def search(stimset='../Search'):
# Connect network handler
ip = '127.0.0.1'
port = 5556
timeout = 1.
url = "tcp://%s:%d" % (ip, port)
songfiles = glob.glob(stimset + '/*.wav')
size = len(songfiles)
present_order = sdk.seqorder(size)
stim, Fs, songname = ss.dBclean(stimset)
print(songname)
with zmq.Context() as context:
with context.socket(zmq.REQ) as socket:
socket.RCVTIMEO = int(timeout * 1000) # timeout in milliseconds
socket.connect(url)
# Start data acquisition
socket.send_string('StartAcquisition')
print(socket.recv().decode())
time.sleep(5)
socket.send_string('IsAcquiring')
print("IsAcquiring:", socket.recv().decode())
print("")
print("Playing search stimuli...")
print("To end, press CTRL-C")
try:
while True:
for i in range(len(present_order)):
sd.play(ss.pulsestim(stim[present_order[i] - 1], True),
Fs)
sd.wait()
time.sleep(2)
except KeyboardInterrupt:
print("")
socket.send_string('StopAcquisition')
print(socket.recv().decode())
time.sleep(1)
socket.send_string('IsAcquiring')
print("IsAcquiring:", socket.recv().decode())
pass
def align_audio(fp,log,thresh=1):
data = h5py.File(fp)
with open(log) as json_file:
meta = json.load(json_file)
n = len(meta['presentation'])
present_order = sdk.seqorder(n//10)
total_start = 0
df = pd.DataFrame(index=np.arange(0, n), columns = ['rec','song','condition','stim','total_start','total_stop','total_pulse','local_pulse'])
for i in range(n):
try:
data['rec_'+str(i)]
if len({len(data['rec_0/'+i]) for i in list(data['rec_0'].keys()) if 'channel' in i}) == 1:
pulse = data['rec_'+str(i)+'/channel37']
points = np.where(pulse[:]>thresh)[0]
dist = np.diff(points)
points = np.delete(points,np.where(dist==1))
ind = present_order[i]-1
if len(points) > 1:
print("Expecting 1 pulse, got "+str(len(points))+". Adjust threshold.")
else:
if type(meta['presentation']) == list:
song = meta['presentation'][i].split(' ')[0]
condition = ' '.join(meta['presentation'][i].split(' ')[1:])
else:
song = meta['presentation'][str(i)]['song']
condition = meta['presentation'][str(i)]['type']
df.loc[i]=[i,song,condition,ind,total_start,total_start+len(pulse)-1,total_start+points[0],points[0]]
total_start = total_start+len(pulse)
else:
raise ValueError('Not all channels have same length!')
except:
ind = present_order[i]-1
if type(meta['presentation']) == list:
song = meta['presentation'][i].split(' ')[0]
condition = ' '.join(meta['presentation'][i].split(' ')[1:])
else:
song = meta['presentation'][str(i)]['song']
condition = meta['presentation'][str(i)]['type']
df.loc[i]=[i,song,condition,ind,np.nan,np.nan,np.nan,np.nan]
df.to_csv('/'.join(fp.split('/')[:-1])+'/alignment.csv',index=False)
data.close()
def make_stims(stimset, seed):
if '180B' in stimset or '178B' in stimset:
song, Fs, songname = ss.dBclean('../178B')
song2, Fs, songname2 = ss.dBclean('../180B')
song = np.concatenate((song, song2), 0)
songname = songname + songname2
else:
song, Fs, songname = ss.dBclean(stimset)
stype = [
'continuous', 'continuousnoise1', 'continuousnoise2', 'gap1', 'gap2',
'gapnoise1', 'gapnoise2', 'noise1', 'noise2'
]
syllables = pd.read_csv('../gap-locations.csv')
conditions = len(stype)
trials = 10
size = len(songname) * conditions
present_order = sdk.seqorder(size, trials)
presentation = {}
stims = []
np.random.seed(seed)
for i in range(len(songname)):
ssyll = syllables.loc[syllables.song == songname[i]]
blocks = np.zeros((2, 2))
blocks[:, 0] = np.asarray(
(ssyll.start1.values[0], ssyll.start2.values[0]))
blocks[:, 1] = np.asarray((ssyll.end1.values[0], ssyll.end2.values[0]))
blocks = blocks * Fs
blocks = blocks.astype(int)
gsize = int(0.1 * Fs)
wnamp = 25000
wndB = 85
if blocks[0, 1] - blocks[0, 0] > gsize:
blocks[0, 1] = blocks[0, 0] + gsize
if blocks[1, 1] - blocks[1, 0] > gsize:
blocks[1, 1] = blocks[1, 0] + gsize
N = 120
ix = np.arange(N * 3)
signal = np.cos(2 * np.pi * ix / float(N * 2)) * 0.5 + 0.5
fadein = signal[120:240]
fadeout = signal[0:120]
wn1 = np.random.normal(0, wnamp, size=blocks[0, 1] - blocks[0, 0])
wn1 = (wn1 / rms(wn1)) * scale(wndB)
wn2 = np.random.normal(0, wnamp, size=blocks[1, 1] - blocks[1, 0])
wn2 = (wn2 / rms(wn2)) * scale(wndB)
for k in range(conditions):
order = (i * conditions) + k
presentation[order] = {
"song": songname[i],
"type": stype[k],
}
if k == 0:
stims.append(song[i])
elif k == 1:
presentation[order]['gaps'] = blocks[0].tolist()
stims.append(
np.concatenate((song[i][:blocks[0, 0]],
song[i][blocks[0, 0]:blocks[0, 1]] + wn1,
song[i][blocks[0, 1]:])))
elif k == 2:
presentation[order]['gaps'] = blocks[1].tolist()
stims.append(
np.concatenate((song[i][:blocks[1, 0]],
song[i][blocks[1, 0]:blocks[1, 1]] + wn2,
song[i][blocks[1, 1]:])))
elif k == 3:
presentation[order]['gaps'] = blocks[0].tolist()
stims.append(
np.concatenate(
(song[i][:blocks[0, 0]],
song[i][blocks[0, 0]:blocks[0, 0] + len(fadein)] *
fadeout,
np.zeros(blocks[0, 1] - blocks[0, 0] -
len(fadein) * 2),
song[i][blocks[0, 1] - len(fadein):blocks[0, 1]] *
fadein, song[i][blocks[0, 1]:])))
elif k == 4:
presentation[order]['gaps'] = blocks[1].tolist()
stims.append(
np.concatenate(
(song[i][:blocks[1, 0]],
song[i][blocks[1, 0]:blocks[1, 0] + len(fadein)] *
fadeout,
np.zeros(blocks[1, 1] - blocks[1, 0] -
len(fadein) * 2),
song[i][blocks[1, 1] - len(fadein):blocks[1, 1]] *
fadein, song[i][blocks[1, 1]:])))
elif k == 5:
presentation[order]['gaps'] = blocks[0].tolist()
stims.append(
np.concatenate(
(song[i][:blocks[0, 0]], wn1, song[i][blocks[0, 1]:])))
elif k == 6:
presentation[order]['gaps'] = blocks[1].tolist()
stims.append(
np.concatenate(
(song[i][:blocks[1, 0]], wn2, song[i][blocks[1, 1]:])))
elif k == 7:
presentation[order]['gaps'] = blocks[0].tolist()
temp = np.zeros(len(song[i]))
stims.append(
np.concatenate(
(temp[:blocks[0, 0]], wn1, temp[blocks[0, 1]:])))
elif k == 8:
presentation[order]['gaps'] = blocks[1].tolist()
temp = np.zeros(len(song[i]))
stims.append(
np.concatenate(
(temp[:blocks[1, 0]], wn2, temp[blocks[1, 1]:])))
else:
print("Undefined stim type")
stims = np.asarray(stims)
return (Fs, stype, present_order, presentation, stims, songname)
def run_chorus(bird,
location,
seed,
rec_dir='/home/melizalab/Data',
stimset='../Chorus'):
# Basic start/stop commands
start_cmd = 'StartRecord'
stop_cmd = 'StopRecord'
experiment = 'chorus'
print("Bird:", bird)
print("Recording location:", location)
print("Randomization seed:", str(seed))
print("Stimulus directory:", stimset)
print("Outer directory:", rec_dir)
command = start_cmd + ' RecDir=%s' % rec_dir + ' PrependText=%s' % bird + ' AppendText=%s' % experiment
# Connect network handler
ip = '127.0.0.1'
port = 5556
timeout = 1.
url = "tcp://%s:%d" % (ip, port)
songfiles = glob.glob('../Chorus/*stim*wav')
scenefiles = glob.glob('../Chorus/scene*wav')
songfiles.sort()
scenefiles.sort()
songname = [os.path.basename(song).strip('.wav') for song in songfiles]
scenename = [os.path.basename(scene).strip('.wav') for scene in scenefiles]
songs = []
scenes = []
name = []
for i in songfiles:
Fs, s = read(i)
songs.append(s)
songs = np.asarray(songs)
for i in scenefiles:
Fsc, c = read(i)
scenes.append(c)
scenes = np.asarray(scenes)
stype = ['silence', 'chorus']
conditions = len(stype)
size = len(songname) * conditions
present_order = sdk.seqorder(size)
presentation = {}
stim = np.zeros((len(songs), len(scenes[0])))
scenestim = np.zeros((len(songs) * len(scenes), len(scenes[0])))
for i in range(len(songs)):
stimstart = (len(scenes[0]) - len(songs[i])) // 2
stim[i, stimstart:stimstart + len(songs[i])] = songs[i]
for j in range(len(scenes)):
scenestim[(i * len(scenes)) + j] = scenes[j] + stim[i]
name.append(songname[i] + ' ' + scenename[j])
np.random.seed(seed)
presentation = {}
pstims = np.zeros((len(songs), len(scenes[0]), 2))
pscenes = np.zeros((len(songs) * len(scenes), len(scenes[0]), 2))
for i in range(len(stim)):
pstims[i] = ss.pulsestim(stim[i])
for i in range(len(scenestim)):
pscenes[i] = ss.pulsestim(scenestim[i])
with zmq.Context() as context:
with context.socket(zmq.REQ) as socket:
socket.RCVTIMEO = int(timeout * 1000) # timeout in milliseconds
socket.connect(url)
# Start data acquisition
socket.send_string('StartAcquisition')
print(socket.recv().decode())
time.sleep(5)
socket.send_string('IsAcquiring')
print("IsAcquiring:", socket.recv().decode())
print("")
socket.send_string(command)
print(socket.recv().decode())
for i in range(len(present_order)):
if i == 0:
socket.send_string('IsRecording')
print("IsRecording:", socket.recv().decode())
socket.send_string('GetRecordingPath')
recpath = socket.recv()
print("Recording path:", recpath.decode())
print("")
else:
socket.send_string(start_cmd)
print(socket.recv().decode())
socket.send_string('IsRecording')
print("IsRecording:", socket.recv().decode())
time.sleep(0.5)
if present_order[i] <= len(pstims):
songnum = present_order[i] - 1
print("Presentation number " + str(i + 1) + ": " +
songname[songnum])
sd.play(pstims[songnum], Fs)
sd.wait()
presentation[i] = {
"song": songname[songnum],
"type": "no-scene",
}
else:
scenenum = (present_order[i] - len(songname) -
1) + (len(songname) * (i //
(len(songname) * 2)))
songnum = scenenum // 10
print("Presentation number " + str(i + 1) + ": " +
songname[songnum] + " with chorus")
sd.play(pscenes[scenenum], Fsc)
sd.wait()
presentation[i] = {
"song": name[scenenum].split(' ')[0],
"type": name[scenenum].split(' ')[1],
}
socket.send_string(stop_cmd)
print(socket.recv().decode())
socket.send_string('IsRecording')
print("IsRecording:", socket.recv().decode())
print("")
time.sleep(0.5)
time.sleep(0.5)
write_log(bird, location, stimset, experiment, songname, recpath,
presentation, seed)
socket.send_string('StopAcquisition')
print(socket.recv().decode())
def run_clean(bird,
location,
rec_dir='/home/melizalab/Data',
stimset='../Stims1'):
# Basic start/stop commands
start_cmd = 'StartRecord'
stop_cmd = 'StopRecord'
experiment = 'selectivity'
print("Bird:", bird)
print("Recording location:", location)
print("Stimulus directory:", stimset)
print("Outer directory:", rec_dir)
command = start_cmd + ' RecDir=%s' % rec_dir + ' PrependText=%s' % bird + ' AppendText=%s' % stimtype
# Connect network handler
ip = '127.0.0.1'
port = 5556
timeout = 1.
url = "tcp://%s:%d" % (ip, port)
songfiles = glob.glob(stimset + '/*.wav')
size = len(songfiles)
present_order = sdk.seqorder(size)
presentation = []
stim, Fs, songname = ss.clean(stimset)
pstims = np.zeros((size, len(stim[0]), 2))
for i in range(len(stim)):
pstims[i] = ss.pulsestim(stim[i])
with zmq.Context() as context:
with context.socket(zmq.REQ) as socket:
socket.RCVTIMEO = int(timeout * 1000) # timeout in milliseconds
socket.connect(url)
# Start data acquisition
socket.send_string('StartAcquisition')
print(socket.recv().decode())
time.sleep(5)
socket.send_string('IsAcquiring')
print("IsAcquiring:", socket.recv().decode())
print("")
socket.send_string(command)
print(socket.recv().decode())
for i in range(len(present_order)):
if i == 0:
socket.send_string('IsRecording')
print("IsRecording:", socket.recv().decode())
socket.send_string('GetRecordingPath')
recpath = socket.recv()
print("Recording path:", recpath.decode())
print("")
else:
socket.send_string(start_cmd)
print(socket.recv().decode())
socket.send_string('IsRecording')
print("IsRecording:", socket.recv().decode())
time.sleep(1)
next_stim(pstims, present_order, i, songname, Fs)
time.sleep(1)
socket.send_string(stop_cmd)
print(socket.recv().decode())
socket.send_string('IsRecording')
print("IsRecording:", socket.recv().decode())
print("")
time.sleep(0.5)
#socket.send_string(stop_cmd)
#print(socket.recv().decode())
#socket.send_string('IsRecording')
#print("IsRecording:", socket.recv().decode())
# Finally, stop data acquisition; it might be a good idea to
# wait a little bit until all data have been written to hard drive
time.sleep(0.5)
write_log(bird, location, stimset, experiment, songname, recpath,
presentation)
socket.send_string('StopAcquisition')
print(socket.recv().decode())
def make_stims(stimset, seed):
song, Fs, songname = ss.clean(stimset)
stype = [
'continuous', 'continuous+mask', 'continuous+noise', 'gap+mask', 'gap',
'gap+noise', 'noise'
]
syllables = pd.read_csv('../syllables.csv')
conditions = len(stype)
trials = 10
size = len(songname) * conditions
present_order = sdk.seqorder(size, trials)
presentation = {}
stims = np.zeros((size, len(song[0])))
np.random.seed(seed)
for i in range(len(songname)):
ssyll = syllables.loc[syllables.songid == songname[i]]
sg = np.random.choice(len(ssyll.loc[ssyll.motif == 1][1:-1]))
blocks = np.zeros((2, 2))
blocks[:, 0] = np.asarray(
[list(ssyll.start[ssyll.motif == x])[sg + 1] for x in range(1, 3)])
blocks[:, 1] = np.asarray(
[list(ssyll.end[ssyll.motif == x])[sg + 1] for x in range(1, 3)])
blocks = blocks * Fs
blocks = blocks.astype(int)
gsize = int(0.1 * Fs)
wnamp = 25000
if blocks[0, 1] - blocks[0, 0] > gsize:
middle = np.mean(blocks, 1)
blocks[:, 0] = middle - int(gsize / 2) - 50
blocks[:, 1] = blocks[:, 0] + gsize
N = 50
ix = np.arange(N * 3)
signal = np.cos(2 * np.pi * ix / float(N * 2)) * 0.5 + 0.5
fadein = signal[50:100]
fadeout = signal[0:50]
for k in range(conditions):
order = (i * conditions) + k
presentation[order] = {
"song": songname[i],
"type": stype[k],
}
if k == 0:
stims[order] = song[i]
elif k == 1:
mask = np.zeros((2, 2))
mask[:, 0] = np.asarray([
list(ssyll.start[ssyll.motif == x])[0]
for x in range(1, 3)
])
mask[:, 1] = np.asarray([
list(ssyll.end[ssyll.motif == x])[-1] for x in range(1, 3)
])
mask = mask * Fs
mask = mask.astype(int)
wn1 = np.random.normal(0, wnamp, size=mask[0, 1] - mask[0, 0])
wn2 = np.random.normal(0, wnamp, size=mask[1, 1] - mask[1, 0])
stims[order] = np.concatenate(
(song[i][:mask[0,
0]], song[i][mask[0, 0]:mask[0, 1]] + wn1,
song[i][mask[0, 1]:mask[1, 0]],
song[i][mask[1, 0]:mask[1, 1]] + wn2, song[i][mask[1,
1]:]))
elif k == 2:
presentation[order]['gaps'] = blocks.tolist()
wn1 = np.random.normal(0,
wnamp,
size=blocks[0, 1] - blocks[0, 0])
wn2 = np.random.normal(0,
wnamp,
size=blocks[1, 1] - blocks[1, 0])
stims[order] = np.concatenate(
(song[i][:blocks[0, 0]],
song[i][blocks[0, 0]:blocks[0, 1]] + wn1,
song[i][blocks[0, 1]:blocks[1, 0]],
song[i][blocks[1, 0]:blocks[1, 1]] + wn2,
song[i][blocks[1, 1]:]))
elif k == 3:
presentation[order]['gaps'] = blocks.tolist()
mask = np.zeros((2, 2))
mask[:, 0] = np.asarray([
list(ssyll.start[ssyll.motif == x])[0]
for x in range(1, 3)
])
mask[:, 1] = np.asarray([
list(ssyll.end[ssyll.motif == x])[-1] for x in range(1, 3)
])
mask = mask * Fs
mask = mask.astype(int)
wn1 = np.random.normal(0, wnamp, size=mask[0, 1] - mask[0, 0])
wn2 = np.random.normal(0, wnamp, size=mask[1, 1] - mask[1, 0])
temp = np.concatenate(
(song[i][:blocks[0, 0]],
song[i][blocks[0, 0]:blocks[0, 0] + len(fadein)] *
fadeout,
np.zeros(blocks[0, 1] - blocks[0, 0] - len(fadein) * 2),
song[i][blocks[0, 1] - len(fadein):blocks[0, 1]] * fadein,
song[i][blocks[0, 1]:blocks[1, 0]],
song[i][blocks[1, 0]:blocks[1, 0] + len(fadein)] *
fadeout,
np.zeros(blocks[1, 1] - blocks[1, 0] - len(fadein) * 2),
song[i][blocks[1, 1] - len(fadein):blocks[1, 1]] * fadein,
song[i][blocks[1, 1]:]))
stims[order] = np.concatenate(
(temp[:mask[0, 0]], temp[mask[0, 0]:mask[0, 1]] + wn1,
temp[mask[0, 1]:mask[1, 0]],
temp[mask[1, 0]:mask[1, 1]] + wn2, temp[mask[1, 1]:]))
elif k == 4:
presentation[order]['gaps'] = blocks.tolist()
stims[order] = np.concatenate(
(song[i][:blocks[0, 0]],
song[i][blocks[0, 0]:blocks[0, 0] + len(fadein)] *
fadeout,
np.zeros(blocks[0, 1] - blocks[0, 0] - len(fadein) * 2),
song[i][blocks[0, 1] - len(fadein):blocks[0, 1]] * fadein,
song[i][blocks[0, 1]:blocks[1, 0]],
song[i][blocks[1, 0]:blocks[1, 0] + len(fadein)] *
fadeout,
np.zeros(blocks[1, 1] - blocks[1, 0] - len(fadein) * 2),
song[i][blocks[1, 1] - len(fadein):blocks[1, 1]] * fadein,
song[i][blocks[1, 1]:]))
elif k == 5:
presentation[order]['gaps'] = blocks.tolist()
wn1 = np.random.normal(0,
wnamp,
size=blocks[0, 1] - blocks[0, 0])
wn2 = np.random.normal(0,
wnamp,
size=blocks[1, 1] - blocks[1, 0])
stims[order] = np.concatenate(
(song[i][:blocks[0, 0]], wn1, song[i][blocks[0,
1]:blocks[1,
0]],
wn2, song[i][blocks[1, 1]:]))
elif k == 6:
presentation[order]['gaps'] = blocks.tolist()
wn1 = np.random.normal(0,
wnamp,
size=blocks[0, 1] - blocks[0, 0])
wn2 = np.random.normal(0,
wnamp,
size=blocks[1, 1] - blocks[1, 0])
temp = np.zeros(len(song[i]))
stims[order] = np.concatenate(
(temp[:blocks[0, 0]], wn1, temp[blocks[0, 1]:blocks[1, 0]],
wn2, temp[blocks[1, 1]:]))
# elif k==7:
# presentation[order]['gaps'] = blocks.tolist()
# wn1 = np.random.normal(0,wnamp,size=(blocks[0,1]-blocks[0,0]-len(fadein)*2)-800)
# wn2 = np.random.normal(0,wnamp,size=(blocks[1,1]-blocks[1,0]-len(fadein)*2)-800)
# stims[order] = np.concatenate((song[i][:blocks[0,0]],
# song[i][blocks[0,0]:blocks[0,0]+len(fadein)]*fadeout,
# np.zeros(400),
# wn1,
# np.zeros(400),
# song[i][blocks[0,1]-len(fadein):blocks[0,1]]*fadein,
# song[i][blocks[0,1]:blocks[1,0]],
# song[i][blocks[1,0]:blocks[1,0]+len(fadein)]*fadeout,
# np.zeros(400),
# wn2,
# np.zeros(400),
# song[i][blocks[1,1]-len(fadein):blocks[1,1]]*fadein,
# song[i][blocks[1,1]:]))
else:
print("Undefined stim type")
scale = np.max(stims)
pstims = np.zeros((size, len(song[0]), 2))
for i in range(len(pstims)):
temp = (stims[i] / scale) * (2**15 - 1)
pstims[i] = ss.pulsestim(temp)
return (Fs, stype, present_order, presentation, pstims, songname)
def make_stims(stimset, seed, dBval):
if '180B' in stimset or '178B' in stimset:
song, Fs, songname = ss.dBclean('../178B', 54)
song2, Fs, songname2 = ss.dBclean('../180B', 54)
song = np.concatenate((song, song2), 0)
songname = songname + songname2
else:
song, Fs, songname = ss.dBclean(stimset)
stype = [
'continuous', 'continuousnoise1', 'continuousnoise2', 'gap1', 'gap2',
'gapnoise1', 'gapnoise2', 'noise1', 'noise2', 'continuousmask',
'gapmask'
]
syllables = pd.read_csv('../gap-locations.csv')
conditions = len(stype)
trials = 10
size = len(songname) * conditions
present_order = sdk.seqorder(size, trials)
presentation = {}
stims = []
np.random.seed(seed)
for i in range(len(songname)):
ssyll = syllables.loc[syllables.song == songname[i]]
#sg = np.random.choice(ssyll.iloc[1:-1].index,2,replace=False)
blocks = np.zeros((2, 2))
#print(ssyll.start1.values[0])
#print(ssyll.start2)
blocks[:, 0] = np.asarray(
(ssyll.start1.values[0], ssyll.start2.values[0]))
blocks[:, 1] = np.asarray((ssyll.end1.values[0], ssyll.end2.values[0]))
blocks = blocks * Fs
blocks = blocks.astype(int)
gsize = int(0.1 * Fs)
wnamp = 25000
wndB = dBval
if blocks[0, 1] - blocks[0, 0] > gsize:
#middle = np.mean(blocks,1)
#blocks[0,0] = middle[0]-int(gsize/2)-50
blocks[0, 1] = blocks[0, 0] + gsize
if blocks[1, 1] - blocks[1, 0] > gsize:
#middle = np.mean(blocks,1)
#blocks[1,0] = middle[1]-int(gsize/2)-50
blocks[1, 1] = blocks[1, 0] + gsize
N = 120
ix = np.arange(N * 3)
signal = np.cos(2 * np.pi * ix / float(N * 2)) * 0.5 + 0.5
fadein = signal[120:240]
fadeout = signal[0:120]
wn1 = np.random.normal(0, wnamp, size=blocks[0, 1] - blocks[0, 0])
wn1 = (wn1 / rms(wn1)) * scale(wndB)
wn2 = np.random.normal(0, wnamp, size=blocks[1, 1] - blocks[1, 0])
wn2 = (wn2 / rms(wn2)) * scale(wndB)
mask = np.random.normal(0, wnamp, size=len(song[i]))
mask = (mask / rms(mask)) * scale(wndB)
mN = 1000
mix = np.arange(mN * 3)
msignal = np.cos(2 * np.pi * mix / float(mN * 2)) * 0.5 + 0.5
mfadein = msignal[1000:2000] * np.random.normal(0, wnamp, size=1000)
mfadein = (mfadein / rms(mfadein)) * scale(wndB)
mfadeout = msignal[0:1000] * np.random.normal(0, wnamp, size=1000)
mfadeout = (mfadeout / rms(mfadeout)) * scale(wndB)
for k in range(conditions):
order = (i * conditions) + k
presentation[order] = {
"song": songname[i],
"type": stype[k],
}
if k == 0:
stims.append(song[i])
elif k == 1:
presentation[order]['gaps'] = blocks[0].tolist()
stims.append(
np.concatenate((song[i][:blocks[0, 0]],
song[i][blocks[0, 0]:blocks[0, 1]] + wn1,
song[i][blocks[0, 1]:])))
elif k == 2:
presentation[order]['gaps'] = blocks[1].tolist()
stims.append(
np.concatenate((song[i][:blocks[1, 0]],
song[i][blocks[1, 0]:blocks[1, 1]] + wn2,
song[i][blocks[1, 1]:])))
elif k == 3:
presentation[order]['gaps'] = blocks[0].tolist()
stims.append(
np.concatenate(
(song[i][:blocks[0, 0]],
song[i][blocks[0, 0]:blocks[0, 0] + len(fadein)] *
fadeout,
np.zeros(blocks[0, 1] - blocks[0, 0] -
len(fadein) * 2),
song[i][blocks[0, 1] - len(fadein):blocks[0, 1]] *
fadein, song[i][blocks[0, 1]:])))
elif k == 4:
presentation[order]['gaps'] = blocks[1].tolist()
stims.append(
np.concatenate(
(song[i][:blocks[1, 0]],
song[i][blocks[1, 0]:blocks[1, 0] + len(fadein)] *
fadeout,
np.zeros(blocks[1, 1] - blocks[1, 0] -
len(fadein) * 2),
song[i][blocks[1, 1] - len(fadein):blocks[1, 1]] *
fadein, song[i][blocks[1, 1]:])))
elif k == 5:
presentation[order]['gaps'] = blocks[0].tolist()
stims.append(
np.concatenate(
(song[i][:blocks[0, 0]], wn1, song[i][blocks[0, 1]:])))
elif k == 6:
presentation[order]['gaps'] = blocks[1].tolist()
stims.append(
np.concatenate(
(song[i][:blocks[1, 0]], wn2, song[i][blocks[1, 1]:])))
elif k == 7:
presentation[order]['gaps'] = blocks[0].tolist()
temp = np.zeros(len(song[i]))
stims.append(
np.concatenate(
(temp[:blocks[0, 0]], wn1, temp[blocks[0, 1]:])))
elif k == 8:
presentation[order]['gaps'] = blocks[1].tolist()
temp = np.zeros(len(song[i]))
stims.append(
np.concatenate(
(temp[:blocks[1, 0]], wn2, temp[blocks[1, 1]:])))
elif k == 9:
stims.append(
np.concatenate((mfadein, song[i] + mask, mfadeout)))
elif k == 10:
stims.append(
np.concatenate(
(mfadein, song[i][:blocks[1, 0]] + mask[:blocks[1, 0]],
song[i][blocks[1, 0]:blocks[1, 0] + len(fadein)] *
fadeout +
mask[blocks[1, 0]:blocks[1, 0] + len(fadein)],
mask[blocks[1, 0] + len(fadein):blocks[1, 1] -
len(fadein)],
song[i][blocks[1, 1] - len(fadein):blocks[1, 1]] *
fadein +
mask[blocks[1, 1] - len(fadein):blocks[1, 1]],
song[i][blocks[1, 1]:] + mask[blocks[1, 1]:],
mfadeout)))
else:
print("Undefined stim type")
stims = np.asarray(stims)
#scale = np.max(stims)
#pstims = np.zeros((size,len(song[0]),2))
#for i in range(len(pstims)):
#temp = (stims[i]/scale)*(2**15-1)
#pstims[i] = ss.pulsestim(temp)
#pstims[i] = ss.pulsestim(stims[i])
return (Fs, stype, present_order, presentation, stims, songname)
