DecibelsChallenge_PersonalData

For the third Decibels Challenge, the prompt for data is personal data. I decided to use data from 10 workouts I did and use SonicPi to sonify the process and Python to clean it up.

Wanted the sounds to represent the various stages a heart goes through in a workout. This explains some of the chaotic tension/feelings that can be heard in this track

Sounds

IllestPreacha · Decibels 2023 March Challenge ; Personal Data : Heart Edition

Data

Table

	HR_AVG	HR_MAX	HR_MIN	Calories	CardioLoad	Zone3	Zone2	HR_Max-MIn	Zone2+3	Other Zones	CaloLoad
0	147	174	98	554	71	0.44	0.1	76	0.54	0.46	7.802816901
1	123	156	108	876	80	0.36	0.37	48	0.73	0.27	10.95
2	110	135	74	44	3	0.16	0.61	61	0.77	0.23	14.66666667
3	100	126	80	224	16	0	0.12	46	0.12	0.88	14
4	130	156	74	292	29	0.56	0.38	82	0.94	0.06	10.06896552
5	116	140	95	137	11	0.05	0.72	45	0.77	0.23	12.45454545
6	126	158	93	434	42	0.34	0.49	65	0.83	0.17	10.33333333
7	123	154	86	593	58	0.44	0.31	68	0.75	0.25	10.22413793
8	136	136	85	570	69	0.24	0.17	51	0.41	0.59	8.260869565
9	90	123	73	157	12	0	0.15	50	0.15	0.85	13.08333333
10	138	172	108	497	57	0.64	0.16	64	0.8	0.2	8.719298246

Column Names

HR_AVG - Average Heart Rate
HR_Max - Max Heart Rate
HR_Min - Min Heart Rate
Calories - Calories Burnt
CardioLoad - the cardio intensity given for that workout
Zone2, Zone3, Other Zones - Refer to the Five Heart zones
CaloLoad - Is the ratio of Calories Burnt: Cardio Load

These columns are used in the SonicPi Code to make the sounds and can be used to understand which columns are affecting which part

Code

SonicPi Code

Comments in Code for more information

require 'csv'

#naming the Dataset DipInCode and going to read the file
Heart = CSV.parse(File.read("C:/Creatuve Code Challenges/Sonification Challenges/March 2023/HeartRatePrep.csv"), headers: true)


i = 0


live_loop :HeartZone do

  #these effects are going to be affected by the zones, since they are between 0 and 1, which is the range for the mix function
  with_fx :echo, mix: Heart[i]['Other Zones'].to_f  do
    with_fx :ixi_techno, mix: Heart[i]['Zone3'].to_f do
      with_fx :krush, mix: Heart[i]['Zone2+3'].to_f do

        #a trio of synths to add more musicality/variance to the listening experience
        use_synth [:organ_tonewheel,:mod_sine,:mod_sine].choose

        #Corresponds to the note being played
        play Heart[i]['HR_MIN'].to_f, decay: Heart[i]['Other Zones'].to_f, amp: dice(12)
        #We can even pass a list of times which it will treat as a circle of times:
        play_pattern_timed chord(Heart[i]['HR_MIN'].to_f, :m13), [Heart[i]['Zone2+3'].to_f, Heart[i]['Other Zones'].to_f ,Heart[i]['HR_MAX'].to_f/ Heart[i]['HR_MIN'].to_f]
      end
    end
  end

  i += 1 #counter

  if i == Heart.length #make the loop nonstop
    i = 0
  end

  #spacing
  sleep Math.sin(Heart[i]['HR_MAX'].to_f/ Heart[i]['HR_MIN'].to_f)
end


live_loop :HeartBeat do

#same as the previous loop, with the difference being a random choosing of 3 effects instead of 3 synths in this portion 

  with_fx :flanger, depth: Heart[i]['Other Zones'].to_f  do
    with_fx :echo,  mix: Heart[i]['Zone2+3'].to_f do
      with_fx [:ping_pong,:vowel,:whammy].choose, mix: Heart[i]['Other Zones'].to_f do
        sample :ambi_piano,beat_stretch: Heart[i]['Zone2'].to_f ,pitch: Heart[i]['CaloLoad'].to_f
        sample :perc_impact1,beat_stretch: Math.cos(Heart[i]['Other Zones'].to_f), decay: Heart[i]['CaloLoad'].to_f
      end
    end
  end

  i += 1

  if i == Heart.length #make the loop nonstop
    i = 0
  end

  sleep Heart[i]['HR_MAX'].to_f/ Heart[i]['HR_MIN'].to_f
end

Python Code

import pandas as pd

df = pd.read_csv('Jan16_23_heart - Sheet1.csv')

df.info()

<class 'pandas.core.frame.DataFrame'> RangeIndex: 11 entries, 0 to 10 Data columns (total 7 columns): # Column Non-Null Count Dtype
--- ------ -------------- -----
0 HR_AVG 11 non-null int64
1 HR_MAX 11 non-null int64
2 HR_MIN 11 non-null int64
3 Calories 11 non-null int64
4 CardioLoad 11 non-null int64
5 Zone3 11 non-null float64 6 Zone2 11 non-null float64 dtypes: float64(2), int64(5) memory usage: 744.0 bytes

df. describe()

	HR_AVG	HR_MAX	HR_MIN	Calories	CardioLoad	Zone3	Zone2
count	11.000000	11.000000	11.000000	11.000000	11.000000	11.000000	11.000000
mean	121.727273	148.181818	88.545455	398.000000	40.727273	0.293636	0.325455
std	16.870631	17.325231	12.902431	249.911184	27.756408	0.222003	0.210778
min	90.000000	123.000000	73.000000	44.000000	3.000000	0.000000	0.100000
25%	113.000000	135.500000	77.000000	190.500000	14.000000	0.105000	0.155000
50%	123.000000	154.000000	86.000000	434.000000	42.000000	0.340000	0.310000
75%	133.000000	157.000000	96.500000	562.000000	63.500000	0.440000	0.435000
max	147.000000	174.000000	108.000000	876.000000	80.000000	0.640000	0.720000

#time to make new columns based on the information provided and get more Numbers for the sonification


#HeartRate Max-MIn , getting the difference between Heart Rates
df['HR_Max-MIn'] = df['HR_MAX']- df['HR_MIN'] 

#Total percentage of HeartRatein Zone3 + Zone2
df['Zone2+3'] = df['Zone2'] + df['Zone3'] 

#Total percentage of Other Heart Zones
df['Other Zones'] = 1 - df['Zone2'] - df['Zone3'] 

#Calories divided by Cardio Load
df['CaloLoad'] = df['Calories'] / df['CardioLoad']

df

	HR_AVG	HR_MAX	HR_MIN	Calories	CardioLoad	Zone3	Zone2	HR_Max-MIn	Zone2+3	Other Zones	CaloLoad
0	147	174	98	554	71	0.44	0.10	76	0.54	0.46	7.802817
1	123	156	108	876	80	0.36	0.37	48	0.73	0.27	10.950000
2	110	135	74	44	3	0.16	0.61	61	0.77	0.23	14.666667
3	100	126	80	224	16	0.00	0.12	46	0.12	0.88	14.000000
4	130	156	74	292	29	0.56	0.38	82	0.94	0.06	10.068966
5	116	140	95	137	11	0.05	0.72	45	0.77	0.23	12.454545
6	126	158	93	434	42	0.34	0.49	65	0.83	0.17	10.333333
7	123	154	86	593	58	0.44	0.31	68	0.75	0.25	10.224138
8	136	136	85	570	69	0.24	0.17	51	0.41	0.59	8.260870
9	90	123	73	157	12	0.00	0.15	50	0.15	0.85	13.083333
10	138	172	108	497	57	0.64	0.16	64	0.80	0.20	8.719298

df.to_csv('HeartRatePrep.csv')

DecibelsChallenge_PersonalData

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