Last night we got a quarter of an inch of rain at our house, making roads “impassable” according to the Fairbanks Police Department, and turning the dog yard, deck, and driveway into an icy mess. There are videos floating around Facebook showing Fairbanks residents playing hockey in the street in front of their houses, and a reported seven vehicles off the road on Ballaine Hill.

Here’s a video of a group of Goldstream Valley musicians ice skating on Golstream Road: http://youtu.be/_afC7UF0NXk

Let’s check out the weather database and take a look at how often Fairbanks experiences this type of event, and when they usually happen. I’m going to skip the parts of the code showing how we get pivoted daily data from the database, but they’re in this post.

Starting with pivoted data we want to look for dates from November through March with more than a tenth of an inch of precipitation, snowfall less than two tenths of an inch and a daily high temperature above 20°F. Then we group by the winter year and month, and aggregate the rain events into a single event. These occurrences are rare enough that this aggregation shoudln’t combine events from different parts of the month.

Here’s the R code:

winter_rain <-
   fai_pivot %>%
      mutate(winter_year=year(dte - days(92)),
               wdoy=yday(dte + days(61)),
               month=month(dte),
               SNOW=ifelse(is.na(SNOW), 0, SNOW),
               TMAX=TMAX*9/5+32,
               TAVG=TAVG*9/5+32,
               TMIN=TMIN*9/5+32,
               PRCP=PRCP/25.4,
               SNOW=SNOW/25.4) %>%
      filter(station_name == 'FAIRBANKS INTL AP',
               winter_year < 2014,
               month %in% c(11, 12, 1, 2, 3),
               TMAX > 20,
               PRCP > 0.1,
               SNOW < 0.2) %>%
      group_by(winter_year, month) %>%
      summarize(date=min(dte), tmax=mean(TMAX),
                prcp=sum(PRCP), days=n()) %>%
      ungroup() %>%
      mutate(month=month(date)) %>%
      select(date, month, tmax, prcp, days) %>%
      arrange(date)

And the results:

This year’s event doesn’t compare to 2010 when almost and inch of rain fell over the course of three days in November, but it does look like it comes at an unusual part of the year.

Here’s the counts and frequency of winter rainfall events by month:

by_month <-
   winter_rain %>%
      group_by(month) %>%
      summarize(n=n()) %>%
      mutate(freq=n/sum(n)*100)

There haven’t been any rain events in December, which is a little surprising, but next to that, February rains are the least common.

I looked at this two years ago (Winter freezing rain) using slightly different criteria. At the bottom of that post I looked at the frequency of rain events over time and concluded that they seem to come in cycles, but that the three events in this decade was a bad sign. Now we can add another rain event to the total for the 2010s.

Abstract (tl;dr)

We’re getting some bad home Internet service from Alaska Communications, and it’s getting worse. There are clear patterns indicating lower quality service in the evening, and very poor download rates over the past couple days. Scroll down to check out the plots.

Introduction

Over the past year we’ve started having trouble watching streaming video over our Internet connection. We’re paying around $100/month for phone, long distance and a 4 Mbps DSL Internet connection, which is a lot of money if we’re not getting a quality product. The connection was pretty great when we first signed up (and frankly, it’s better than a lot of people in Fairbanks), but over time, the quality has degraded and despite having a technician out to take a look, it hasn’t gotten better.

Methods

In September last year I started monitoring our bandwidth, once every two hours, using the Python speedtest-cli tool, which uses speedtest.net to get the data.

To use it, install the package:

$ pip install speedtest-cli

Then set up a cron job on your server to run this once every two hours. I have it running on the raspberry pi that collects our weather data. I use this script, which appends data to a file each time it is run. You’ll want to change the server to whichever is closest and most reliable at your location.

#! /bin/bash
results_dir="/path/to/results"
date >> ${results_dir}/speedtest_results
speedtest --server 3191 --simple >> ${results_dir}/speedtest_results

The raw output file just keeps growing, and looks like this:

Mon Sep  1 09:20:08 AKDT 2014
Ping: 199.155 ms
Download: 2.51 Mbits/s
Upload: 0.60 Mbits/s
Mon Sep  1 10:26:01 AKDT 2014
Ping: 158.118 ms
Download: 3.73 Mbits/s
Upload: 0.60 Mbits/s
...

This isn’t a very good format for analysis, so I wrote a Python script to process the data into a tidy data set with one row per observation, and columns for ping time, download and upload rates as numbers.

From here, we can look at the data in R. First, let’s see how our rates change during the day. One thing we’ve noticed is that our Internet will be fine until around seven or eight in the evening, at which point we can no longer stream video successfully. Hulu is particularly bad at handling a lower quality connection.

Code to get the data and add some columns to group the data appropriately for plotting:

#! /usr/bin/env Rscript
# Prep:
# parse_speedtest_results.py speedtest_results speedtest_results.csv

library(lubridate)
library(ggplot2)
library(dplyr)
speed <- read.csv('speedtest_results.csv', header=TRUE) %>%
   tbl_df() %>%
   mutate(date=ymd_hms(as.character(date)),
         yyyymm=paste(year(date), sprintf("%02d", month(date)), sep='-'),
         month=month(date),
         hour=hour(date))

Plot it:

q <- ggplot(data=speed, aes(x=factor(hour), y=download)) +
   geom_boxplot() +
   scale_x_discrete(name="Hour of the day") +
   scale_y_continuous(name="Download speed (Mbps)") +
   ggtitle(paste("Speedtest results (",
                  min(floor_date(speed$date, "day")), " - " ,
                  max(floor_date(speed$date, "day")), ")", sep="")) +
   theme_bw() +
   facet_wrap(~ yyyymm)

Results and Discussion

Here’s the result:

Box and whisker plots (boxplots) show how data is distributed. The box represents the range where half the data lies (from the 25th to the 75th percentile) and the line through the box represents the median value. The vertical lines extending above and below the box (the whiskers), show where most of the rest of the observations are, and the dots are extreme values. The figure above has a single boxplot for each two hour period, and the plots are split into month-long periods so we can see if there are any trends over time.

There are some clear patterns across all months: our bandwidth is pretty close to what we’re paying for for most of the day. The boxes are all up near 4 Mbps and they’re skinny, indicating that most of the observations are close to 4 Mbps. Starting in the early evening, the boxes start getting larger, demonstrating that we’re not always getting our expected rate. The boxes are very large between eight and ten, which means we’re as likely to get 2 Mbps as the 4 we pay for.

Patterns over time are also showing up. Starting in January, there’s another drop in our bandwidth around noon and by February it’s rare that we’re getting the full speed of our connection at any time of day.

One note: it is possible that some of the decline in our bandwidth during the evening is because the download script is competing with the other things we are doing on the Internet when we are home from work. This doesn’t explain the drop around noon, however, and when I look at the actual Internet usage diagrams collected from our router using SMTP / MRTG, it doesn’t appear that we are really using enough bandwidth to explain the dramatic and consistent drops seen in the plot above.

February is starting to look different from the other months, I took a closer look at the data for that month. I’m filtering the data to just February, and based on a look at the initial version of this plot, I added trend lines for the period before and after noon on the 12th of February.

library(dplyr)
library(lubridate)
library(ggplot2)
library(scales)
speeds <- tbl_df(read.csv('speedtest_results.csv', header=TRUE))
speed_plot <-
   speeds %>%
      mutate(date=ymd_hms(date),
            grp=ifelse(date<'2015-02-12 12:00:00', 'before', 'after')) %>%
      filter(date > '2015-01-31 23:59:59') %>%
      ggplot(aes(x=date, y=download)) +
            geom_point() +
            theme_bw() +
            geom_smooth(aes(group=grp), method="lm", se=FALSE) +
            scale_y_continuous(limits=c(0,4.5),
                           breaks=c(0,1,2,3,4),
                           name="Download speed (Mbps)") +
            theme(axis.title.x=element_blank())

The result:

Ouch. Throughout the month our bandwidth has been going down, but you can also see that after noon on the 12th, we’re no longer getting 4 Mpbs no matter what time of day it is. The trend line probably isn’t statistically significant for this period, but it’s clear that our Internet service, for which we pay a lot of money for, is getting worse and worse, now averaging less than 2 Mbps.

Conclusion

I think there’s enough evidence here that we aren’t getting what we are paying for from our ISP. Time to contact Alaska Communications and get them to either reduce our rates based on the poor quality of service they are providing, or upgrade their equipment to handle the traffic on our line. I suspect they probably oversold the connection and the equipment can’t handle all the users trying to get their full bandwidth at the same time.

Whenever we’re in the middle of a cold snap, as we are right now, I’m tempted to see how the current snap compares to those in the past. The one we’re in right now isn’t all that bad: sixteen days in a row where the minimum temperature is colder than −20°F. In some years, such a threshold wouldn’t even qualify as the definition of a “cold snap,” but right now, it feels like one.

Getting the length of consecutive things in a database isn’t simple. What we’ll do is get a list of all the days where the minimum daily temperature was warmer than −20°F. Then go through each record and count the number of days between the current row and the next one. Most of these will be one, but when the number of days is greater than one, that means there’s one or more observations in between the “warm” days where the minimum temperature was colder than −20°F (or there was missing data).

For example, given this set of dates and temperatures from earlier this year:

Once we select for rows where the temperature is above −20°F we get this:

Now we can grab the start and end of the period (January 2nd + one day and January 8th - one day) and get the length of the cold snap. You can see why missing data would be a problem, since it would create a gap that isn’t necessarily due to cold temperatures.

I couldn't figure out how to get the time periods and check them for validity all in one step, so I wrote a simple function that counts the days with valid data between two dates, then used this function in the real query. Only periods with non-null data on each day during the cold snap were included.

CREATE FUNCTION valid_n(date, date)
RETURNS bigint AS
  'SELECT count(*)
   FROM ghcnd_pivot
   WHERE station_name = ''FAIRBANKS INTL AP''
      AND dte BETWEEN $1 AND $2
      AND tmin_c IS NOT NULL'
LANGUAGE SQL
RETURNS NULL ON NULL INPUT;

Here we go:

SELECT rank() OVER (ORDER BY days DESC) AS rank,
       start, "end", days FROM (
   SELECT start + interval '1 day' AS start,
         "end" - interval '1 day' AS end,
         interv - 1 AS days,
         valid_n(date(start + interval '1 day'),
                  date("end" - interval '1 day')) as valid_n
   FROM (
      SELECT dte AS start,
            lead(dte) OVER (ORDER BY dte) AS end,
            lead(dte) OVER (ORDER BY dte) - dte AS interv
      FROM (
         SELECT dte
         FROM ghcnd_pivot
         WHERE station_name = 'FAIRBANKS INTL AP'
            AND tmin_c > f_to_c(-20)
      ) AS foo
   ) AS bar
   WHERE interv >= 17
) AS f
WHERE days = valid_n
ORDER BY days DESC;

And the top 10:

There have been seven cold snaps that lasted 16 days (including the one we’re currently in), tied for 45th place.

Keep in mind that defining days where the daily minimum is −20°F or colder is a pretty generous definition of a cold snap. If we require the minimum temperatures be below −40° the lengths are considerably shorter:

I think it’s also interesting that only three (marked with a grey background) of the top ten cold snaps defined at −20°F appear in those that have a −40° threshold.

I’ve been a bit behind on mentioning the 2015 Tournament of Books. The contestants were announced last month. As usual, here’s the list with a three star rating system for those I've read: ☆ - not worthy, ☆☆ - good, ★★★ - great.

• Silence Once Begun by Jesse Ball ☆☆
• A Brave Man Seven Storeys Tall by Will Chancellor ☆
• All the Light We Cannot See by Anthony Doerr ★★★
• Those Who Leave and Those Who Stay by Elena Ferrante
• An Untamed State by Roxane Gay ★★★
• Wittgenstein Jr by Lars Iyer
• A Brief History of Seven Killings by Marlon James
• Redeployment by Phil Klay
• Station Eleven by Emily St. John Mandel ☆☆
• The Bone Clocks by David Mitchell ★★★
• Everything I Never Told You by Celeste Ng ☆☆
• Dept. of Speculation by Jenny Offill ★★★
• Adam by Ariel Schrag
• The Paying Guests by Sarah Waters ☆
• Annihilation by Jeff VanderMeer ☆☆
• All the Birds, Singing by Evie Wyld ★★★

Thus far, my early favorite is, of course, The Bone Clocks by David Mitchell. It's a fantastic book, similar in design to Cloud Atlas, but better. Both All the Light We Cannot See and Dept. of Speculation are distant runner's up. All the Light is great story, told in very short and easy to digest chapters, and Speculation is a funny, heartrending, strange, and ultimately redemptive story of marriage.