def useExplicitX(ds, dpath="/", chanX=0):
ds = ds.getSubData(dpath)
dat = ds.getData()
head = ds.header()
chan = dat[:, chanX]
steps = chan[1:] - chan[:-1]
smin = steps.min()
sdev = (steps.max() - smin) / smin
nonmono = False
if smin < 0:
print("data are not monotonic increasing. Sorting")
nonmono = True
smin = abs(steps).min()
sdev = abs(sdev)
head["StartTime"] = chan.min()
head["Labels"] = [head["Labels"][i] for i in range(dat.shape[1]) if not i == chanX]
if smin <= 0:
print("Data do not represent a function on the indicated X variable. Aborting.")
return
head["SamplesPerSecond"] = 1.0 / smin
if sdev < 0.001:
if nonmono:
dat = dat[N.arange(dat.shape[0] - 1, -1, -1), :]
print("Data are uniformly sampled")
dat = dat[:, [x for x in range(dat.shape[1]) if not x == chanX]]
else:
print("Data are not uniformly sampled. Interpolating")
if chanX != 0:
ind = [chanX] + [x for x in range(dat.shape[1]) if not x == chanX]
dat = dat[:, ind]
dat = uniformsample(dat, smin)
ds.datinit(dat, head)
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 allTFsRs(fnames):
ndoc = nmpml.blankDocument()
for fname in fnames:
doc = io.read(fname)
ds = doc.getElements("Data", depth=1)[0]
bn=os.path.splitext(fname)[0]
tf = tffmax(ds, False)
if not tf:
continue
tf = tf.getData()
if fname.startswith("2009") or fname.startswith("2010"):
tf[:,2]+=pi
tf[:,2] = tf[:,2] - ( 2*pi*(tf[:,2]>pi))
tf = row_stack( [array([[0,0,0]]), tf, array([[250,tf[-1,1],tf[-1,2]]])])
tf = uniformsample(tf, 1.0)
tf = mdat.newData(tf, {'Name':bn, 'SampleType':'timeseries', 'SamplesPerSecond':1.0, "StartTime":0})
ndoc.newElement(tf)
io.write(ndoc, "allTFsResamp.mdat")
def orderPlotsByAttribute(ds, attrib="length", channel=1):
dats = ds.getElements("Data", {"SampleType": "function"})
ifv = min([d.data[:, 0].min() for d in dats])
mfv = max([d.data[:, 0].max() for d in dats])
vals = array([e.attrib(attrib) for e in dats])
order = vals.argsort()
chans = []
for i in order:
t = dats[i].data[:, [0, channel]]
ii = t[:, 0].argmin()
iv = t[ii, 0]
if iv > ifv:
t = row_stack([array([ifv, t[ii, 1]]), t])
mi = t[:, 0].argmax()
mv = t[mi, 0]
if mv < mfv:
t = row_stack([t, array([mfv, t[mi, 1]])])
d = uniformsample(t, 5.0)[:, 0]
chans.append(d)
d = column_stack(chans)
ds.datinit(d, {"SampleType": "timeseries", "SamplesPerSecond": 0.2})