本文整理汇总了Python中scipy.signal.argrelextrema函数的典型用法代码示例。如果您正苦于以下问题：Python argrelextrema函数的具体用法？Python argrelextrema怎么用？Python argrelextrema使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了argrelextrema函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: findBump
def findBump(twoder):
mins = argrelextrema(twoder[20:70], np.less, order = 5)[0]
maxes = argrelextrema(twoder[10:65], np.greater, order = 5)[0]
max2der = max(abs(twoder))
realmins = []
realmaxes = []
thresh = 0.00
for i in range(len(mins)):
ind = mins[i] + 20
if twoder[min(len(twoder)-1,ind+6)] - twoder[ind] > thresh*max2der and twoder[ind-6] - twoder[ind] > thresh*max2der:
realmins.append(ind)
if twoder[-1] < 0 and twoder[-3] < 0:
realmins.append(len(twoder)-1)
for i in range(len(maxes)):
ind = maxes[i] + 10
if twoder[ind] - twoder[min(len(twoder)-1,ind+6)] > thresh*max2der and twoder[ind] - twoder[ind-6] > thresh*max2der:
realmaxes.append(ind)
if len(realmaxes) <= 1:
return -1
startLoc = -1
for i in range(len(realmins)):
if realmins[-i-1] < realmaxes[1]:
startLoc = (realmins[-i-1] + realmaxes[1])/2
break
midLoc = -1
for i in range(len(realmins)):
if realmins[i] > realmaxes[1]:
midLoc = (realmins[i] + realmaxes[1])/2
break
return [startLoc, midLoc]示例2: localMinMax
def localMinMax(self, y, box_pts=None):
# detect local minima and maxima using the smoothed curve
self.resetLocalMinMax()
leny = len(y)
# smooth the signal
# print box_pts,len(y)
if box_pts is None: # detail segmentation box
n = int(leny/10)
box_pts = 3 if n<3 else n
if leny>52 and box_pts>=leny: # segment > 0.3s but box is too large
n = int(leny/5)
box_pts = n
if box_pts < leny:
self.ySmooth = uf.smooth(y, box_pts)
half_box_pts = np.ceil(box_pts/2.0)
if len(self.ySmooth):
# for local maxima
self.maximaInd = argrelextrema(self.ySmooth, np.greater)
# remove the boundary effect of convolve
self.maximaInd = [mi for mi in self.maximaInd[0] if (mi>half_box_pts and mi<leny-half_box_pts)]
# for local minima
self.minimaInd = argrelextrema(self.ySmooth, np.less)
# remove the boundary effect of convolve
self.minimaInd = [mi for mi in self.minimaInd[0] if (mi>half_box_pts and mi<leny-half_box_pts)]
return box_pts示例3: extract
def extract(self, spikes, sample_rate):
channels_data = spikes
number_of_channels = len(channels_data)
number_of_points_in_channel = len(channels_data[0])
lower_boundary = 0
upper_boundary = int(number_of_points_in_channel / 2) - 1
lmda = int(upper_boundary * 1.5)
dt = 1.0 / sample_rate
step_tau = int((upper_boundary - lower_boundary) / 50) ## was constant 1000 ## ensure that integer
number_of_iterations = len(np.r_[lower_boundary:upper_boundary:step_tau]) ##(upper_boundary-lower_boundary)/step_tau
# TODO: From the auditory nerve activity compute the autocorrelation of each nerve activity
iteration = np.zeros(number_of_iterations)
for i in range(number_of_iterations):
for j in range(number_of_channels):
# TODO: Sum these autocorrelation across nerves to construct the summary autocorrelation
iteration[i] += self.integration(lower_boundary, upper_boundary, sample_rate, dt, channels_data, j, i * step_tau, lmda)
iteration = iteration / iteration[0] ##normalization
# TODO: Extract the argument of the first non-zero peak in the autocorrelation
peak_tau = step_tau * argrelextrema(iteration, np.greater)[0][iteration[argrelextrema(iteration, np.greater)[0]].argmax()]
# TODO: Pitch matching maybe?
# TODO: Return pitch estimate
return 1.0 / (peak_tau * dt)示例4: most_like_imf
def most_like_imf(data, imf, imf_index1, imf_index2):
residual_short_sample_width, residual_short_std, short_distance = most_like_gaussian(data,imf,imf_index1)
print "short sample width %s"%residual_short_sample_width
print "short std %s"%residual_short_std
residual_long_sample_width, residual_long_std, long_distance = most_like_gaussian(data,imf,imf_index2)
i = 1
while residual_short_sample_width == residual_long_sample_width:# and residual_short_factor == residual_long_factor:
residual_long_sample_width, residual_long_std, _ = most_like_gaussian(data,imf,imf_index2,i)
print "long sample width %s"%residual_long_sample_width
print "long std %s"%residual_long_std
i += 1
print "long sample width %s"%residual_long_sample_width
print "long std %s"%residual_long_std
confidence = 0
if (short_distance/np.mean(data[-20:]))<0.01 and (long_distance/np.mean(data[-20:]))<0.01:
confidence = 1
long_period = gaussian_smooth(data, residual_long_sample_width, residual_long_std)
short_period = gaussian_smooth(data, residual_short_sample_width, residual_short_std)
diff = [short_period[i]-long_period[i] for i in range(len(long_period))]
#diff = gaussian_smooth(diff, 21, 4)
data = np.array(diff)
max_index = list(argrelextrema(data,np.greater)[0])
min_index = list(argrelextrema(data,np.less)[0])
return max_index, min_index, residual_long_sample_width, residual_short_sample_width, diff, confidence示例5: calc_para
def calc_para(list1,list2):
list1_max_index = list(argrelextrema(np.array(list1),np.greater)[0])
list1_min_index = list(argrelextrema(np.array(list1),np.less)[0])
list2_max_index = list(argrelextrema(np.array(list2),np.greater)[0])
list2_min_index = list(argrelextrema(np.array(list2),np.less)[0])
error=10000
#######
# if list1_max_index!=[] and list2_max_index!=[]:
# error = abs(abs(list1[0]-list2[0])/list2[0])+abs(abs(list1[-1]-list2[-1])/list2[-1])+abs(abs(list1[list1_max_index[0]]-list2[list2_max_index[0]])/list2[list2_max_index[0]])+abs(1-list1_max_index[0]/list2_max_index[0])+abs(1-(len(list1)-list1_max_index[0])/(len(list2)-list2_max_index[0]))
# if list1_min_index!=[] and list2_min_index!=[]:
# error = abs(abs(list1[0]-list2[0])/list2[0])+abs(abs(list1[-1]-list2[-1])/list2[-1])+abs(abs(list1[list1_min_index[0]]-list2[list2_min_index[0]])/list2[list2_min_index[0]])+abs(1-list1_min_index[0]/list2_min_index[0])+abs(1-(len(list1)-list1_min_index[0])/(len(list2)-list2_min_index[0]))
#error = abs(list1[0]-list2[0])+abs(list1[-1]-list2[-1])+abs(list1[list1_min_index[0]]-list2[list2_min_index[0]])
#######
if list1_max_index!=[] and list2_max_index!=[]:
# list1 = [i/abs(list1[list1_max_index[0]]-list1[0]) for i in list1]
# list2 = [i/abs(list2[list2_max_index[0]]-list2[0]) for i in list2]
# error = abs((list2[0]/list1[0])-1)+abs((list2_max_index[0]/list1_max_index[0])-1)+abs((len(list2)-list2_max_index[0])/(len(list1)-list1_max_index[0])-1)
#error = abs((list2[0]-list1[0])/list2[0])+abs((list2_max_index[0]-list1_max_index[0])/list2_max_index[0])+abs(((len(list2)-list2_max_index[0])-(len(list1)-list1_max_index[0]))/(len(list2)-list2_max_index[0]))+abs((list1[list1_max_index[0]]-list2[list2_max_index[0]])/list1[list1_max_index[0]])+abs((list2[-1]-list1[-1])/list2[-1])
error = abs((list2[0]-list1[0])/min(list2[0],list1[0]))+abs((list2_max_index[0]-list1_max_index[0])/min(list2_max_index[0],list1_max_index[0]))+abs(((len(list2)-list2_max_index[0])-(len(list1)-list1_max_index[0]))/min((len(list2)-list2_max_index[0]),(len(list1)-list1_max_index[0])))+abs((list1[list1_max_index[0]]-list2[list2_max_index[0]])/min(list1[list1_max_index[0]],list2[list2_max_index[0]]))+abs((list2[-1]-list1[-1])/min(list1[-1],list2[-1]))+abs((len(list1)-len(list2))/min(len(list1),len(list2)))
if list1_min_index!=[] and list2_min_index!=[]:
# list1 = [i/abs(list1[list1_min_index[0]]-list1[0]) for i in list1]
# list2 = [i/abs(list2[list2_min_index[0]]-list2[0]) for i in list2]
# error = abs((list2[0]/list1[0])-1)+abs((list2_min_index[0]/list1_min_index[0])-1)+abs((len(list2)-list2_min_index[0])/(len(list1)-list1_min_index[0])-1)
#error = abs((list2[0]-list1[0])/list2[0])+abs((list2_min_index[0]-list1_min_index[0])/list2_min_index[0])+abs(((len(list2)-list2_min_index[0])-(len(list1)-list1_min_index[0]))/(len(list2)-list2_min_index[0]))+abs((list1[list1_min_index[0]]-list2[list2_min_index[0]])/list1[list1_min_index[0]])+abs((list2[-1]-list1[-1])/list2[-1])
error = abs((list2[0]-list1[0])/min(list2[0],list1[0]))+abs((list2_min_index[0]-list1_min_index[0])/min(list2_min_index[0],list1_min_index[0]))+abs(((len(list2)-list2_min_index[0])-(len(list1)-list1_min_index[0]))/min((len(list2)-list2_min_index[0]),(len(list1)-list1_min_index[0])))+abs((list1[list1_min_index[0]]-list2[list2_min_index[0]])/min(list1[list1_min_index[0]],list2[list2_min_index[0]]))+abs((list2[-1]-list1[-1])/min(list1[-1],list2[-1]))+abs((len(list1)-len(list2))/min(len(list1),len(list2)))
return error示例6: simplify3
def simplify3(nk):
result=[]
nk=np.array(nk)
xk = nk/float(np.sum(nk))
#print nk
#X_plot = np.linspace(0, len(nk), 1000)[:, np.newaxis]
sdiv=1000
X_plot = np.linspace(0, len(xk), sdiv)[:, np.newaxis]
custm = stats.rv_discrete(name='custm',a=0,b=7, values=(range(len(xk)), xk))
yk= custm.rvs(size=100000)
#yk.flatten()
#fig, ax = plt.subplots(1, 1)
#ax.hist(yk, normed=True, histtype='stepfilled', alpha=0.2)
# gaussian KDE
X=yk.reshape(-1, 1)
kde = KernelDensity(kernel='gaussian', bandwidth=0.6).fit(X)
log_dens = kde.score_samples(X_plot)
mi, ma = argrelextrema(log_dens, np.less)[0], argrelextrema(log_dens, np.greater)[0]
mi=np.rint(mi*float(len(xk))/float(sdiv))
ma=np.rint(ma*float(len(xk))/float(sdiv))
start=0
#print mi
for i in mi:
i=int(i)
if start!=i:
val=np.average(nk[start:i])
for j in xrange(start,i):
result.append(val)
start=i
val=np.average(nk[start:])
for j in xrange(start,len(nk)):
result.append(val)
return np.array(result)示例7: get_extrema
def get_extrema(self, limits=(0.0, float('inf')), order=5):
"""Computes masks (i.e. arrays of indices) of the extrema of the force.
Parameters
----------
order: integer, optional
Number of neighboring points used to define an extremum;
default: 5.
Returns
-------
minima: 1D array of integers
Index of all minima.
maxima: 1D array of integers
Index of all maxima.
"""
minima = signal.argrelextrema(self.values, numpy.less_equal,
order=order)[0][:-1]
maxima = signal.argrelextrema(self.values, numpy.greater_equal,
order=order)[0][:-1]
mask = numpy.where(numpy.logical_and(self.times >= limits[0],
self.times <= limits[1]))[0]
minima = numpy.intersect1d(minima, mask, assume_unique=True)
maxima = numpy.intersect1d(maxima, mask, assume_unique=True)
# remove indices that are too close
minima = minima[numpy.append(True, minima[1:]-minima[:-1] > order)]
maxima = maxima[numpy.append(True, maxima[1:]-maxima[:-1] > order)]
return minima, maxima示例8: peaksValleys
def peaksValleys(data, attrib):
for i in range(0,len(attrib)-2):
k = [[], []] # k[0] picos e k[1] vales
[k[0].append(data[j]) for j in argrelextrema(data, np.greater)[0]]
[k[1].append(data[j]) for j in argrelextrema(data, np.less)[0]]
#print (k[0])
#print (k[1])
aux = max(k[0])
aux2 = min(k[1])
for i in range(len(k[0])):
try:
if aux/k[0][i] < 0.7:
k[0].pop(i)
except:
break
for i in range(len(k[1])):
try:
if aux2/k[1][i] < 0.1:
k[1].pop(i)
except:
break
#print (k[0])
#print (k[1])
return k[0], k[1]示例9: tribocycle_finder
def tribocycle_finder(y_disp):
"""
finds tribo-cycles using the y displacement curve
:param y_disp:
:return:
"""
y_disp = moving_average(y_disp)
maxima = argrelextrema(y_disp, np.greater, order=1000)
minima = argrelextrema(y_disp, np.less, order=500)
if maxima[0].size > minima[0].size:
cycle_ends = maxima
cycle_mid = minima
elif minima[0].size > maxima[0].size:
cycle_ends = minima
cycle_mid = maxima
else:
print 'Error in tribocycle finder, y displacement waveform incorrect'
plt.plot(y_disp)
plt.show()
cycle_ends = np.nan
cycle_mid = np.nan
return cycle_ends, cycle_mid示例10: find_order_edge_peaks
def find_order_edge_peaks(reduced):
from scipy.signal import argrelextrema
# make top and bottom edge images
rolled = np.roll(reduced.flat, 5, axis=0)
reduced.topEdgesImg = rolled - reduced.flat
reduced.botEdgesImg = reduced.flat - rolled
# take a vertical cut of edges
reduced.topEdgesProfile = np.median(reduced.topEdgesImg[:, 40:50], axis=1)
reduced.botEdgesProfile = np.median(reduced.botEdgesImg[:, 40:50], axis=1)
# find the highest peaks in crosscut, search +/- 15 pixels to narrow down list
top_extrema = argrelextrema(reduced.topEdgesProfile, np.greater, order=35)[0]
bot_extrema = argrelextrema(reduced.botEdgesProfile, np.greater, order=35)[0]
# find crosscut values at those extrema
top_intensities = reduced.topEdgesProfile[top_extrema]
bot_intensities = reduced.botEdgesProfile[bot_extrema]
reduced.topEdgePeaks = zip(top_extrema, top_intensities)
reduced.botEdgePeaks = zip(bot_extrema, bot_intensities)
return示例11: plotPlots
def plotPlots(linearray, sampletitle, roi):
plt.figure(figsize=(14, 14))
gs1 = matplotlib.gridspec.GridSpec(8, 8)
gs1.update(wspace=0.25, hspace=0.3)
from scipy.signal import argrelextrema
for i, li in enumerate(linearray): # [0, :]):
axarr = plt.subplot(gs1[i])
lin = linearray[:, i]
axarr.plot(lin)
try:
ma = np.mean(lin[argrelextrema(lin, np.greater)[0]])
mi = np.mean(lin[argrelextrema(lin, np.less)[0]])
axarr.plot([0,len(lin)],[ma,ma])
axarr.plot([0,len(lin)],[mi,mi])
axarr.set_title('{:.2%}'.format((ma-mi)/ma))
except IndexError:
print "Index error."
axarr.set_ylim(0.6,1)
# set_title('%.4f, %.4f' % (float(a[i]), float(b[i])))
axarr.xaxis.set_major_formatter(plt.NullFormatter())
axarr.yaxis.set_major_formatter(plt.NullFormatter())
print data.directory, sampletitle, str(roi)
# plt.savefig('{}/{}_{}_lines.pdf'.format(data.directory, sampletitle, str(roi)))
plt.savefig(data.directory + '/%s_%s_lines.pdf' % (sampletitle, str(roi)))示例12: find_strong_lines
def find_strong_lines(x, xo, gggf, gggf_infm, cfile, logfile):
# We need to find strong lines in spectrum and ingnore all small changes in flux.
# Here - code checks the change in signal around inflection points and compares it
# to noise multiplied by rejection parameter
max_ind = argrelextrema(gggf, np.greater)
min_ind = argrelextrema(gggf, np.less)
max_tab = np.array([x[max_ind], gggf[max_ind]])
min_tab = np.array([x[min_ind], gggf[min_ind]])
thold = get_thold(gggf, cfile.getfloat('Lines', 'r_lvl'))
str_lines = []
if not (max_tab.size != 0 and min_tab.size != 0 and thold == 0.0):
for item in gggf_infm:
indx = np.abs(max_tab[0, :] - item).argmin()
indm = np.abs(min_tab[0, :] - item).argmin()
if ((np.abs(max_tab[1, indx]) > thold) and
(np.abs(min_tab[1, indm]) > thold)):
str_lines.append(item)
str_lines = evaluate_lines(xo, str_lines,
cfile.getfloat('Lines', 'det_level'),
gggf_infm, logfile)
return str_lines示例13: accumulateFrame
def accumulateFrame(symbols, order, mindate, maxdate):
connection = sqlite3.connect('../data/historical.sl')
c = connection.cursor()
df = pd.DataFrame(columns=['ts','min','max']).set_index('ts')
for symbol in symbols:
query = "SELECT ts,close,high,low FROM eod WHERE symbol = '" + symbol + "' AND ts BETWEEN " + str(mindate) + " AND " + str(maxdate) + " ORDER BY ts;"
c.execute(query)
data = c.fetchall()
dates = array([datetime.datetime.fromordinal(q[0]) for q in data])
closes = array([q[1] for q in data])
highs = array([q[2] for q in data])
lows = array([q[3] for q in data])
# compute extrema
if len(lows) > 0:
cmin = argrelextrema(data=lows, comparator=np.less, order=order)
# create frame for minima indexed by date
if(len(cmin[0]) > 0):
mins = np.vstack((dates[cmin], np.negative(np.ones(cmin[0].size)), np.zeros(cmin[0].size))).transpose()
df1 = pd.DataFrame(mins, columns=['ts','min','max']).set_index('ts')
df = df.add(df1, fill_value=0)
if len(highs) > 0:
cmax = argrelextrema(data=highs, comparator=np.greater, order=order)
# create frame for maxima indexed by date
if(len(cmax[0]) > 0):
maxes = np.vstack((dates[cmax], np.zeros(cmax[0].size), np.ones(cmax[0].size))).transpose()
df2 = pd.DataFrame(maxes, columns=['ts','min','max']).set_index('ts')
df = df.add(df2, fill_value=0)
return df示例14: get_local_Extrema
def get_local_Extrema(time,data):
''' # Function to get the local extrema for a response
#
# Inputs:
# time = time array corresponding to the data
# data = the response data array (only pass a single dimension/state at at time)
#
# Output:
# localMaxes = the amplitude of the local maxes
# localMax_Times = the times of the local maxes
#
# Created: 03/28/14
# - Joshua Vaughan
# - [email protected]
# - http://www.ucs.louisiana.edu/~jev9637
######################################################################################
'''
from scipy import signal
# Get local maximums
localMax_indexes = signal.argrelextrema(data, np.greater)
localMaxes = data[localMax_indexes]
localMax_Times = time[localMax_indexes]
# Get local minimums
localMin_indexes = signal.argrelextrema(data, np.less)
localMins = data[localMin_indexes]
localMin_Times = time[localMin_indexes]
return localMaxes, localMax_Times, localMins, localMin_Times示例15: check
def check(self, data, date):
dataarr = np.asarray(data)
extmax = argrelextrema(dataarr, np.greater)
extmin = argrelextrema(dataarr, np.less)
newdata = [[],[]]
retpattern = []
for i,d in enumerate(data):
if i in extmax[0] or i in extmin[0]:
newdata[0].append(date[i])
newdata[1].append(d)
for idx, val in enumerate(newdata[0]):
if idx + 6 > len(newdata[0]):
break
itemx = newdata[0][idx: idx+6]
itemy = newdata[1][idx: idx+6]
for p in self.pattern:
try:
ret = p.pattern(itemy)
if ret:
print p.name
print itemx, itemy
#pylab.scatter(itemx, itemy)
retpattern.append({'name': p.name, "iyd": itemy, \
"ixd": itemx})
except:
print "error in %s" % p.name
return retpattern