def __init__(self, *args, **kwargs):
FigureWindow.__init__(self, *args, **kwargs)
self.currentData = None
# algorithm dict and current data
self.algorithms = AlgorithmDict()
self.lockAttribute('algorithms')
#
# The toolbar
toolTabs = self.toolTabs
figureBook = self.figureBook
frmAlgo = Frame(toolTabs)
grpAlgoSel = AlgoSelGroup(frmAlgo, topwin=self)
grpAlgoSel.pack(side=LEFT, fill=Y)
grpParams = ParamsGroup(frmAlgo, topwin=self)
grpParams.pack(side=LEFT, fill=Y)
grpSolve = OptimizeGroup(frmAlgo, topwin=self)
grpSolve.pack(side=LEFT, fill=Y)
toolTabs.add(frmAlgo, text='Algorithm')
with self.attributeLock:
setMultiAttr(self,
grpAlgoSel = grpAlgoSel,
grpParams = grpParams,
grpSolve = grpSolve
)
self.makeViewTab()
self.makeMarkerTab()
self.makeExportTab()
# End toolbar
figureBook.makeFigures(
figureMeta = [
dict(name='Envelope', polar=False),
dict(name='Phase', polar=False),
dict(name='AutoCorrelation', polar=False),
dict(name='PSD', polar=False)
]
)
with open(Application.instance.configFileName) as f:
config = json.load(f)
algorithms = config['SingleWaveformAlgorithms']
for algo in algorithms:
self.loadAlgorithm(moduleName=algo['ModuleName'], className=algo['ClassName'])
self.grpAlgoSel.algoList.current(len(algorithms)-1)
figEnvelope = figureBook[0]
figEnvelope.plotFunction = lambda currentData, *args, **kwargs:\
figEnvelope.plot(abs(currentData), *args, **kwargs)
figPhase = figureBook[1]
figPhase.plotFunction = lambda currentData, *args, **kwargs:\
figPhase.plot(angle(currentData), *args, **kwargs)
def plot_acdb(currentData, *args, **kwargs):
s = currentData
if not isinstance(s, ndarray):
s = array(s)
N = len(s)
ac = convolve(s, conj(s[::-1]))
acdb = 20*log10(abs(ac))
acdb = acdb - max(acdb)
figAuto.plot(r_[(-N+1):N], acdb, *args, **kwargs)
figAuto = figureBook[2]
figAuto.plotFunction = plot_acdb
figPSD = figureBook[3]
figPSD.plotFunction = lambda currentData, *args, **kwargs:\
figPSD.plot(abs(fft.fft(currentData)), *args, **kwargs)
def on_connect(self):
# algorithm dict and current data
self.algorithms = AlgorithmDict()
self.lock_attribute('algorithms')
#
# The toolbar
tool_tabs = self._tool_tabs
figure_book = self.figure_book
frmAlgo = Frame(tool_tabs)
algorithm_selection_group = AlgoSelGroup(frmAlgo, topwin=self)
algorithm_selection_group.pack(side='left', fill='y')
parameter_group = ParamsGroup(frmAlgo,
topwin=self,
wavesyn_root=self.root_node)
parameter_group.pack(side='left', fill='y')
solve_group = OptimizeGroup(frmAlgo,
topwin=self,
wavesyn_root=self.root_node)
solve_group.pack(side='left', fill='y')
tool_tabs.add(frmAlgo, text='Algorithm')
with self.attribute_lock:
set_attributes(self,
algorithm_selection_group=algorithm_selection_group,
parameter_group=parameter_group,
solve_group=solve_group)
self._make_view_tab()
self._make_marker_tab()
self._make_export_tab()
self._make_window_manager_tab()
# End toolbar
figure_book.make_figures(figure_meta=[
dict(name='Envelope', polar=False),
dict(name='Phase', polar=False),
dict(name='AutoCorrelation', polar=False),
dict(name='PSD', polar=False)
])
with open(self.root_node.config_file_path) as f:
config = json.load(f)
algorithms = config['SingleWaveformAlgorithms']
for algo in algorithms:
self.load_algorithm(module_name=algo['ModuleName'],
class_name=algo['ClassName'])
self.algorithm_selection_group.algorithm_list.current(
len(algorithms) - 1)
envelope_figure = figure_book[0]
envelope_figure.plot_function = lambda current_data, *args, **kwargs:\
envelope_figure.plot(abs(current_data), *args, **kwargs)
phase_figure = figure_book[1]
phase_figure.plot_function = lambda current_data, *args, **kwargs:\
phase_figure.plot(angle(current_data), *args, **kwargs)
def plot_acdb(current_data, *args, **kwargs):
s = current_data
if not isinstance(s, ndarray):
s = array(s)
N = len(s)
ac = convolve(s, conj(s[::-1]))
acdb = 20 * log10(abs(ac))
acdb = acdb - max(acdb)
autocorrelatino_figure.plot(r_[(-N + 1):N], acdb, *args, **kwargs)
autocorrelatino_figure = figure_book[2]
autocorrelatino_figure.plot_function = plot_acdb
psd_figure = figure_book[3]
psd_figure.plot_function = lambda current_data, *args, **kwargs:\
psd_figure.plot(abs(fft.fft(current_data)), *args, **kwargs)
