def tffmax(ds, usamp=True):
ftt.scanForTFValues(ds, window=.2, minFreq=4.9, maxFreq = 250.0)
tfv = ds.getSubData('/ftvals')
tf = _cleanupTFV(tfv.getData())
if tf.shape[0]==0:
print("No values in fmax tf")
return None
if usamp:
sv = tf[:,0].min()
tf = uniformsample(tf, 1.0)
dat = mdat.newData(tf, {'SampleType':'timeseries', 'SamplesPerSecond':1.0, "StartTime":sv})
else:
dat = mdat.newData(tf, {'SampleType':'function'})
return dat
def getTF(vidfn, mffn):
mff=os.path.join(os.path.split(ftt.__file__)[0], 'MicroflownCalib.ncl')
doc = io.read(mffn)
mfds = doc.getElements('Data')[0]
mfds.delChans([z for z in range(mfds.data.shape[1]) if not z==MFCHAN])
fd = io.read(mff)
fd = fd.getElements('Data')[0]
mfds.newElement(fd)
fd.setName('filter')
cal.applyFilterToSignal(mfds, dpathSig='/', dpathFilt="/filter", channel=0, newpath='velocity')
vdoc = io.read(vidfn)
vds = vdoc.getElements('Data')[0]
vds.newElement(mfds.getSubData('velocity'))
combine(vds, '/', '/velocity', False)
ftt.scanForTFValues(vds)
tfv = vds.getSubData('/ftvals')
tf = _cleanupTFV(tfv.getData())
f=open('trans_func.txt', 'w')
for i in range(tf.shape[0]):
f.write(" ".join(map(str, tf[i]))+"\n")
f.close()