def yvar(self):
"""
The y coordinates.
"""
yvar = _np.shape(linspacestep(self._ystart, self._ystop, self.step))[0] - 1
yvar = linspacestep(1, yvar)
return yvar
def yvar(self):
"""
The y coordinates.
"""
yvar = _np.shape(linspacestep(self._ystart, self._ystop,
self.step))[0] - 1
yvar = linspacestep(1, yvar)
return yvar
def __init__(self, img, xbounds=None, ybounds=None, step=1):
# ======================================
# Save things
# ======================================
self._img = img
self._xbounds = xbounds
self._ybounds = ybounds
self._step = step
self._ind = None
# ======================================
# Translate to clearer variables
# ======================================
if xbounds is None:
xstart = 0
xstop = img.shape[0]
else:
xstart = xbounds[0]
xstop = xbounds[1]
if ybounds is None:
ystart = 0
ystop = img.shape[1]
else:
ystart = ybounds[0]
ystop = ybounds[1]
self._ystart = ystart
self._ystop = ystop
self._xstart = xstart
self._xstop = xstop
xrange = slice(xstart, xstop)
yrange = slice(ystart, ystop)
img = img[xrange, yrange]
# ======================================
# Check number of points and
# initialize arrays
# ======================================
num_pts = (ystop - ystart) / step
self._variance = _np.zeros(num_pts)
self._gr = _np.empty(num_pts, object)
# ======================================
# Fit individual slices
# ======================================
for i, val in enumerate(linspacestep(0, ystop - ystart - step, step)):
# Take a strip of the image
strip = img[:, slice(val, val + step)]
# Sum over the strip to get an average of sorts
histdata = _np.sum(strip, 1)
xbins = len(histdata)
x = _np.linspace(1, xbins, xbins)
# Fit with a Gaussian to find spot size
try:
self._gr[i] = GaussResults(
x,
histdata,
# sigma_y = _np.ones(xbins),
variance = True,
background = True
)
self._variance[i] = self._gr[i].popt[2]
except RuntimeError as e:
print(e)
def __init__(self, img, xbounds=None, ybounds=None, step=1):
# ======================================
# Save things
# ======================================
self._img = img
self._xbounds = xbounds
self._ybounds = ybounds
self._step = step
self._ind = None
# ======================================
# Translate to clearer variables
# ======================================
if xbounds is None:
xstart = 0
xstop = img.shape[0]
else:
xstart = xbounds[0]
xstop = xbounds[1]
if ybounds is None:
ystart = 0
ystop = img.shape[1]
else:
ystart = ybounds[0]
ystop = ybounds[1]
self._ystart = ystart
self._ystop = ystop
self._xstart = xstart
self._xstop = xstop
xrange = slice(xstart, xstop)
yrange = slice(ystart, ystop)
img = img[xrange, yrange]
# ======================================
# Check number of points and
# initialize arrays
# ======================================
num_pts = (ystop - ystart) / step
self._variance = _np.zeros(num_pts)
self._gr = _np.empty(num_pts, object)
# ======================================
# Fit individual slices
# ======================================
for i, val in enumerate(linspacestep(0, ystop - ystart - step, step)):
# Take a strip of the image
strip = img[:, slice(val, val + step)]
# Sum over the strip to get an average of sorts
histdata = _np.sum(strip, 1)
xbins = len(histdata)
x = _np.linspace(1, xbins, xbins)
# Fit with a Gaussian to find spot size
try:
self._gr[i] = GaussResults(
x,
histdata,
# sigma_y = _np.ones(xbins),
variance=True,
background=True)
self._variance[i] = self._gr[i].popt[2]
except RuntimeError as e:
print(e)
