In virtually all the applications and operations I write, I try to incorporate some level of logging so that my code can be adequately supported, particularly in Production environments. Some time ago, I wrote about how I generally log in my Python applications. Well, lately, I’ve switched from that approach to using Loguru and I must say I’m rather satisfied with its ease of use. Here’s a quick example I put together recently of the package:
Step 1: Do your standard imports
As I explained in other postson logging, I like adding a “run id” to each log line so that I can easily group lines together belonging to a single instance of my application, so I import the uuid package to help in that regard:
import os
import sys
import uuid
from loguru import logger
Step 2: Setup/customize my logging context
In one line, I can customize how each log line is written and set logging behavior like rolling the file when it hits 10 Mb in size:
def main(argv):
logger_ctx.info('Starting run of the loguru_example.py script')
# do some stuff
logger_ctx.info('Completing run of the loguru_example.py script')
if __name__ == '__main__':
main(sys.argv[1:])
And now you have a nice and easy log for your application:
Pretty darn simple! So now there’s no excuse: start logging today!
This year I’ve started taking guitar lessons. While I’m anxious to jump into learning a bunch of songs, my instructor is keen on me developing foundational knowledge in music theory, scales, modes, and so forth–which I’m perfectly fine with, as well.
So far, we’ve covered several ways to play major scales, the pentatonic minor scale, and the natural minor scale. We also talked about scale “relatives:” how every major scale has a minor scale and every minor scale is a subset of a major scale, the two being relatives of each other.
My instructor then gave me this assignment: play any major scale from the low E string to the high E string, transition into the scale’s relative minor by dropping down three frets, and finish playing out the relative minor scale.
As I’ve been practicing this task, though, I often find myself off by a fret. I have to ask myself, “self, what major scale did you start in? C major? So why are you playing the G# natural minor scale?”
What would really help my practice is to have a handy cheatsheet to show me all the notes in each major scale and highlight the relative minor scale of each major. I could write it all out by hand, but why do that when I have Python and Pandas at my disposal! Here’s what I came up with:
Here’s the code I came up with to calculate all the notes in each scale. Each scale consists of 15 notes spanning three octaves:
# make up my list of notes
chromatic_scale_ascending = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']
# since I usually start on the low E string, rearrange the notes starting on E
scale_from_e = (chromatic_scale_ascending + chromatic_scale_ascending)[4:16]
# the scale pattern:
# root, whole step, whole step, half step, whole step, whole step, whole step, half step
key_steps = [2, 2, 1, 2, 2, 2] # on the guitar, a whole step is two frets
major_keys = []
for root in scale_from_e:
three_octaves = scale_from_e * 3
steps_from_root = three_octaves.index(root)
major_scale = [root]
# construct the unique notes in the scale
for step in key_steps:
steps_from_root += step
major_scale.append(three_octaves[steps_from_root])
# span the scale across 3 octaves
major_keys.append(major_scale * 2 + [root])
Drop the scales into Pandas for the looks
Writing my list of lists to a pandas dataframe and then writing that dataframe out in a jupyter notebook makes everything look nice. More importantly, I can use the style function in pandas to highlight the relative minor scales of each major scale:
df_major_keys = pd.DataFrame(major_keys)
# use this function to highlight the relative minor scales in orange
def highlight_natural_minor(data):
df = data.copy()
df.iloc[:,5:13] = 'background-color: orange'
return df
df_major_keys.style.apply(highlight_natural_minor, axis=None)
…and here’s my handy major/minor scale cheatsheet:
Column 0 is the tonic/root of the major scale while columns 5 through 12 represent the relative minor scale of that major. So we can see that that the E major scale contains the C# minor scale. For example, Ozzy’s Crazy Train apparently moves between A major and F# minor scales which sound just great together–assuming you ignore Ozzy’s personal eccentricities.
So here’s a cool way to merge my interests in music and Python and Pandas into one large mash of goodness.
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!
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