def _decompose_threaded(self,
callback,
label,
signal=None,
algorithm=None,
ns=None):
if ns is None:
ns = Namespace()
ns.autosig = signal is None
ns.s, signal = self._get_signal(signal)
def do_threaded():
ns.s = self._do_decomposition(ns.s, algorithm=algorithm)
def on_error(message=None):
em = QErrorMessage(self.ui)
msg = tr("An error occurred during decomposition")
if message:
msg += ":\n" + message
em.setWindowTitle(tr("Decomposition error"))
em.showMessage(msg)
t = ProgressThreaded(self.ui,
do_threaded,
lambda: callback(ns),
label=label)
t.worker.error[str].connect(on_error)
t.run()
def fft(self,
signals=None,
shift=True,
power_spectrum=True,
inverse=False,
on_complete=None):
if signals is None:
signals = self.ui.get_selected_signals()
if isinstance(signals, hs.signals.BaseSignal):
signals = (signals, )
fftsignals = []
def do_ffts():
for i, signal in enumerate(signals):
if inverse:
fft = signal.ifft(shift=shift)
else:
fft = signal.fft(shift=shift)
fftsignals.append(fft)
yield i + 1
def on_ffts_complete():
for fs in fftsignals:
fs.plot(power_spectrum=power_spectrum)
sw = self.ui.lut_signalwrapper[fs]
if on_complete is not None:
on_complete(sw)
if len(signals) > 1:
if inverse:
label = tr('Performing inverse FFT')
else:
label = tr('Performing FFT')
t = ProgressThreaded(
self.ui,
do_ffts(),
on_ffts_complete,
label=label,
)
# This breaks the progress bar...
# generator_N=len(signals))
t.run()
else:
for i in do_ffts():
pass
on_ffts_complete()
def _decompose_threaded(self,
callback,
label,
signal=None,
algorithm=None,
ns=None):
if ns is None:
ns = Namespace()
ns.autosig = signal is None
ns.s, signal = self._get_signal(signal)
def do_threaded():
ns.s = self._do_decomposition(ns.s, algorithm=algorithm)
t = ProgressThreaded(self.ui,
do_threaded,
lambda: callback(ns),
label=label)
t.run()
def _decompose_threaded(self, callback, label, signal=None,
algorithm=None, ns=None):
if ns is None:
ns = Namespace()
ns.autosig = signal is None
ns.s, signal = self._get_signal(signal)
def do_threaded():
ns.s = self._do_decomposition(ns.s, algorithm=algorithm)
def on_error(message=None):
em = QErrorMessage(self.ui)
msg = tr("An error occurred during decomposition")
if message:
msg += ":\n" + message
em.setWindowTitle(tr("Decomposition error"))
em.showMessage(msg)
t = ProgressThreaded(self.ui, do_threaded, lambda: callback(ns),
label=label)
t.worker.error[str].connect(on_error)
t.run()
def nfft(self, signals=None, inverse=False):
if signals is None:
signals = self.ui.get_selected_signals()
if signals is None:
return
# Make sure we can iterate
if isinstance(signals, hyperspy.signal.BaseSignal):
signals = (signals,)
if len(signals) < 1:
return
fftsignals = []
def on_ftts_complete():
for fs in fftsignals:
fs.plot()
def do_ffts():
j = 0
for s in signals:
ffts = s.deepcopy()
if ffts.data.itemsize <= 4:
ffts.change_dtype(np.complex64)
else:
ffts.change_dtype(np.complex128)
am = AxesManager(s.axes_manager._get_axes_dicts())
for idx in am:
fftdata = s.data[am._getitem_tuple]
fftdata = scipy.fftpack.fftn(fftdata)
fftdata = scipy.fftpack.fftshift(fftdata)
ffts.data[am._getitem_tuple] = fftdata
j += 1
yield j
for i in range(ffts.axes_manager.signal_dimension):
axis = ffts.axes_manager.signal_axes[i]
s_axis = s.axes_manager.signal_axes[i]
axis.scale = 1 / (s_axis.size * s_axis.scale)
shift = (axis.high_value - axis.low_value) / 2
axis.offset -= shift
u = s_axis.units
if u is traits.Undefined:
pass # Leave unit as undefined
elif u.endswith('-1'):
u = u[:-2]
else:
u += '-1'
axis.units = u
indstr = ' ' + str(s.axes_manager.indices) \
if len(s.axes_manager.indices) > 0 else ''
ffts.metadata.General.title = 'FFT of ' + \
ffts.metadata.General.title + indstr
fftsignals.append(ffts)
def do_iffts():
j = 0
for s in signals:
ffts = s.deepcopy()
if ffts.data.itemsize <= 4:
ffts.change_dtype(np.float32)
else:
ffts.change_dtype(np.float64)
am = AxesManager(s.axes_manager._get_axes_dicts())
for i in range(ffts.axes_manager.signal_dimension):
axis = ffts.axes_manager.signal_axes[i]
s_axis = s.axes_manager.signal_axes[i]
shift = (axis.high_value - axis.low_value) / 2
axis.offset += shift
axis.scale = 1 / (s_axis.size * s_axis.scale)
u = s_axis.units
if u is traits.Undefined:
pass # Leave unit as undefined
elif u.endswith('-1'):
u = u[:-2]
else:
u += '-1'
axis.units = u
for idx in am:
fftdata = s.data[am._getitem_tuple]
fftdata = scipy.fftpack.ifftshift(fftdata)
fftdata = scipy.fftpack.ifftn(fftdata)
fftdata = np.abs(fftdata)
ffts.data[am._getitem_tuple] = fftdata
j += 1
yield j
indstr = ' ' + str(s.axes_manager.indices) \
if len(s.axes_manager.indices) > 0 else ''
ffts.metadata.General.title = 'Inverse FFT of ' + \
ffts.metadata.General.title + indstr
fftsignals.append(ffts)
n_ffts = np.product([d for s in signals
for d in s.axes_manager.navigation_shape])
if inverse:
label = tr('Performing inverse FFT')
f = do_iffts()
else:
label = tr('Performing FFT')
f = do_ffts()
t = ProgressThreaded(self.ui, f, on_ftts_complete,
label=label,
cancellable=True,
generator_N=n_ffts)
t.run()
def fft(self, signals=None, inverse=False, on_complete=None):
if signals is None:
signals = self.ui.get_selected_signals()
# Make sure we can iterate
if isinstance(signals, hyperspy.signal.BaseSignal):
signals = (signals,)
fftsignals = []
def on_ffts_complete():
for fs in fftsignals:
fs.plot()
sw = self.ui.lut_signalwrapper[fs]
if on_complete is not None:
on_complete(sw)
def do_ffts():
for i, s in enumerate(signals):
if inverse:
fftdata = scipy.fftpack.ifftshift(s())
fftdata = scipy.fftpack.ifftn(fftdata)
fftdata = np.abs(fftdata)
else:
fftdata = scipy.fftpack.fftn(s())
fftdata = scipy.fftpack.fftshift(fftdata)
ffts = s.__class__(
fftdata,
axes=s.axes_manager._get_signal_axes_dicts(),
metadata=s.metadata.as_dictionary(),)
ffts.axes_manager._set_axis_attribute_values("navigate", False)
indstr = ' ' + str(s.axes_manager.indices) \
if len(s.axes_manager.indices) > 0 else ''
invstr = "Inverse " if inverse else ""
ffts.metadata.General.title = invstr + 'FFT of ' + \
ffts.metadata.General.title + indstr
for i in range(ffts.axes_manager.signal_dimension):
axis = ffts.axes_manager.signal_axes[i]
s_axis = s.axes_manager.signal_axes[i]
if not inverse:
axis.scale = 1 / (s_axis.size * s_axis.scale)
shift = (axis.high_value - axis.low_value) / 2
if inverse:
shift = -shift
axis.offset -= shift
if inverse:
axis.scale = 1 / (s_axis.size * s_axis.scale)
u = s_axis.units
if u is traits.Undefined:
pass # Leave unit as undefined
elif u.endswith('-1'):
u = u[:-2]
else:
u += '-1'
axis.units = u
fftsignals.append(ffts)
yield i + 1
if len(signals) > 1:
if inverse:
label = tr('Performing inverse FFT')
else:
label = tr('Performing FFT')
t = ProgressThreaded(self.ui, do_ffts(), on_ffts_complete,
label=label,
generator_N=len(signals))
t.run()
else:
for i in do_ffts():
pass
on_ffts_complete()
def nfft(self, signals=None, inverse=False):
if signals is None:
signals = self.ui.get_selected_signals()
if signals is None:
return
# Make sure we can iterate
if isinstance(signals, hyperspy.signal.BaseSignal):
signals = (signals, )
if len(signals) < 1:
return
fftsignals = []
def on_ftts_complete():
for fs in fftsignals:
fs.plot()
def do_ffts():
j = 0
for s in signals:
ffts = s.deepcopy()
if ffts.data.itemsize <= 4:
ffts.change_dtype(np.complex64)
else:
ffts.change_dtype(np.complex128)
am = AxesManager(s.axes_manager._get_axes_dicts())
for idx in am:
fftdata = s.data[am._getitem_tuple]
fftdata = scipy.fftpack.fftn(fftdata)
fftdata = scipy.fftpack.fftshift(fftdata)
ffts.data[am._getitem_tuple] = fftdata
j += 1
yield j
for i in range(ffts.axes_manager.signal_dimension):
axis = ffts.axes_manager.signal_axes[i]
s_axis = s.axes_manager.signal_axes[i]
axis.scale = 1 / (s_axis.size * s_axis.scale)
shift = (axis.high_value - axis.low_value) / 2
axis.offset -= shift
u = s_axis.units
if u.endswith('-1'):
u = u[:-2]
else:
u += '-1'
axis.units = u
indstr = ' ' + str(s.axes_manager.indices) \
if len(s.axes_manager.indices) > 0 else ''
ffts.metadata.General.title = 'FFT of ' + \
ffts.metadata.General.title + indstr
fftsignals.append(ffts)
def do_iffts():
j = 0
for s in signals:
ffts = s.deepcopy()
if ffts.data.itemsize <= 4:
ffts.change_dtype(np.float32)
else:
ffts.change_dtype(np.float64)
am = AxesManager(s.axes_manager._get_axes_dicts())
for i in range(ffts.axes_manager.signal_dimension):
axis = ffts.axes_manager.signal_axes[i]
s_axis = s.axes_manager.signal_axes[i]
shift = (axis.high_value - axis.low_value) / 2
axis.offset += shift
axis.scale = 1 / (s_axis.size * s_axis.scale)
u = s_axis.units
if u.endswith('-1'):
u = u[:-2]
else:
u += '-1'
axis.units = u
for idx in am:
fftdata = s.data[am._getitem_tuple]
fftdata = scipy.fftpack.ifftshift(fftdata)
fftdata = scipy.fftpack.ifftn(fftdata)
fftdata = np.abs(fftdata)
ffts.data[am._getitem_tuple] = fftdata
j += 1
yield j
indstr = ' ' + str(s.axes_manager.indices) \
if len(s.axes_manager.indices) > 0 else ''
ffts.metadata.General.title = 'Inverse FFT of ' + \
ffts.metadata.General.title + indstr
fftsignals.append(ffts)
n_ffts = np.product([
d for s in signals for d in s.signal.axes_manager.navigation_shape
])
if inverse:
label = tr('Performing inverse FFT')
f = do_iffts()
else:
label = tr('Performing FFT')
f = do_ffts()
t = ProgressThreaded(self.ui,
f,
on_ftts_complete,
label=label,
cancellable=True,
generator_N=n_ffts)
t.run()
def fft(self, signals=None, inverse=False, on_complete=None):
if signals is None:
signals = self.ui.get_selected_signals()
# Make sure we can iterate
if isinstance(signals, hyperspy.signal.BaseSignal):
signals = (signals, )
fftsignals = []
def on_ffts_complete():
for fs in fftsignals:
fs.plot()
sw = self.ui.lut_signalwrapper[fs]
if on_complete is not None:
on_complete(sw)
def do_ffts():
for i, s in enumerate(signals):
if inverse:
fftdata = scipy.fftpack.ifftshift(s())
fftdata = scipy.fftpack.ifftn(fftdata)
fftdata = np.abs(fftdata)
else:
fftdata = scipy.fftpack.fftn(s())
fftdata = scipy.fftpack.fftshift(fftdata)
ffts = s.__class__(
fftdata,
axes=s.axes_manager._get_signal_axes_dicts(),
metadata=s.metadata.as_dictionary(),
)
ffts.axes_manager._set_axis_attribute_values("navigate", False)
indstr = ' ' + str(s.axes_manager.indices) \
if len(s.axes_manager.indices) > 0 else ''
invstr = "Inverse " if inverse else ""
ffts.metadata.General.title = invstr + 'FFT of ' + \
ffts.metadata.General.title + indstr
for i in range(ffts.axes_manager.signal_dimension):
axis = ffts.axes_manager.signal_axes[i]
s_axis = s.axes_manager.signal_axes[i]
if not inverse:
axis.scale = 1 / (s_axis.size * s_axis.scale)
shift = (axis.high_value - axis.low_value) / 2
if inverse:
shift = -shift
axis.offset -= shift
if inverse:
axis.scale = 1 / (s_axis.size * s_axis.scale)
u = s_axis.units
if u.endswith('-1'):
u = u[:-2]
else:
u += '-1'
axis.units = u
fftsignals.append(ffts)
yield i + 1
if len(signals) > 1:
if inverse:
label = tr('Performing inverse FFT')
else:
label = tr('Performing FFT')
t = ProgressThreaded(self.ui,
do_ffts(),
on_ffts_complete,
label=label,
generator_N=len(signals))
t.run()
else:
for i in do_ffts():
pass
on_ffts_complete()