# 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](https://soundcloud.com/llestreacha) ยท [Decibels 2023 March Challenge ; Personal Data : Heart Edition](https://soundcloud.com/llestreacha/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 ```ruby 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 ```python import pandas as pd ``` ```python df = pd.read_csv('Jan16_23_heart - Sheet1.csv') ``` ```python 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 ```python 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 | ```python #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'] ``` ```python 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 | ```python df.to_csv('HeartRatePrep.csv') ```