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()
