On my workstations and phone, I’ve been trying to switch my applications over to “dark mode” themes where, instead of black letters on bright white backgrounds, you get white letters on dark backgrounds. This color change just seems to feel better on my eyes.
On the more recent versions of Windows 10, switching to dark mode is pretty easy. What’s nicer still is that many applications–like Firefox, Chrome, and Windows Explorer–will simply inherit that dark mode setting from the operating system, so you don’t have to go hunt around in the settings of these applications individually.
On my Android phone, many applications are also beginning to provide dark mode options and, for my background wallpaper, I simply used Zedge to find a free, dark space image to complete the theme.
Give dark mode a try on your devices and see if it helps reduce the strain on your eyes!
There are a few goodarticles out there on Windows PowerToys–Microsoft’s return to authoring little, helpful utilities for those looking to work more effectively in their Windows environments.
As of this writing, three utilities come bundled with the project: FancyZones, PowerRename, and Shortcut Guide (although Shortcut Guide is less a utility and more a handy cheatsheet on the cool shortcuts you can trigger with the Windows key on your keyboard).
The tool I’m really looking forward to is PowerLauncher. This tool purports to be an application launcher on par with the likes of Launchy and WoX. Currently, I’m a big user of SlickRun. It’s handy and very flexible. If I can’t get SlickRun to perform a particular action, I can always wrap the action with a PowerShell or batch script and have SlickRun launch that. I’ve used SlickRun for years, but I think it’s high time Microsoft themselves provide a more flexible way to launch applications and other actions from the keyboard–there’s clearly a market for it.
I don’t see any timelines for the release of the utility, but I will be eagerly checking in on the github page for updates.
I’ve alluded to my interest in reading a fewtimesin the past. Several years ago, I made the switch from physical books to digital and use an Amazon Kindle as my main reading vehicle.
One frustration I have with the Kindle, though, is either its inability to track the reading time metrics I’m interested in collecting or its poor way of sharing those metrics with data nerds like me.
Earlier in the year, I decided to spend more than five minutes solving this problem and found out about Kindle FreeTime. Kindle FreeTime is an application on Kindle devices with the primary focus of getting kids to read. Parents can use FreeTime to decide what books their children can read and what minimum daily reading goals they want their children to meet. A side benefit of FreeTime, though, is that it captures a lot of the metrics I’m interested in in a SQLite database: all you have to do is plug your kindle into your workstation, download the database at system\freetime\freetime.db, and start exploring.
Dayinfo
One of the tables in the FreeTime database is dayinfo. This is probably a good place to start gathering some general reading metrics. Here’s how I went about digging into the data.
Load all the standard packages
In my notebook, I started by loading all the normal packages I use including the sqlite3 package:
import pandas as pd
import numpy as np
import sqlite3
from datetime import datetime
import matplotlib.pyplot as plt
%matplotlib inline
Load and clean the data from the table
Next, I queried the data from the dayinfo table and added a few helpful columns:
conn = sqlite3.connect('./data/freetime.db')
query = "SELECT * FROM dayinfo;"
df_dayinfo = pd.read_sql_query(query,conn)
# clean up fields and do some feature engineering
df_dayinfo['accessdate'] = pd.to_datetime(df_dayinfo.accessdate)
df_dayinfo['access_month'] = df_dayinfo.accessdate.dt.month
df_dayinfo['access_dow'] = df_dayinfo.accessdate.dt.dayofweek
df_dayinfo['read_mins'] = df_dayinfo.timeread / 60
df_dayinfo['read_hours'] = df_dayinfo.timeread / 3600
Calculate some preliminary metrics
Finally, I wanted to calculate my total reading time for the year 2019 and my average daily reading time. I only started using FreeTime in March 2019, so I had to pro-rate some of my calculations. Here’s what I came up with:
df_dayinfo_2019 = df_dayinfo[(df_dayinfo.accessdate > datetime(2019, 1, 1)) & (df_dayinfo.accessdate < datetime(2020, 1, 1))]
days_in_2019 = (df_dayinfo_2019.accessdate.max() - df_dayinfo_2019.accessdate.min()).days
print('From {0:%d %b %Y} to {1:%d %b %Y} ({2} days):'.format(df_dayinfo_2019.accessdate.min(),
df_dayinfo_2019.accessdate.max(), days_in_2019))
print('I read {0:.2f} hours'.format(df_dayinfo_2019.read_hours.sum()))
print("That's an average of {0:.2f} minutes per day".format((df_dayinfo_2019.read_mins.sum())/days_in_2019))
From 10 Mar 2019 to 29 Dec 2019 (294 days):
I read 111.18 hours
That's an average of 22.69 minutes per day
Bah! Only 22 minutes reading time per day on average?! Well, I know one goal I’ll need to work on for 2020. Lets see what this data looks like in some charts:
My monthly reading totals starting in March: May was a good month
fig, ax = plt.subplots(figsize=(10, 8))
df_dayinfo_2019[['access_dow', 'read_hours']].sort_values('access_dow').groupby('access_dow').sum().plot.bar(ax=ax)
_ = ax.set_title('Hours Read by Day of Week: {0:%d %b %Y} to {1:%d %b %Y}'.format(df_dayinfo_2019.accessdate.min(),
df_dayinfo_2019.accessdate.max()))
_ = ax.set_xlabel('Day of Week')
_ = ax.set_ylabel('Hours')
_ = ax.set_xticklabels(['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'])
I read the most on Wednesdays. That makes sense because most of my Wednesday evenings in Spring are sitting in the parking lot outside a bandroom while my kid practices with his middle school orchestra. I get a lot of reading time in on those days.
There are other tables in the database including details on each of the books that I’ve read over the year. Hopefully, at some point, I’ll dig in to those details, as well. But for now, this data is sufficient to get me motivated to read more in 2020.
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