def configure(self, time_series, segment_length=None, window_function=None, detrend=None):
"""
Do any configuration needed before launching.
:param time_series: the input time series to which the fft is to be applied
:param segment_length: the block size which determines the frequency resolution \
of the resulting power spectra
:param window_function: windowing functions can be applied before the FFT is performed
:type window_function: None; ‘hamming’; ‘bartlett’; ‘blackman’; ‘hanning’
:param detrend: None; specify if detrend is performed on the time series
"""
shape = time_series.read_data_shape()
LOG.debug("time_series shape is %s" % (str(shape)))
LOG.debug("Provided segment_length is %s" % (str(segment_length)))
LOG.debug("Provided window_function is %s" % (str(window_function)))
LOG.debug("Detrend is %s" % (str(detrend)))
##-------------------- Fill Algorithm for Analysis -------------------##
#The enumerate set function isn't working well. A get around strategy is to create a new algorithm
algorithm = fft.FFT()
if segment_length is not None:
algorithm.segment_length = segment_length
algorithm.window_function = window_function
algorithm.time_series = time_series
algorithm.detrend = detrend
self.algorithm = algorithm
LOG.debug("Using segment_length is %s" % (str(self.algorithm.segment_length)))
LOG.debug("Using window_function is %s" % (str(self.algorithm.window_function)))
LOG.debug("Using detrend is %s" % (str(self.algorithm.detrend)))
def get_input_tree(self):
"""
Return a list of lists describing the interface to the analyzer. This
is used by the GUI to generate the menus and fields necessary for
defining a simulation.
"""
algorithm = fft.FFT()
algorithm.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
tree = algorithm.interface[self.INTERFACE_ATTRIBUTES]
for node in tree:
if node['name'] == 'time_series':
node['conditions'] = entities_filter.FilterChain(
fields=[entities_filter.FilterChain.datatype + '._nr_dimensions'],
operations=["=="], values=[4])
return tree
def __init__(self):
super(FourierAdapter, self).__init__()
self.algorithm = fft.FFT()
self.memory_factor = 1
def get_traited_datatype(self):
return fft.FFT()
