def correlate_section(sound, mvmt, cluster, sections):
""" Calculates correlation between cluster results and sound and movement
levels """
#Load files
input_file_orig = sound
input_file_sound = butter_lowpass_filter(input_file_orig, 0.1, 30)
input_file_mvmt = mvmt
input_file_cluster = cluster
#Sampling Rate
sr = 30
sound_thresh = 0.5 * 10**13 #np.percentile(input_file_sound, 75)
mvmt_thresh = 1.5 #np.percentile(input_file_mvmt, 99)
#Number of frames in unit
nf = sr * 16.0
sound_reduced = np.zeros(input_file_cluster.shape[0])
mvmt_reduced = np.zeros(input_file_cluster.shape[0])
for i in xrange(input_file_cluster.shape[0]):
# Reduce Sound and movement resolution by percentage
# of values above threshold
sound_reduced[i] = np.where(
input_file_sound[i * nf:
(i + 1) * nf] > sound_thresh)[0].shape[0] / nf
mvmt_reduced[i] = np.where(
input_file_mvmt[i * nf:
(i + 1) * nf] > mvmt_thresh)[0].shape[0] / nf
sound_sections = []
mvmt_sections = []
cluster_sections = []
for section in sections:
sound_sections.append(sound_reduced[section[0]:section[0] +
section[1]])
mvmt_sections.append(mvmt_reduced[section[0]:section[0] + section[1]])
cluster_sections.append(input_file_cluster[section[0]:section[0] +
section[1], :])
sound_sections = np.hstack(sound_sections)
mvmt_sections = np.hstack(mvmt_sections)
cluster_sections = np.vstack(cluster_sections)
sound_corr = np.zeros(input_file_cluster.shape[1])
mvmt_corr = np.zeros(input_file_cluster.shape[1])
for c, values in enumerate(cluster_sections.T):
sound_corr[c] = pearsonr(sound_sections, values)[0]
mvmt_corr[c] = pearsonr(mvmt_sections, values)[0]
mvmt_channel = np.where(mvmt_corr == np.nanmax(mvmt_corr))[0][0]
sound_channel = np.where(sound_corr == np.nanmax(sound_corr))[0][0]
if (mvmt_channel == sound_channel):
if np.nanmax(mvmt_corr) > np.nanmax(sound_corr):
sound_corr_temp = copy.deepcopy(sound_corr)
sound_corr_temp[mvmt_channel] = 0
sound_channel = np.where(
sound_corr_temp == np.nanmax(sound_corr_temp))[0][0]
else:
mvmt_corr_temp = copy.deepcopy(mvmt_corr)
mvmt_corr_temp[sound_channel] = 0
mvmt_channel = np.where(
mvmt_corr_temp == np.nanmax(mvmt_corr_temp))[0][0]
average = np.mean(np.vstack([mvmt_corr, sound_corr]), axis=0)
rest_channel = np.where(average == np.nanmin(average))[0][0]
# plt.plot(sound_sections, label="actual sound")
# plt.plot(mvmt_sections, label="actual mvmt")
# plt.plot(cluster_sections[:,mvmt_channel], label="cluster mvmt")
# plt.plot(cluster_sections[:,sound_channel], label="cluster sound")
# plt.plot(cluster_sections[:,rest_channel], label="cluster rest")
# plt.legend()
# plt.show()
#
# pdb.set_trace()
print str(mvmt_channel) + str(mvmt_corr[mvmt_channel])
print str(sound_channel) + str(sound_corr[sound_channel])
print str(rest_channel) + str(average[rest_channel])
return {'Mvmt': mvmt_channel, 'Sound': sound_channel, 'Rest': rest_channel}
def correlate_section(sound, mvmt, cluster, sections):
""" Calculates correlation between cluster results and sound and movement
levels """
#Load files
input_file_orig = sound
input_file_sound = butter_lowpass_filter(input_file_orig, 0.1, 30)
input_file_mvmt = mvmt
input_file_cluster = cluster
#Sampling Rate
sr = 30
sound_thresh = 0.5*10**13#np.percentile(input_file_sound, 75)
mvmt_thresh = 1.5 #np.percentile(input_file_mvmt, 99)
#Number of frames in unit
nf=sr*16.0
sound_reduced = np.zeros(input_file_cluster.shape[0])
mvmt_reduced = np.zeros(input_file_cluster.shape[0])
for i in xrange(input_file_cluster.shape[0]):
# Reduce Sound and movement resolution by percentage
# of values above threshold
sound_reduced[i] = np.where(input_file_sound[i*nf:(i+1)*nf]
> sound_thresh)[0].shape[0]/nf
mvmt_reduced[i] = np.where(input_file_mvmt[i*nf:(i+1)*nf]
> mvmt_thresh)[0].shape[0]/nf
sound_sections=[]
mvmt_sections=[]
cluster_sections=[]
for section in sections:
sound_sections.append(sound_reduced[section[0]:section[0]+section[1]])
mvmt_sections.append(mvmt_reduced[section[0]:section[0]+section[1]])
cluster_sections.append(input_file_cluster[section[0]:section[0]+section[1],:])
sound_sections=np.hstack(sound_sections)
mvmt_sections=np.hstack(mvmt_sections)
cluster_sections=np.vstack(cluster_sections)
sound_corr = np.zeros(input_file_cluster.shape[1])
mvmt_corr = np.zeros(input_file_cluster.shape[1])
for c, values in enumerate(cluster_sections.T):
sound_corr[c]=pearsonr(sound_sections, values)[0]
mvmt_corr[c]=pearsonr(mvmt_sections, values)[0]
mvmt_channel = np.where(mvmt_corr==np.nanmax(mvmt_corr))[0][0]
sound_channel = np.where(sound_corr==np.nanmax(sound_corr))[0][0]
if (mvmt_channel==sound_channel):
if np.nanmax(mvmt_corr)>np.nanmax(sound_corr):
sound_corr_temp = copy.deepcopy(sound_corr)
sound_corr_temp[mvmt_channel]=0
sound_channel = np.where(sound_corr_temp==np.nanmax(sound_corr_temp))[0][0]
else:
mvmt_corr_temp = copy.deepcopy(mvmt_corr)
mvmt_corr_temp[sound_channel]=0
mvmt_channel = np.where(mvmt_corr_temp==np.nanmax(mvmt_corr_temp))[0][0]
average = np.mean(np.vstack([mvmt_corr, sound_corr]), axis=0)
rest_channel = np.where(average==np.nanmin(average))[0][0]
# plt.plot(sound_sections, label="actual sound")
# plt.plot(mvmt_sections, label="actual mvmt")
# plt.plot(cluster_sections[:,mvmt_channel], label="cluster mvmt")
# plt.plot(cluster_sections[:,sound_channel], label="cluster sound")
# plt.plot(cluster_sections[:,rest_channel], label="cluster rest")
# plt.legend()
# plt.show()
#
# pdb.set_trace()
print str(mvmt_channel) + str(mvmt_corr[mvmt_channel])
print str(sound_channel) + str(sound_corr[sound_channel])
print str(rest_channel) + str(average[rest_channel])
return {'Mvmt':mvmt_channel,'Sound':
sound_channel,'Rest':rest_channel}
def correlate(sound, mvmt, cluster):
""" Calculates correlation between cluster results and sound and movement
levels """
#Load files
input_file_orig = pickle.load(open(sound, "rb"))
input_file_sound = butter_lowpass_filter(input_file_orig, 0.1, 30)
input_file_mvmt = pickle.load(open(mvmt, "rb"))
input_file_cluster = pickle.load(open(cluster, "rb"))
#Sampling Rate
sr = 30
start = 0
end = input_file_cluster.shape[0]
sound_thresh = 3 * 10**11 #np.percentile(input_file_sound, 75)
mvmt_thresh = 1.1 #np.percentile(input_file_mvmt, 99)
#Number of frames in unit
nf = sr * 16.0
sound_reduced = np.zeros(input_file_cluster.shape[0])
mvmt_reduced = np.zeros(input_file_cluster.shape[0])
for i in xrange(start, end):
# Reduce Sound and movement resolution by percentage
# of values above threshold
sound_reduced[i] = np.where(
input_file_sound[i * nf:
(i + 1) * nf] > sound_thresh)[0].shape[0] / nf
mvmt_reduced[i] = np.where(
input_file_mvmt[i * nf:
(i + 1) * nf] > mvmt_thresh)[0].shape[0] / nf
sound_corr = np.zeros(input_file_cluster.shape[1])
mvmt_corr = np.zeros(input_file_cluster.shape[1])
for c, values in enumerate(input_file_cluster.T):
sound_corr[c] = pearsonr(sound_reduced, values[start:end])[0]
mvmt_corr[c] = pearsonr(mvmt_reduced, values[start:end])[0]
mvmt_channel = np.where(mvmt_corr == np.nanmax(mvmt_corr))[0][0]
sound_channel = np.where(sound_corr == np.nanmax(sound_corr))[0][0]
if (mvmt_channel == sound_channel):
if np.nanmax(mvmt_corr) > np.nanmax(sound_corr):
sound_corr_temp = copy.deepcopy(sound_corr)
sound_corr_temp[mvmt_channel] = np.nan
sound_channel = np.where(
sound_corr_temp == np.nanmax(sound_corr_temp))[0][0]
else:
mvmt_corr_temp = copy.deepcopy(mvmt_corr)
mvmt_corr_temp[sound_channel] = 0
mvmt_channel = np.where(
mvmt_corr_temp == np.nanmax(mvmt_corr_temp))[0][0]
average = np.mean(np.vstack([mvmt_corr, sound_corr]), axis=0)
rest_channel = np.where(average == np.nanmin(average))[0][0]
#plt.plot(input_file_cluster[:100,mvmt_channel], label="cluster mvmt")
# plt.plot(input_file_cluster[:,sound_channel], label="cluster sound")
#plt.plot(input_file_cluster[:,rest_channel], label="cluster rest")
#plt.plot(sound_reduced[:], label="actual sound")
#plt.plot(mvmt_reduced[:], label="actual mvmt")
#plt.legend()
#plt.show()
print str(mvmt_channel) + ":" + str(mvmt_corr[mvmt_channel])
print mvmt_corr
print str(sound_channel) + ":" + str(sound_corr[sound_channel])
print sound_corr
print str(rest_channel) + ":" + str(average[rest_channel])
print average
return {'Mvmt': mvmt_channel, 'Sound': sound_channel, 'Rest': rest_channel}
def correlate(sound, mvmt, cluster):
""" Calculates correlation between cluster results and sound and movement
levels """
#Load files
input_file_orig = pickle.load(open(sound, "rb"))
input_file_sound = butter_lowpass_filter(input_file_orig, 0.1, 30)
input_file_mvmt = pickle.load(open(mvmt, "rb"))
input_file_cluster = pickle.load(open(cluster, "rb"))
#Sampling Rate
sr = 30
start = 0
end = input_file_cluster.shape[0]
sound_thresh = 3*10**11#np.percentile(input_file_sound, 75)
mvmt_thresh = 1.1 #np.percentile(input_file_mvmt, 99)
#Number of frames in unit
nf=sr*16.0
sound_reduced = np.zeros(input_file_cluster.shape[0])
mvmt_reduced = np.zeros(input_file_cluster.shape[0])
for i in xrange(start,end):
# Reduce Sound and movement resolution by percentage
# of values above threshold
sound_reduced[i] = np.where(input_file_sound[i*nf:(i+1)*nf]
> sound_thresh)[0].shape[0]/nf
mvmt_reduced[i] = np.where(input_file_mvmt[i*nf:(i+1)*nf]
> mvmt_thresh)[0].shape[0]/nf
sound_corr = np.zeros(input_file_cluster.shape[1])
mvmt_corr = np.zeros(input_file_cluster.shape[1])
for c, values in enumerate(input_file_cluster.T):
sound_corr[c]=pearsonr(sound_reduced, values[start:end])[0]
mvmt_corr[c]=pearsonr(mvmt_reduced, values[start:end])[0]
mvmt_channel = np.where(mvmt_corr==np.nanmax(mvmt_corr))[0][0]
sound_channel = np.where(sound_corr==np.nanmax(sound_corr))[0][0]
if (mvmt_channel==sound_channel):
if np.nanmax(mvmt_corr)>np.nanmax(sound_corr):
sound_corr_temp = copy.deepcopy(sound_corr)
sound_corr_temp[mvmt_channel]=np.nan
sound_channel = np.where(sound_corr_temp==np.nanmax(sound_corr_temp))[0][0]
else:
mvmt_corr_temp = copy.deepcopy(mvmt_corr)
mvmt_corr_temp[sound_channel]=0
mvmt_channel = np.where(mvmt_corr_temp==np.nanmax(mvmt_corr_temp))[0][0]
average = np.mean(np.vstack([mvmt_corr, sound_corr]), axis=0)
rest_channel = np.where(average==np.nanmin(average))[0][0]
#plt.plot(input_file_cluster[:100,mvmt_channel], label="cluster mvmt")
# plt.plot(input_file_cluster[:,sound_channel], label="cluster sound")
#plt.plot(input_file_cluster[:,rest_channel], label="cluster rest")
#plt.plot(sound_reduced[:], label="actual sound")
#plt.plot(mvmt_reduced[:], label="actual mvmt")
#plt.legend()
#plt.show()
print str(mvmt_channel) + ":" + str(mvmt_corr[mvmt_channel])
print mvmt_corr
print str(sound_channel) + ":" + str(sound_corr[sound_channel])
print sound_corr
print str(rest_channel) + ":" +str(average[rest_channel])
print average
return {'Mvmt':mvmt_channel,'Sound':
sound_channel,'Rest':rest_channel}
