def csv_to_miditime(self, infile, outfile, octave):
raw_data = list(self.read_csv(infile))
mymidi = MIDITime(self.tempo, outfile, self.seconds_per_year, self.base_octave, self.octave_range, self.epoch)
note_list = []
for r in raw_data:
began_date = datetime.strptime(r["began_date"], "%Y-%m-%d %H:%M:%S+00:00") # 2009-01-15 16:15:00+00:00
ended_date = datetime.strptime(r["ended_date"], "%Y-%m-%d %H:%M:%S+00:00")
began_days_since_epoch = mymidi.days_since_epoch(began_date)
ended_days_since_epoch = mymidi.days_since_epoch(ended_date)
start_beat = mymidi.beat(began_days_since_epoch)
end_beat = mymidi.beat(ended_days_since_epoch)
duration_in_beats = end_beat - start_beat
if duration_in_beats < 3:
duration_in_beats = 3
# print start_beat, duration_in_beats
note_list = note_list + self.bigger_boat(round(start_beat), duration_in_beats, mymidi, octave)
# Add a track with those notes
mymidi.add_track(note_list)
# Output the .mid file
mymidi.save_midi()
def csv_to_miditime(self, infile, outfile, octave):
raw_data = list(self.read_csv(infile))
mymidi = MIDITime(self.tempo, outfile, self.seconds_per_year,
self.base_octave, self.octave_range, self.epoch)
note_list = []
start_note_index = 0
for r in raw_data:
began_date = datetime.strptime(
r["began_date"],
"%Y-%m-%d %H:%M:%S+00:00") # 2009-01-15 16:15:00+00:00
ended_date = datetime.strptime(r["ended_date"],
"%Y-%m-%d %H:%M:%S+00:00")
began_days_since_epoch = mymidi.days_since_epoch(began_date)
ended_days_since_epoch = mymidi.days_since_epoch(ended_date)
start_beat = mymidi.beat(began_days_since_epoch)
end_beat = mymidi.beat(ended_days_since_epoch)
duration_in_beats = end_beat - start_beat
# if duration_in_beats < 3:
# duration_in_beats = 3
# print start_beat, duration_in_beats
new_notes, start_note_index = self.bigger_boat_2(
start_beat, start_note_index, duration_in_beats, mymidi,
octave)
note_list = note_list + new_notes
# Add a track with those notes
mymidi.add_track(note_list)
# Output the .mid file
mymidi.save_midi()
def testDatetimeNaive(self):
m = MIDITime()
dt = datetime.datetime.strptime('1970-01-01', '%Y-%m-%d')
self.assertEqual(m.days_since_epoch(dt), 0)
import math
# instantiate the MITITime class with tempo 120 and 5sec/year
mymidi = MIDITime(120, 'data_sonfication.mid', 1, 5, 1)
# load in climate data as dictionary
climate_data = []
with open('GLB.Ts+dSST.csv') as csvfile:
readCSV = csv.reader(csvfile, delimiter=',')
# skip headers (first 2 rows)
next(readCSV)
next(readCSV)
for row in readCSV:
climate_data.append({
'days_since_epoch':
mymidi.days_since_epoch(datetime.strptime(row[0], '%Y')),
'magnitude_change':
row[17]
})
my_data_timed = [{
'beat': mymidi.beat(d['days_since_epoch']),
'magnitude_change': float(d['magnitude_change'])
} for d in climate_data]
start_time = my_data_timed[0]['beat']
data_list = [d['magnitude_change'] for d in my_data_timed]
def mag_to_pitch_tuned(magnitude):
"""
Consumes some magnitude value and normalizes it over the range of note
class Coal2Midi(object):
''' Adapted from Jordan Wirfs-Brock's awesome coal production sonification.
Post here: http://insideenergy.org/2016/05/03/listen-to-u-s-coal-production-fall-off-a-cliff/
Code and data here: https://github.com/InsideEnergy/Data-for-stories/tree/master/20160503-coal-production-sonification
'''
epoch = datetime(1970, 1, 1) # TODO: Allow this to override the midtime epoch
mymidi = None
tempo = 120
min_attack = 30
max_attack = 255
min_duration = 1
max_duration = 5
seconds_per_year = 26
c_major = ['C', 'D', 'E', 'F', 'G', 'A', 'B']
c_minor = ['C', 'D', 'Eb', 'F', 'G', 'Ab', 'Bb']
a_minor = ['A', 'B', 'C', 'D', 'E', 'F', 'F#', 'G', 'G#']
c_blues_minor = ['C', 'Eb', 'F', 'F#', 'G', 'Bb']
d_minor = ['D', 'E', 'F', 'G', 'A', 'Bb', 'C']
c_gregorian = ['C', 'D', 'Eb', 'F', 'G', 'Ab', 'A', 'Bb']
current_key = c_major
base_octave = 4
octave_range = 3
def __init__(self):
self.csv_to_miditime()
def read_csv(self, filepath):
csv_file = open(filepath, 'rU')
return csv.DictReader(csv_file, delimiter=',', quotechar='"')
def remove_weeks(self, csv_obj):
return [r for r in csv_obj if int(r['Week']) not in [53]]
def round_to_quarter_beat(self, input):
return round(input * 4) / 4
def round_to_half_beat(self, input):
return round(input * 2) / 2
def make_notes(self, data_timed, data_key):
note_list = []
start_time = data_timed[0]['beat']
for d in data_timed:
note_list.append([
# self.round_to_half_beat(d['beat'] - start_time),
round(d['beat'] - start_time),
self.data_to_pitch_tuned(d[data_key]),
100,
#mag_to_attack(d['magnitude']), # attack
1 # duration, in beats
])
return note_list
def data_to_pitch_tuned(self, datapoint):
# Where does this data point sit in the domain of your data? (I.E. the min magnitude is 3, the max in 5.6). In this case the optional 'True' means the scale is reversed, so the highest value will return the lowest percentage.
scale_pct = self.mymidi.linear_scale_pct(0, self.maximum, datapoint)
# Another option: Linear scale, reverse order
# scale_pct = mymidi.linear_scale_pct(0, self.maximum, datapoint, True)
# Another option: Logarithmic scale, reverse order
# scale_pct = mymidi.log_scale_pct(0, self.maximum, datapoint, True)
# Pick a range of notes. This allows you to play in a key.
mode = self.current_key
#Find the note that matches your data point
note = self.mymidi.scale_to_note(scale_pct, mode)
#Translate that note to a MIDI pitch
midi_pitch = self.mymidi.note_to_midi_pitch(note)
return midi_pitch
def mag_to_attack(self, datapoint):
# Where does this data point sit in the domain of your data? (I.E. the min magnitude is 3, the max in 5.6). In this case the optional 'True' means the scale is reversed, so the highest value will return the lowest percentage.
scale_pct = self.mymidi.linear_scale_pct(0, self.maximum, datapoint)
#max_attack = 10
adj_attack = (1 - scale_pct) * max_attack + 70
#adj_attack = 100
return adj_attack
def csv_to_miditime(self):
self.mymidi = MIDITime(self.tempo, 'coaltest.mid', self.seconds_per_year, self.base_octave, self.octave_range)
raw_data = self.read_csv('data/coal_prod_1984_2016_weeks_summed.csv')
filtered_data = self.remove_weeks(raw_data)
self.minimum = self.mymidi.get_data_range(filtered_data, 'CoalProd')[0] / 1000000.0
self.maximum = self.mymidi.get_data_range(filtered_data, 'CoalProd')[1] / 1000000.0
timed_data = []
# Get the first day in the dataset, so we can use it's day of the week to anchor our other weekly data.
first_day = self.mymidi.map_week_to_day(filtered_data[0]['Year'], filtered_data[0]['Week'])
for r in filtered_data:
# Convert the week to a date in that week
week_start_date = self.mymidi.map_week_to_day(r['Year'], r['Week'], first_day.weekday())
# To get your date into an integer format, convert that date into the number of days since Jan. 1, 1970
days_since_epoch = self.mymidi.days_since_epoch(week_start_date)
# Convert that integer date into a beat
beat = self.mymidi.beat(days_since_epoch)
timed_data.append({
'days_since_epoch': days_since_epoch,
'beat': beat,
'CoalProdMillions': float(r['CoalProd']) / 1000000.0
})
note_list = self.make_notes(timed_data, 'CoalProdMillions')
# Add a track with those notes
self.mymidi.add_track(note_list)
# Output the .mid file
self.mymidi.save_midi()
def testDatetimeAware(self):
m = MIDITime()
utc = pytz.utc
dt = utc.localize(datetime.datetime.strptime('1970-01-01', '%Y-%m-%d'))
self.assertEqual(m.days_since_epoch(dt), 0)
def testDatetimeNaive(self):
m = MIDITime()
dt = datetime.datetime.strptime('1970-01-01', '%Y-%m-%d')
self.assertEqual(m.days_since_epoch(dt), 0)
class Coal2Midi(object):
''' Adapted from Jordan Wirfs-Brock's awesome coal production sonification.
Post here: http://insideenergy.org/2016/05/03/listen-to-u-s-coal-production-fall-off-a-cliff/
Code and data here: https://github.com/InsideEnergy/Data-for-stories/tree/master/20160503-coal-production-sonification
'''
epoch = datetime(1970, 1,
1) # TODO: Allow this to override the midtime epoch
mymidi = None
tempo = 120
min_attack = 30
max_attack = 255
min_duration = 1
max_duration = 5
seconds_per_year = 26
c_major = ['C', 'D', 'E', 'F', 'G', 'A', 'B']
c_minor = ['C', 'D', 'Eb', 'F', 'G', 'Ab', 'Bb']
a_minor = ['A', 'B', 'C', 'D', 'E', 'F', 'F#', 'G', 'G#']
c_blues_minor = ['C', 'Eb', 'F', 'F#', 'G', 'Bb']
d_minor = ['D', 'E', 'F', 'G', 'A', 'Bb', 'C']
c_gregorian = ['C', 'D', 'Eb', 'F', 'G', 'Ab', 'A', 'Bb']
current_key = c_major
base_octave = 4
octave_range = 3
def __init__(self):
self.csv_to_miditime()
def read_csv(self, filepath):
csv_file = open(filepath, 'rU')
return csv.DictReader(csv_file, delimiter=',', quotechar='"')
def remove_weeks(self, csv_obj):
return [r for r in csv_obj if int(r['Week']) not in [53]]
def round_to_quarter_beat(self, input):
return round(input * 4) / 4
def round_to_half_beat(self, input):
return round(input * 2) / 2
def make_notes(self, data_timed, data_key):
note_list = []
start_time = data_timed[0]['beat']
for d in data_timed:
note_list.append([
# self.round_to_half_beat(d['beat'] - start_time),
round(d['beat'] - start_time),
self.data_to_pitch_tuned(d[data_key]),
100,
#mag_to_attack(d['magnitude']), # attack
1 # duration, in beats
])
return note_list
def data_to_pitch_tuned(self, datapoint):
# Where does this data point sit in the domain of your data? (I.E. the min magnitude is 3, the max in 5.6). In this case the optional 'True' means the scale is reversed, so the highest value will return the lowest percentage.
scale_pct = self.mymidi.linear_scale_pct(0, self.maximum, datapoint)
# Another option: Linear scale, reverse order
# scale_pct = mymidi.linear_scale_pct(0, self.maximum, datapoint, True)
# Another option: Logarithmic scale, reverse order
# scale_pct = mymidi.log_scale_pct(0, self.maximum, datapoint, True)
# Pick a range of notes. This allows you to play in a key.
mode = self.current_key
#Find the note that matches your data point
note = self.mymidi.scale_to_note(scale_pct, mode)
#Translate that note to a MIDI pitch
midi_pitch = self.mymidi.note_to_midi_pitch(note)
return midi_pitch
def mag_to_attack(self, datapoint):
# Where does this data point sit in the domain of your data? (I.E. the min magnitude is 3, the max in 5.6). In this case the optional 'True' means the scale is reversed, so the highest value will return the lowest percentage.
scale_pct = self.mymidi.linear_scale_pct(0, self.maximum, datapoint)
#max_attack = 10
adj_attack = (1 - scale_pct) * max_attack + 70
#adj_attack = 100
return adj_attack
def csv_to_miditime(self):
self.mymidi = MIDITime(self.tempo, 'coaltest.mid',
self.seconds_per_year, self.base_octave,
self.octave_range)
raw_data = self.read_csv('data/coal_prod_1984_2016_weeks_summed.csv')
filtered_data = self.remove_weeks(raw_data)
self.minimum = self.mymidi.get_data_range(filtered_data,
'CoalProd')[0] / 1000000.0
self.maximum = self.mymidi.get_data_range(filtered_data,
'CoalProd')[1] / 1000000.0
timed_data = []
# Get the first day in the dataset, so we can use it's day of the week to anchor our other weekly data.
first_day = self.mymidi.map_week_to_day(filtered_data[0]['Year'],
filtered_data[0]['Week'])
for r in filtered_data:
# Convert the week to a date in that week
week_start_date = self.mymidi.map_week_to_day(
r['Year'], r['Week'], first_day.weekday())
# To get your date into an integer format, convert that date into the number of days since Jan. 1, 1970
days_since_epoch = self.mymidi.days_since_epoch(week_start_date)
# Convert that integer date into a beat
beat = self.mymidi.beat(days_since_epoch)
timed_data.append({
'days_since_epoch':
days_since_epoch,
'beat':
beat,
'CoalProdMillions':
float(r['CoalProd']) / 1000000.0
})
note_list = self.make_notes(timed_data, 'CoalProdMillions')
# Add a track with those notes
self.mymidi.add_track(note_list)
# Output the .mid file
self.mymidi.save_midi()
my_data = my_data_chg[1:]
#print("raw data:\n{0}".format(carbon_data))
#print("changes:\n{0}".format(my_data_print[1:]))
# Data should look like this
# my_data = [
# {'event_date': <datetime object>, 'magnitude': 3.4},
# {'event_date': <datetime object>, 'magnitude': 3.2},
# {'event_date': <datetime object>, 'magnitude': 3.6},
# {'event_date': <datetime object>, 'magnitude': 3.0},
# {'event_date': <datetime object>, 'magnitude': 5.6},
# {'event_date': <datetime object>, 'magnitude': 4.0}
# ]
# Convert date/time into an integer
my_data_epoched = [{
'days_since_epoch': mymidi.days_since_epoch(d['event_date']),
'magnitude': d['magnitude']
} for d in my_data]
# Convert integer date/time into something reasonable for a song
my_data_timed = [{
'beat': mymidi.beat(d['days_since_epoch']),
'magnitude': d['magnitude']
} for d in my_data_epoched]
start_time = my_data_timed[0]['beat']
def mag_to_pitch_tuned(magnitude):
# Where does this data point sit in the domain of your data? (I.E. the min magnitude is 3, the max in 5.6). In this case the optional 'True' means the scale is reversed, so the highest value will return the lowest percentage.
scale_pct = mymidi.linear_scale_pct(-17, 34, magnitude)
def testDatetimeAware(self):
m = MIDITime()
utc = pytz.utc
dt = utc.localize(datetime.datetime.strptime('1970-01-01', '%Y-%m-%d'))
self.assertEqual(m.days_since_epoch(dt), 0)
class bomb2midi(object):
''' Submitted by Jennifer LaFleur. '''
epoch = datetime(
1945, 1, 1) # Not actually necessary, but optional to specify your own
mymidi = None
min_value = 0
max_value = 5.7
tempo = 120
min_attack = 30
max_attack = 255
min_duration = 1
max_duration = 5
seconds_per_year = 3
c_major = ['C', 'D', 'E', 'F', 'G', 'A', 'B']
c_minor = ['C', 'D', 'Eb', 'F', 'G', 'Ab', 'Bb']
a_minor = ['A', 'B', 'C', 'D', 'E', 'F', 'F#', 'G', 'G#']
c_blues_minor = ['C', 'Eb', 'F', 'F#', 'G', 'Bb']
d_minor = ['D', 'E', 'F', 'G', 'A', 'Bb', 'C']
c_gregorian = ['C', 'D', 'Eb', 'F', 'G', 'Ab', 'A', 'Bb']
current_key = c_major
base_octave = 2
octave_range = 5
def __init__(self):
self.csv_to_miditime()
def read_csv(self, filepath):
csv_file = open(filepath, 'rU')
return csv.DictReader(csv_file, delimiter=',', quotechar='"')
def remove_weeks(self, csv_obj):
return [r for r in csv_obj if r['Date'] not in ['']]
def round_to_quarter_beat(self, input):
return round(input * 4) / 4
def make_notes(self, data_timed, data_key):
note_list = []
start_time = data_timed[0]['beat']
for d in data_timed:
note_list.append([
self.round_to_quarter_beat(d['beat'] - start_time),
self.data_to_pitch_tuned(d[data_key]),
100,
#mag_to_attack(d['magnitude']), # attack
1 # duration, in beats
])
return note_list
def csv_to_miditime(self):
raw_data = list(self.read_csv('data/bombs.csv'))
filtered_data = self.remove_weeks(raw_data)
self.mymidi = MIDITime(self.tempo, 'bombtest_log.mid',
self.seconds_per_year, self.base_octave,
self.octave_range, self.epoch)
self.minimum = self.mymidi.get_data_range(filtered_data, 'Yieldnum')[0]
self.maximum = self.mymidi.get_data_range(filtered_data, 'Yieldnum')[1]
timed_data = []
for r in filtered_data:
python_date = datetime.strptime(r["Date"], "%m/%d/%Y")
days_since_epoch = self.mymidi.days_since_epoch(python_date)
beat = self.mymidi.beat(days_since_epoch)
timed_data.append({
'days_since_epoch': days_since_epoch,
'beat': beat,
'BombYieldMillions': float(r['Yieldnum'])
})
note_list = self.make_notes(timed_data, 'BombYieldMillions')
# Add a track with those notes
self.mymidi.add_track(note_list)
# Output the .mid file
self.mymidi.save_midi()
def data_to_pitch_tuned(self, datapoint):
# Where does this data point sit in the domain of your data? (I.E. the min magnitude is 3, the max in 5.6). In this case the optional 'True' means the scale is reversed, so the highest value will return the lowest percentage.
#scale_pct = self.mymidi.linear_scale_pct(0, self.maximum, datapoint)
# Another option: Linear scale, reverse order
# scale_pct = self.mymidi.linear_scale_pct(0, self.maximum, datapoint, True)
# print 10**self.maximum
# Another option: Logarithmic scale, reverse order
scale_pct = self.mymidi.log_scale_pct(0, self.maximum, datapoint, True,
'log')
# Pick a range of notes. This allows you to play in a key.
mode = self.current_key
#Find the note that matches your data point
note = self.mymidi.scale_to_note(scale_pct, mode)
#Translate that note to a MIDI pitch
midi_pitch = self.mymidi.note_to_midi_pitch(note)
print scale_pct, note
return midi_pitch
def mag_to_attack(self, datapoint):
# Where does this data point sit in the domain of your data? (I.E. the min magnitude is 3, the max in 5.6). In this case the optional 'True' means the scale is reversed, so the highest value will return the lowest percentage.
scale_pct = self.mymidi.linear_scale_pct(0, self.maximum, datapoint)
#max_attack = 10
adj_attack = (1 - scale_pct) * max_attack + 70
#adj_attack = 100
return adj_attack
{'event_date': datetime (1912,7,12) , 'trips': 1 },
{'event_date': datetime (1912,7,13) , 'trips': 1 },
{'event_date': datetime (1912,7,14) , 'trips': 2 },
{'event_date': datetime (1912,7,15) , 'trips': 2 },
{'event_date': datetime (1912,7,16) , 'trips': 2 },
{'event_date': datetime (1912,7,17) , 'trips': 2 },
{'event_date': datetime (1912,7,18) , 'trips': 1 },
{'event_date': datetime (1912,7,19) , 'trips': 1 },
{'event_date': datetime (1912,7,20) , 'trips': 0 },
{'event_date': datetime (1912,7,21) , 'trips': 0 },
{'event_date': datetime (1912,7,22) , 'trips': 0 },
{'event_date': datetime (1912,7,23) , 'trips': 0 },
{'event_date': datetime (1912,7,24) , 'trips': 0 }
]
my_data_epoched = [{'days_since_epoch': mymidi.days_since_epoch(d['event_date']), 'trips': d['trips']} for d in my_data]
my_data_timed = [{'beat': mymidi.beat(d['days_since_epoch']), 'trips': d['trips']} for d in my_data_epoched]
start_time = my_data_timed[0]['beat']
def mag_to_pitch_tuned(trips):
scale_pct = mymidi.linear_scale_pct(0, 2, trips)
# Pick a range of notes. This allows you to play in a key.
c_major = ['C', 'C#', 'D', 'D#', 'E', 'E#', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B', 'B#']
#Find the note that matches your data point
note = mymidi.scale_to_note(scale_pct, c_major)
#Translate that note to a MIDI pitch
midi_pitch = mymidi.note_to_midi_pitch(note)
class Pebble(object):
'''
Lots of stuff cribbed from here: https://www.angio.net/personal/climb/speed
'''
g = 9.8
mass_grams = 141 # 5 oz, or a baseball
epoch = datetime(
2004, 1, 1) # Not actually necessary, but optional to specify your own
mymidi = None
tempo = 120
min_velocity = 30
max_velocity = 127
min_impact_duration = 1
max_impact_duration = 4
seconds_per_year = 1
c_major = ['C', 'D', 'E', 'F', 'G', 'A', 'B']
c_minor = ['C', 'D', 'Eb', 'F', 'G', 'Ab', 'Bb']
a_minor = ['A', 'B', 'C', 'D', 'E', 'F', 'F#', 'G', 'G#']
c_blues_minor = ['C', 'Eb', 'F', 'F#', 'G', 'Bb']
d_minor = ['D', 'E', 'F', 'G', 'A', 'Bb', 'C']
c_gregorian = ['C', 'D', 'Eb', 'F', 'G', 'Ab', 'A', 'Bb']
current_key = c_major
base_octave = 2
octave_range = 4
def __init__(self):
self.csv_to_miditime()
def get_yearly_averages(self, rows, date_var, date_format, distance_var,
unit):
years = {}
for r in rows:
# filter out nulls
if r[distance_var]:
if r[distance_var] != '':
# extract year
year = datetime.strptime(r[date_var], date_format).year
# make a decade
decade = int('%s0' % (str(year)[:-1], ))
# convert to meters (if feet):
if unit == 'feet':
distance_meters = self.feet_to_meters(
float(r[distance_var]))
else:
distance_meters = float(r[distance_var])
if decade not in years:
years[decade] = [distance_meters]
else:
years[decade].append(distance_meters)
# now get averages
output = []
for year, values in years.iteritems():
yearly_avg = {
'year': year,
'median_distance_meters': median(values)
}
output.append(yearly_avg)
print yearly_avg
# sort them
return sorted(output, key=lambda k: k['year'])
def feet_to_meters(self, feet):
return float(feet) * 0.3048
def time_to_impact(self, height_meters):
return math.sqrt(2 * float(height_meters) / self.g)
def seconds_to_beats(self, seconds): # Just for manually setting seconds
return seconds * (self.tempo / 60)
def read_csv(self, filepath):
csv_file = open(filepath, 'rU')
return csv.DictReader(csv_file, delimiter=',', quotechar='"')
#
# def round_to_quarter_beat(self, input):
# return round(input * 4) / 4
def velocity_on_impact(self, height_meters): # sqrt( 2 * g * height )
return math.sqrt(2 * self.g * float(height_meters))
def energy_on_impact(
self, mass, velocity
): # Energy at splat time: 1/2 * mass * velocity2 = mass * g * height
return (mass * velocity) / 2
def energy_to_velocity(self, datapoint):
# Where does this data point sit in the domain of your data? (I.E. the min magnitude is 3, the max in 5.6). In this case the optional 'True' means the scale is reversed, so the highest value will return the lowest percentage.
#scale_pct = self.mymidi.linear_scale_pct(0, self.maximum, datapoint)
# Another option: Linear scale, reverse order
scale_pct = self.mymidi.linear_scale_pct(0, self.maximum_energy,
datapoint)
# print 10**self.maximum
# Another option: Logarithmic scale, reverse order
# scale_pct = self.mymidi.log_scale_pct(0, self.maximum, datapoint, True, 'log')
velocity_range = self.max_velocity - self.min_velocity
velocity = self.min_velocity + (scale_pct * velocity_range)
return velocity
def data_to_pitch_tuned(self, datapoint):
# Where does this data point sit in the domain of your data? (I.E. the min magnitude is 3, the max in 5.6). In this case the optional 'True' means the scale is reversed, so the highest value will return the lowest percentage.
#scale_pct = self.mymidi.linear_scale_pct(0, self.maximum, datapoint)
# Another option: Linear scale, reverse order
scale_pct = self.mymidi.linear_scale_pct(0, self.maximum_energy,
datapoint, True)
# print 10**self.maximum
# Another option: Logarithmic scale, reverse order
# scale_pct = self.mymidi.log_scale_pct(0, self.maximum, datapoint, True, 'log')
# Pick a range of notes. This allows you to play in a key.
mode = self.current_key
#Find the note that matches your data point
note = self.mymidi.scale_to_note(scale_pct, mode)
#Translate that note to a MIDI pitch
midi_pitch = self.mymidi.note_to_midi_pitch(note)
return midi_pitch
def energy_to_duration(self, datapoint): # For impact duration, not fall
scale_pct = self.mymidi.linear_scale_pct(self.minimum_energy,
self.maximum_energy,
datapoint)
duration_range = self.max_impact_duration - self.min_impact_duration
duration = self.min_impact_duration + (scale_pct * duration_range)
return duration
def make_falling_notes(self, data_timed, data_key, channel):
note_list = []
start_time = data_timed[0]['beat']
for d in data_timed:
note_list.append([
[
d['beat'] - start_time,
self.mymidi.note_to_midi_pitch(
"C4"), # pitch (set manually for drop)
100, # velocity
self.seconds_to_beats(
d['duration_secs']) # duration, in beats
],
channel
])
return note_list
def make_splashing_notes(self, data_timed, data_key, channel):
note_list = []
start_time = data_timed[0]['beat']
for d in data_timed:
velocity = self.velocity_on_impact(d['distance_meters'])
energy = self.energy_on_impact(self.mass_grams, velocity)
note_list.append([
[
d['beat'] - start_time + self.seconds_to_beats(
d[data_key]), # falling start plus duration of fall
self.data_to_pitch_tuned(energy), # pitch
self.energy_to_velocity(energy), # velocity
self.energy_to_duration(energy) # duration, in beats
],
channel
])
return note_list
def csv_to_miditime(self):
# raw_data = list(self.read_csv('data/groundwater_test.csv'))
raw_data = list(self.read_csv('data/15S18E30L001M_clean.csv'))
# yearly_data = self.get_yearly_averages(raw_data, 'Date', "%m/%d/%Y", 'wl(m)', 'meters')
yearly_data = self.get_yearly_averages(raw_data, 'Measurement_Date',
"%m-%d-%Y", 'GSWS', 'feet')
self.mymidi = MIDITime(self.tempo, 'media_out/pebble_longterm.mid',
self.seconds_per_year, self.base_octave,
self.octave_range, self.epoch)
self.minimum_depth = self.mymidi.get_data_range(
yearly_data, 'median_distance_meters')[0]
self.maximum_depth = self.mymidi.get_data_range(
yearly_data, 'median_distance_meters')[1]
self.minimum_energy = self.energy_on_impact(
self.mass_grams,
self.velocity_on_impact(
self.mymidi.get_data_range(yearly_data,
'median_distance_meters')[0]))
self.maximum_energy = self.energy_on_impact(
self.mass_grams,
self.velocity_on_impact(
self.mymidi.get_data_range(yearly_data,
'median_distance_meters')[1]))
timed_data = []
for r in yearly_data:
# python_date = datetime.strptime(r["Date"], "%Y-%m-%d")
python_date = datetime.strptime('1/1/%s' % r["year"], "%m/%d/%Y")
distance_meters = r['median_distance_meters']
days_since_epoch = self.mymidi.days_since_epoch(python_date)
beat = self.mymidi.beat(days_since_epoch)
timed_data.append({
'days_since_epoch':
days_since_epoch,
'beat':
beat,
'distance_meters':
distance_meters,
'duration_secs':
self.time_to_impact(distance_meters)
})
falling_note_list = self.make_falling_notes(timed_data,
'duration_secs', 0)
splashing_note_list = self.make_splashing_notes(
timed_data, 'duration_secs', 1)
# Add a track with those notes
self.mymidi.add_track(falling_note_list)
self.mymidi.add_track(splashing_note_list)
# Output the .mid file
self.mymidi.save_midi()
class bomb2midi(object):
''' Submitted by Jennifer LaFleur. '''
epoch = datetime(1945, 1, 1) # Not actually necessary, but optional to specify your own
mymidi = None
min_value = 0
max_value = 5.7
tempo = 120
min_attack = 30
max_attack = 255
min_duration = 1
max_duration = 5
seconds_per_year = 3
c_major = ['C', 'D', 'E', 'F', 'G', 'A', 'B']
c_minor = ['C', 'D', 'Eb', 'F', 'G', 'Ab', 'Bb']
a_minor = ['A', 'B', 'C', 'D', 'E', 'F', 'F#', 'G', 'G#']
c_blues_minor = ['C', 'Eb', 'F', 'F#', 'G', 'Bb']
d_minor = ['D', 'E', 'F', 'G', 'A', 'Bb', 'C']
c_gregorian = ['C', 'D', 'Eb', 'F', 'G', 'Ab', 'A', 'Bb']
current_key = c_major
base_octave = 2
octave_range = 5
def __init__(self):
self.csv_to_miditime()
def read_csv(self, filepath):
csv_file = open(filepath, 'rU')
return csv.DictReader(csv_file, delimiter=',', quotechar='"')
def remove_weeks(self, csv_obj):
return [r for r in csv_obj if r['Date'] not in ['']]
def round_to_quarter_beat(self, input):
return round(input * 4) / 4
def make_notes(self, data_timed, data_key):
note_list = []
start_time = data_timed[0]['beat']
for d in data_timed:
note_list.append([
self.round_to_quarter_beat(d['beat'] - start_time),
self.data_to_pitch_tuned(d[data_key]),
100,
#mag_to_attack(d['magnitude']), # attack
1 # duration, in beats
])
return note_list
def csv_to_miditime(self):
raw_data = list(self.read_csv('data/bombs.csv'))
filtered_data = self.remove_weeks(raw_data)
self.mymidi = MIDITime(self.tempo, 'bombtest_log.mid', self.seconds_per_year, self.base_octave, self.octave_range, self.epoch)
self.minimum = self.mymidi.get_data_range(filtered_data, 'Yieldnum')[0]
self.maximum = self.mymidi.get_data_range(filtered_data, 'Yieldnum')[1]
timed_data = []
for r in filtered_data:
python_date = datetime.strptime(r["Date"], "%m/%d/%Y")
days_since_epoch = self.mymidi.days_since_epoch(python_date)
beat = self.mymidi.beat(days_since_epoch)
timed_data.append({
'days_since_epoch': days_since_epoch,
'beat': beat,
'BombYieldMillions': float(r['Yieldnum'])
})
note_list = self.make_notes(timed_data, 'BombYieldMillions')
# Add a track with those notes
self.mymidi.add_track(note_list)
# Output the .mid file
self.mymidi.save_midi()
def data_to_pitch_tuned(self, datapoint):
# Where does this data point sit in the domain of your data? (I.E. the min magnitude is 3, the max in 5.6). In this case the optional 'True' means the scale is reversed, so the highest value will return the lowest percentage.
#scale_pct = self.mymidi.linear_scale_pct(0, self.maximum, datapoint)
# Another option: Linear scale, reverse order
# scale_pct = self.mymidi.linear_scale_pct(0, self.maximum, datapoint, True)
# print 10**self.maximum
# Another option: Logarithmic scale, reverse order
scale_pct = self.mymidi.log_scale_pct(0, self.maximum, datapoint, True, 'log')
# Pick a range of notes. This allows you to play in a key.
mode = self.current_key
#Find the note that matches your data point
note = self.mymidi.scale_to_note(scale_pct, mode)
#Translate that note to a MIDI pitch
midi_pitch = self.mymidi.note_to_midi_pitch(note)
print scale_pct, note
return midi_pitch
def mag_to_attack(self, datapoint):
# Where does this data point sit in the domain of your data? (I.E. the min magnitude is 3, the max in 5.6). In this case the optional 'True' means the scale is reversed, so the highest value will return the lowest percentage.
scale_pct = self.mymidi.linear_scale_pct(0, self.maximum, datapoint)
#max_attack = 10
adj_attack = (1 - scale_pct) * max_attack + 70
#adj_attack = 100
return adj_attack
try:
format = "%m/%d/%y %H:%M"
print(format, date_string)
datetime.datetime.strptime(date_string, format)
# print("true")
date_time_obj = datetime.datetime.strptime(
data[i]['time'], '%m/%d/%y %H:%M')
except ValueError:
print("")
#array with columns ['hours_since_epoch', 'in'].
#in each row 'hours_since_epoch' has the time in terms of hours elapsed since epoch along with the corresponding value given in the 'in' column
#hours since epoch is taken out using days_since_epoch() * 24
my_in_data_epoched_hours.append({
'hours_since_epoch':
mymidiIN.days_since_epoch(date_time_obj) * 24,
'in':
data[i]['in']
})
#array with columns ['hours_since_epoch', 'out'].
#in each row 'hours_since_epoch' has the time in terms of hours elapsed since epoch along with the corresponding value given in the 'out' column
#hours since epoch is taken out using days_since_epoch() * 24
my_out_data_epoched_hours.append({
'hours_since_epoch':
mymidiOUT.days_since_epoch(date_time_obj) * 24,
'out':
data[i]['out']
})
#Step 4