The electoral college is states vs. federal, not urban vs. rural

The electoral college (EC) is the system used in the US to determine how individual's votes for president get turned into the numbers that actually determine who becomes president. Each state and D. C. is allocated a number of electors based partially on the population of the state from the last census. The number of electors is equivalent to the number of senators + the number of representatives for each state (D. C. gets 3), see here for details about how the allocations are calculated.

I've heard people say that one of the things the EC does is prevent voters in the cities from dominating rural voters. This has always seemed a bit odd to me since, on its face, the allocation is just based on state populations and not demographics. So, I decided to look at the relationship between rural, urban, and total population and how they related to the number of electoral college votes. The code and data for reproducing the plots are here. This is all based on 2010 Census data.

OK, first let's just look at how many EC votes each state gets. There are a total of 538 electors. The plot below shows the distribution of votes for each state along with a line showing the number each state would be allocated if it was done exactly proportional to population. I've labeled a few states of interest.

Plots of the number of electoral college votes per state. Plot on the right in an inset of the bottom left corner of the plot on the left. Blue line is the number of votes states would have if the number of votes was proportional. The red vertical lines are the differences between the proportional number and actual number. (click to get larger version)

As you can see, states with smaller populations tend to have larger than proportional representation and larger states have fewer votes.

We can look at the number of electoral votes that different people get, i.e. how much is your vote worth in a presidential election. I'm leaving out a lot of important details, like racist voter suppression, the number of actual people able to vote in each state versus total population, and changes in population/demographics since 2010. Given the 538 electors and the 2010 population of 308,745,538, the average person gets. 1.7e-6 or 1.7 millionths of a vote. But, this will vary state-to-state based on the number of electors allocated to each state.

EC votes per person for different states and D.C.. Plotted against total state population, rural population, and urban population (rural and urban add up to total). (click to get larger version)

As you can see, the number of EC votes per person varies from about 1.5 millionths (California) to 5.3 millionths (Wyoming). about a factor of 3.5. State with populations above about 10 million all have similar EC votes per person, but small states can have much larger votes per person.

The solid blue line is the national average EC votes per person (1.74 millionths), the solid green line is the national average EC votes for someone living in a urban area (1.72 millionths, barely below the blue line), and the solid orange line is the national average EC votes for someone living in a rural area (1.85 millionths). So, on average, a person living in a rural area has about 8 percent more voting power compared to someone living in an urban area.

But!, the 601,723 people living in urban D. C. have 338 percent more voting power than the 1,880,350 people living in a rural area of California.

Finally, let's look at how the total state population correlates to the fraction of people living in rural areas.

Fraction of population which lives in rural areas versus total population. There is a trend that states with larger populations tend to have a smaller fraction of people living in urban areas. For states with a total population less than than 10 million, there is much more variance in the fraction of people living in urban areas.

This shows that there is indeed a negative correlation, i.e. smaller states tend to have more people living in rural areas (this leads to the 8 percent difference above).

The thing that I take away from all of this is that the electoral college is actually weighting your vote as a member of the US lower than your vote as member of your state. Because of the current state demographics, it also weights rural votes slightly higher than urban votes, but this is a very small effect compared to the small state versus large state effect (8 vs. 350 percent). So, if you currently live in a big city in California, New York, or Texas and want your vote for president to have more impact, you'll get more value for your vote if you move to an urban area in Wyoming, D. C., or Vermont rather than a rural area of your state,  although you can still have an impact on House and state reps within your state.

I should also note that all of this analysis misses a larger problem of the electoral college: most states have a winner-take-all system where the candidate with the popular majority takes 100 percent of the electoral votes. This means that a candidate who wins 51 percent of the votes in a state gets 100 percent of the EC votes. This system is also used for state reps. and when coupled with gerrymandering, can lead to skews in the state representation compared to state voting demographics.

Edit: Thanks Dylan for catching some spelling errors!

The Bay Area has weird weather: part 3

I started this series of posts to understand why the weather in the Bay Area seems different than weather in the places I've previously lived. In this post, I'll show one analysis that I think answers this question. As a reminder, in part 1, I showed some basic visualization of the raw data and an annual summary. In part 2, I went over two analyses that showed that the Bay Area has different weather as compared to Detroit and Ithaca, but neither really got at the heart of why my experience was different.

In this post I'll present two more analyses. The first shows another interesting difference between the Bay Area and Detroit and Ithaca. The second post really gets at the question that I've been trying to answer and introduces the jacket crossing probability (something I made up).

Based on the power-spectrum analysis in part 2, I decided to look in more detail at the daily fluctuations (right side of the plot). For any given day of the year in a city, say May 17th, there is an average temperature, maybe 70 degrees. In addition to the average, there are also the year-to-year fluctuations. These fluctuations can be averaged over all days in a year and plotted.

Again, the Bay Area looks very different than Detroit and Ithaca. The distributions of daily high and low fluctuations for Detroit and Ithaca both look very symmetric and fairly Gaussian. The fluctuations have a standard deviation of about 15 degrees and look almost identical for the daily highs and lows. In contrast, the Bay Area distributions are much narrower, with standard deviations less than 10 degrees for all highs and lows. The daily highs tend to have their modes skewed towards lower temperature with longer tails into the highs. The daily low temperatures tend to be more symmetric and have smaller standard deviations. This means that each day's daily high or low is more predictable in the Bay Area.

Now, to really get at the question of why the Bay Area's weather is weird I came up with a metric I'm calling the jacket crossing probability: for a given day, what are the odds that the daily high is above and temperature where I'd want a jacket and the daily low is below that temperature. We can plot this probability for all days.

I personally need a jacket when it gets below 60 degrees. If I set this as the threshold, I get the above jacket crossing probabilities. So, Detroit and Ithaca only have two relatively short periods where, with greater than 50% odds, you'll both want and not want a jacket. They align with late spring and late fall. Similar periods for Oakland and San Francisco extend from spring through summer and into fall. In San Jose, this period extends for almost the entire year outside. So, in the Bay Area, the annoying time when you might both want and not want a jacket extends for the better part of the year. In Detroit and Ithaca, summers are hot and winters are cold and you can prepare for the entire day easily. I think these plots really get at the differences in weather I've experienced in the Bay Area.

I'll follow up with maybe one more post with some additional analyses that others have suggested or done themselves (yay for collaboration!).

The Bay Area has weird weather: part 2

[Edit: more explanation for second plot.]

In part 1, I showed some raw temperature data for a few different cities I've lived in. I also had a plot of the daily average temperature over a year for the cities. Code for making the plots are here.

The goal of this project is to try and understand why my perception of the weather in the Bay Area is so different from other places I've lived. This post will start to look into the question of daily temperature fluctuations versus annual temperature fluctuation.

The first way I thought of to visualize this question was to look at a plot of 1:the difference between the daily highs and lows versus 2:the difference between the highest temperature in a year. I can measure the mean value and standard deviations of both of these quantities.

For each city, I've plotted the mean of the differences described above and the shaded ellipses show the standard deviation of the quantities.

One thing becomes very clear from this plot: there is something very different about Detroit and Ithaca compared to the Bay Area cities. I was surprised that the daily fluctuations for Oakland and SF were smaller than the ones in Detroit and Ithaca, but it is clear that they are still relatively large compared to the annual fluctuations.

This plot made me think that it might be interesting to not only try and compare the daily and annual fluctuations, but the fluctuations for timescales in between as well. The power spectrum of the temperatures can be used to measure these fluctuations across different time scales.

Annual temperature power spectrum for different cities.

The y-axis of this plot is proportional to the amplitude of the `temperature fluctuations at a given time scale. The x-axis are the different timescales (log-scale) from annual fluctuations on the left (1 per year), to day-to-day fluctuations on the right (365/2 per year). I've also marked the monthly and weekly fluctuations with the vertical lines.

I noticed a few things from these plots. For Ithaca and Detroit, the short-timescale fluctuations seem to be similar for the daily highs and daily lows and there is only much of a difference in the annual timescales (and maybe a little bit sub-weekly, I haven't done any careful stats). In contrast, in the Bay Area there are noticeable differences between daily highs and lows across timescales which are pretty prevalent at about the week timescale. Detroit and Ithaca also have a large kink at 2 cycles/year which means that temperature fluctuations at annual timescales are much larger than any of the shorter timescales. For the Bay Area, it's a much more smooth transition.

This still does quite answer the question I'm interested in, and there is one more analysis I'll describe which, I think, gets at why the Bay Area weather is weird.

The Bay Area has weird weather

[Update: removed San Diego]

The weather in the San Francisco Bay Area is weird. At least it is weird compared to most of the other places I've lived in the US. In the suburbs of Detroit where I grew up and in upstate New York where I went to college, you could be comfortable in the same clothes basically all day or night. If you've ever been to the Bay Area, you know that this is not true. It can be in the 50s in the morning and evening and then 80 during the day.

So, I've always thought that the Bay Area must have larger daily temperature swings relative to the seasonal swings compared to other places I've lived. I wanted to find some historical data to look at this phenomenon and finally found it at the National Oceanic and Atmospheric Administation (NOAA) website, which has a nice search function for different databases.

I finally got around to downloading some data for cities that I've lived in or near. I'll write a few posts looking at the data and also exploring different ways of visualizing the data.

You can find the analysis and plotting code I'm writing on my github here. It's a work in progress, so there'll be more updates and cleanup.

This first post is basically just trying to take a broad look at the data. So, first I just want to plot all of the data for each city. Click on the plot for a larger version. The first plot as the daily high (red) and daily low (blue) along with a local median filtered version (darker squiggly line) and the average over all time (darker straight horizontal line) for the high and low temps. The y-axes are all the same, but notice that the x-axes have different numbers of years.

From this plot I noticed a few things. Different cities have very different annual temperature swings. But, some cities have much larger separation between the daily minimum and maximum. In fact, for San Jose, it looks like the daily swings are almost as large and the annual swings!

We can also look at the data where we take the average for a year. These plots show the daily average maximum and minimum temperatures (top and bottom of red shaded area) and the halfway point (black line). Again, we can see that some cities have large annual swings (Detroit and Ithaca) and the Bay Area has a relatively small annual swing. In the next post, I'll do a more careful comparison of the daily and seasonal swings!

Part 2 is here.

How does learning happen in machine learning?

Machine learning is a broad set of techniques which can be used to identify patterns in data and use these patterns to help with some task like early seizure-detection/prediction, automated image captioning, automatic translation, etc. Most machine learning techniques have a set of parameters which need to be tuned. Many of these techniques rely on a very simple idea from calculus in order to tune these parameters.

Machine learning is a mapping problem

Machine learning algorithms generally have a set of parameters, which we'll call \(\theta\), that need to be tuned in order for the algorithm to perform well at a particular task. An important question in machine learning is how to choose the parameters, \(\theta\), so that my algorithm performs the task well. Let's look at a broad class of machine learning algorithms which take in some data, \(X\), and use that data to make a prediction, \(\hat y\). The algorithm can be represented by a function which makes these predictions,

\( \hat y = f(X;\theta)\).

This notation means that we have some function or mapping \(f(.; \theta)\) which has parameters \(\theta\). Given some piece of data \(X\), the algorithm will made some prediction \(\hat y\). If we change the parameters, \(\theta\), then the function will produce a different prediction.

If we choose random values for \(\theta\), there is no reason to believe that our mapping, \(f(.; \theta)\), will do anything useful. But, in machine learning, we always have some training data which we can use to tune the parameters, \(\theta\). This training data will have a bunch of input data which we can label as: \(X_i,\ i \in 1,\ 2,\ 3,\ldots\), and a bunch of paired labels: \(y_i,\ i \in 1,\ 2,\ 3,\ldots\), where \(y_i\) is the correct prediction for \(X_i\). Often, this training data has either been created by a simulation or labeled by hand (which is generally very time/money consuming).

Learning is parameter tuning

Now that we have ground-truth labels, \(y_i\), for our training data, \(X_i\), we can then evaluate how bad our mapping, \(f(.; \theta)\), is. There are many possible ways to measure how bad \(f(.; \theta)\) is, but a simple one is to compare the prediction of the mapping \(\hat y_i\) to the ground-truth label \(y_i\),

\( y_i-\hat y_i \).

Generally, mistakes which cause this error to be positive or negative are equally bad so a good measure of the error would be:

\(( y_i-\hat y_i)^2 =( y_i-f(X_i;\theta))^2\).

When this quantity is equal to zero for every piece of data we are doing a perfect mapping, and the larger this quantity is the worse our function is at prediction. Let's call this quantity summed over all of the training data the error

\(E(\theta)=\sum_i( y_i-f(X_i;\theta))^2\).

So, how do we make this quantity small? One simple idea from calculus is called gradient descent. If we can calculate the derivative or gradient of the error with respect to \(\theta\) then we know that if we go in the opposite direction (downhill), then our error should be smaller. In order to calculate this derivative, our mapping, \(f(.,\theta)\), needs to be differentiable with respect to \(\theta\).

So, if we have a differentiable \(f(.,\theta)\) we can compute the gradient of the cost function with respect to \(\theta\)

\(\frac{\partial E(\theta)}{\partial \theta}=\frac{\partial}{\partial \theta}\sum_i( y_i-f(X_i;\theta))^2=-2\sum_i( y_i-f(X_i;\theta))\frac{\partial f(X_i;\theta)}{\partial \theta}\).

If we have this derivative, we can then adjust our parameters, \(\theta^t\) such that our error is a bit smaller

\(\theta^{t+1} = \theta^t - \epsilon\frac{\partial f(X_i;\theta)}{\partial \theta}\)

where \(\epsilon\) is a small scalar. Now, if we repeat this process over and over, the value of the error, \(E(\theta)\) should get smaller and smaller as we keep updating \(\theta\). Eventually, we should get to a (local) minimum at which point our gradients will become zero and we can stop updating the parameters. This process is shown (in a somewhat cartoon way) in this figure.

If the error function is shaped somewhat like a bowl as a function of some parameter theta, we can calculate the derivative of the bowl and walk downhill to the bottom.
If the error function is shaped somewhat like a bowl as a function of some parameter theta, we can calculate the derivative of the bowl and walk downhill to the bottom.

Extensions and exceptions

This post presented a slightly simplified picture of learning in machine learning. I'll briefly mention a few of the simplifications.

The terms error function, objective function, and cost function are all used basically interchangeably in machine learning. In probabilistic models you may also see likelihoods or log-likelihoods which are similar to a cost function except they are setup to be maximized rather than minimized. Since people (physicists?) like to minimize things, negative log-likelihoods are also used.

The squared-error function was a somewhat arbitrary choice of error function. It turns out that depending on what sort of problem you are working on, e.g. classification or regression, you may want a different type of cost function. Many of the commonly used error function can be derived from the idea of maximum likelihood learning in statistics.

There are many extensions to simple gradient descent which are more commonly used such as stochastic gradient descent (sgd), sgd with momentum and other fancier things like Adam, second-order methods, and many more methods.

Not all learning techniques for all models are (or were initially) done through gradient descent. The first learning rule for Hopfield networks was not based on gradient descent although the proof of the convergence of inference was based on (not-gradient) descent. Infact, it has been replaced with a more modern version based on gradient descent of an objective function.

Vectors and Fourier Series: Part 3

In Part 2, we adapted three tools developed for vectors to functions: a Basis in which to represent our function, Projection Operators to find the components of our function, and a Function Rebuilder which allows us to recreate our vector in the new basis. This is the third (and final!) post in a series of three:

  • Part 1: Developing tools from vectors
  • Part 2: Using these tools for Fourier series
  • Part 3: A few examples using these tools

We can apply these tools to two problems that are common in Fourier Series analysis. First we'll look at the square wave and then the sawtooth wave. Since we've chosen a sine and cosine basis (a frequency basis), there are a few questions we can ask ourselves before we begin:

  1. Will these two functions contain a finite or infinite number of components?
  2. Will the amplitude of the components grow or shrink as a function of their frequency?

Let's try and get an intuitive answer to these questions first.

For 1., another way of asking this question is "could you come up with a way to combine a few sines and cosines to create the function?" The smoking guns here are the corners. Sines and cosines do not have sharp corners and so making a square wave or sawtooth wave with a finite number of them should be impossible.

For 2., one way of thinking about this is that the function we are decomposing are mostly smooth with a few corners. To get them to be smooth, we can't have more and more high frequency components, so the amplitude of the components should shrink.

Let's see if these intuitive answers are borne out.

Square Wave

We'll center the square wave vertically at zero and let it range from \([-L, L]\). In this case, the square wave function is

\(f(x)=\begin{cases}1&-L\leq x\leq 0 \\-1&0\leq x\lt L\end{cases}.\)


If we imagine this function being repeated periodically outside the range \([-L, L]\), it would be an odd (antisymmetric) function. Since sine functions are odd and cosine functions are even, an arbitrary odd function should only be built out of sums of other odd functions. So, we get to take one shortcut and only look at the projections onto the sine function (the cosine projections will be zero). You should work this out if this explanation wasn't clear.

Since the square wave is defined piecewise, our projection integral will also be piecewise:

\(a_n = \text{Proj}_{s_n}(f(x)) \\= \int_{-L}^{0}dx\tfrac{1}{\sqrt{L}} \sin(\tfrac{n\pi x}{L}) (1)+\int_{0}^Ldx\tfrac{1}{\sqrt{L}} \sin(\tfrac{n\pi x}{L})(-1).\)

Both of these integrals can be done exactly.

\(a_n = \tfrac{1}{\sqrt{L}} \frac{-L}{n \pi}\cos(\tfrac{n\pi x}{L})|_{-L}^0 +\tfrac{1}{\sqrt{L}} \frac{L}{n \pi}\cos(\tfrac{n\pi x}{L})|_0^L \\= -\frac{\sqrt{L}}{n \pi}\cos(0)+\frac{\sqrt{L}}{n \pi}\cos(-n\pi)+\frac{\sqrt{L}}{n \pi}\cos(n\pi)-\frac{\sqrt{L}}{n \pi}\cos(0)\\= \frac{2\sqrt{L}}{n \pi}\cos(n\pi)-\frac{2\sqrt{L}}{n \pi}\cos(0).\)

\(\cos(n\pi)\) will be \((-1)^{n}\) and \(\cos(0)\) is \(1\). And so we have

\(a_n=\frac{4\sqrt{L}}{n \pi}\begin{cases}1&n~\text{odd}\\ 0 &n~\text{even}\end{cases}.\)

\(f(x)=\sum_{1,3, 5,\ldots}\frac{4}{n \pi}\sin(\tfrac{n\pi x}{L})\)

So we can see the answer to our questions. The square wave has an infinite number of components and those components shrink as \(1/n\).

Sawtooth Wave

We'll have the sawtooth wave range from -1 to 1 vertically and span \([-L, L]\). In this case, the sawtooth wave function is



This function is also odd. The sawtooth wave coefficients will only have one contributing integral:

\(a_n = \text{Proj}_{s_n}(f(x)) = \int_{-L}^Ldx\sqrt{\tfrac{1}{L}} \sin(\tfrac{n\pi x}{L})\frac{x}{L}.\)

This integral can be done exactly with integration by parts.

\(a_n = \sqrt{\tfrac{1}{L}}(-\frac{x}{n \pi}\cos(\tfrac{n\pi x}{L})+\frac{L}{n^2 \pi^2}\sin(\tfrac{n\pi x}{L}))|_{-L}^L\\=(-\frac{\sqrt{L}}{n \pi}\cos(n\pi)+\frac{\sqrt{L}}{n^2 \pi^2}\sin(n\pi))-(\frac{\sqrt{L}}{n \pi}\cos(n\pi)-\frac{\sqrt{L}}{n^2 \pi^2}\sin(n\pi))\\=\frac{2\sqrt{L}}{n^2 \pi^2}\sin(n\pi)-\frac{2\sqrt{L}}{n \pi}\cos(n\pi)\).

\(\cos(n\pi)\) will be \((-1)^n\) and \(\sin(n\pi)\) will be 0. And so we have

\(a_n=(-1)^{n+1}\frac{2\sqrt{L}}{n \pi}.\)

\(g(x)=\sum_n(-1)^{n+1}\frac{2}{n \pi}\sin(\tfrac{n\pi x}{L})\)

Again we see that an infinite number of frequencies are represented in the signal but that their amplitude falls off at higher frequency.

Vectors and Fourier Series: Part 2

In Part 1, we developed three tools: a Basis in which to represent our vectors, Projection Operators to find the components of our vector, and a Vector Rebuilder which allows us to recreate our vector in the new basis. This is the second post is a series of three:

  • Part 1: Developing tools from vectors
  • Part 2: Using these tools for Fourier series
  • Part 3: A few examples using these tools

We now want to develop these tools and apply the intuition to Fourier Series. The goal will be to represent a function (vectors) as the sum of sines and cosines (our basis). To do this we will need to define a basis, create projection operators, and create a functions rebuilder.

We will restrict ourselves to functions on the interval: \([-L,L]\). A more general technique is the Fourier Transform, which can be applied to functions on more general intervals. Many of the ideas we develop for Fourier Series can be applied to Fourier Transforms.

Note: I originally wrote this post with the interval \([0,L]\). It's more standard (and a bit easier) to use \([-L,L]\), so I've since changed things to this convention. Video has not been updated, sorry :/

Choosing Basis Functions

Our first task will be to choose a set of basis function. We have some freedom to choose a basis as long as each basis function is normalized and is orthogonal to every other basis function (an orthonormal basis!). To check this, we need to define something equivalent to the dot product for vectors. A dot product tells us how much two vectors overlap. A similar operation for functions is integration.

Let's look at the integral of two functions multiplied together over the interval: \([-L, L]\). This will be our guess for the definition of a dot product for functions, but it is just a guess.


If we imagine discretizing the integral, the integral becomes a sum of values from \(f(x)\) multiplied by values of \(g(x)\), which smells a lot like a dot product. In the companion video, I'll look more at this intuition.

Now, we get to make another guess. We could choose many different basis functions in principle. Our motivation will be our knowledge that we already think about many things in terms of a frequency basis, e.g. sound, light, planetary motion. Based on this motivation, we'll let our basis functions be:

\(s_n(x) = A_n \sin(\tfrac{n\pi x}{L})\)


\(c_n(x) = B_n \cos(\tfrac{n\pi x}{L})\).

We need to normalize these basis functions and check that they are orthogonal. Both of these can be done through some quick integrals using Wolfram Alpha. We get

\(s_n(x) = \tfrac{1}{\sqrt{L}} \sin(\tfrac{n\pi x}{L})\)


\(c_n(x) = \tfrac{1}{\sqrt{L}} \cos(\tfrac{n\pi x}{L})\).

This is a different convention from what is commonly used in Fourier Series (see the Wolfram MathWorld page for more details), but it will be equivalent. You might call what I'm doing the "normalized basis" convention and the typical one is more of a physics convention (put the \(\pi\)s in the Fourier space).

Projection Operators

Great! Now we need to find Projection Operators to help us write functions in terms of our basis. Taking a cue from the projection operators for normal vectors, we should take the "dot product" of our function with the basis vectors.

\(\text{Proj}_{s_n}(f(x)) = \int_{-L}^L dx~\tfrac{1}{\sqrt{L}} \sin(\tfrac{n\pi x}{L})f(x)=a_n\)


\(\text{Proj}_{c_n}(f(x)) = \int_{-L}^L dx~\tfrac{1}{\sqrt{L}} \cos(\tfrac{n\pi x}{L})f(x) = b_n\).

Vector Rebuilder

Now we can rebuild our function from the coefficients from the dot product multiplied by our basis functions, just like regular vectors.

\(f(x)=\sum\limits_{n=0}^\infty\text{Proj}_{s_n}(f(x))+\text{Proj}_{c_n}(f(x))\\ = \sum\limits_{n=0}^\infty \int_{-L}^L dx~\tfrac{1}{\sqrt{L}} \sin(\tfrac{n\pi x}{L})f(x)+\int_{-L}^L dx~\tfrac{1}{\sqrt{L}} \cos(\tfrac{n\pi x}{L})f(x)\\ = \sum\limits_{n=0}^\infty a_n \tfrac{1}{\sqrt{L}} \sin(\tfrac{n\pi x}{L})+b_n \tfrac{1}{\sqrt{L}} \cos(\tfrac{n\pi x}{L})\)

To recap, we've guessed a seemingly useful way of defining basis vectors, dot products, and projection operators for functions. Using these tools, we can write down a formal way of breaking down a function into a sum of sines and cosines. This is what people call writing out a Fourier Series. In the think post of the series, I'll go through a couple of problems so that you can get a flavor for how this pans out.

Youtube companion to this post:

Vectors and Fourier Series: Part 1

When I was first presented with Fourier series, I mostly viewed them as a bunch of mathematical tricks to calculate a bunch of coefficients. I didn't have a great idea about why we were calculating these coefficients, or why it was nice to always have these sine and cosine functions. It wasn't until later that I realized that I could apply much of the intuition I had for vectors to Fourier series. This post will be the first in a series of three that develop this intuition:

  • Part 1: Developing tools from vectors
  • Part 2: Using these tools for Fourier series
  • Part 3: A few examples using these tools

We can start with the abstract notion of a vector. We can think about a vectors as just an arrow that points in some direction with some length. This is a nice geometrical picture of a vectors, but it is difficult to use a picture to do a calculation. We want to turn our geometrical picture into the usual algebraic picture of vectors.

\(\vec r=a\hat x+b\hat y\)

Choosing a Basis

We will need to develop some tools to do this. One tool we will need is a basis. In our algebraic picture, choosing a basis means that we choose to describe our vector, \(\vec r\), in terms of the components in the \(\hat x\) and \(\hat y\) direction. As usual, we need our basis vectors to be linearly independent, have unit length, and we need one basis vector per dimension (they span the space).

Three steps in creating a vector.

Projection Operators

Now the question becomes: how can we calculate the components in the different directions? The way we learn to do this with vectors is by projection. So, we need projection operators. Things that eat our vector, \(\vec r\), and spit out the components in the \(\hat x\) and \(\hat y\) directions. For the example vector above, this would be:

\(\begin{aligned}\text{Proj}_x(\vec r)&=a,\\\text{Proj}_y(\vec r)&=b.\end{aligned}\)

 We want these projection operators to have a few properties, and as long as they have these properties, any operator we can cook up will work. We want the projection operator in the \(x\) direction to only pick out the \(x\) component of our vector. If there is an \(x\)  component, the projection operator should return it, and if there is no \(x\) component, it should return zero.

Great! Because we have used vectors before, we know that the projection operators are dot products with the unit vectors.

\(\begin{aligned}\text{Proj}_x(\vec r) &= \hat x\cdot\vec r=a\\\text{Proj}_y(\vec r) &= \hat y\cdot\vec r = b\end{aligned}\)

We can also check that these projection operators satisfy the properties that we wanted our operators to have.

So, now we have a way to take some arbitrary vector—maybe it was given to us in magnitude and angle form—and break it into components for our chosen basis.

Vector Rebuilder

The last tool we want is a way of taking our components and rebuilding our vector in our chosen basis. I don't know of a mathematical name for this, so I'm going to call it a vector rebuilder. We know how to do this with our basis unit vectors:

\(\vec r = a\hat x+b\hat y = \text{Proj}_x(\vec r) \hat x+\text{Proj}_y(\vec r)\hat y = \sum_{e=x,y}\text{Proj}_e(\vec r)\hat e.\)

So, to recap, we have developed three tools:

  • Basis: chosen set of unit vectors that allow us to describe any vector as a unique linear combination of them.
  • Projection Operators: set of operators that allow us to calculate the components of a vector along any basis vector.
  • Vector Rebuilder: expression that gives us our vector in terms of our basis and projection operators.

This may seem silly or overly pedantic, but during Part 2, I'll (hopefully) make it clear how we can develop these same tools for Fourier analysis and use them to gain some intuition for the goals and techniques used.

Youtube companion to this post: