I will do it as a demo. You will also have time to do the UN exercise session. So what we'll do here is that we will import a data set from using pandas. So the exercise is this one, customization one, log scale in maplotlib and I will now switch over to do it in the notebook. So I start with copying this text snippet here. And please remind me because I was also distracting answering something. Should we now all do the same thing as you or should we watch? You can watch. So this is now imported a data set. This is about statistics of countries. It's the GapMinder data set and we will then use a plot command that I paste. Before we go into the plot, we can maybe reconnect to the previous lesson about pandas. So what people see here is that we load a CSV file from the internet, but then there is this read CSV command, which we have seen in the previous episode. And at the same time, there is dot query. So we filter out and we are only interested in the data for the year 2007. And here we have four different countries. We have the life expectancy. And we are interested in GDP, the gross domestic product. So roughly how wealthy the country is. And now take this snippet here, I paste it in here and execute it. And what we really get here is that what we choose to work with here is the life expectancy. We will visualize life expectancy as a function of the GBP per capita in terms of perching parity. What's that? That's perching parity. Yeah, so it's some inflation adjusted US dollars that are also adjusted to have a more fair comparison. So it's not US dollars, but for our purposes, we can think of inflation adjusted US dollars. Yes, the data is here distributed so that it's seen here. A lot of it is along the vertical line here. And then around this horizontal line here, horizontal region here in upper most of the plot. So the question is here, how can we make better use of the resuscitation to highlight the data here? So do you have any suggestions on the one? What could be? Yeah, so one step that we can do is we can try a logarithmic axis, which will then, because we have such a big difference in orders of magnitude on the x-axis, so by switching from a linear to a log axis, we will probably see the trend hopefully clearer. So we need to set the axis scale to a logarithmic one. And this we will do by setting this attribute, the set x scale, we set it to log. Take that line and I go through notebook and add it here, set x.setScale.xcale log. Yes, and now we have then the logarithmic scale on the x-axis here and what we have here is much more evenly distributed within the canvas. There's also here one attribute, the alpha attribute that we can play around with here. So the alpha attribute is at first here, it's a half. You can see what happens if we change it to 0.8. Yeah, then you can see that this affects the transparency of points. So by using this higher value, we have a more dense visualization of the dots. Great, and a question to both of us. So do you remember all these things, like how to set a log axis, how to set the transparency, like I admitted I don't. So I almost never remember this, I always have to look it up. Yes, that's a good point. And one can then, so we have here, if you see here, what are the objects that created, they've created the handle fig and they've created the handle x. And the logarithmic scale is something that is set by working with the x object. And let's see what attributes do we have here for this object. So we can type help and x. And then we can see that we have a very lengthy listing here. Here's first a general description of what the orbit is, what class it is. And then you can see that we have all of these listings. And among these listings here, we will have then the logarithmic scale, the opportunity to set the logarithmic scale. Good, we will now have, we will, not now, soon let you go into the exercise session, where you will play around with customization of figures. And perhaps introduce the exercises here. It's working with the same data sets. Your task is that you will make the tick marks and access labels once larger. And you need them, you can search on the web for what are these attributes that you need to work with. And the target that you're aiming for is to arrive to a figure which is looking like this. So this is one of the exercises that you can do. And the second exercise that you could, oh, we should hide the solution there. The third exercise that you can work with here is that you can adopt a gallery example. And here you have some links to some of the other resources that are, yeah, like Seaborn, which is based on Matloplib. And then there's also Vega Altair, which is a standalone partner package for visualization. Yeah, and here, oh, this, so people will choose exercise two or three. And the exercise three is really close to at least how our work in real life. So in real life, I don't remember all of these commands. I often look through the gallery examples for something that looks similar to what I have in mind. And here you will try to, first, I often take the example and I try to run it on my computer. And then once I get it to work, then I try to change the data. And then I try to put in my data. And then I tweak. So that can be a really fun exploration, which is at least close to how I work. I don't know about Johan, but I can't remember on almost anything. I always thought from something that already works that somebody else created. That's also how I do it. So looking at this gallery is a very good source of inspiration. And that's also where you then get exposed to what are the different functions and attributes that you can work with. So is there anything that we can bring up from the HackMD for now? I'm looking. So most questions are answered and they are relatively detailed. One bigger picture question was whether we will talk about interactive plots. So plots where you don't get just an image, but you get something that you can interact with, like with the slider. And I believe that we will not do it in this course, but I will link to a lesson where this is demonstrated. But I think we are almost ready for exercise session. It will be customization two or three until what time, like when we will be back? We will take 20 minutes for this exercise. All right. So we'll be back to 55 parts and then we will summarize. We will connect this a bit with Pandas data frames and then hand over to the next episode. So exercise customization two or three starting now. See you again in 20 minutes. Bye. All right, we are back. Five more minutes of matplotlib. So we want to wrap up the session and then we will go into a break and then we will go into something else. And we thought that in the five minutes we could try to do this exploration together on an example. And I also realized now during the exercise session that the solution that we have listed here doesn't match anymore perfectly the gallery of Seaborn because they change their examples. Anyway, let's try this together. I will now take Seaborn, which is something that builds on top of matplotlib. And I will take that for a specific reason that I will come back to before the hour is over. So this is often how I start. I open up one of these libraries. I go to the gallery and I look for an example that looks close to what I have in mind. And here I will try to be somehow close to the exercise. I want to do a violin plot, which is a way to show distribution of points and the statistical spread. And so this is something I want to have something like this. Here, this is what it looks and there is an example code. And the way I start often is I take what they have and I try to run it on my computer first. Let's try that. Seaborn is a library that should be in your environment. It is in the Python for Sarkom. It is also a part of Anaconda base environment. And now I'm crossing fingers and running the cell. And I get a plot that looks like what they have. So that's already a big success story. I don't fully understand what's going on here, but my next step often is to I want to get an insight into the data because I want to replace it with my own data. And here I have a feeling that solo example tips data set. This is some data set about some bird. No, it's awesome smoking and not smoking. But what I do because I don't know this data set, I would often, I would actually split this cell into two. And here I would print. I can do either this. Or if I'm in a notebook, I can do this directly tips. I want to see how it looks. Let's run all cells. And this turns out to be, we already recognize this. This is a pandas data frame with columns columns and rows. And I think this data set shows the different tipping behavior of smokers and non smokers. And now I also maybe understand that what this library is able to do is that we load the data set, and then we can map X values to a certain column. And we can map the Y values to a different column. And we can map the color to a yet different column. And now I would go in and instead of using this data set, I would try to put in my own pandas data frame. And I would try to plot that. And only then I would start tweaking and adjusting and customizing. And here I wanted to show you that Seaborn is a library that is able to really use pandas data frames directly and map columns to visual channels. X, Y, color. Can we do the same thing in my protlib? And back to our lesson. And I learned very recently that you can do almost the same thing in my protlib. So instead of what we were doing, that we were sending a slice, a column of data into defined as X or as Y, we can use this instead. I can say that data is a data frame. And then I can map X values to particular column, Y values to a different column. So that's very nice. If you then try to do a bit more, like if you try to map color to continent, then it becomes a little bit harder. And these are, so we wanted you to know that there are libraries in Python that make this easier. And this is also for those of you who come from R and ggplot2, you can do the same things in Python with libraries like Seaborn, Altair, Vega. But I see now that we are out of time. We will continue answering the questions. So please keep asking questions about plotting about map protlib and we will continue answering, but I don't want to eat into the future sessions. So thanks from my side. Johan, any concluding words here? Thank you. You covered it all. And I said we will continue to answer questions on the HackMD. Yeah, more details there. I will answer there. Thanks so much everybody for listening. Thanks to Johan for co-teaching and looking forward to the next sessions. And I think now we go into a break, into a 10-minute break, if I understand correctly. Yeah, break to 11 past the hour. Yep. Bye.


What's going on everybody and welcome in to the week eight edition of the fan duel DFS studs here on the hurry up Ryan Williams you can find me on Twitter at Ryan Alexander underscore w and I'm here to give you the top three plays that I'm targeting for my week eight main slate lineups on the fan duel slate let's get into the plays starting off at quarterback we got Josh Allen who's coming in at 8800 he tops the slate going against this Miami defense coming or Miami Dolphins excuse me against this Miami defense that has given up the fifth most fan duel points to opposing quarterbacks in his career Josh Allen has had success against this team we absolutely love that multiple scores against this defense and I know that they you know they got back Xavier and Howard and Byron Jones last week which you know kind of held Kevin Ridley there for a little bit but what we love about Josh Allen is he passes the ball around so the best thing about him is you can see the points coming through him but maybe he runs into touchdown where he finds Sanders and digs and whoever the case may be the other options there Cole Beasley and others to score in the end zone and we don't have a game where you necessarily need to stack him we can play him naked not having anybody back with him I do think he has some intriguing appeal here I can see him having a Tom Brady like game last week where you know maybe he doesn't get there on yards but the touchdowns are just so so valuable here on fan duel that I think that we can trust Josh Allen this week and I'm paying that 8800 price tag for the floor there let's talk about running back position and we're going to talk about Joe Mixon here 7600 going against the New York Jets and what you love about the New York Jets is they're allowing the most fan duel points per game to opposing backs he's got a great game script while albeit not at home but he is a road favorite almost at double digit points there and the Jets are going to be rolling out either a backup quarterback in Matt White or a backup once a stud quarterback I guess you could argue maybe not for fantasy purposes in Joe Flacco that they just traded for and this is just going to be a matchup that I think Cincinnati will be able to take advantage of their defense has absolutely been stout they've been able to get to the quarterback at a high rate top five and sack rate I believe this year and even with him being a road back you know something that we wouldn't normally target I think that we can get back to seeing Joe Mixon you know kind of take advantage of this game I think the Bengals you know definitely want to make sure that they get a win here can stay at top of that AFC North and and really bring it home and so you love that I know that last week it kind of didn't come to fruition this guy saw zero targets out of the backfield coming off of a game where he had six catches but you do love that the Jets are allowing the second most receptions and yards to opposing backs so maybe he can get right in that spot and then just the touch totals I feel like on the ground will come to fruition with Joe Mixon so he's a guy that I'm looking at targeting this week as well finally we got to wrap it up with the boy Cooper Cup for the Los Angeles Rams here coming in at 9200 the second highest price player on the slate who's playing I know that Christian McCaffrey carries that 10k price tag but we know he's not going to be playing at least at this point without a miracle and so you're looking at Cooper Cup here who this guy's just doing everything right now he leads the league in red zone targets targets inside the 10 targets inside the five which you absolutely love when you're talking about his ability to get into the end zone nine touchdowns on the year he's leading the league in that category also leading the league in yards and receptions I mean what can this guy not do and by playing Cooper Cup it's going to be tough I feel like people see that 9200 price tag they're going to feel like they can't get exposure to him there it's just too high of a price but this guy has been doing it every single week I mean you can say that you want to fade him but why would you be fading him on this team that hasn't implied team total over 30 one of only two teams that are going to be on the main slate with that implied team total over 30 and the volume is just so heavy against this Houston team that has just been absolutely trashed by opponents it's it's been pretty ridiculous you're looking at also Tyra Taylor being healthy enough to play in this game maybe that keeps this game close and that just even more so vaults Cooper Cup Cooper Cup into that 25 point range 30 point range on FanDuel you love the volume he's seeing listen Robert Woods and Van Jefferson have combined for 83 targets on the year Cooper Cup has 81 he's the alpha in this offense Matt Stafford loves him I got to get exposure to him I hope you guys do as well this week that's going to do it there for the FanDuel Hurry Up Week 8 edition of the FanDuel Hurry Up's DFS Studs I hope you guys are subscribed to the YouTube channel make sure you give us a thumbs up we appreciate all the interaction that we're getting on the YouTube channel we're appreciating all the interaction that we're getting on Twitter you can find me on Twitter at Ryan Alexander underscore W and I'll catch you guys next week for the week 9 DFS Studs until then get that money peace


Hello and welcome to the session. Let us discuss the following problem. Find the zeroes of the following quadratic polynomial and verify the relationship between the zero and the coefficient fx is equal to x square minus 2x minus 8. Let us write the key idea. A real number alpha is the zero of a polynomial f of x if f of alpha is equal to 0 and if alpha and beta are the coefficients of a quadratic polynomial f of x is equal to A x square plus B x plus c alpha plus beta is equal to minus B by A that is minus coefficient of x divided by coefficient of x square and alpha beta is equal to c by A which is equal to constant term divided by coefficient of x square. Now let us write the solution for the problem. Now the solution. We have f of x is equal to x square minus 2x minus 8. Now we will split the middle term x square minus 4x plus 2x minus 8. Now grouping and taking common factors from these two terms we take x common so we are left with x minus 4 and from these two terms we take plus 2 common and we are left with x minus 4. Now we get x plus 2 into x minus 4 as our factors which is equal to f of x therefore zeros of f of x are given by equating f of x is equal to 0 which implies x plus 2 into x minus 4 is equal to 0 which implies x plus 2 is equal to 0 or x minus 4 is equal to 0 which implies x is equal to minus 2 or x is equal to 4 thus the zeros of f of x is equal to f minus 2x minus 8 are alpha is equal to minus 2 and beta is equal to 4. Now second part of the question to verify the relationship between the zeros and the coefficient here sum of the zeros plus beta is equal to minus 2 plus 4 which is equal to 2 that is minus coefficient of x divided by coefficient of x square which is equal to minus 2 by 1. Similarly product of zeros is equal to alpha beta which is equal to minus 2 into 4 which is equal to minus 8 by 1 which is equal to constant term by coefficient of x that is minus 8 by 1 hence verified. I hope you enjoyed this session. Bye and have a nice day.


Hey everyone, it's Gabrielle. I'm over here at Oxmer Toyota today for our weekly use car specials. This week we have three pre-owned Honda's to share with you all. Let's go over some information on the first one. Now this is a 2019 Honda CR-V LX that exterior color is in a Lunar Silver Metallic. Now the vehicle has a Bluetooth. There's a backup camera. Vehicle has a clean car fax with one previous owner. That interior is in an Ivory. That mileage is 23,912. The price is 24,000 and this one is one of the best values within 400 miles. For our second Honda, this is a 2018 Honda Civic EXL. That exterior color is called Polished Metal Metallic. This vehicle does have a sunroof. There is leather. It has the eight-way power driver's seat with the lumbar support. So there's Bluetooth backup camera. Now this vehicle has a clean car fax with one previous owner. That interior color is in a gray. The mileage is 16,891. The price is 1,000 and this is priced right at 96% of the market rate. Now for our third this is a 2017 Honda CR-V LX. This is all-wheel drive. That exterior color is in a gray. So the vehicle has a Bluetooth. There's a backup camera. This vehicle also has a clean car fax with one previous owner. That interior is in a gray. The mileage is 35,151. The price is 19,500. This vehicle is priced 94% of the market rate. If you guys have any questions, feel free to let us know. All of our contact information is listed right here. Thanks everyone. Bye.


Clark, you're set to make another spring start tomorrow. What will you be focusing on the most when you take them out? Yeah, I think for me it's really just continuing to get to work in, being able to go out there and fill out the workload is a big deal right now. And doing that, being healthy is the big deal. So continue to feel good and go out there, fill out the zone, throw some strikes and then continue to my job. There's a lot of talk surrounding this new pitch that you've adopted, your cutter, you threw it a lot in your previous start. Is that going to be the same plan as you try to fine tune that pitch? Yeah, I think just continuing to gain that comfortability with it and throwing it in any count I think is the big deal. So I would probably say that's why the usage is up a little bit right now. But I've been very encouraged with the shape of it and also the execution of it. So I think it could be a high percentage pitch for me like tomorrow and throughout the rest of the season and as we go on. So I'm very encouraged by it with the shape of it and the execution of it. So for me, it's just continuing to harp on that and get more comfortable with it. The narrative is that that pitch is designed to help you get some left handers out more. But what can it possibly do against right handers? Yeah, I think so for me, a lot of where I get my success is sinking guys, especially right handers. I'll sink them inside, sink them away. So for me, when you're showing that cutter, the difference in shape I think is big for the right-handed hitters. When they're expecting them all to sink and it stays straight or cuts a few inches, you're talking about a big difference on the barrel. So if you can get off the barrel and can induce weak contact, but by, you know, not falling into the same patterns, I think that's going to be the big pitch for me. It's going to be your second time back out dealing with the pitch clock. What were your first impressions and do you have to make any type of adjustment? For me, I think I like to naturally work quicker. So it's not going to be a major adjustment for me. I do think there are times when, you know, I think it can be beneficial in both ways. When you're rolling and things are going really well, you can really put pressure on the hitter and you can really keep things rolling. But I think if you've got to start scrambling out there and you're having long innings, long ABs, there's times when you can get tired. So it's being able to continue to control your pace, control your breathing, and being able to be on the aggression vice, you know, the hitter be on you. No secret, you're in competition for a spot in the starting rotation, but what's the key to not think about competing for a roster spot, so to speak? Right. Yeah. I think the big thing for me is just being able to control what I can control.  You know, for me, it's I got to focus on what I got going on my routine and I'm going to go out there and just continue to compete every single time out there. And, you know, if it's if I go out there and I win the starting job, if I don't win the starting job and I'm back in a similar role as I was last year or whatever the case may be, I think just being able to go out there and control what I can control, what advice, you know, for my routine and, you know, the way I feel and stuff like that. So for me, it's just being able to be out there on the aggression and control my game. Clark, thanks for the time. Best of luck tomorrow. Thank you. Thank you so much.