def init(self): if self.interface == None: try: self.interface = mlabwrap.init( which(self.exec_path) ) except IOError: self.interface._mlab = None # Complete shutdown self.interface = mlabwrap.init( which(self.exec_path) ) return self.interface
def __init__(self, **kwargs): super(NMRLabMetabolabTool, self).__init__(**kwargs) self.addDataToolBar() self.addFigureToolBar() self.data.add_input('input') # Add input slot self.data.add_output('output') self.table.setModel(self.data.o['output'].as_table) self.views.addTab(MplSpectraView(self), 'View') # Start matlab interface self.matlab = mlabwrap.init() #code = "addpath('%s')" % os.path.abspath( self.plugin.path ) #r = self.matlab.run_code(code) #print r,"!!!!" # Setup data consumer options self.data.consumer_defs.append( DataDefinition( 'input', { 'labels_n': ('>1', None), 'entities_t': (None, None), 'scales_t': (None, ['float']), })) self.config.set_defaults({ 'bin_size': 0.01, 'bin_offset': 0, })
def __init__(self, **kwargs): super(PLSToolboxApp, self).__init__(**kwargs) self.addDataToolBar() self.addFigureToolBar() self.data.add_input('input') # Add input slot self.data.add_output('output') self.table.setModel(self.data.o['output'].as_table) self.views.addTab(MplSpectraView(self), 'View') # Start matlab interface self.matlab = mlabwrap.init() #code = "addpath('%s')" % os.path.abspath( self.plugin.path ) #r = self.matlab.run_code(code) #print r,"!!!!" # Setup data consumer options self.data.consumer_defs.append( DataDefinition('input', { 'labels_n': ('>1', None), 'entities_t': (None, None), 'scales_t': (None, ['float']), }) ) self.config.set_defaults({ 'bin_size': 0.01, 'bin_offset': 0, })
def __init__(self, **kwargs): super(MATLABTool, self).__init__(**kwargs) self.addDataToolBar() self.addFigureToolBar() self.data.add_input('input') # Add input slot self.data.add_output('output') self.table.setModel(self.data.o['output'].as_table) self.views.addTab(MplSpectraView(self), 'View') # Start matlab interface self.matlab = mlabwrap.init() # Setup data consumer options self.data.consumer_defs.append( DataDefinition( 'input', { 'labels_n': ('>1', None), 'entities_t': (None, None), 'scales_t': (None, ['float']), })) self.config.set_defaults({ 'bin_size': 0.01, 'bin_offset': 0, })
def __init__(self, **kwargs): super(MATLABScriptTool, self).__init__(**kwargs) self.addDataToolBar() self.addCodeEditorToolbar() self.data.add_input('input') # Add input slot self.data.add_output('output') # Add output slot # We need an input filter for this type; accepting *anything* self.data.consumer_defs.append(DataDefinition('input', {})) self.config.set_defaults({ 'source': ''' % %%%%% MATLAB Scripting for Pathomx %%%% % % Source data from input ports is available as same-named variables in the MATLAB % workspace (default input). The data matrix is therefore available under input.data % Put your modified data output into the variable output. % For more information on the Pathomx Dataset object structure see: % http://docs.pathomx.org/en/latest/ % % Have fun! ''' }) self.editor = ui.CodeEditor() self.config.add_handler('source', self.editor) highlighter = MATLABHighlighter(self.editor.document()) self.views.addView(self.editor, 'Editor') self.matlab = mlabwrap.init() self.finalise()
def __init__(self, **kwargs): super(MATLABScriptTool, self).__init__(**kwargs) self.addDataToolBar() self.addCodeEditorToolbar() self.data.add_input('input') # Add input slot self.data.add_output('output') # Add output slot # We need an input filter for this type; accepting *anything* self.data.consumer_defs.append( DataDefinition('input', { }) ) self.config.set_defaults({ 'source': ''' % %%%%% MATLAB Scripting for Pathomx %%%% % % Source data from input ports is available as same-named variables in the MATLAB % workspace (default input). The data matrix is therefore available under input.data % Put your modified data output into the variable output. % For more information on the Pathomx Dataset object structure see: % http://docs.pathomx.org/en/latest/ % % Have fun! ''' }) self.editor = ui.CodeEditor() self.config.add_handler('source', self.editor) highlighter = MATLABHighlighter(self.editor.document()) self.views.addView(self.editor, 'Editor') self.matlab = mlabwrap.init() self.finalise()
def __init__(self): self.mlab = mlabwrap.init() self.mlab._do("clear all") self.mlab._do("cd('{}}')".format(os.getcwd())) self.mlab._do('global modelPoints dataPoints x0 lb ub')
def main(): print 'loading...' # Loading mlabwrap to enable matlab usage global mlab mlab = mlabwrap.init() global counter counter = 0 l = ['/Applications/Sublime\ Text\ 2.app', '/Applications/Spotify.app', '/Applications/Safari.app', '/Applications/Keynote.app', 'www.youtube.com', 'www.facebook.com'] actor = Actor([], l) #### Pu_Ro_Jay features #### initialF = loadGroundTruth(['pu_ro_jay']) instructF = loadGroundTruth(['open', 'kill', 'gotoWeb', 'switchDesktop', 'switchTabWindow', 'facebookPost', 'musicControl']) apps = ['sublime', 'spotify', 'safari', 'keynote', 'youtube', 'facebook'] appsF = loadGroundTruth(apps) paras = ['playpause', 'prevtrack', 'nexttrack', 'volumedown', 'volumeup', 'mute','left', 'right'] parasF = loadGroundTruth(paras) con = Condition() Recorder(con, CHUNK, FORMAT, CHANNELS, RATE, CHUNK_BUFFER_SIZE).start() # signal.signal(signal.SIGINT, signal_handler_SIGINT) time.sleep(5) tmpData = [] play=pyaudio.PyAudio() stream_play = play.open(format=FORMAT, channels=CHANNELS, rate=RATE, output=True) print "* Start playing" data = "" # counter = 0 frame = [] dists = [10000, 10000] DETECTING_FLAG = 0 INSTRUCT_FLAG = 0 SECOND_FLAG = 0 buff = [] while(1): data = read_from_recorder(con, 1) if tmpData != data: print counter counter += 1 f1 = getFeatures(data, play) # write2wav(data, 'wavFile/tmp' + str(counter) + '_.wav', play) if DETECTING_FLAG == False: DETECTING_FLAG = True # f1 = getFeatures(data, play) tmpDist = getDistances(f1, initialF[0][1])[0] dists.pop(0) dists.append(tmpDist) print tmpDist if sum(dists) < 180: print "DETECTING!!" DETECTING_FLAG = True #counter = STOP_POINT + 1 #break elif INSTRUCT_FLAG == False: tmpDists = getAllDistances(f1, instructF) newList = sorted(tmpDists, key = lambda t: t[1]) for tup in newList: print tup[0], tup[1] m = min(tmpDists, key = lambda t: t[1]) if m[1] < 93: ### instruction name ### INSTRUCT_FLAG = True buff.append(m[0]) print m[0] # ['open', 'kill', 'gotoWeb', 'switchDesktop', 'switchTabWindow', 'facebookPost', 'musicControl'] if m[0] == 'open' or m[0] == 'kill': os.system('say what') elif m[0] == 'gotoWeb' or m[0] == 'switchTabWindow' or m[0] == 'switchDesktop': os.system('say 哪邊') elif m[0] == 'musicControl': os.system('say 請說') # counter = STOP_POINT + 1 # break if m[0] == 'facebookPost': os.system('say okay') doAction(actor, apps, paras, buff) buff = [] DETECTING_FLAG = False INSTRUCT_FLAG = False else: print "PARA" tmpDists = [] if buff[0] == 'open' or buff[0] == 'kill' or buff[0] == 'gotoWeb': tmpDists = getAllDistances(f1, appsF) else: tmpDists = getAllDistances(f1, parasF) newList = sorted(tmpDists, key = lambda t: t[1]) for tup in newList: print tup[0], tup[1] m = min(tmpDists, key = lambda t: t[1]) if m[1] < 93: buff.append(m[0]) print m[0] os.system('say okay') doAction(actor, apps, paras, buff) buff = [] DETECTING_FLAG = False INSTRUCT_FLAG = False tmpData = data if counter > STOP_POINT: break stream_play.stop_stream() stream_play.close()