def stratBallot(cls, voter):
frontUtils = [voter[frontId], voter[targId]] #utils of frontrunners
stratGap = frontUtils[1] - frontUtils[0]
if stratGap is 0:
strat = extraStrat = [(4 if (util >= frontUtils[0]) else 0)
for util in voter]
isStrat = True
else:
if stratGap < 0:
#winner is preferred; be complacent.
isStrat = False
else:
#runner-up is preferred; be strategic in iss run
isStrat = True
#sort cuts high to low
frontUtils = (frontUtils[1], frontUtils[0])
top = max(voter)
#print("lll312")
#print(self.baseCuts, front)
cutoffs = [( (min(frontUtils[0], self.baseCuts[i]))
if (i < floor(targResult)) else
( (frontUtils[1])
if (i < floor(frontResult) + 1) else
min(top, self.baseCuts[i])
))
for i in range(len(self.baseCuts))]
strat = [toVote(cutoffs, util) for util in voter]
extraStrat = [max(0,min(10,floor(
4.99 * (util-frontUtils[1]) / (frontUtils[0]-frontUtils[1])
)))
for util in voter]
return dict(strat=strat, extraStrat=extraStrat, isStrat=isStrat,
stratGap = stratGap)
def count_histogram_for_bin(positions, assignments, im_width, im_height, num_bins, i, j, num_words): xmin = floor(im_width / float(num_bins) * i) xmax = floor(im_width / float(num_bins) * (i + 1)) ymin = floor(im_height / float(num_bins) * j) ymax = floor(im_height / float(num_bins) * (j + 1)) indices = get_indices_for_pos(positions, xmin, xmax, ymin, ymax) return count_histogram(indices, assignments, num_words)
def count_histogram_for_bin(positions, assignments, im_width, im_height,
num_bins, i, j, num_words):
xmin = floor(im_width / float(num_bins) * i)
xmax = floor(im_width / float(num_bins) * (i + 1))
ymin = floor(im_height / float(num_bins) * j)
ymax = floor(im_height / float(num_bins) * (j + 1))
indices = get_indices_for_pos(positions, xmin, xmax, ymin, ymax)
return count_histogram(indices, assignments, num_words)
def __call__(self, X, n = None, x0 = None):
'''
Generates n seed points for the lpc algorithm.
X, 2 dimensional [#points, #dimension of points] array containing the data for which local density modes is to calculated
n, required number of seed points, if n = None, returns exactly the local density modes, otherwise lpcRandomStartPoints is called with x0 equal
to the local density modes (local density modes are the cluster centers)
x0, 2-dimensional array containing #rows equal to number of explicitly defined mean shift seed points and #columns equal
to dimension of the individual data points (called number of features in MeanShift docs).
Returns the lpc seed points as a 2 dimensional [#seed points, #dimension of seed points] array
'''
self._Xi = X
if x0 is None:
N = self._Xi.shape[0]
ms_sub = float(self._lpcParameters['ms_sub'])
#guarantees ms_sub <= ms_sub % of N <= 10 * ms_sub seed points (could give the option of using seed point binning in MeanShift)
Nsub = int(min(max(ms_sub, floor(ms_sub * N / 100)), 10 * ms_sub))
ms_seeds = self._Xi[sample(xrange(0, N), Nsub),:]
else:
ms_seeds = x0
self._meanShift.seeds = ms_seeds
self._meanShift.fit(self._Xi)
cluster_respresentatives = self._removeNonTracklikeClusterCenters()
if len(cluster_respresentatives) == 0:
cluster_respresentatives = None
lpcRSP = lpcRandomStartPoints()
if n is None:
return lpcRSP(self._Xi, n = 2, x0 = cluster_respresentatives)
else:
return lpcRSP(self._Xi, n = n, x0 = cluster_respresentatives)
def plot_fscores(labels, series):
length = max(list(map(len, series)))
fig = plt.figure()
ax = fig.gca()
ax.set_xticks(np.arange(0, float(len(series))), 1)
ymin = min(list(map(min, series)))
ymax = max(list(map(max, series)))
ymin = floor(ymin * 10) / 10
ymax = ceil(ymax * 10) / 10
ax.set_yticks(np.arange(ymin, ymax, 0.1))
plt.axis([0, length - 1, ymin, ymax])
fontProperties = {'family': 'sans-serif', 'sans-serif': ['Helvetica'],
'weight': 'normal', 'size': 20}
rc('text', usetex=True)
rc('font', **fontProperties)
ax.set_xticklabels(
[r'$\frac{%d}{%d}$' % (i + 1, length - i) for i in range(length)],
fontProperties)
plt.grid()
for i, [l, s] in enumerate(zip(labels, series)):
c = CVALUE[COLORS[i]]
plt.plot(list(range(len(s))), s, '-', marker=MARKERS[i], color=c,
linewidth=2.5, markersize=12, fillstyle='full', label=l)
plt.legend(loc="best")
plt.ylabel(r'$F_1$')
plt.xlabel(r'$k$')
def plot_KLDiv_with_logscale(series):
length = len(series)
fig = plt.figure()
ax = fig.gca()
ax.set_xticks(np.arange(0, float(len(series))), 1)
ymin = min(series)
ymax = max(series)
ymin = floor(ymin * 10) / 10
ymax = ceil(ymax * 10) / 10
ax.set_yticks(np.arange(ymin, ymax, 0.1))
plt.axis([0, length - 1, ymin, ymax])
fontProperties = {'family': 'sans-serif', 'sans-serif': ['Helvetica'],
'weight': 'normal', 'size': 20}
rc('text', usetex=True)
rc('font', **fontProperties)
# ax.set_xticklabels([r'$\frac{%d}{%d}$' % (i+1, length-i) for i in range(length)], fontProperties)
plt.grid()
a = plt.axes() # plt.axis([0, length-1, ymin, ymax])
plt.yscale('log')
c = CVALUE[COLORS[0]]
m = MARKERS[0]
plt.plot(list(range(len(series))), series, '-', marker=m, color=c, linewidth=2.5,
markersize=12, fillstyle='full', label='Label')
c = CVALUE[COLORS[1]]
m = MARKERS[1]
plt.plot(list(range(len(series))), series, '-', marker=m, color=c, linewidth=2.5,
markersize=12, fillstyle='full', label='Label')
plt.legend(loc="best")
plt.ylabel(r'$F_1$')
plt.xlabel(r'$k$')
def generateInt(self, k):
'''
generowanie k liczb
'''
T = [0]*k
for i in range(k):
U = uniform(low=0, high=1)
X = log(U)/log(1-self.p)
T[(int(floor(X)))] = T[(int(floor(X)))] + 1
return T
#A = Geometric(0.1)
#C = A.showIntGenAndCount(A.generateInt(100))
#P = A.probabilityChart(C)
#A = PlotHist()
#A.plotHistgram("Generator dwumianowy", C,P,1,'ilosc','liczby')
#A.showHistogram()
def stratBallot(cls, voter):
frontUtils = [voter[frontId],
voter[targId]] #utils of frontrunners
stratGap = frontUtils[1] - frontUtils[0]
if stratGap is 0:
strat = extraStrat = [(4 if (util >= frontUtils[0]) else 0)
for util in voter]
isStrat = True
else:
if stratGap < 0:
#winner is preferred; be complacent.
isStrat = False
else:
#runner-up is preferred; be strategic in iss run
isStrat = True
#sort cuts high to low
frontUtils = (frontUtils[1], frontUtils[0])
top = max(voter)
#print("lll312")
#print(self.baseCuts, front)
cutoffs = [((min(frontUtils[0], self.baseCuts[i])) if
(i < floor(targResult)) else
((frontUtils[1]) if
(i < floor(frontResult) +
1) else min(top, self.baseCuts[i])))
for i in range(len(self.baseCuts))]
strat = [toVote(cutoffs, util) for util in voter]
extraStrat = [
max(
0,
min(
10,
floor(4.99 * (util - frontUtils[1]) /
(frontUtils[0] - frontUtils[1]))))
for util in voter
]
return dict(strat=strat,
extraStrat=extraStrat,
isStrat=isStrat,
stratGap=stratGap)
def honBallot(cls, utils):
"""Takes utilities and returns an honest ballot (on 0..10)
honest ballots work as expected
>>> Score().honBallot(Score, Voter([5,6,7]))
[0.0, 5.0, 10.0]
>>> Score().resultsFor(DeterministicModel(3)(5,3),Score().honBallot)["results"]
[4.0, 6.0, 5.0]
"""
bot = min(utils)
scale = max(utils)-bot
return [floor((cls.topRank + .99) * (util-bot) / scale) for util in utils]
def honBallot(cls, utils):
"""Takes utilities and returns an honest ballot (on 0..10)
honest ballots work as expected
>>> Score().honBallot(Score, Voter([5,6,7]))
[0.0, 5.0, 10.0]
>>> Score().resultsFor(DeterministicModel(3)(5,3),Score().honBallot)["results"]
[4.0, 6.0, 5.0]
"""
bot = min(utils)
scale = max(utils) - bot
return [
floor((cls.topRank + .99) * (util - bot) / scale)
for util in utils
]
def fillStratBallot(cls, voter, polls, places, n, stratGap, ballot,
frontId, frontResult, targId, targResult):
"""Returns a (function which takes utilities and returns a strategic ballot)
for the given "polling" info."""
cuts = [voter[frontId], voter[targId]]
if stratGap > 0:
#sort cuts high to low
cuts = (cuts[1], cuts[0])
if cuts[0] == cuts[1]:
strat = [(cls.topRank if (util >= cuts[0]) else 0) for util in voter]
else:
strat = [max(0,min(cls.topRank,floor(
(cls.topRank + .99) * (util-cuts[1]) / (cuts[0]-cuts[1])
)))
for util in voter]
for i in range(n):
ballot[i] = strat[i]
def stratBallot(cls, voter):
cuts = [voter[places[0][0]], voter[places[1][0]]]
stratGap = cuts[1] - cuts[0]
if stratGap <= 0:
#winner is preferred; be complacent.
isStrat = False
else:
#runner-up is preferred; be strategic in iss run
isStrat = True
#sort cuts high to low
cuts = (cuts[1], cuts[0])
if cuts[0] == cuts[1]:
strat = [(cls.topRank if (util >= cuts[0]) else 0) for util in voter]
else:
strat = [max(0,min(cls.topRank,floor(
(cls.topRank + .99) * (util-cuts[1]) / (cuts[0]-cuts[1])
)))
for util in voter]
return dict(strat=strat, isStrat=isStrat, stratGap=stratGap)
def test_changing_the_shape_of_an_array(self):
a = floor(10*random.random((3,4)))
a = array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])
numpy.testing.assert_array_equal(a.ravel(), array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]))
a.shape = (6,2)
numpy.testing.assert_array_equal(a, array([[ 1, 2],
[ 3, 4],
[ 5, 6],
[ 7, 8],
[ 9, 10],
[11, 12]]))
numpy.testing.assert_array_equal(a.transpose(), array([[ 1, 3, 5, 7, 9, 11],
[ 2, 4, 6, 8, 10, 12]]))
a = a.reshape(2,6)
numpy.testing.assert_array_equal(a, array([[ 1, 2, 3, 4, 5, 6],
[ 7, 8, 9, 10, 11, 12]]))
a = a.reshape(3,-1)
numpy.testing.assert_array_equal(a, array([[1,2,3,4],
[5,6,7,8],
[9,10,11,12]]))
def fillStratBallot(cls, voter, polls, places, n, stratGap, ballot,
frontId, frontResult, targId, targResult):
"""Returns a (function which takes utilities and returns a strategic ballot)
for the given "polling" info."""
cuts = [voter[frontId], voter[targId]]
if stratGap > 0:
#sort cuts high to low
cuts = (cuts[1], cuts[0])
if cuts[0] == cuts[1]:
strat = [(cls.topRank if (util >= cuts[0]) else 0)
for util in voter]
else:
strat = [
max(
0,
min(
cls.topRank,
floor((cls.topRank + .99) * (util - cuts[1]) /
(cuts[0] - cuts[1])))) for util in voter
]
for i in range(n):
ballot[i] = strat[i]
def stratBallot(cls, voter):
cuts = [voter[places[0][0]], voter[places[1][0]]]
stratGap = cuts[1] - cuts[0]
if stratGap <= 0:
#winner is preferred; be complacent.
isStrat = False
else:
#runner-up is preferred; be strategic in iss run
isStrat = True
#sort cuts high to low
cuts = (cuts[1], cuts[0])
if cuts[0] == cuts[1]:
strat = [(cls.topRank if (util >= cuts[0]) else 0)
for util in voter]
else:
strat = [
max(
0,
min(
cls.topRank,
floor((cls.topRank + .99) * (util - cuts[1]) /
(cuts[0] - cuts[1])))) for util in voter
]
return dict(strat=strat, isStrat=isStrat, stratGap=stratGap)
from matplotlib import pyplot
from scipy import ndimage
import numpy
from numpy.ma.core import floor
from string import replace
from scipy.misc.pilutil import imsave
import os
print ('********************************************************************************')
print ('* Changing the intensity levels of an image using SciPy, NumPy and MatPlotLib *')
print ('********************************************************************************')
import sys
sys.path.append('../utils')
import userinput
fpath = userinput.get_img_path()
img_array = userinput.get_gray_img(fpath)
# builds new images...
intensities = [2, 4, 8, 16, 32, 64, 128, 255];
for intensity in intensities:
print ('building new image using an intensity level of \'' + str(intensity) + '\'...')
new_img_array = floor(numpy.array(img_array) / intensity) * intensity
new_fpath = '%s/%i-intensity-%s' % (os.path.dirname(fpath), intensity, os.path.basename(fpath))
print ('saving new image to \'' + new_fpath + '\'...')
imsave(new_fpath, new_img_array)
print ('********************************************************************************')
class ParametersAlgo(object):
ALPHA = 0.97
FOR_JINGJU = 0
FOR_MAKAM = 0
OBS_MODEL = 'GMM'
OBS_MODEL = 'MLP'
OBS_MODEL = 'MLP_fuzzy'
EVAL_LEVEL = tierAliases.words
# eval level phonemes does not work
# EVAL_LEVEL = tierAliases.pinyin # in Jingju only level is syllable
# use duraiton-based decoding (HMMDuraiton package) or just plain viterbi (HMM package)
# if false, use transition probabilities from htkModels
WITH_DURATIONS= 1
USE_PERSISTENT_PPGs = 0
# level into which to segments decoded result stateNetwork
# DETECTION_TOKEN_LEVEL= 'syllables'
DETECTION_TOKEN_LEVEL= 'words'
# DETECTION_TOKEN_LEVEL= 'phonemes'
Q_WEIGHT_TRANSITION = 3.5
DECODE_WITH_HTK = 0
GLOBAL_WAIT_PROB = 0.9
THRESHOLD_PEAKS = -70
DEVIATION_IN_SEC = 0.1
# unit: num frames
NUMFRAMESPERSECOND = 100
# same as WINDOWSIZE in wavconfig singing. unit: seconds. TOOD: read from there automatically
WINDOW_SIZE = 0.025
# in frames
ONLY_MIDDLE_STATE = 1
WITH_SHORT_PAUSES = 0
# padded a short pause state at beginning and end of sequence
WITH_PADDED_SILENCE = 0
# no feature vectors at all. all observ, probs. set to 1
# WITH_ORACLE_PHONEMES = -1
WITH_ORACLE_PHONEMES = 0
PATH_TO_HCOPY= '/usr/local/bin/HCopy'
PATH_TO_HVITE = '/usr/local/bin/HVite'
# On kora.s.upf.edu
# PATH_TO_HCOPY = '/homedtic/georgid/htkBuilt/bin/HCopy'
projDir = os.path.abspath(os.path.join(os.path.dirname(os.path.realpath(__file__)) , os.path.pardir ))
PATH_TO_CONFIG_FILES= projDir + '/models_makam/input_files/'
parentDir = os.path.abspath(os.path.join(os.path.dirname(os.path.realpath(__file__) ), os.path.pardir))
MODELS_DIR = os.path.join(parentDir, 'models_jingju/' + '3' + 'folds/')
POLYPHONIC = 1
WITH_ORACLE_ONSETS = -1
### no onsets at all.
# WITH_ORACLE_ONSETS = -1
# Sigma of onset smoothing function g: normal distribution
ONSET_SIGMA = 0.075
# ONSET_SIGMA = 0.15
ONSET_SIGMA_IN_FRAMES = int(floor(ONSET_SIGMA * NUMFRAMESPERSECOND))
if ONSET_SIGMA_IN_FRAMES % 2 == 0:
ONSET_SIGMA_IN_FRAMES += 1
# ONSET_TOLERANCE_WINDOW = 0.02 # seconds. to work implement decoding with one onset only
ONSET_TOLERANCE_WINDOW = 0 # seconds
# in _ContinousHMM.b_map cut probabilities
CUTOFF_BIN_OBS_PROBS = 30
# for for_jingju
CONSONANT_DURATION_IN_SEC = 0.3
# for for_makam
# CONSONANT_DURATION_IN_SEC = 0.1
CONSONANT_DURATION = NUMFRAMESPERSECOND * CONSONANT_DURATION_IN_SEC;
CONSONANT_DURATION_DEVIATION = 0.7
#####
LOGGING_LEVEL = logging.INFO
VISUALIZE = 0
ANNOTATION_RULES_ONSETS_EXT = 'annotationOnsets.txt'
ANNOTATION_SCORE_ONSETS_EXT = 'alignedNotes.txt' # use this ont to get better impression on recall, compared to annotationOnsets.txt, which are only on note onsets with rules of interest
WRITE_TO_FILE = True
image_data_raw = image_generator.GetBGR24ImageMapRaw()
depth_data_raw = depth_generator.GetGrayscale16DepthMapRaw()
cv.SetData(current_depth_frame, depth_data_raw)
cv.Convert(current_depth_frame, for_thresh)
cv.SetData(current_image_frame, image_data_raw)
# initialize matrices for drawing and start timing
t0 = time.time()
cv.SetZero(hist_img)
cv.SetZero(out)
cv.SetZero(contours)
# compute and smooth histogram
depth = np.asarray(current_depth_frame)
hist, bins = np.histogram(depth, n_bins, range=(min_range, max_range), normed=False)
hist_half = floor(n_bins/2)
hist[:hist_half] = np.convolve(hist[:hist_half], np.ones(k_width) / k_width, 'same')
hist[hist_half:] = np.convolve(hist[hist_half:], np.ones(k_width2) / k_width2, 'same')
max_hist = np.max(hist)
timing['t_histo'] += time.time() - t0
# histogram clustering
start, end = 0, 0
c = 1
conts_list = []
for i in range(len(hist)-1):
cur_value = hist[i]
next_value = hist[i + 1]
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
]