Space Launches and frequencies

For the Second Decibels Challenge, the dataset provided was from OurWorldInData

IllestPreacha · Space Launch Frequencies

Sonification Steps

While cleaning the data in Python, I decided to make 8 CSV Files
- 1 for each of the 6 Continents
- 1 for the Space Agencies
- 1 for the total launches in the world
For the Sonification in Sonic Pi, I decided to track the frequency of all the countries and agencies that participated in a launch during a single year. With the earlier cleaning, this was done per region and in intervals of four years (simulating the Olympics), with each year in a cycle being represented by a different sound.
- There are three distinct sounds in the 8 Sonifications
  - Originally the sounds included the bd dub sound
  - Then for the next set, that sound was replaced with a drone-type sound
  - For the world launches, the sounds are still in intervals but they are also pitched to represent the higher frequency of launches per year

SonicPi Code

Set 1 ( African, Asian & European Launches)

require 'csv'

#reading the csv fields
AfricanSpaceLaunch = CSV.parse(File.read("C:/Creatuve Code Challenges/Sonification Challenges/Febuary 2023/African_SpaceObject.csv"),headers: true)
#AsianSpaceLaunch = CSV.parse(File.read("C:/Creatuve Code Challenges/Sonification Challenges/Febuary 2023/Asian_SpaceObject.csv"),headers: true)
#making a counter
i = 0

live_loop :ASL do
  with_fx :flanger do
    with_fx :reverb do

      puts AfricanSpaceLaunch[i]['Year']
      #now going to give distinct sounds to the year based on a 4 year sound
      #giving decay effect to show how some years it lingers more

     #case is set to the interval of four years
      case AfricanSpaceLaunch[i]['Year'].to_f % 4
      when 0
        sample :bd_808, decay: AfricanSpaceLaunch[i]['yearly_launches'].to_f
      when 1
        sample :ambi_dark_woosh, decay: AfricanSpaceLaunch[i]['yearly_launches'].to_f
      when 2
        sample :drum_snare_hard, decay: AfricanSpaceLaunch[i]['yearly_launches'].to_f
      when 3
        sample :elec_bell, decay: AfricanSpaceLaunch[i]['yearly_launches'].to_f
      else
        sample :elec_ping, decay: AfricanSpaceLaunch[i]['yearly_launches'].to_f
      end
      #counter based on the amount of rows
      if i >= 27
        i = 0
      end
      i += 1
      sleep 0.5
    end
  end
end

Set 2 ( Oceanic, Agencies, North American & South American Launches)

require 'csv'

#reading the csv fields
OSL = CSV.parse(File.read("C:/Creatuve Code Challenges/Sonification Challenges/Febuary 2023/Oceanic_SpaceObject.csv"),headers: true)
#making a counter
i = 0

#after first three swapped out the bd_dub for a drone sound

live_loop :OSL do
  with_fx :flanger do
    with_fx :reverb do
      puts OSL[i]['Year']
      #now going to give distinct sounds to the year based on a 4 year sound
      #giving decay effect to show how some years it lingers more

    #case is set to the interval of four years
      case OSL[i]['Year'].to_f % 4
      when 0
        sample :ambi_drone, decay: OSL[i]['yearly_launches'].to_f
      when 1
        sample :ambi_dark_woosh, decay: OSL[i]['yearly_launches'].to_f
      when 2
        sample :drum_snare_hard, decay: OSL[i]['yearly_launches'].to_f
      when 3
        sample :elec_bell, decay: OSL[i]['yearly_launches'].to_f
      else
        sample :elec_ping, decay: OSL[i]['yearly_launches'].to_f
      end
         #counter based on the amount of rows
      if i >= 25
        i = 0
      end
      i += 1
      sleep 0.5
    end
  end
end

Set 3 ( World Launches)

require 'csv'

#reading the csv fields
W = CSV.parse(File.read("C:/Creatuve Code Challenges/Sonification Challenges/Febuary 2023/World_SpaceObject.csv"),headers: true)
#making a counter
i = 0

#for the world decided to do something a little different
#it still follows the dividing the data into intervals of 4 but uses notes to describe the intensity

live_loop :AOSL, amp: 6 do
  with_fx :flanger do
    with_fx :reverb do

      puts W[i]['Year']
#case is set to the interval of four years
      case W[i]['Year'].to_f % 4
      when 0
        use_synth :piano
        play W[i]['yearly_launches'].to_f / 10
      when 1
        use_synth :chipnoise
        play W[i]['yearly_launches'].to_f / 10
      when 2
        use_synth :chiplead
        play W[i]['yearly_launches'].to_f / 10
      when 3
        use_synth :pretty_bell
        play W[i]['yearly_launches'].to_f / 10
      else
        use_synth :piano
        play W[i]['yearly_launches'].to_f / 10
      end
      #counter based on the amount of rows
      if i >= 65
        i = 0
      end
      i += 1
      sleep 1
    end
  end
end

Python Data Cleaning Code

#reading the csv file

import pandas as pd
df = pd.read_csv("SpaceObjects.csv")
print(df)

Entity Code Year yearly_launches 0 Algeria DZA 2002 1 1 Algeria DZA 2010 1 2 Algeria DZA 2016 3 3 Algeria DZA 2017 1 4 Angola AGO 2017 1 ... ... ... ... ... 1114 World OWID_WRL 2018 453 1115 World OWID_WRL 2019 586 1116 World OWID_WRL 2020 1274 1117 World OWID_WRL 2021 1810 1118 World OWID_WRL 2022 2163

[1119 rows x 4 columns]

#getting information on the type of columns and the amount
df.info()

<class 'pandas.core.frame.DataFrame'> RangeIndex: 1119 entries, 0 to 1118 Data columns (total 4 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Entity 1119 non-null object 1 Code 981 non-null object 2 Year 1119 non-null int64 3 yearly_launches 1119 non-null int64 dtypes: int64(2), object(2) memory usage: 35.1+ KB

#getting information from the csv
df.describe()

	Year	yearly_launches
count	1119.000000	1119.000000
mean	2001.115282	25.587131
std	16.517192	124.276918
min	1957.000000	1.000000
25%	1991.000000	1.000000
50%	2005.000000	2.000000
75%	2015.000000	6.000000
max	2022.000000	2163.000000

df.Entity.unique()

array(['Algeria', 'Angola', 'Arabsat', 'Argentina', 'Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bangladesh', 'Belarus', 'Belgium', 'Bhutan', 'Bolivia', 'Brazil', 'Bulgaria', 'Canada', 'Chile', 'China', 'Colombia', 'Costa Rica', 'Czechia', 'Denmark', 'EUMETSAT', 'Ecuador', 'Egypt', 'Estonia', 'Ethiopia', 'European Space Agency', 'European Union', 'Eutelsat', 'Finland', 'France', 'Germany', 'Ghana', 'Greece', 'Guatemala', 'Hungary', 'India', 'Indonesia', 'Inmarsat', 'Intelsat', 'Intersputnik', 'Iran', 'Israel', 'Italy', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Laos', 'Latvia', 'Lithuania', 'Luxembourg', 'Malaysia', 'Mauritius', 'Mexico', 'Moldova', 'Monaco', 'Mongolia', 'Morocco', 'NATO', 'Nepal', 'Netherlands', 'New Zealand', 'Nigeria', 'North Korea', 'Norway', 'Pakistan', 'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Qatar', 'RASCOM', 'Romania', 'Russia', 'Rwanda', 'Saudi Arabia', 'Sea Launch', 'Singapore', 'Slovakia', 'Slovenia', 'South Africa', 'South Korea', 'Spain', 'Sri Lanka', 'Starsem', 'Sweden', 'Switzerland', 'Taiwan', 'Thailand', 'Tunisia', 'Turkey', 'Turkmenistan', 'Ukraine', 'United Arab Emirates', 'United Kingdom', 'United States', 'Uruguay', 'Venezuela', 'Vietnam', 'World'], dtype=object)

'''
African Countries

'Algeria', 'Angola', 'Ethiopia', 'Ghana', 'Egypt', 'Morocco', 'Mauritius', 'Nigeria', 'South Africa'.

European Countries:
'Austria', 'Belarus', 'Belgium', 'Bulgaria', 'Czechia', 'Denmark', 'Estonia', 'Finland', 'France', 'Germany', 
'Greece', 'Hungary', 'Italy', 'Lithuania', 'Luxembourg', 'Monaco', 'Netherlands', 'Norway', 'Poland', 
'Portugal', 'Romania', 'Russia', 'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'Switzerland', 'Ukraine', 'United Kingdom'

North American Countries:
'Canada', 'Costa Rica', 'Mexico', 'United States'.

South American Countries:
'Argentina', 'Bolivia', 'Brazil', 'Chile', 'Colombia', 'Ecuador', 'Paraguay', 'Peru', 'Uruguay', 'Venezuela'.

Asian Countries:
'Arabsat', 'Bangladesh', 'Bhutan', 'China', 'India', 'Indonesia', 'Iran', 'Israel', 'Japan', 
'Jordan', 'Kazakhstan', 'Laos', 'Malaysia', 'North Korea', 'Pakistan', 'Philippines', 'Singapore', 
'South Korea', 'Taiwan', 'Thailand', 'Turkey', 'Turkmenistan', 'Vietnam'.

Oceanic Countries:
'Australia', 'New Zealand'.

The non-countries in the list are:
'Arabsat', 'EUMETSAT', 'European Space Agency', 'European Union', 'Eutelsat', 'Inmarsat', 
'Intelsat', 'Intersputnik', 'NATO', 'RASCOM', 'Sea Launch', 'Starsem', 'World'.

'''

"\nAfrican Countries\n\n'Algeria', 'Angola', 'Ethiopia', 'Ghana', 'Egypt', 'Morocco', 'Mauritius', 'Nigeria', 'South Africa'.\n\nEuropean Countries:\n'Austria', 'Belarus', 'Belgium', 'Bulgaria', 'Czechia', 'Denmark', 'Estonia', 'Finland', 'France', 'Germany', \n'Greece', 'Hungary', 'Italy', 'Lithuania', 'Luxembourg', 'Monaco', 'Netherlands', 'Norway', 'Poland', \n'Portugal', 'Romania', 'Russia', 'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'Switzerland', 'Ukraine', 'United Kingdom'\n\nNorth American Countries:\n'Canada', 'Costa Rica', 'Mexico', 'United States'.\n\nSouth American Countries:\n'Argentina', 'Bolivia', 'Brazil', 'Chile', 'Colombia', 'Ecuador', 'Paraguay', 'Peru', 'Uruguay', 'Venezuela'.\n\nAsian Countries:\n'Arabsat', 'Bangladesh', 'Bhutan', 'China', 'India', 'Indonesia', 'Iran', 'Israel', 'Japan', \n'Jordan', 'Kazakhstan', 'Laos', 'Malaysia', 'North Korea', 'Pakistan', 'Philippines', 'Singapore', \n'South Korea', 'Taiwan', 'Thailand', 'Turkey', 'Turkmenistan', 'Vietnam'.\n\nOceanic Countries:\n'Australia', 'New Zealand'.\n\nThe non-countries in the list are:\n'Arabsat', 'EUMETSAT', 'European Space Agency', 'European Union', 'Eutelsat', 'Inmarsat', \n'Intelsat', 'Intersputnik', 'NATO', 'RASCOM', 'Sea Launch', 'Starsem', 'World'.\n\n"

#making new dataframes by breaking the it into Continents and Agencies

African = df[df["Entity"].isin(['Algeria', 'Angola', 'Ethiopia', 'Ghana', 'Egypt', 'Morocco', 'Mauritius', 'Nigeria', 'South Africa'])]

NorthAmerican = df[df["Entity"].isin(['Canada', 'Costa Rica', 'Mexico', 'United States'])]

SouthAmerican = df[df["Entity"].isin(['Argentina', 'Bolivia', 'Brazil', 'Chile', 'Colombia', 'Ecuador', 'Paraguay', 'Peru', 'Uruguay', 'Venezuela'])]

European = df[df["Entity"].isin(['Austria', 'Belarus', 'Belgium', 'Bulgaria', 'Czechia', 'Denmark', 'Estonia', 'Finland', 'France', 'Germany', 
'Greece', 'Hungary', 'Italy', 'Lithuania', 'Luxembourg', 'Monaco', 'Netherlands', 'Norway', 'Poland', 
'Portugal', 'Romania', 'Russia', 'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'Switzerland', 'Ukraine', 'United Kingdom'])]

Asian = df[df["Entity"].isin(['Bangladesh', 'Bhutan', 'China', 'India', 'Indonesia', 'Iran', 'Israel', 'Japan', 
'Jordan', 'Kazakhstan', 'Laos', 'Malaysia', 'North Korea', 'Pakistan', 'Philippines', 'Singapore', 
'South Korea', 'Taiwan', 'Thailand', 'Turkey', 'Turkmenistan', 'Vietnam'])]

Oceanic = df[df["Entity"].isin(['Australia', 'New Zealand'])]

Agencies = df[df["Entity"].isin(['Arabsat', 'EUMETSAT', 'European Space Agency', 'European Union', 'Eutelsat', 'Inmarsat', 
'Intelsat', 'Intersputnik', 'NATO', 'RASCOM', 'Sea Launch', 'Starsem'])]

World = df[df["Entity"].isin(['World'])]

#sorted by year
African = African.sort_values(by=['Year'],ascending=True)
NorthAmerican = NorthAmerican.sort_values(by=['Year'],ascending=True)
SouthAmerican = SouthAmerican.sort_values(by=['Year'],ascending=True)
European = European.sort_values(by=['Year'],ascending=True)
Asian = Asian.sort_values(by=['Year'],ascending=True)
Oceanic = Oceanic.sort_values(by=['Year'],ascending=True)
Agencies = Agencies.sort_values(by=['Year'],ascending=True)
World = World.sort_values(by=['Year'],ascending=True)

African.head()

	Entity	Code	Year	yearly_launches
218	Egypt	EGY	1998	1
814	South Africa	ZAF	1999	1
219	Egypt	EGY	2000	1
663	Morocco	MAR	2001	1
0	Algeria	DZA	2002	1

World.describe()

	Year	yearly_launches
count	66.000000	66.000000
mean	1989.500000	216.378788
std	19.196354	357.906725
min	1957.000000	2.000000
25%	1973.250000	108.250000
50%	1989.500000	134.500000
75%	2005.750000	158.000000
max	2022.000000	2163.000000

African.describe()

	Year	yearly_launches
count	28.000000	28.000000
mean	2012.607143	1.285714
std	7.563750	0.762896
min	1998.000000	1.000000
25%	2008.500000	1.000000
50%	2015.000000	1.000000
75%	2018.000000	1.000000
max	2022.000000	4.000000

NorthAmerican.describe()

	Year	yearly_launches
count	118.000000	118.000000
mean	1994.364407	62.991525
std	18.664682	219.515103
min	1958.000000	1.000000
25%	1978.250000	2.000000
50%	1996.000000	18.000000
75%	2011.000000	35.750000
max	2022.000000	1796.000000

SouthAmerican.describe()

	Year	yearly_launches
count	61.000000	61.000000
mean	2009.360656	1.852459
std	10.125928	1.930769
min	1985.000000	1.000000
25%	2000.000000	1.000000
50%	2013.000000	1.000000
75%	2017.000000	2.000000
max	2022.000000	13.000000

#saving each dataframe to a csv

African.to_csv('African_SpaceObject.csv')
SouthAmerican.to_csv('SouthAmerican_SpaceObject.csv')
NorthAmerican.to_csv('NorthAmerican_SpaceObject.csv')
Asian.to_csv('Asian_SpaceObject.csv')
European.to_csv('European_SpaceObject.csv')
World.to_csv('World_SpaceObject.csv')
Oceanic.to_csv('Oceanic_SpaceObject.csv')
Agencies.to_csv('Agencies_SpaceObject.csv')

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Decibels Challenge

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Space Launches and frequencies

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Set 1 ( African, Asian & European Launches)

Set 2 ( Oceanic, Agencies, North American & South American Launches)

Set 3 ( World Launches)

Python Data Cleaning Code