def future(self, i): #到底需不需要考虑拓扑顺序呢？？？
if type(self.node_list[i]) != Varnode or self.node_list[i].I == None:
return #第一次future是提前做的，因此现在I还可能是空呢
for item in self.node_list[i].next_dashed_list:
if self.node_list[item].lval != None: #lval != -inf
#e.g. 原来是[j_3, a)，现在有[, 99]
#把lval换成-inf，变成[-inf, a)，然后把这个字符串代入Interval进行初始化
tmp = copy.deepcopy(self.node_list[item])
tmp.lval = str(self.node_list[i].I.lower)
s = str(tmp).replace('-inf', '').replace('inf', '')
if tmp._type == 'int':
e = IntInterval.from_string(s)
else:
e = FloatInterval.from_string(s)
else: #rval != inf
#e.g. 原来是(a,j_3)，现在有[, 99]
#把rval=j_3换成99，变成(a, 99)，然后把这个字符串代入Interval进行初始化
tmp = copy.deepcopy(self.node_list[item])
tmp.rval = str(self.node_list[i].I.upper)
s = str(tmp).replace('-inf',
'').replace('inf',
'') #用字符串来初始化不能有inf，而要留空
if tmp._type == 'int':
e = IntInterval.from_string(s)
else:
e = FloatInterval.from_string(s)
self.node_list[item].I = e
def load_last_dependency_segment(self):
viewport = self._view.dependencies_segment_choose_scrolled_win.get_child()
main_box = viewport.get_child()
boxes = main_box.get_children()
last_box = boxes[-1]
last_combobox = last_box.get_children()[1]
segment_activated = last_combobox.get_active()
model = last_combobox.get_model()
_iter = model.get_iter_from_string('{}'.format(segment_activated))
segment_str = model.get_value(_iter, 0)
last_segment = FloatInterval.from_string(segment_str)
return last_segment
def load_chosen_dependency_segment(self, dependency_id):
viewport = self._view.dependencies_segment_choose_scrolled_win.get_child()
main_box = viewport.get_child()
boxes = main_box.get_children()
for box in boxes:
children = box.get_children()
label = children[0]
depend = int(label.get_text())
if dependency_id == depend:
combobox = children[1]
segment_activated = combobox.get_active()
model = combobox.get_model()
_iter = model.get_iter_from_string('{}'.format(segment_activated))
segment_str = model.get_value(_iter, 0)
segment = FloatInterval.from_string(segment_str)
return segment
def load_focus_depend(self):
dependencies_segment_choose_scrolled_win = self._view.dependencies_segment_choose_scrolled_win
viewport = dependencies_segment_choose_scrolled_win.get_child()
main_box = viewport.get_child()
focus_box = main_box.get_focus_child()
focus_box_children = focus_box.get_children()
focus_label = focus_box_children[0]
focus_depend_id = int(focus_label.get_text())
focus_combobox = focus_box_children[1]
segment_activated = focus_combobox.get_active()
model = focus_combobox.get_model()
_iter = model.get_iter_from_string('{}'.format(segment_activated))
segment_str = model.get_value(_iter, 0)
focus_segment = FloatInterval.from_string(segment_str)
return focus_depend_id, focus_segment
def load_parameter_node_path(self):
viewport = self._view.dependencies_segment_choose_scrolled_win.get_child()
main_box = viewport.get_child()
boxes = main_box.get_children()
calibrate_parameter = self.load_chosen_calibrate_parameter()
parameter_path = [[calibrate_parameter, None]]
for box in boxes:
children = box.get_children()
label = children[0]
depend_id = int(label.get_text())
combobox = children[1]
segment_activated = combobox.get_active()
model = combobox.get_model()
_iter = model.get_iter_from_string('{}'.format(segment_activated))
segment_str = model.get_value(_iter, 0)
focus_segment = FloatInterval.from_string(segment_str)
parameter_path.append([depend_id, focus_segment])
return parameter_path
def spectrogram_by_eeg_bandwidth(sig, sample_rate=125, timeIntervalSec=30):
slow = FloatInterval.from_string('[0.5, 2.0)')
delta = FloatInterval.from_string('[2, 4.0)')
theta = FloatInterval.from_string('[4.0, 8.0)')
alpha = FloatInterval.from_string('[8.0, 16.0)')
beta = FloatInterval.from_string('[16.0, 32.0)')
gamma = FloatInterval.from_string('[32.0, 100.0)')
##above100Hz = FloatInterval.from_string('[100.0,)')
# no signal above100Hz
nperseg = int(sample_rate * timeIntervalSec)
noverlap = None #will take default value
freqs, times, spec = signal.spectrogram(sig,
fs=sample_rate,
window='hann',
nperseg=sample_rate*timeIntervalSec,
noverlap=noverlap,
detrend=False)
#Edelta, Etheta, Ealpha, Ebeta, Egamma, Eabove100Hz = np.zeros(6)
result = pd.DataFrame(np.zeros(spec.shape[1]*6).reshape((6, spec.shape[1])), index=['Slow', 'Delta', 'Theta', 'Alpha', 'Beta', 'Gamma'])
#
for i in range(0, spec.shape[0]):
for j in range(0, spec.shape[1]):
if freqs[i] in slow:
result.loc["Slow", j]= result.loc["Slow", j] + spec[i, j]
if freqs[i] in delta:
result.loc["Delta", j]= result.loc["Delta", j] + spec[i, j]
elif freqs[i] in theta:
result.loc["Theta", j]= result.loc["Theta", j] + spec[i, j]
elif freqs[i] in alpha:
result.loc["Alpha", j]= result.loc["Alpha", j] + spec[i, j]
elif freqs[i] in beta:
result.loc["Beta", j]= result.loc["Beta", j] + spec[i, j]
elif freqs[i] in gamma:
result.loc["Gamma", j]= result.loc["Gamma", j] + spec[i, j]
# else: we are filtering out frequencies lower than 0.5 Hz
# print("error at cell " + str((i, j)))
return result
def spectrogram_by_eeg_bandwidth(sig, sample_rate=125, timeIntervalSec=2):
delta = FloatInterval.from_string('[0, 4.0)')
theta = FloatInterval.from_string('[4.0, 8.0)')
alpha = FloatInterval.from_string('[8.0, 16.0)')
beta = FloatInterval.from_string('[16.0, 32.0)')
gamma = FloatInterval.from_string('[32.0, 100.0)')
above100Hz = FloatInterval.from_string('[100.0,)')
#
nperseg = int(sample_rate * timeIntervalSec)
noverlap = 0
freqs, times, spec = signal.spectrogram(sig,
fs=sample_rate,
window='hann',
nperseg=sample_rate*timeIntervalSec,
noverlap=noverlap,
detrend=False)
#Edelta, Etheta, Ealpha, Ebeta, Egamma, Eabove100Hz = np.zeros(6)
result = pd.DataFrame(np.zeros(spec.shape[1]*6).reshape((6, spec.shape[1])), index=['Delta', 'Theta', 'Alpha', 'Beta', 'Gamma', 'Above100Hz'])
#
for i in range(0, spec.shape[0]):
for j in range(0, spec.shape[1]):
if freqs[i] in delta:
result.loc["Delta", j]= result.loc["Delta", j] + spec[i, j]
elif freqs[i] in theta:
result.loc["Theta", j]= result.loc["Theta", j] + spec[i, j]
elif freqs[i] in alpha:
result.loc["Alpha", j]= result.loc["Alpha", j] + spec[i, j]
elif freqs[i] in beta:
result.loc["Beta", j]= result.loc["Beta", j] + spec[i, j]
elif freqs[i] in gamma:
result.loc["Gamma", j]= result.loc["Gamma", j] + spec[i, j]
elif freqs[i] in above100Hz:
print(str((i, j)) + "added tp above100Hz")
result.loc["Above100Hz", j]= result.loc["Above100Hz", j] + spec[i, j]
else:
print("error at cell " + str((i, j)))
return result
