def make_average_datamask_from_mean(donor_dict, fmask = True):
'''Will create the mean mask file from the donor_dict or hybdatadict information
meanmask_file = a file called donor_id.amsk.1.
This will be used when computing the spike similarity measure
to assess the quality of spike detection'''
clufilename = donor_dict['donor_path'] + donor_dict['donor']+'_'+ donor_dict['donorcluid']+'.clu.1'
clusters = klustersloader.read_clusters(clufilename)
if fmask == True:
fmaskfilename = donor_dict['donor_path']+ donor_dict['donor']+'.fmask.1'
else:
fmaskfilename = donor_dict['donor_path']+ donor_dict['donor']+'.mask.1'
[fmasks,fmasks_full] = klustersloader.read_masks(fmaskfilename,donor_dict['numchannels'])
#output [False, True, False, False, ...] where True indicates
#the indices corresponding to the chosen donor cluster
selected_cluster_indices = np.in1d(clusters, donor_dict['donorcluster'])
num_selected_spikes = np.sum(selected_cluster_indices)
selected_fmasks = fmasks_full[selected_cluster_indices,:]
donor_id_amsk_data = np.sum(selected_fmasks,axis = 0)/num_selected_spikes
return donor_id_amsk_data
def create_average_hybrid_wave_spike(donor_dict):
'''Input a dictionary donor_dict or a hybdatadict
will create the mean spike file from taking the average of
spikes in a donor cluster of the donor dataset
meanspike_file_id = a file called donor_id.msua.1.'''
clufilename = donor_dict['donor_path'] + donor_dict['donor']+'_'+ donor_dict['donorcluid']+'.clu.1'
spkfilename = donor_dict['donor_path']+ donor_dict['donor']+'.spk.1'
uspkfilename = donor_dict['donor_path']+ donor_dict['donor']+'.uspk.1'
clusters = klustersloader.read_clusters(clufilename)
#uspk = klustersloader.read_waveforms(uspkfilename,20,32)
##print uspkfilename
uspk = np.array(load_binary(uspkfilename), dtype = np.int16)
# uspk = np.array(uspk, dtype = np.int16)
# uspk = normalize(uspk, symmetric = True)
uspk = uspk.reshape((-1, 20, 32))
#uspk = np.transpose(uspk, (0,2,1))
#print uspk[1,:,:]
#print 'the 8th column, 8th channel recording ', uspk[1,:,7]
#print uspk.shape
#print ' uspk.dtype = ' , uspk.dtype
#output [False, True, False, False, ...] where True indicates
#the indices corresponding to the chosen donor cluster
selected_cluster_indices = np.in1d(clusters, donor_dict['donorcluster'])
num_selected_spikes = np.sum(selected_cluster_indices)
selected_spikes = uspk[selected_cluster_indices,:,:]
#print 'second selected spike ' , selected_spikes[20,:,:]
#print 'the 8th channel recording of a selected uspk ', selected_spikes[20,:,7]
#print 'number of selected spikes ', num_selected_spikes
#print ' sum of spikes ', np.sum(selected_spikes,axis = 0)
donor_id_msua_data = np.sum(selected_spikes,axis = 0)/num_selected_spikes
#print donor_id_msua_data
#print donor_id_msua_data.dtype
return donor_id_msua_data
