def extract_waveforms(chunk_detect=None,
threshold=None,
chunk_fil=None,
chunk_raw=None,
probe=None,
components=None,
**prm):
# For now, we use the same binary chunk for detection and extraction
# +/-chunk, or abs(chunk), depending on the parameter 'detect_spikes'.
chunk_extract = chunk_detect # shape: (nsamples, nchannels)
# This is a list of Waveform instances.
waveforms = []
for component in components:
w = extract_waveform(component,
chunk_extract=chunk_extract,
chunk_fil=chunk_fil,
chunk_raw=chunk_raw,
threshold_strong=threshold.strong,
threshold_weak=threshold.weak,
probe=probe,
**prm)
if w is not None:
waveforms.append(w)
# Remove skipped waveforms (in overlapping chunk sections).
# waveforms = [w for w in waveforms if w is not None]
return waveforms
def test_extract_waveform_1():
waveform = extract_waveform(Component(COMPONENT),
chunk_extract=CHUNK_EXTRACT,
chunk_fil=CHUNK_EXTRACT,
chunk_raw=CHUNK_EXTRACT,
extract_s_before=1,
extract_s_after=2,
threshold_strong=THRESHOLD_STRONG,
threshold_weak=THRESHOLD_WEAK,
probe=PROBE)
assert waveform.raw.shape == (3, 5)
assert waveform.fil.shape == (3, 5)
assert waveform.masks.shape == (5, )
def test_extract_waveform_1():
waveform = extract_waveform(Component(COMPONENT),
chunk_extract=CHUNK_EXTRACT,
chunk_fil=CHUNK_EXTRACT,
chunk_raw=CHUNK_EXTRACT,
extract_s_before=1,
extract_s_after=2,
threshold_strong=THRESHOLD_STRONG,
threshold_weak=THRESHOLD_WEAK,
probe=PROBE)
assert waveform.raw.shape == (3, 5)
assert waveform.fil.shape == (3, 5)
assert waveform.masks.shape == (5,)
def extract_waveforms(chunk_detect=None, threshold=None,
chunk_fil=None, chunk_raw=None,
probe=None, components=None,
**prm):
# For now, we use the same binary chunk for detection and extraction
# +/-chunk, or abs(chunk), depending on the parameter 'detect_spikes'.
chunk_extract = chunk_detect # shape: (nsamples, nchannels)
# This is a list of Waveform instances.
waveforms = []
for component in components:
w = extract_waveform(component,
chunk_extract=chunk_extract,
chunk_fil=chunk_fil,
chunk_raw=chunk_raw,
threshold_strong=threshold.strong,
threshold_weak=threshold.weak,
probe=probe,
**prm)
if w is not None:
waveforms.append(w)
# Remove skipped waveforms (in overlapping chunk sections).
# waveforms = [w for w in waveforms if w is not None]
return waveforms
def diagnostics(threshold = None, probe = None,components = None,chunk = None,chunk_detect= None,chunk_threshold=None, chunk_fil=None, chunk_raw=None,prm = None, **extra_params):
multdetection_times = prm['diagnostics_time_samples']
s_start = chunk.s_start # Absolute start of the chunk
print 's_start ' , s_start
#debug_fd = GlobalVariables['debug_fd']
samplingrate= prm['sample_rate']
chunk_size_less= prm['chunk_size']-prm['chunk_overlap']
#-Parameters['CHUNK_OVERLAP']
# print 'Parameters: \n', Parameters
# probefilename = Parameters['PROBE_FILE']
# print 'chunk_size_less = ', chunk_size_less
# window_width = 120
# samples_backward = 60
window_width = 140
samples_backward = 70
#path = Parameters['OUTPUT_DIR']
for interestpoint in multdetection_times:
if (interestpoint - chunk_size_less) <= s_start < (interestpoint):
#print interestpoint_ms, ':\n'
#debug_fd.write(str(interestpoint_ms)+':\n')
print 'interestpoint ', interestpoint, ':\n'
#debug_fd.write(str(interestpoint)+':\n')
# sampmin = interestpoint - s_start - 3
sampmin = np.amax([0,interestpoint - s_start - samples_backward])
sampmax = sampmin + window_width
print 'sampmin, sampmax ',sampmin, sampmax
#embed()
connected_comp_enum = np.zeros_like(chunk_fil)
j = 0
debugnextbits = []
waveslist = []
for k,indlist in enumerate(components):
indtemparray = np.array(indlist.items)
#print k,':',indlist, '\n'
#print 'indlist.s_start', indlist.s_start
#print indlist.keep_start
#print indlist.keep_end
# print '\n'
# j = j+1
# connected_comp_enum[indtemparray[:,0],indtemparray[:,1]] = j
# debug_fd.write(str(k)+': '+'\n')
# debug_fd.write(str(indlist)+'\n')
# debug_fd.write('\n')
# debug_fd.flush()
if (set(indtemparray[:,0]).intersection(np.arange(int(sampmin),int(sampmax+1))) != set()):
nut = set(indtemparray[:,0]).intersection(np.arange(int(sampmin),int(sampmax+1)))
print nut
#print 'Am I even getting here'
#embed()
print k,':',indlist, '\n'
print '\n'
j = j+1
connected_comp_enum[indtemparray[:,0],indtemparray[:,1]] = j
#debug_fd.write(str(k)+': '+'\n')
#debug_fd.write(str(indlist)+'\n')
#debug_fd.write('\n')
#debug_fd.flush() # makes sure everything is written to the debug file as program proceeds
#N_CH = prms['nchannels']
chunk_extract = chunk_detect
wv = extract_waveform(indlist,
chunk_extract=chunk_extract,
chunk_fil=chunk_fil,
chunk_raw=chunk_raw,
threshold_strong=threshold.strong,
threshold_weak=threshold.weak,
probe=probe,
**prm)
s_peak = wv.sf_offset - wv.s_start
sf_peak= s_peak + wv.s_frac_part
print 'wv.s_min', wv.s_min,'\n'
print 'wv.s_start', wv.s_start,'\n'
print 'wv.sf_offset', wv.sf_offset,'\n'
print 'wv.s_frac_part',wv.s_frac_part,'\n'
debugnextbits.append((s_peak, sf_peak))
print 'debugnextbits =', debugnextbits
waveslist.append(wv)
#debug_fd.write('debugnextbits ='+ str(debugnextbits)+'\n')
#debug_fd.flush()
#embed()
total_height = 4
total_width = 4
gs = gridspec.GridSpec(total_height,total_width)
fig1 = plt.figure()
#filtchunk_normalised = np.maximum((filteredchunk - ThresholdWeak) / (ThresholdStrong - ThresholdWeak),0)
#filtchunk_normalised_power = np.power(filtchunk_normalised,Parameters['WEIGHT_POWER'])
print 'plotting figure now'
plt.suptitle('%s samples'%(interestpoint), fontsize=10, fontweight='bold')
plt.subplots_adjust(hspace = 0.5)
#plt.subplots_adjust(hspace = 0.25,left= 0.12, bottom = 0.10, right = 0.90, top = 0.90, wspace = 0.2)
#Raw data
dataxis = fig1.add_subplot(gs[0,0:total_width])
dataxis.set_title('DatChunks',fontsize=10)
imdat = dataxis.imshow(np.transpose(chunk_raw[sampmin:sampmax,:]),interpolation="nearest",aspect="auto")
#dataxis.set_xlabel('Samples')
dataxis.set_ylabel('Channels')
#Filtered data
filaxis = fig1.add_subplot(gs[1,0:total_width])
filaxis.set_title('FilteredChunks',fontsize=10)
imfil = filaxis.imshow(np.transpose(chunk_fil[sampmin:sampmax,:]),interpolation="nearest",aspect="auto")
#filaxis.set_xlabel('Samples')
filaxis.set_ylabel('Channels')
#Connected components
compaxis = fig1.add_subplot(gs[2,0:total_width])
compaxis.set_title('Threshold Crossings',fontsize=10)
imcomp = compaxis.imshow(np.transpose(chunk_threshold.weak[sampmin:sampmax,:].astype(int)+chunk_threshold.strong[sampmin:sampmax,:].astype(int)),interpolation="nearest",aspect="auto")
#compaxis.set_xlabel('Samples')
compaxis.set_ylabel('Channels')
for spiketimedebug in debugnextbits:
compaxis.axvline(spiketimedebug[1]-sampmin,color = 'w') #plot a vertical line for s_fpeak
print spiketimedebug[1]-sampmin
conaxis = fig1.add_subplot(gs[3,0:total_width])
conaxis.set_title('Connected Components',fontsize=10)
imcon = conaxis.imshow(np.transpose(connected_comp_enum[sampmin:sampmax,:]),interpolation="nearest",aspect="auto");#plt.colorbar(imcon);
conaxis.set_xlabel('Samples')
conaxis.set_ylabel('Channels')
for spiketimedebug in debugnextbits:
conaxis.axvline(spiketimedebug[1]-sampmin,color = 'w') #plot a vertical line for s_fpeak
print spiketimedebug[1]-sampmin
##offset = 2*np.amax(np.absolute(chunk_raw[sampmin:sampmax,:]))
#offset = 2*np.amax(chunk_raw[sampmin:sampmax,:])
#gain = 1
##rawdataxis = fig1.add_subplot(6,1,5)
#rawdataxis = fig1.add_subplot(gs[4:7,0:total_width])
#rawdataxis.set_title('Raw data',fontsize=10)
#rawdataxis.hold(True)
#for i in np.arange(prm['nchannels']):
#rawdataxis.plot(gain*chunk_raw[sampmin:sampmax,i]+(prm['nchannels']-i)*offset)
#for spiketimedebug in debugnextbits:
#rawdataxis.axvline(spiketimedebug[1]-sampmin,color = 'k') #plot a vertical line for s_fpeak
##offsetfil = 2*np.amax(np.absolute(chunk_fil[sampmin:sampmax,:]))
#offsetfil = 2*np.amax(chunk_fil[sampmin:sampmax,:])
#gain_fil = 1
##fildataxis = fig1.add_subplot(6,1,6)
#fildataxis = fig1.add_subplot(gs[8:11,0:total_width])
#fildataxis.set_title('Filtered data',fontsize=10)
#fildataxis.hold(True)
#for i in np.arange(prm['nchannels']):
#fildataxis.plot(gain_fil*chunk_fil[sampmin:sampmax,i]+(prm['nchannels']-i)*offsetfil)
#for spiketimedebug in debugnextbits:
#fildataxis.axvline(spiketimedebug[1]-sampmin,color = 'k') #plot a vertical line for s_fpeak
#Set this variable to True, if you want to see and adjust each plot in matplotlib as they arise.
if prm['show_plots_as_they_arise']:
plt.show()
fig1.savefig('Debug_SD2floodfillchunk_%s_samples'%(interestpoint))
if prm['save_graph_data']:
tosave = [waveslist,debugnextbits,interestpoint,chunk_threshold, waveslist,chunk_fil,chunk_raw,connected_comp_enum,sampmin,sampmax,prm]
with open('savegraphdata_%s.p'%(interestpoint),'wb') as f:
pickle.dump(tosave,f)