def plot_rec_dwt_random_pairs(self):
reclist = self.qt.getQTrecnamelist()
sel1213 = conf['sel1213']
sel1213set = set(sel1213)
out_reclist = set(reclist) # - sel1213set
# dwt coef
dwtECG = WTfeature()
# records selected
selected_record_list = []
for ind, recname in enumerate(out_reclist):
# inspect
print 'processing record : {}'.format(recname)
print '{} records left.'.format(len(out_reclist) - ind - 1)
# debug selection
if not recname.startswith(ur'sele0704'):
continue
sig = self.qt.load(recname)
# wavelet
waveletobj = dwtECG.gswt_wavelet()
self.plot_dwt_rswt(sig['sig'],
waveletobj=waveletobj,
ECGrecordname=recname,
auto_plot=True)
def PlotDWTCoef(self, sig=None, figID=1, waveletobj=pywt.Wavelet('sym2')):
if sig is None:
sig = self.sig['sig']
rawsig = sig
wtf = WTfeature()
# DWT
Level = 7
#waveletobj = wtf.gswt_wavelet()
#waveletobj = pywt.Wavelet('sym2')
res = pywt.wavedec(rawsig, waveletobj, level=Level)
plt.figure(figID)
N_subplot = Level + 2
plt.subplot(N_subplot, 1, 1)
plt.plot(rawsig)
plt.title('Original Signal')
detail_i = 1
for i in xrange(Level, 0, -1):
plt.subplot(N_subplot, 1, detail_i + 1)
plt.plot(res[i])
plt.xlim(0, len(res[i]) - 1)
plt.title('Detail Level {}'.format(detail_i))
detail_i += 1
plt.subplot(N_subplot, 1, N_subplot)
plt.plot(res[0])
plt.xlim(0, len(res[0]) - 1)
plt.title('Approximation Level')
plt.show()
def PlotDWT():
dat = scipy.io.loadmat('test.mat')
dat = dat['sig']
dat = [float(x) for x in dat]
#plt.plot(dat)
#plt.show()
wtf = WTfeature()
# DWT
Level = 5
res = pywt.wavedec(dat, wtf.gswt_wavelet(), level=Level)
plt.figure(2)
plt.plot(dat)
plt.figure(1)
for i in xrange(0, Level + 1):
plt.subplot(Level + 1, 1, i + 1)
plt.plot(res[i])
plt.title('Level {}'.format(i + 1))
plt.show()
def plot_dwt_pairs_no_arrow(self, rawsig, relation_importance):
## =========================================V
# 展示RSWT示意图
# No Arrow, only points
## =========================================V
N = 5
figureID = 1
fs = conf['fs']
FixedWindowLen = conf['winlen_ratio_to_fs'] * fs
print 'Fixed Window Length:{}'.format(FixedWindowLen)
xL = 1000
xR = xL + FixedWindowLen
tarpos = 1500
# -----------------
# get median
# -----------------
importance_arr = []
for rel_layer in relation_importance:
for rel, imp in rel_layer:
importance_arr.append(imp)
N_imp = len(importance_arr)
# ascending order:0->1
importance_arr.sort()
IMP_Thres = importance_arr[int(N_imp / 2)]
# get wavelet obj
# dwt coef
dwtECG = WTfeature()
waveletobj = dwtECG.gswt_wavelet()
# props of ARROW
#arrowprops = dict(width = 1,headwidth = 4,facecolor='black', shrink=0)
pltxLim = range(xL, xR)
sigAmp = [rawsig[x] for x in pltxLim]
cA = rawsig
# ====================
# plot raw signal input
# ====================
plt.figure(figureID)
# plot raw ECG
plt.subplot(N + 1, 1, 1)
# get handle for annote arrow
# hide axis
#frame = plt.gca()
#frame.axes.get_xaxis().set_visible(False)
#frame.axes.get_yaxis().set_visible(False)
plt.plot(pltxLim, sigAmp)
# plot reference point
#plt.plot(tarpos,rawsig[tarpos],'ro')
plt.title('ECG sample')
#plt.xlim(pltxLim)
for i in range(2, N + 2):
# relation&importance
rel_layer = relation_importance[i - 2]
cA, cD = pywt.dwt(cA, waveletobj)
xL /= 2
xR /= 2
tarpos /= 2
# crop x range out
xi = range(xL, xR)
cDamp = [cD[x] for x in xi]
# get relation points
rel_x = []
rel_y = []
cur_N = len(cDamp)
for rel_pair, imp in rel_layer:
rel_x.append(rel_pair[0])
if rel_x[-1] >= cur_N:
rel_y.append(0)
else:
rel_y.append(cDamp[rel_pair[0]])
rel_x.append(rel_pair[1])
if rel_x[-1] >= cur_N:
rel_y.append(0)
else:
rel_y.append(cDamp[rel_x[-1]])
# plot
fig = plt.subplot(N + 1, 1, i)
#plt.grid(True)
plt.plot(rel_x, rel_y, '.b')
plt.plot(cDamp)
# reference point
# plt.plot(tarpos,cDamp[tarpos-xL],'ro')
plt.xlim(0, len(cDamp) - 1)
plt.title('DWT Level ({}):'.format(i - 1))
# plot result
plt.show()
def plot_dwt_pairs_arrow(self,
rawsig,
relation_importance,
Window_Left=1200,
savefigname=None,
figsize=(10, 8),
figtitle='ECG Sample',
showFigure=True):
## =========================================V
# 展示RSWT示意图
# Plot Arrow
## =========================================V
#
#================
# constants
#================
N = 5
N_subplot = N + 2
# importance pairs lower than this threshold is not shown in the figure
Thres_min_importance = self.get_min_Importance_threshold(
relation_importance)
figureID = 1
fs = conf['fs']
FixedWindowLen = conf['winlen_ratio_to_fs'] * fs
print 'Fixed Window Length:{}'.format(FixedWindowLen)
xL = Window_Left
xR = xL + FixedWindowLen
tarpos = 1500
# props of ARROW
arrowprops = dict(width=1, headwidth=4, facecolor='black', shrink=0)
# -----------------
# get median
# -----------------
importance_arr = []
for rel_layer in relation_importance:
for rel, imp in rel_layer:
importance_arr.append(imp)
N_imp = len(importance_arr)
# ascending order:0->1
importance_arr.sort()
IMP_Thres = importance_arr[int(N_imp / 2)]
IMP_MAX = importance_arr[-1]
# get wavelet obj
# dwt coef
dwtECG = WTfeature()
waveletobj = dwtECG.gswt_wavelet()
# props of ARROW
#arrowprops = dict(width = 1,headwidth = 4,facecolor='black', shrink=0)
pltxLim = range(xL, xR)
sigAmp = [rawsig[x] for x in pltxLim]
cA = rawsig
# ====================
# plot raw signal input
# ====================
Fig_main = plt.figure(figureID, figsize=figsize)
# plot raw ECG
plt.subplot((N_subplot + 1) / 2, 2, 1)
# get handle for annote arrow
# hide axis
#frame = plt.gca()
#frame.axes.get_xaxis().set_visible(False)
#frame.axes.get_yaxis().set_visible(False)
plt.plot(pltxLim, sigAmp)
# plot reference point
#plt.plot(tarpos,rawsig[tarpos],'ro')
#plt.title(figtitle+'[Window Left = {}]'.format(Window_Left))
plt.title(figtitle)
plt.xlim(pltxLim[0], pltxLim[-1])
for i in range(2, N_subplot):
# relation&importance
rel_layer = relation_importance[i - 2]
cA, cD = pywt.dwt(cA, waveletobj)
xL /= 2
xR /= 2
tarpos /= 2
# crop x range out
xi = range(xL, xR)
cDamp = [cD[x] for x in xi]
# get relation points
rel_x = []
rel_y = []
cur_N = len(cDamp)
# ------------
# sub plot
# ------------
#fig = plt.subplot(N+1,1,i)
fig = plt.subplot((N_subplot + 1) / 2, 2, i)
plt.title('DWT Detail Coefficient {}'.format(i - 1))
#------------
# find pair&its amplitude
# -----------
# sort rel_layer with imp
rel_layer.sort(key=lambda x: x[1])
for rel_pair, imp in rel_layer:
# do not show imp lower than threshold
if imp < Thres_min_importance:
continue
# importance thres
alpha = (imp - Thres_min_importance) / (IMP_MAX -
Thres_min_importance)
arrow_color = self.get_RGB_from_Alpha((1, 0, 0), alpha,
(1, 1, 1))
arrowprops = dict(width=0.15,
linewidth=0.15,
headwidth=1.5,
headlength=1.5,
facecolor=arrow_color,
edgecolor=arrow_color,
shrink=0)
rel_x.append(rel_pair[0])
if rel_x[-1] >= cur_N:
rel_y.append(0)
else:
rel_y.append(cDamp[rel_pair[0]])
rel_x.append(rel_pair[1])
if rel_x[-1] >= cur_N:
rel_y.append(0)
else:
rel_y.append(cDamp[rel_x[-1]])
fig.annotate('',
xy=(rel_x[-2], rel_y[-2]),
xytext=(rel_x[-1], rel_y[-1]),
arrowprops=arrowprops)
#plt.grid(True)
plt.plot(rel_x, rel_y, '.b')
plt.plot(cDamp)
# reference point
# plt.plot(tarpos,cDamp[tarpos-xL],'ro')
plt.xlim(0, len(cDamp) - 1)
# plot Approximation Level
rel_x = []
rel_y = []
rel_layer = relation_importance[-1]
#fig = plt.subplot((N_subplot + 1)/2,2,N_subplot)
fig = plt.subplot(4, 1, 4)
plt.title('Approximation Coefficient')
cAamp = [cA[x] for x in xi]
# sort rel_layer with imp
rel_layer.sort(key=lambda x: x[1])
for rel_pair, imp in rel_layer:
# do not show imp lower than threshold
if imp < Thres_min_importance:
continue
# importance thres
alpha = (imp - Thres_min_importance) / (IMP_MAX -
Thres_min_importance)
arrow_color = self.get_RGB_from_Alpha((1, 0, 0), alpha, (1, 1, 1))
#arrowprops = dict(width = 1,headwidth = 4,facecolor=arrow_color,edgecolor = arrow_color,shrink=0)
arrowprops = dict(width=0.15,
linewidth=0.15,
headwidth=1.5,
headlength=1.5,
facecolor=arrow_color,
edgecolor=arrow_color,
shrink=0)
rel_x.append(rel_pair[0])
if rel_x[-1] >= cur_N:
rel_y.append(0)
else:
rel_y.append(cAamp[rel_pair[0]])
rel_x.append(rel_pair[1])
if rel_x[-1] >= cur_N:
rel_y.append(0)
else:
rel_y.append(cAamp[rel_x[-1]])
fig.annotate('',
xy=(rel_x[-2], rel_y[-2]),
xytext=(rel_x[-1], rel_y[-1]),
arrowprops=arrowprops)
# reference point
plt.plot(rel_x, rel_y, '.b')
plt.plot(cAamp)
plt.xlim(0, len(cAamp) - 1)
# plot result
if showFigure == True:
plt.show()
# save fig
if savefigname is not None:
Fig_main.savefig(savefigname, dpi=Fig_main.dpi)
Fig_main.clf()
def plot_dwt_pairs_arrow_partial_window_compare(self,
rawsig,
relation_importance,
Window_Left=1200,
savefigname=None,
figsize=(10, 8),
figtitle='ECG Sample',
showFigure=True):
## =========================================V
# 展示RSWT示意图
## =========================================V
#
#================
# constants
#================
N = 5
figureID = 1
fs = conf['fs']
FixedWindowLen = conf['winlen_ratio_to_fs'] * fs
print 'Fixed Window Length:{}'.format(FixedWindowLen)
xL = Window_Left
xR = xL + FixedWindowLen
tarpos = 1500
# props of ARROW
arrowprops = dict(width=1, headwidth=4, facecolor='black', shrink=0)
# -----------------
# get median
# -----------------
importance_arr = []
for rel_layer in relation_importance:
for rel, imp in rel_layer:
importance_arr.append(imp)
N_imp = len(importance_arr)
# ascending order:0->1
importance_arr.sort()
IMP_Thres = importance_arr[int(N_imp / 2)]
IMP_MAX = importance_arr[-1]
# get wavelet obj
# dwt coef
dwtECG = WTfeature()
waveletobj = dwtECG.gswt_wavelet()
# props of ARROW
#arrowprops = dict(width = 1,headwidth = 4,facecolor='black', shrink=0)
pltxLim = range(xL, xR)
sigAmp = [rawsig[x] for x in pltxLim]
cA = rawsig
# ====================
# plot raw signal input
# ====================
Fig_main = plt.figure(figureID, figsize=figsize)
# plot raw ECG
plt.subplot(N + 1, 2, 1)
# get handle for annote arrow
# hide axis
#frame = plt.gca()
#frame.axes.get_xaxis().set_visible(False)
#frame.axes.get_yaxis().set_visible(False)
plt.plot(pltxLim, sigAmp)
# plot reference point
#plt.plot(tarpos,rawsig[tarpos],'ro')
plt.title('Window the signal before DWT')
#plt.xlim(pltxLim)
# ===========Col2=================
plt.subplot(N + 1, 2, 2)
plt.plot(pltxLim, sigAmp)
cA_col2 = cA[xL:xR]
plt.title('Window the signal after DWT')
for i in range(2, N + 2):
# ====================
# subplot col2
# ====================
cA_col2, cD_col2 = pywt.dwt(cA_col2, waveletobj)
plt.subplot(N + 1, 2, i * 2 - 1)
plt.plot(cA_col2)
# relation&importance
rel_layer = relation_importance[i - 2]
cA, cD = pywt.dwt(cA, waveletobj)
xL /= 2
xR /= 2
tarpos /= 2
# crop x range out
xi = range(xL, xR)
cDamp = [cD[x] for x in xi]
# get relation points
rel_x = []
rel_y = []
cur_N = len(cDamp)
# ------------
# sub plot
# ------------
# plot
fig = plt.subplot(N + 1, 2, 2 * i)
#------------
# find pair&its amplitude
# -----------
for rel_pair, imp in rel_layer:
# importance thres
arrowprops = dict(width=1,
headwidth=4,
facecolor='r',
edgecolor='r',
alpha=imp / IMP_MAX,
shrink=0)
rel_x.append(rel_pair[0])
if rel_x[-1] >= cur_N:
rel_y.append(0)
else:
rel_y.append(cDamp[rel_pair[0]])
rel_x.append(rel_pair[1])
if rel_x[-1] >= cur_N:
rel_y.append(0)
else:
rel_y.append(cDamp[rel_x[-1]])
fig.annotate('',
xy=(rel_x[-2], rel_y[-2]),
xytext=(rel_x[-1], rel_y[-1]),
arrowprops=arrowprops)
#plt.grid(True)
plt.plot(rel_x, rel_y, '.b')
plt.plot(cDamp)
# reference point
# plt.plot(tarpos,cDamp[tarpos-xL],'ro')
plt.xlim(0, len(cDamp) - 1)
plt.title('DWT Level ({}):'.format(i - 1))
# plot result
if showFigure == True:
plt.show()
# save fig
if savefigname is not None:
Fig_main.savefig(savefigname, dpi=Fig_main.dpi)
Fig_main.clf()
plt.title('Original Signal')
detail_i = 1
for i in xrange(Level, 0, -1):
plt.subplot(N_subplot, 1, detail_i + 1)
plt.plot(res[i])
plt.xlim(0, len(res[i]) - 1)
plt.title('Detail Level {}'.format(detail_i))
detail_i += 1
plt.subplot(N_subplot, 1, N_subplot)
plt.plot(res[0])
plt.xlim(0, len(res[0]) - 1)
plt.title('Approximation Level')
plt.show()
if __name__ == '__main__':
wt = WT_choose()
wtf = WTfeature()
reclist = wt.reclist
for ind in xrange(0, 105):
if reclist[ind] != 'sel46':
continue
wt.loadsig(ind)
rawsig = wt.sig['sig']
rawsig = rawsig[1000:2000]
wt.PlotDWTCoef(sig=rawsig, waveletobj=pywt.Wavelet('db2'))
# gswt
waveletobj = wtf.gswt_wavelet()
wt.PlotDWTCoef(sig=rawsig, figID=2, waveletobj=waveletobj)