Working with MIDI data in Python using Mido

Originally published by SAM AGNEW at twilio.com

Let's walk through the basics of working with MIDI data using the Mido Python library.

Setting up

Before moving on, you will need to make sure you have an up to date version of Python 3 and pip installed. Make sure you create and activate a virtual environment before installing Mido.

Run the following command to install Mido in your virtual environment:

pip install mido==1.2.9

In the rest of this post, we will be working with these two MIDI files as examples. Download them and save them to the directory where you want your code to run.

VampireKillerCV1.mid is the stage 1 music from the original Castlevania game on the NES. VampireKillerCV3.mid is the Castlevania 3 version of the same song, which is slightly remixed and plays later in the game when you enter Dracula's Castle (hence the track name "Deja Vu")!

Working with MidiFile objects and track data

The MidiFile object is one of the basic types you're going to work with when using Mido. As the name implies, it can be used to read, write and play back MIDI files. You can create a new MIDI file or open up an existing file as a MidiFile object. Any changes you make to this object won't be written until you call the save() method with a filename.

Let's start by opening VampireKillerCV1.mid and examining its properties. Open up your Python shell and follow along with this code which will create a new MidiFile object with the contents of the file we want:

from mido import MidiFile
mid = MidiFile(‘VampireKillerCV1.mid’, clip=True)

print(mid)

We are using clip=True just in case we end up opening a file with notes over 127 velocity, the maximum for a note in a MIDI file. This isn’t a typical scenario and would usually mean the data is corrupted, but when working with large amounts of files it’s good to keep in mind that this is a possibility. This would clip the velocity of all notes to 127 if they are higher than that.

You should see some output similar to this:

<midi file ‘VampireKillerCV1.mid’ type 1, 9 tracks, 4754 messages>

This means that the MIDI file has 9 synchronous tracks, with 4754 messages inside of them. Each MidiFilehas a type property that designates how the tracks interact with each other.

There are three types of MIDI files:

• type 0 (single track): all messages are saved in one track
• type 1 (synchronous): all tracks start at the same time
• type 2 (asynchronous): each track is independent of the others

Let’s loop through some of the tracks and see what we find:

for track in mid.tracks:

print(track)

Your output should look something like this:

<midi track ‘’ 5 messages>

<midi track ‘CV1- Vampire Killer’ 7 messages>

<midi track ‘Staff-2’ 635 messages>

<midi track ‘Staff-3’ 659 messages>

<midi track ‘Staff-4’ 728 messages>

<midi track ‘Staff-5’ 635 messages>

<midi track ‘Staff-6’ 659 messages>

<midi track ‘Staff-7’ 1421 messages>

<midi track ‘Staff-1’ 5 messages>

This allows you to see the track titles and how many messages are in each track. You can loop through the messages in a track:

for msg in mid.tracks[0]:

print(msg)

This particular track contains only meta information about the MIDI file in general such as the tempo and time signature, but other tracks contain actual musical data:

<meta message time_signature numerator=4 denominator=4 clocks_per_click=24 notated_32nd_notes_per_beat=8 time=0>

<meta message key_signature key=‘C’ time=0>

<meta message smpte_offset frame_rate=24 hours=33 minutes=0 seconds=0 frames=0 sub_frames=0 time=0>

<meta message set_tempo tempo=468750 time=0>

<meta message end_of_track time=0>

Now let’s actually do something with this info!

Manipulating MIDI tracks and writing new files

If you’ve opened this file in a music program such as GarageBand, you might have noticed that there are duplicate tracks for the main melody and harmony (corresponding to the NES square wave channels 1 and 2 in the source tune). This kind of thing is pretty common when dealing with video game music MIDIs, and might seem unnecessary.

Let’s write some code to clean this file up and remove the duplicate tracks to make it more closely resemble the original NES version. Create and open a file called remove_duplicates.py, and add the following code to it:

import os

 

from mido import MidiFile

 

 

cv1 = MidiFile(‘VampireKillerCV1.mid’, clip=True)

 

message_numbers = []

duplicates = []

 

for track in cv1.tracks:

    if len(track) in message_numbers:

        duplicates.append(track)

    else:

        message_numbers.append(len(track))

 

for track in duplicates:

    cv1.tracks.remove(track)

 

cv1.save(‘new_song.mid’)

This code loops through the tracks in our MIDI file, searches for tracks that have the same exact number of messages, and removes them from the overall MIDI file to get rid of the duplicates.

The new MIDI file is saved to new_song.mid to keep the original file intact. Run the code with the following command:

python remove_duplicates.py

Now open up new_song.mid and see for yourself that the MIDI file has no duplicate tracks!

Let’s make some new music

Cleaning up MIDI files is cool, but making new music is a lot more fun! If you open VampireKillerCV3.mid you can hear the Castlevania 3 version of this song is remixed to be a little more upbeat. The melody stays mostly the same but the harmony adds some more flavor, and the bass and drums are both played at a more consistent pace as opposed to the original game.

Let’s write some code to take the bass and drum tracks from the Castlevania 3 version and mash them up with the original melody and harmony tracks from the first Castlevania game.

Open a file called mashup.py and add the following code to it:

import os

 

from mido import MidiFile

 

 

cv1 = MidiFile(‘new_song.mid’, clip=True)

cv3 = MidiFile(‘VampireKillerCV3.mid’, clip=True)

 

del cv1.tracks[4]

del cv1.tracks[4]

 

cv1.tracks.append(cv3.tracks[4])

cv1.tracks.append(cv3.tracks[5])

 

cv1.save(‘mashup.mid’)

This code deletes the bass and drum tracks from the first file, and adds the bass and drum tracks from the second file. Notice that we are opening new_song.mid so that we have the version of the MIDI with no duplicate tracks, and saving the new tune to a file called mashup.mid.

Run this code and open mashup.mid and jam out to our new remix of Vampire Killer from Castlevania 1 and 3.

Now what?

There is a lot you can do with MIDI data. You can use it to write interfaces for digital instruments, or use it to clean up data to train on a neural network with Magenta. This post only brushed the surface of the functionality of the Mido library, but I hope you feel a little more equipped to take this further and use it for your own MIDI files!

Feel free to reach out for any questions or to show off any cool music you make with Python:

Originally published by SAM AGNEW at twilio.com

 ===================================================================

Thanks for reading :heart: If you liked this post, share it with all of your programming buddies! Follow me on Facebook | Twitter

Learn More

☞ Complete Python Bootcamp: Go from zero to hero in Python 3

☞ Python for Time Series Data Analysis

☞ The complete beginner’s guide to JSON

☞ The Complete Guide to JSON Web Tokens

☞ Python Programming For Beginners From Scratch

☞ Python Network Programming | Network Apps & Hacking Tools

☞ Intro To SQLite Databases for Python Programming

☞ Ethical Hacking With Python, JavaScript and Kali Linux

☞ Beginner’s guide on Python: Learn python from scratch! (New)

☞ Python for Beginners: Complete Python Programming

#python

What is GEEK

Buddha Community

top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

…

#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object

a**=25+**85j

type**(a)**

output**:<class’complex’>**

b**={1:10,2:“Pinky”****}**

id**(b)**

output**:**238989244168

Built-in data types in Python

a**=str(“Hello python world”)****#str**

b**=int(18)****#int**

c**=float(20482.5)****#float**

d**=complex(5+85j)****#complex**

e**=list((“python”,“fast”,“growing”,“in”,2018))****#list**

f**=tuple((“python”,“easy”,“learning”))****#tuple**

g**=range(10)****#range**

h**=dict(name=“Vidu”,age=36)****#dict**

i**=set((“python”,“fast”,“growing”,“in”,2018))****#set**

j**=frozenset((“python”,“fast”,“growing”,“in”,2018))****#frozenset**

k**=bool(18)****#bool**

l**=bytes(8)****#bytes**

m**=bytearray(8)****#bytearray**

n**=memoryview(bytes(18))****#memoryview**

Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger

age**=**18

print**(age)**

Output**:**18

Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).

String

The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.

“Hello”+“python”

output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

---

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management