Why I’m going back to Linux after five years of using macOS

I’ve been a supporter of the Electronic Frontier Foundation since 2004. Their work on privacy, free expression and technology are all things I am passionate about. For the last year or so, I have become more concerned with privacy issues in technology. The rise in big data and how everything is tracking everything we do has given me significant concerns. I’ve been giving a lot of thought to which ecosystems I want to stay in. I’m not going to say I trust any of these technology companies, but I can control (or minimize) my footprint with some of these companies.

Last year I took a number of steps in this direction:

  • I deleted my Facebook and Instagram accounts. I don’t think I need to go into detail here, but Facebook isn’t something you would ever equate with the word “privacy”.
  • After Evernote said they would access your notes and data (only to backtrack later) I quickly stopped using Evernote.
  • I’m paying cash for most of my personal purchases and now shopping local and not online – even if I have to pay a bit more for things such as records, books or cycling gear.
  • I went through and deleted over a hundred online accounts over the Christmas break and used a password manager to make sure I wasn’t using duplicate passwords online and also that I was using secure passwords.
  • I’m no longer using Flickr (and Yahoo services in general) for my photos and I have a tough decision to make about whether I delete that account and remove access to the photos there. (Wikipedia using a number of my Green Bay Packer photos under a Creative Commons license).
  • I switched to DuckDuckGo instead of Google as my default search engine.
  • As much as I’m intrigued by Amazon’s Alexa and Google Home, I won’t buy a voice activated device. Just think about what data it knows about you – what smart devices in your house, what your saying around it – and the recent story in the news how a police department wants the data scares the shit out of me.
  • I’m not using TouchID on my iOS devices. Courts have ruled multiple times that your fingerprint is not protected under the Fifth Amendment – but a passcode is.

Yes, I sound paranoid. But at the end of the day, this is my decision and my choice. I may not have anything to hide, but I don’t believe just because we have the technology means that it always needs to be used to collect everything about you. While I will never be able to erase everything about me online or with these technology companies – nor would I necessarily want to – I can control with whom I do business and make conscious choices about it. This way I can be eyes wide open that yes, I’ve been using Gmail since it first launched and that Google knows almost everything about me. But that’s my choice to stay within Google’s ecosystem (for now). even if I start to use less of their services, such as switching to DuckDuckGo for internet searches.

I stopped using Microsoft Windows in 2003 when I switched to using Linux full time until about 2012 when I started using macOS after buying my first MacBook. I love Apple’s hardware and I like macOS – the same Unix internals underneath, lots of polish, and excellent apps. Everything just works – you don’t have to fiddle with video card drivers or wireless. But you will have to do things the way Apple wants you to (see: iTunes). Integreation with iOS is great – answer phone calls on your Mac, reply to text messages. But who knows what Apple is tracking as well as the apps you’re using (I’m looking at you Evernote). And don’t get me started on the Touch Bar on the new MacBooks. (No Escape key? Really?)

So I’m going back to using Linux on the desktop after five+ years away. There is no question that the macOS user experience is significantly better. But using the GNOME desktop on Fedora is pretty close and gets better every release. I’ll know my computing experience is secure and private. I’ll probably share some thoughts on what key applications I’ll miss most in a separate blog post. I’ll still need to use macOS at my day job, but I can control what I use at home and have the peace of mind that nothing is tracking me (outside of what’s in my web browser) when using my own computers.

Dwayne Crooks on learning Python efficiently

Dwayne Crooks wrote a fabulous blog post this week with his advice on learning Python efficiently.

Being a year into my journey, I couldn’t agree with him more. He lists five mistakes that hamper our ability to learn efficiently. Below I’ve listed his five mistakes with where I am in my journey in italics.

  1. Reading a book cover to cover. I strongly agree with this one. This was the first mistake I made a year ago when I decided I wanted to learn Python. I bought Think Python and Learning Python and quickly realized I am not the type of learner who can learn from reading and trying to follow along.
  2. Diving in without a plan. Check! Yes, I have a plan. I know what I want to build. Whew.
  3. Failing to narrow your scope. I think I’m ok on this one? Let’s just quote this one in full from Mr. Crooks:

    Having clear boundaries makes it easy to decide whether or not a new resource is worth your time. That’s why learning Python by trying to build something in it is a great way to go. You’d realize how much of Python you don’t need to know in order to accomplish any one task. You’ll find that the more you narrow your scope at the beginning, the more you’ll learn and the faster you’ll progress.

    The challenge for me in understanding this one, is if you’re new to Python, how do you know where to draw the boundaries? When I get stuck, I revisit some of the classes I’ve taken or search Stack Overflow. I quickly realize how much I don’t know when I find a new way to do something or come across something related but that I don’t need. But knowing what I want to build probably expands my scope instead of narrowing it.

  4. Trying to learn 2 (or more) things at the same time. I’m being very careful with this one. I want to have a prototype of my application working before I move on to my next class, Python for Entrepreneurs, which will teach me how to build my application using Pyramid. The course will also cover CSS, Bootstrap and more web technologies. Where I’m struggling though is on my prototype – do I just build the prototype or do I try and learn some basic SQL, which is what the web app version will need? My head has been in the right spot on this one as I’ve tried to avoid learning SQL up until now.
  5. Spending too much time studying before you have experience doing. Mr. Crooks hits this one on the head and is basically describing me: Because we’re afraid to fail, we want to know what we’re doing before we ever try. So we spend a lot of time learning before ever trying to apply any of it. I’m wired to be a “learner” and do a deep dive into anything before I pull the trigger. Whether it’s a ton of research before buying a new TV or learning a new skill, this describes me well. But I think I’m ok on this one. If you were to look through my Github repo for nflpool (please don’t), you would see a mishmash of Python. There’s probably 25 files in my repo that is basically just a scratchpad for me trying to figure out how to parse JSON or trying to write a for loop to get the results I need. There’s nothing Pythonic in there (yet). For example, I’m not using functions like I should. But once I get the different pieces working, I’ll refactor it the right way. You can argue whether I should be starting it right or not, but I’m diving in and trying to figure it out piece by piece. You have to start somewhere…

Mr. Crooks then goes on and shares his five steps to get started. I’m happy to see I’m on the right track.

One Year of Python

It was Black Friday of 2015. O’Reilly put on a sale of their programming ebooks and I was finally ready to take the plunge and learn Python. I bought three books:

I then signed up for a Coursera class, Python for Everybody, taught by Dr. Charles Severance and started the class. I was ready to do this. I needed a hobby. I had a problem to solve.

Then real life got in the way. A few months earlier, we started building a new house. In January it was time to sell our house, which meant hours of work. Then in February, we moved.

I put learning Python on the back burner. Before I knew it, it was July, and another six months had gone by. It was now fantasy football season and that was the problem I had to solve. I needed a program that would keep track of all football statistics and standings and automatically calculate each player’s points. It was time.

I re-started the Coursera course and spent the time. I was easily spending twenty hours a week reading the course materials, watching the videos and doing the homework.

I confirmed what I knew about myself: I learn best by doing, not just reading or watching videos. The books I had bought were helpful, but just sitting down and reading them, trying to follow along and do the exercises was difficult. Python for Everybody on Coursera was great.

I finished that and moved on to Python Jumpstart by Building 10 Apps by Michael Kennedy, which I had purchased in early 2016 via a Kickstarter campaign. I’m almost done with that a year after I started this journey.

Learning to code in Python is hard. I don’t have a background in computer science and with some of the concepts that the books and courses teach I just don’t have the base knowledge necessary. This sometimes makes it harder and takes longer to understand the concepts. I’m lucky that my wife has worked professionally as a programmer in multiple languages, including Java and SQL. But I drive her crazy when I ask her questions about concepts I clearly don’t understand. I use the wrong terminology or fail to grasp what I’ve been taught.

I don’t know how much I’ve retained from the classes and books. I’m trying to build my application in parallel with my learning. I’m convinced the only way I’m going to learn is to build something, which is a piece of advice most often found online for people aspiring to learn programming. I’m constantly hitting up Google and Stack Overflow when I get stuck. I’ll copy bits and pieces of code from these search results and I’m always doubting whether I understand what I’m copying. I’ve signed up for multiple newsletters and bookmarked dozens of websites with articles on how to learn, code snippets, programming challenges and more. I’m overwhelmed with the concepts I’m learning and I know I don’t understand, let alone use, these concepts.

But I’m going to keep trying. The only way I’ll learn is by building something. The code will be ugly. It will break. And I’ll keep updating it until it works and as I learn more, I’ll make it more elegant.

Here’s to another year.

Importing Team Data into NFLPool

Last weekend I discovered how to pretty print the five JSON files I get from MySportsFeeds. This was helpful to understand just how much data is nested within each file. I also spent a good chunk of the weekend writing in a notebook. I mostly did some data modeling on what each table in the database should store and what their primary keys would be. I also captured things I need to research and started breaking the project into chunks. As I tweeted out over the weekend:

Monday was a holiday so I did the first four courses of Python Jumpstart. I took a break and went back to the JSON files I had worked with. My goal was to build with what should be the easiest table and pull the team data out. This is a dictionary that includes the team name (Texans), city (Houston), abbreviation (HOU) and id (64). The ID number is supplied in the JSON feed and is unique, so I will use that as the primary key. There will be two more columns in the table for conference and division, but I wanted to deal with that later.

I wrote a for loop to try and pull out each team’s information. I quickly got stuck and nothing was working. At one point, the loop I had written worked, but only pulled out the data for the first ranked team. I showed my wife my code and she pointed out that it wasn’t iterating in a loop.

I was stuck for two nights working on this after dinner. I finally stepped back and modified my pretty print Python program and started breaking down all of the information in the JSON file again. I figured out what was a list and what was a dictionary and what was nested where. (It looks like I didn’t commit this to the git repo, oops! Will have to fix that.)

After doing this last night, I found the list I needed to work with. I then re-wrote my for loop and I was able to iterate through all 16 teams in the AFC:

for afc_team_list in teamlist:

afc_team_name = data["conferenceteamstandings"]["conference"][0]["teamentry"][x]["team"]["Name"]

afc_team_city = data["conferenceteamstandings"]["conference"][0]["teamentry"][x]["team"]["City"]

afc_team_id = data["conferenceteamstandings"]["conference"][0]["teamentry"][x]["team"]["ID"]

afc_team_abbr = data["conferenceteamstandings"]["conference"][0]["teamentry"][x]["team"]["Abbreviation"]

print((afc_team_name),",",(afc_team_city),",",(afc_team_id),",",(afc_team_abbr))

x = x + 1

I then copied and pasted and did it again for the NFC. I did try, unsuccessfully, to modify the conference list – “conference”0 – so I could just write one for loop instead of one for each of the two conferences. But it was working, so I’ll leave it for now. (I’m sure my code is ugly, but hey, I’m just starting).

After that it was all about writing the SQL insert statements to put this into a SQLite3 database. (For now, later it will go into MySQL). That took me a an hour, but at the end, I got it working and was even able to add the conference name to each row.

Next up, I need to take the data in the Division standings JSON file. In it is stored the division name for each division in a conference: AFC/AFC-East. I’ll need to write a for loop to grab it, slice it to remove the “AFC/“ and then stick that in the Division field for each team in the Teams table. I’ll also need to stop dropping and re-creating the table each time I insert data, but it’s working.

Progress!

Building the NFLPool webapp – Starting with JSON

I’m glad I started with the Python for Everybody specialization at Coursera before jumping into Python Jumpstart by building 10 Python Apps by Michael Kennedy. Mr. Kennedy moves fast. I’ve completed the first four apps and it’s good to get a refresher on the information I learned in Python for Everybody.

I also spent part of the weekend sketching in a notebook. I did some brainstorming about the database design I’ll need for NFLPool. I learned one of the bigger differences between MySQL and Postgresql is that MySQL does not have the ability to use foreign keys but MySQL is much faster. The lack of foreign keys may make the design a bit tougher, but more on that later in a different blog post.

I also sketched out some ideas for the functions I’m going to need to write so I’m not writing the same bit of code over and over again. From there, I created a to-do list of things to start working through. I find this whole process of building an app overwhelming. I never thought I’d be using paper and pencil so much, but I’ve found it helpful to break this into smaller chunks and attack them one at a time.

Then I started working on the import process for the JSON. This quickly derailed as I realized just how many stats MySportsFeeds captures from an NFL game. That quickly turned in to writing a JSON pretty print statement so I could see how the five different JSON files nested their dictionaries.

I currently download five JSON files every Tuesday via a cron job with all the statistics. I know my app won’t be ready for the 2016 season and my hope is by having 17 weeks of data, I can re-create the season to test my app to make sure it’s scoring each player correctly as we move through the season week by week. When I download the JSON via curl, it includes all the web headers, such as:

HTTP/1.1 200 OK
Date: Wed, 21 Sep 2016 12:16:07 GMT
Server: Apache-Coyote/1.1
Cache-Control: must-revalidate, no-store, s-maxage=0, max-age=0, private
Access-Control-Allow-Headers: Origin, Content-Type, Accept, Accept-Encoding, Accept-Language, Authorization
Access-Control-Allow-Origin: *
Access-Control-Allow-Credentials: true
Content-Encoding: gzip
Access-Control-Allow-Methods: GET, OPTIONS
Content-Type: application/json
Set-Cookie: JSESSIONID=B7548F2309747418749B5421282A5E08; Path=/leaguemanager-web/; HttpOnly
Vary: User-Agent
Connection: close
Transfer-Encoding: chunked

And then the JSON starts right after that with curly braces. I was proud of myself as I wrote an if statement to load the file, read the lines, and load the JSON when finding the curly braces. Then I wrote code to first print out all the statistics categories (commented out below) and pretty print all the JSON:

import json
import pprint
import os

#Open the JSON file that includes headers


#Change the name of the file to open to match the query below:
with open('json/20160921-division-team-standings.json') as file:
    alltext = file.readlines()  #Put each line into a list

# division-team-standings.json
for lines in alltext:
    if lines.startswith('{'):
        rawdata = lines
        data = json.loads(rawdata)
#        for stat_categories in data["divisionteamstandings"]["division"][0]["teamentry"][0]["stats"]:
#            pprint.pprint(stat_categories)   #Print all the categories in "stats"
        pprint.pprint(data)  #Print the JSON

I had five files to review and I just manually changed the code to the file I wanted and had a code block for each of the files. I know I probably should have just wrote a function, but I was in the zone. (My code probably isn’t very Pythonic either, but I have to start somewhere on this journey). I also know that when it comes time to build the real app I’ll be loading the JSON across the network and not from a local file, but future Paul gets to deal with that.

I also spent some time playing around with the nflgame and mlbgame Python modules. I need to spend some more time with them and I’ll share some thoughts on those in another blog post.