1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
|
import * as util from './lib/util'
import * as data from './data'
import * as player from './player'
/* initialization */
const mass_fields = [
"date", "timestamp",
"fatalities", "injured", "total_victims",
"age", "case", "weapon_type", "weapon_details"
].reduce((a,b,i) => {
a[b] = i
return a
}, {})
const gv_fields = [
"incident_id", "date",
"state", "city_or_county", "address",
"n_killed", "n_injured",
"incident_url", "source_url", "incident_url_fields_missing",
"congressional_district",
"gun_stolen", "gun_type", "incident_characteristics",
"latitude", "location_description", "longitude",
"n_guns_involved",
"notes",
"participant_age", "participant_age_group", "participant_gender", "participant_name", "participant_relationship", "participant_status", "participant_type",
"sources",
"state_house_district", "state_senate_district"
].reduce((a,b,i) => {
a[b] = i
return a
}, {})
const year_days_by_month = [
31, 28, 31, 30,
31, 30, 31, 31,
30, 31, 30, 31,
0
].reduce((a, b, i) => {
if (i === 0) {
return [b]
}
return a.concat(a[i-1] + b)
}, [])
let i = 0, max_i = 0, mass_i = 0
let datasets = {}, dataset = {}, bounds = {}, diff = []
let play_fn = play_sequence
data.load().then(lists => {
console.log(lists)
const ar15 = lists.ar_15_2016_18
datasets['AR-15 2016-18'] = {}
datasets['AR-15 2016-18'].name = 'AR-15 (2016-18)'
datasets['AR-15 2016-18'].pedal = true
datasets['AR-15 2016-18'].play_fn = play_mass_shootings
const ar_lines = ar15.lines.map(l => {
if (l[gv_fields.incident_characteristics].match('Shots Fired - No Injuries')) {
return null
}
if (l[gv_fields.n_killed] + l[gv_fields.n_injured] < 4) return null
const [y, m, d] = l[gv_fields.date].split('-')
if (parseInt(y) > 2017) return null
const yy = (parseInt(y) - 2016) * 365
const mm = year_days_by_month[parseInt(m)]
const dd = Math.floor(parseInt(d)) + 14
const date = Math.floor((yy + mm + dd) / 7)
// console.log(date, y, m, d)
let total = l[gv_fields.n_killed] + l[gv_fields.n_injured]
if (l[gv_fields.n_killed] === 0) {
total = - l[gv_fields.n_injured]
}
return [
date,
Math.log(Math.log(total + 10) + 1),
"** !!, $$, {} killed, [] injured".replace('**', l[gv_fields.date]).replace('!!', l[gv_fields.city_or_county]).replace('$$', l[gv_fields.state]).replace('{}', l[gv_fields.n_killed]).replace('[]', l[gv_fields.n_injured]),
l[gv_fields.n_killed],
l[gv_fields.n_injured],
]
}).filter(n => !!n)
datasets['AR-15 2016-18'].dates = ar_lines.map(a => a[0])
datasets['AR-15 2016-18'].dates.push(ar_lines.length)
datasets['AR-15 2016-18'].lines = [ar_lines.map(a => a[1])]
datasets['AR-15 2016-18'].labels = ar_lines.map(a => a[2])
const fm = lists.firearms_manufactured
datasets['Firearms Manufactured'] = {}
datasets['Firearms Manufactured'].name = 'Firearms Manufactured'
datasets['Firearms Manufactured'].play_fn = play_sequence
datasets['Firearms Manufactured'].h = fm.h.slice(1, 5)
datasets['Firearms Manufactured'].labels = fm.lines.map(l => l.slice(0, 1))
datasets['Firearms Manufactured'].lines = fm.lines.map(l => l.slice(1, 5))
datasets["Mass Shootings"] = lists.mass_shootings_from_columbine
datasets["Mass Shootings"].name = "Mass Shootings"
datasets["Mass Shootings"].pedal = true
datasets["Mass Shootings"].isMass = true
datasets["Mass Shootings"].play_fn = play_mass_shootings
const lines = datasets["Mass Shootings"].lines.reverse()
const [min_y, ...rest_a] = lines[0][mass_fields.date].split('/')
const [max_y, ...rest_b] = lines[lines.length-1][mass_fields.date].split('/')
datasets["Mass Shootings"].dates = lines.map(row => {
const [y, m, d] = row[mass_fields.date].split('/')
return (parseInt(y) - parseInt(min_y)) * 12 + parseInt(m)
})
datasets["Mass Shootings"].max_i = (parseInt(max_y) - parseInt(min_y)) * 12 + parseInt(12)
// console.log('max i', max_i)
datasets["Mass Shootings"].data = lines
datasets["Mass Shootings"].lines = [lines.map(row => row[mass_fields.total_victims])]
ready()
})
/* play function for mass shooting data w/ custom timing */
function play_mass_shootings(i, bounds, diff, note_time, channel="all", exporting) {
const { min, max } = bounds
const total = dataset.dates.length
let pedal_note
let notes = [], midi_notes = []
let cases = []
let timings
let week = Math.floor((i)/4) % 4
let year = Math.floor((i - (4*4*3)) / 48) // + 2
console.log(year)
let yy = -year
if (year > 0) year += 1
let this_one = 0
// console.log(i, mass_i, dataset.dates[mass_i], channel, exporting)
while (i >= dataset.dates[mass_i] && mass_i < total) {
// console.log(i, dataset.dates[mass_i])
notes.push(dataset.lines[0][mass_i])
if (dataset.isMass) {
cases.push(dataset.data[mass_i][mass_fields.date] + ' ' + dataset.data[mass_i][mass_fields.case] +
", " + dataset.data[mass_i][mass_fields.fatalities] + ' dead, ' + dataset.data[mass_i][mass_fields.injured] + ' injured')
} else {
cases.push(dataset.labels[mass_i])
// console.log(dataset.labels[mass_i])
}
// console.log('push case', dataset.data[mass_i][mass_fields.date] + ' ' + dataset.data[mass_i][mass_fields.case])
mass_i += 1
this_one += 1
if (this_one >= 4) break
}
if (cases.length) {
document.querySelector('#cases').innerHTML = cases.join('<br>')
}
if (total <= mass_i) {
mass_i = 0
i = 0
} else {
i += 1
}
return [i, [], [], pedal_note]
}
/* play the next note in sequence */
function play_sequence(i, bounds, diff, note_time, channel="all", exporting) {
const { rows, min, max } = bounds
const count = rows.length * rows[0].length
if (i >= count) i = 0
const y = Math.floor(i / rows[0].length)
const x = i % rows[0].length
// if (!x) console.log(y)
const n = rows[y][x]
i += 1
if (i >= count) i = 0
const midi_note = play_note( norm(n, min, max) * nx.multiply.value, note_time, channel, exporting)
return [i, [midi_note], [128]]
}
/* play next note according to sonification */
function play_next(){
if (paused) return
let note_time = 120000 / Tone.Transport.bpm.value * note_values[nx.timing.active][0]
clearTimeout(playTimeout)
playTimeout = setTimeout(play_next, note_time)
let [new_i, notes, timings] = play_fn(i, bounds, diff, note_time)
if (dataset.labels) {
// const j = Math.floor(i / bounds.rows[0].length)
// document.querySelector('#cases').innerHTML = dataset.labels[j]
}
i = new_i
if (recording) {
let timing = note_values[nx.timing.active][2]
if (timing.length) timing = timing[i % timing.length]
recorder.addEvent(new MidiWriter.NoteEvent({ pitch: notes, duration: 't' + timing }))
}
}
/* build and bind the UI */
function ready() {
document.querySelector('.loading').classList.remove('loading')
}
|