def find_distn(lgbinwidth, numlgbins, transient, N, M_t, M_i):
totspkhist = zeros((numlgbins, 1))
skiptime = transient * ms
skipbin = int(ceil(skiptime / lgbinwidth))
for i in xrange(numlgbins):
step_start = (i) * lgbinwidth #30*ms #
step_end = (i + 1) * lgbinwidth #30*ms #
for j in xrange(N):
#spks=where(logical_and(M[j]>step_start, M[j]<step_end))
#totspkhist[i]+=len(M[j][spks])
totspkhist[i] += len(M_i[logical_and(M_t > step_start,
M_t < step_end)])
#totspkhist_1D=reshape(totspkhist,len(totspkhist))
###smooth plot first so thresholds work better
#b,a=butter(3,0.4,'low')
#totspkhist_smooth=filtfilt(b,a,totspkhist_1D)
totspkhist_smooth = reshape(
totspkhist, len(totspkhist)
) #here we took out the actual smoothing and left it as raw distn.
#create distn based on hist, but skip first skiptime to cut out transient excessive spiking
if max(totspkhist_smooth[skipbin:]) > 0:
totspkdist_smooth = totspkhist_smooth / max(
totspkhist_smooth[skipbin:])
else:
totspkdist_smooth = totspkhist_smooth
totspkhist_list = [val for subl in totspkhist for val in subl]
return [totspkhist, totspkdist_smooth, totspkhist_list]
def filter_func(x):
ok = logical_and(x.Rload == R, x.phase == phase)
# print 334,x,R, phase
# print ok
return ok
def checkClassification(self,binary):
fp = count_nonzero(logical_and(self.notMask, binary))
tp = count_nonzero(logical_and(self.mask,binary))
return fp,tp
def find_bursts(duration, dt, transient, N, M_t, M_i, max_freq):
base = 2 #round lgbinwidth to nearest 2 so will always divide into durations
expnum = 2.0264 * exp(-0.2656 * max_freq + 2.9288) + 5.7907
lgbinwidth = (int(base * round(
(-max_freq + 33) / base))) * ms #23-good for higher freq stuff
#lgbinwidth=(int(base*round((expnum)/base)))/1000 #use exptl based on some fit of choice binwidths
#lgbinwidth=10*ms
numlgbins = int(ceil(duration / lgbinwidth))
#totspkhist=zeros((numlgbins,1))
totspkhist = zeros(numlgbins)
#totspkdist_smooth=zeros((numlgbins,1))
skiptime = transient * ms
skipbin = int(ceil(skiptime / lgbinwidth))
inc_past_thresh = []
dec_past_thresh = []
#Create histogram given the bins calculated
for i in xrange(numlgbins):
step_start = (i) * lgbinwidth
step_end = (i + 1) * lgbinwidth
totspkhist[i] = len(M_i[logical_and(M_t > step_start, M_t < step_end)])
###smooth plot first so thresholds work better
#totspkhist_1D=reshape(totspkhist,len(totspkhist)) #first just reshape so single row not single colm
#b,a=butter(3,0.4,'low')
#totspkhist_smooth=filtfilt(b,a,totspkhist_1D)
#totspkhist_smooth=reshape(totspkhist,len(totspkhist)) #here we took out the actual smoothing and left it as raw distn. here just reshape so single row not single colm
totspkdist_smooth = totspkhist / max(
totspkhist[skipbin:]
) #create distn based on hist, but skip first skiptime to cut out transient excessive spiking
# ####### FOR MOVING THRESHOLD #################
## find points where increases and decreases over some threshold
dist_thresh = []
thresh_plot = []
mul_fac = 0.35
switch = 0 #keeps track of whether inc or dec last
elim_noise = 1 / (max_freq * 2.5 * Hz)
#For line 95, somehow not required in previous version?
#elim_noise_units = 1/(max_freq*Hz*2.5)
thresh_time = 5 / (max_freq) #capture 5 cycles
thresh_ind = int(floor(
(thresh_time / lgbinwidth) /
2)) #the number of indices on each side of the window
#dist_thresh moves with window capturing approx 5 cycles (need special cases for borders) Find where increases and decreases past threshold (as long as a certain distance apart, based on "elim_noise" which is based on avg freq of bursts
dist_thresh.append(
totspkdist_smooth[skipbin:skipbin + thresh_ind].mean(0) +
mul_fac * totspkdist_smooth[skipbin:skipbin + thresh_ind].std(0))
for i in xrange(1, numlgbins):
step_start = (i) * lgbinwidth
step_end = (i + 1) * lgbinwidth
#moving threshold
if i > (skipbin +
thresh_ind) and (i + thresh_ind) < len(totspkdist_smooth):
#print(totspkdist_smooth[i-thresh_ind:i+thresh_ind])
dist_thresh.append(
totspkdist_smooth[i - thresh_ind:i + thresh_ind].mean(0) +
mul_fac *
totspkdist_smooth[i - thresh_ind:i + thresh_ind].std(0))
elif (i + thresh_ind) >= len(totspkdist_smooth):
dist_thresh.append(totspkdist_smooth[-thresh_ind:].mean(0) +
mul_fac *
totspkdist_smooth[-thresh_ind:].std(0))
else:
dist_thresh.append(
totspkdist_smooth[skipbin:skipbin + thresh_ind].mean(0) +
mul_fac *
totspkdist_smooth[skipbin:skipbin + thresh_ind].std(0))
if (totspkdist_smooth[i - 1] <
dist_thresh[i]) and (totspkdist_smooth[i] >= dist_thresh[i]):
#inc_past_thresh.append(step_start-0.5*lgbinwidth)
if (inc_past_thresh): #there has already been at least one inc,
if (
abs(inc_past_thresh[-1] -
(step_start - 0.5 * lgbinwidth)) > elim_noise
) and switch == 0: #must be at least x ms apart (yHz), and it was dec last..
inc_past_thresh.append(
step_start - 0.5 * lgbinwidth
) #take lower point (therefore first) when increasing. Need to -0.5binwidth to adjust for shift between index of bin width and index of bin distn
#print (['incr=%f'%inc_past_thresh[-1]])
thresh_plot.append(dist_thresh[i])
switch = 1
else:
inc_past_thresh.append(
step_start - 0.5 * lgbinwidth
) #take lower point (therefore first) when increasing. Need to -0.5binwidth to adjust for shift between index of bin width and index of bin distn
thresh_plot.append(dist_thresh[i])
switch = 1 #keeps track of that it was inc. last
elif (totspkdist_smooth[i - 1] >=
dist_thresh[i]) and (totspkdist_smooth[i] < dist_thresh[i]):
# dec_past_thresh.append(step_end-0.5*lgbinwidth) #take lower point (therefore second) when decreasing
if (dec_past_thresh): #there has already been at least one dec
if (
abs(dec_past_thresh[-1] -
(step_end - 0.5 * lgbinwidth)) > elim_noise
) and switch == 1: #must be at least x ms apart (y Hz), and it was inc last
dec_past_thresh.append(
step_end - 0.5 * lgbinwidth
) #take lower point (therefore second) when decreasing
#print (['decr=%f'%dec_past_thresh[-1]])
switch = 0
else:
dec_past_thresh.append(
step_end - 0.5 * lgbinwidth
) #take lower point (therefore second) when decreasing
switch = 0 #keeps track of that it was dec last
if totspkdist_smooth[0] < dist_thresh[
0]: #if you are starting below thresh, then pop first inc. otherwise, don't (since will decrease first)
if inc_past_thresh: #if list is not empty
inc_past_thresh.pop(0)
#
#####################################################################
#
######### TO DEFINE A STATIC THRESHOLD AND FIND CROSSING POINTS
# dist_thresh=0.15 #static threshold
# switch=0 #keeps track of whether inc or dec last
# overall_freq=3.6 #0.9
# elim_noise=1/(overall_freq*5)#2.5)
#
#
# for i in xrange(1,numlgbins):
# step_start=(i)*lgbinwidth
# step_end=(i+1)*lgbinwidth
#
# if (totspkdist_smooth[i-1]<dist_thresh) and (totspkdist_smooth[i]>=dist_thresh): #if cross threshold (increasing)
# if (inc_past_thresh): #there has already been at least one inc,
# if (abs(dec_past_thresh[-1]-(step_start-0.5*lgbinwidth))>elim_noise) and switch==0: #must be at least x ms apart (yHz) from the previous dec, and it was dec last..
# inc_past_thresh.append(step_start-0.5*lgbinwidth) #take lower point (therefore first) when increasing. Need to -0.5binwidth to adjust for shift between index of bin width and index of bin distn
# #print (['incr=%f'%inc_past_thresh[-1]]) #-0.5*lgbinwidth
# switch=1
# else:
# inc_past_thresh.append(step_start-0.5*lgbinwidth) #take lower point (therefore first) when increasing. Need to -0.5binwidth to adjust for shift between index of bin width and index of bin distn
# switch=1 #keeps track of that it was inc. last
# elif (totspkdist_smooth[i-1]>=dist_thresh) and (totspkdist_smooth[i]<dist_thresh):
# if (dec_past_thresh): #there has already been at least one dec
# if (abs(inc_past_thresh[-1]-(step_end-0.5*lgbinwidth))>elim_noise) and switch==1: #must be at least x ms apart (y Hz) from the previous incr, and it was inc last
# dec_past_thresh.append(step_end-0.5*lgbinwidth) #take lower point (therefore second) when decreasing
# #print (['decr=%f'%dec_past_thresh[-1]])
# switch=0
# else:
# dec_past_thresh.append(step_end-0.5*lgbinwidth) #take lower point (therefore second) when decreasing
# switch=0 #keeps track of that it was dec last
#
#
# if totspkdist_smooth[0]<dist_thresh: #if you are starting below thresh, then pop first inc. otherwise, don't (since will decrease first)
# if inc_past_thresh: #if list is not empty
# inc_past_thresh.pop(0)
################################################################
###############################################################
######## DEFINE INTER AND INTRA BURSTS ########
#since always start with dec, intraburst=time points from 1st inc:2nd dec, from 2nd inc:3rd dec, etc.
#interburst=time points from 1st dec:1st inc, from 2nd dec:2nd inc, etc.
intraburst_time_ms_compound_list = []
interburst_time_ms_compound_list = []
intraburst_bins = [] #in seconds
interburst_bins = []
#print(inc_past_thresh)
if len(inc_past_thresh) < len(dec_past_thresh): #if you end on a decrease
for i in xrange(len(inc_past_thresh)):
intraburst_time_ms_compound_list.append(
arange(inc_past_thresh[i] / ms, dec_past_thresh[i + 1] / ms,
1)) #10 is timestep
interburst_time_ms_compound_list.append(
arange((dec_past_thresh[i] + dt) / ms,
(inc_past_thresh[i] - dt) / ms, 1)) #10 is timestep
intraburst_bins.append(inc_past_thresh[i])
intraburst_bins.append(dec_past_thresh[i + 1])
interburst_bins.append(dec_past_thresh[i])
interburst_bins.append(inc_past_thresh[i])
else: #if you end on an increase
for i in xrange(len(inc_past_thresh) - 1):
intraburst_time_ms_compound_list.append(
arange(inc_past_thresh[i] / ms, dec_past_thresh[i + 1] / ms,
1)) #10 is timestep
interburst_time_ms_compound_list.append(
arange((dec_past_thresh[i] + dt) / ms,
(inc_past_thresh[i] - dt) / ms, 1)) #10 is timestep
intraburst_bins.append(inc_past_thresh[i])
intraburst_bins.append(dec_past_thresh[i + 1])
interburst_bins.append(dec_past_thresh[i] + dt)
interburst_bins.append(inc_past_thresh[i] - dt)
if dec_past_thresh and inc_past_thresh: #if neither dec_past_thresh nor inc_past_thresh is empty
interburst_bins.append(dec_past_thresh[-1] +
dt) #will have one more inter than intra
interburst_bins.append(inc_past_thresh[-1] + dt)
interburst_bins = interburst_bins / second
intraburst_bins = intraburst_bins / second
intraburst_time_ms = [
num for elem in intraburst_time_ms_compound_list for num in elem
] #flatten list
interburst_time_ms = [
num for elem in interburst_time_ms_compound_list for num in elem
] #flatten list
num_intraburst_bins = len(
intraburst_bins
) / 2 #/2 since have both start and end points for each bin
num_interburst_bins = len(interburst_bins) / 2
intraburst_bins_ms = [x * 1000 for x in intraburst_bins]
interburst_bins_ms = [x * 1000 for x in interburst_bins]
######################################
#bin_s=[((inc_past_thresh-dec_past_thresh)/2+dec_past_thresh) for inc_past_thresh, dec_past_thresh in zip(inc_past_thresh,dec_past_thresh)]
bin_s = [((x - y) / 2 + y)
for x, y in zip(inc_past_thresh, dec_past_thresh)] / second
binpt_ind = [int(floor(x / lgbinwidth)) for x in bin_s]
########## FIND PEAK TO TROUGH AND SAVE VALUES ###################
########## CATEGORIZE BURSTING BASED ON PEAK TO TROUGH VALUES ###################
########## DISCARD BINPTS IF PEAK TO TROUGH IS TOO SMALL ###################
peaks = []
trough = []
peak_to_trough_diff = []
min_burst_size = 0.2 #defines a burst as 0.2 or larger.
for i in xrange(len(binpt_ind) - 1):
peaks.append(max(totspkdist_smooth[binpt_ind[i]:binpt_ind[i + 1]]))
trough.append(min(totspkdist_smooth[binpt_ind[i]:binpt_ind[i + 1]]))
peak_to_trough_diff = [
max_dist - min_dist for max_dist, min_dist in zip(peaks, trough)
]
#to delete all bins following any <min_burst_size
first_ind_not_burst = next(
(x[0] for x in enumerate(peak_to_trough_diff) if x[1] < 0.2), None)
# if first_ind_not_burst:
# del bin_s[first_ind_not_burst+1:] #needs +1 since bin_s has one additional value (since counts edges)
#to keep track of any bins <0.2 so can ignore in stats later
all_ind_not_burst = [
x[0] for x in enumerate(peak_to_trough_diff) if x[1] < 0.2
] #defines a burst as 0.2 or larger.
bin_ms = [x * 1000 for x in bin_s]
binpt_ind = [int(floor(x / lgbinwidth)) for x in bin_s]
#for moving threshold only
thresh_plot = []
thresh_plot = [dist_thresh[x] for x in binpt_ind]
#for static threshold
#thresh_plot=[dist_thresh]*len(bin_ms)
#
#
# bin_s=[((inc_past_thresh-dec_past_thresh)/2+dec_past_thresh) for inc_past_thresh, dec_past_thresh in zip(inc_past_thresh,dec_past_thresh)]
# bin_ms=[x*1000 for x in bin_s]
# thresh_plot=[]
# binpt_ind=[int(floor(x/lgbinwidth)) for x in bin_s]
# thresh_plot=[dist_thresh[x] for x in binpt_ind]
#
binpts = xrange(int(lgbinwidth * 1000 / 2),
int(numlgbins * lgbinwidth * 1000), int(lgbinwidth * 1000))
totspkhist_list = totspkhist.tolist(
) #[val for subl in totspkhist for val in subl]
#find first index after transient to see if have enough bins to do stats
bin_ind_no_trans = bisect.bisect(bin_ms, transient)
intrabin_ind_no_trans = bisect.bisect(intraburst_bins, transient /
1000) #transient to seconds
if intrabin_ind_no_trans % 2 != 0: #index must be even since format is ind0=start_bin, ind1=end_bin, ind2=start_bin, .... .
intrabin_ind_no_trans += 1
interbin_ind_no_trans = bisect.bisect(interburst_bins, transient / 1000)
if interbin_ind_no_trans % 2 != 0:
interbin_ind_no_trans += 1
return [
bin_s, bin_ms, binpts, totspkhist, totspkdist_smooth, dist_thresh,
totspkhist_list, thresh_plot, binpt_ind, lgbinwidth, numlgbins,
intraburst_bins, interburst_bins, intraburst_bins_ms,
interburst_bins_ms, intraburst_time_ms, interburst_time_ms,
num_intraburst_bins, num_interburst_bins, bin_ind_no_trans,
intrabin_ind_no_trans, interbin_ind_no_trans
]
def filter_func(x):
ok = logical_and(x.Rload == R, x.phase == phase)
# print 334,x,R, phase
# print ok
return ok
