def get_single (self):
"""
gets one block of data
"""
taskHandle = TaskHandle(0)
read = uInt32()
fnum = float64()
lnum = uInt64()
try:
DAQmxLoadTask(self.taskname,ctypes.byref(taskHandle))
if self.numchannels<1:
raise RuntimeError
except RuntimeError:
# no valid task
time_data_import.getdata(self,td)
return
#import data
ac = numpy.empty((self.numsamples,self.numchannels),numpy.float32)
DAQmxGetSampQuantSampPerChan(taskHandle,ctypes.byref(lnum))
max_num_samples = lnum.value
data = numpy.empty((max_num_samples,self.numchannels),dtype=numpy.float64)
DAQmxStartTask(taskHandle)
count = 0L
numsamples = self.numsamples
while count<numsamples:
DAQmxReadAnalogF64(taskHandle,-1,float64(10.0),
DAQmx_Val_GroupByScanNumber,data.ctypes.data,
data.size,ctypes.byref(read),None)
anz = min(read.value,numsamples-count)
ac[count:count+anz]=numpy.array(data[:anz],dtype=numpy.float32)
count+=read.value
DAQmxStopTask(taskHandle)
DAQmxClearTask(taskHandle)
return ac
def get_data(self, td):
"""
main work is done here: imports the data from CSV file into
TimeSamples object td and saves also a '*.h5' file so this import
need not be performed every time the data is needed
"""
taskHandle = TaskHandle(0)
read = uInt32()
fnum = float64()
lnum = uInt64()
try:
DAQmxLoadTask(self.taskname, ctypes.byref(taskHandle))
if self.numchannels < 1:
raise RuntimeError
DAQmxSetSampClkRate(taskHandle, float64(self.sample_freq))
except RuntimeError:
# no valid task
time_data_import.getdata(self, td)
return
#import data
name = td.name
if name == '':
name = datetime.now().isoformat('_').replace(':', '-').replace(
'.', '_')
name = path.join(td_dir, name + '.h5')
f5h = tables.open_file(name, mode='w')
ac = f5h.create_earray(f5h.root, 'time_data',
tables.atom.Float32Atom(),
(0, self.numchannels))
ac.set_attr('sample_freq', self.sample_freq)
DAQmxSetSampQuantSampPerChan(taskHandle, uInt64(100000))
DAQmxGetSampQuantSampPerChan(taskHandle, ctypes.byref(lnum))
max_num_samples = lnum.value
print "Puffergroesse: %i" % max_num_samples
data = numpy.empty((max_num_samples, self.numchannels),
dtype=numpy.float64)
DAQmxStartTask(taskHandle)
count = 0L
numsamples = self.numsamples
while count < numsamples:
#~ DAQmxReadAnalogF64(taskHandle,-1,float64(10.0),
#~ DAQmx_Val_GroupByScanNumber,data.ctypes.data,
#~ data.size,ctypes.byref(read),None)
DAQmxReadAnalogF64(taskHandle, 1024, float64(10.0),
DAQmx_Val_GroupByScanNumber, data.ctypes.data,
data.size, ctypes.byref(read), None)
ac.append(
numpy.array(data[:min(read.value, numsamples - count)],
dtype=numpy.float32))
count += read.value
#~ if read.value>200:
#~ print count, read.value
DAQmxStopTask(taskHandle)
DAQmxClearTask(taskHandle)
f5h.close()
td.name = name
td.load_data()
def get_data (self,td):
"""
main work is done here: imports the data from CSV file into
TimeSamples object td and saves also a '*.h5' file so this import
need not be performed every time the data is needed
"""
taskHandle = TaskHandle(0)
read = uInt32()
fnum = float64()
lnum = uInt64()
try:
DAQmxLoadTask(self.taskname,ctypes.byref(taskHandle))
if self.numchannels<1:
raise RuntimeError
DAQmxSetSampClkRate(taskHandle,float64(self.sample_freq))
except RuntimeError:
# no valid task
time_data_import.getdata(self,td)
return
#import data
name = td.name
if name=='':
name = datetime.now().isoformat('_').replace(':','-').replace('.','_')
name = path.join(td_dir,name+'.h5')
f5h = tables.open_file(name,mode='w')
ac = f5h.create_earray(f5h.root,'time_data',tables.atom.Float32Atom(),(0,self.numchannels))
ac.set_attr('sample_freq',self.sample_freq)
DAQmxSetSampQuantSampPerChan(taskHandle,uInt64(100000))
DAQmxGetSampQuantSampPerChan(taskHandle,ctypes.byref(lnum))
max_num_samples = lnum.value
print "Puffergroesse: %i" % max_num_samples
data = numpy.empty((max_num_samples,self.numchannels),dtype=numpy.float64)
DAQmxStartTask(taskHandle)
count = 0L
numsamples = self.numsamples
while count<numsamples:
#~ DAQmxReadAnalogF64(taskHandle,-1,float64(10.0),
#~ DAQmx_Val_GroupByScanNumber,data.ctypes.data,
#~ data.size,ctypes.byref(read),None)
DAQmxReadAnalogF64(taskHandle,1024,float64(10.0),
DAQmx_Val_GroupByScanNumber,data.ctypes.data,
data.size,ctypes.byref(read),None)
ac.append(numpy.array(data[:min(read.value,numsamples-count)],dtype=numpy.float32))
count+=read.value
#~ if read.value>200:
#~ print count, read.value
DAQmxStopTask(taskHandle)
DAQmxClearTask(taskHandle)
f5h.close()
td.name = name
td.load_data()
def get_single(self):
"""
gets one block of data
"""
taskHandle = TaskHandle(0)
read = uInt32()
fnum = float64()
lnum = uInt64()
try:
DAQmxLoadTask(self.taskname, ctypes.byref(taskHandle))
if self.numchannels < 1:
raise RuntimeError
except RuntimeError:
# no valid task
time_data_import.getdata(self, td)
return
#import data
ac = numpy.empty((self.numsamples, self.numchannels), numpy.float32)
DAQmxGetSampQuantSampPerChan(taskHandle, ctypes.byref(lnum))
max_num_samples = lnum.value
data = numpy.empty((max_num_samples, self.numchannels),
dtype=numpy.float64)
DAQmxStartTask(taskHandle)
count = 0L
numsamples = self.numsamples
while count < numsamples:
DAQmxReadAnalogF64(taskHandle, -1, float64(10.0),
DAQmx_Val_GroupByScanNumber, data.ctypes.data,
data.size, ctypes.byref(read), None)
anz = min(read.value, numsamples - count)
ac[count:count + anz] = numpy.array(data[:anz],
dtype=numpy.float32)
count += read.value
DAQmxStopTask(taskHandle)
DAQmxClearTask(taskHandle)
return ac
