def make_image_0():
P = objs.PlatonicSpheresCollection(pos, rad)
H = psfs.AnisotropicGaussian()
I = ilms.BarnesStreakLegPoly2P1D(npts=(20, 10), local_updates=True)
B = GlobalScalar('bkg', 0.0)
C = GlobalScalar('offset', 0.0)
I.randomize_parameters()
return states.ImageState(im, [B, I, H, P, C], pad=16, model_as_data=True)
def makestate(im, pos, rad, slab=None, mem_level='hi'):
"""
Workhorse for creating & optimizing states with an initial centroid
guess.
This is an example function that works for a particular microscope. For
your own microscope, you'll need to change particulars such as the psf
type and the orders of the background and illumination.
Parameters
----------
im : :class:`~peri.util.RawImage`
A RawImage of the data.
pos : [N,3] element numpy.ndarray.
The initial guess for the N particle positions.
rad : N element numpy.ndarray.
The initial guess for the N particle radii.
slab : :class:`peri.comp.objs.Slab` or None, optional
If not None, a slab corresponding to that in the image. Default
is None.
mem_level : {'lo', 'med-lo', 'med', 'med-hi', 'hi'}, optional
A valid memory level for the state to control the memory overhead
at the expense of accuracy. Default is `'hi'`
Returns
-------
:class:`~peri.states.ImageState`
An ImageState with a linked z-scale, a ConfocalImageModel, and
all the necessary components with orders at which are useful for
my particular test case.
"""
if slab is not None:
o = comp.ComponentCollection(
[
objs.PlatonicSpheresCollection(pos, rad, zscale=zscale),
slab
],
category='obj'
)
else:
o = objs.PlatonicSpheresCollection(pos, rad, zscale=zscale)
p = exactpsf.FixedSSChebLinePSF()
npts, iorder = _calc_ilm_order(im.get_image().shape)
i = ilms.BarnesStreakLegPoly2P1D(npts=npts, zorder=iorder)
b = ilms.LegendrePoly2P1D(order=(9 ,3, 5), category='bkg')
c = comp.GlobalScalar('offset', 0.0)
s = states.ImageState(im, [o, i, b, c, p])
runner.link_zscale(s)
if mem_level != 'hi':
s.set_mem_level(mem_level)
opt.do_levmarq(s, ['ilm-scale'], max_iter=1, run_length=6, max_mem=1e4)
return s
def create_img():
"""Creates an image, as a `peri.util.Image`, which is similar
to the image in the tutorial"""
# 1. particles + coverslip
rad = 0.5 * np.random.randn(POS.shape[0]) + 4.5 # 4.5 +- 0.5 px particles
part = objs.PlatonicSpheresCollection(POS, rad, zscale=0.89)
slab = objs.Slab(zpos=4.92, angles=(-4.7e-3, -7.3e-4))
objects = comp.ComponentCollection([part, slab], category='obj')
# 2. psf, ilm
p = exactpsf.FixedSSChebLinePSF(kfki=1.07, zslab=-29.3, alpha=1.17,
n2n1=0.98, sigkf=-0.33, zscale=0.89, laser_wavelength=0.45)
i = ilms.BarnesStreakLegPoly2P1D(npts=(16,10,8,4), zorder=8)
b = ilms.LegendrePoly2P1D(order=(7,2,2), category='bkg')
off = comp.GlobalScalar(name='offset', value=-2.11)
mdl = models.ConfocalImageModel()
st = states.ImageState(util.NullImage(shape=[48,64,64]),
[objects, p, i, b, off], mdl=mdl, model_as_data=True)
b.update(b.params, BKGVALS)
i.update(i.params, ILMVALS)
im = st.model + np.random.randn(*st.model.shape) * 0.03
return util.Image(im)
plot_averaged_residuals(st)
# Next, let's check the illumination field. For this, we load a different
# image, one that I've taken of just dyed fluid. This image also has a
# coverslip in it, at the bottom. For now, we'll ignore this coverlip by
# setting the tile to be a specific region of z in the image. Moreover,
# since I know that our confocal has some scan issues at the edges of the
# image, I'll also crop out the image edges with the tile:
im_ilm = util.RawImage('./ilm_test.tif',
tile=util.Tile([48, 0, 0], [49, 100, 100]))
# also located in the scripts folder
# Looking at the image, the illlumination is very stripey, due to the line-scan
# nature of our confocal. To account for this, we use a stripe-based ilm:
ilm = ilms.BarnesStreakLegPoly2P1D(npts=(50, 30, 20, 13, 7, 7, 7), zorder=1)
# (we only use a zorder of 1 since we've truncated to 1 pixel in z).
# Our real model will use a point-spread function that will blur out the ilm
# field slightly more. So we check the fit with a model that includes the
# type of point-spread function that we will use. A model that blur with a
# point-spread function takes considerably more time to evaluate than a
# SmoothFieldModel, so if you're not sure if your ilm is high enough order
# you should first check with a faster SmoothFieldModel.
psf = exactpsf.FixedSSChebLinePSF()
st = states.ImageState(im_ilm, [ilm, psf], mdl=models.BlurredFieldModel())
opt.do_levmarq(st, st.params)
# Plotting the residuals shows that they're good, aside from scan noise
# inherent to the line CCD camera:
import numpy
from peri.comp import objs
coverslip = objs.Slab(zpos=6)
particle_positions = numpy.load('../../Downloads/particle-positions.npy')
particle_radii = 5.0
particles = objs.PlatonicSpheresCollection(particle_positions, particle_radii)
from peri.comp import comp
objects = comp.ComponentCollection([particles, coverslip], category='obj')
from peri.comp import ilms
illumination = ilms.BarnesStreakLegPoly2P1D(npts=(16, 10, 8, 4), zorder=8)
background = ilms.LegendrePoly2P1D(order=(7, 2, 2), category='bkg')
offset = comp.GlobalScalar(name='offset', value=0.)
from peri.comp import exactpsf
point_spread_function = exactpsf.FixedSSChebLinePSF()
from peri import models
model = models.ConfocalImageModel()
print(model)
from peri import states, util, models
from peri.comp import psfs, objs, ilms, GlobalScalar, ComponentCollection
from peri.test import nbody
import peri.opt.optimize as opt
import peri.opt.addsubtract as addsub
im = util.NullImage(shape=(32, ) * 3)
pos, rad, tile = nbody.create_configuration(3, im.tile)
P = ComponentCollection(
[objs.PlatonicSpheresCollection(pos, rad),
objs.Slab(2)], category='obj')
H = psfs.AnisotropicGaussian()
I = ilms.BarnesStreakLegPoly2P1D(npts=(25, 13, 3),
zorder=2,
local_updates=False)
B = ilms.LegendrePoly2P1D(order=(3, 1, 1), category='bkg', constval=0.01)
C = GlobalScalar('offset', 0.0)
I.randomize_parameters()
s = states.ImageState(im, [B, I, H, P, C], pad=16, model_as_data=True)