def fitExp(ds, select=(None, [0], None), newpath='/o4'):
dat=getSelection(ds, select)
h=getSelectionHeader(ds, select)
fs=h['SamplesPerSecond']
start=h.get('StartTime', 0)
ld=log(ravel(dat))
m, b = regress(ld, fs, start)
x=start+arange(ld.shape[0])/fs
y=exp(m*x+b)
v=((m, b), y)
if newpath:
np=ds.getSubData(newpath)
if not np:
np=ds.createSubData(newpath)
np.datinit(v[1], h)
else:
setSelection(ds, select, v[1])
def showProjection(doc, upath="/Data:DataFile"): nip='/Data:proj' ndat=doc.getInstance(upath).getData() fromTimeSeries(doc, upath, 2.0, width=-1, outputPath=nip) #pad(doc, nip, 10, 10, 50, 50) idat, h=getImageDataAndHeader(doc, nip) ndat-=array(h['SpatialAnchor'][:2]) m, b = regress(ndat) line=255*_makeline(m, b, idat.shape) line=reshape(line, (line.shape[0], line.shape[1], 1, 1)) df=colorOverlay(idat, line) frames=[] for i in range(idat.shape[3]): x, y = _ptoline(ndat[i,0], ndat[i,1], m, b) ball = 255*makeball(x, y, 1.0, idat.shape) ball=reshape(ball, (ball.shape[0], ball.shape[1], 1, 1)) frames.append(concatenate([idat[:,:,:,i:i+1], line, ball], 2)) df=concatenate(frames, 3) setImageData(df, nip, doc)
def findLine(doc, image, outputPath='line'):
'''finds the best fit line in the indicated frame'''
dat=getImageData(doc, image)
if not isBinary(dat):
print("Warning: dat is not binary. Using an automatic threshold")
me, ma, st= dat.mean(), dat.max(), dat.std()
thresh=min(ma-st, me+st)
dat=dat>=thresh
frames=range(dat.shape[3])
get=[]
lines=[]
for frame in frames:
df=dat[:,:,0,frame]
ind=transpose(array(nonzero(df))).astype(float32)
m, b = regress(ind)
lines.append((m,b))
line=_makeline(m, b, df.shape)
df=colorOverlay(df, line)
get.append(df)
df=concatenate(get, 3)
setImageData(df, outputPath or image, doc)
setTimeseriesData(doc, image, lines, outputPath, ['Slope', 'Intercept'])
def findPoint(doc, image, outputPath='point'):
dat=getImageData(doc, image)
if not isBinary(dat):
print("Warning: dat is not binary. Using an automatic threshold")
me, ma, st= dat.mean(), dat.max(), dat.std()
thresh=min(ma-st, me+st)
dat=dat>=thresh
frames=range(dat.shape[3])
get=[]
points=[]
for frame in frames:
df=dat[:,:,0,frame]
ind=transpose(array(nonzero(df))).astype(float32)
m, b = regress(ind)
x=ind[:,0].mean()
y=m*x+b
points.append((x,y))
pt=zeros_like(df)
pt[x,y]=1.0
df=colorOverlay(df, pt)
get.append(df)
df=concatenate(get, 3)
setImageData(df, outputPath, doc)
setTimeseriesData(doc, image, points, outputPath, ['X', 'Y'])
