/
mielensfit.py
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/
mielensfit.py
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import os
import numpy as np
import holopy as hp
from holopy.scattering import calc_holo, Sphere
from holopy.scattering.theory import MieLens, Mie
from holopy.core.io import load_average
from holopy.core.metadata import get_extents, get_spacing
from holopy.core.process import subimage, normalize, center_find # , bg_correct
from holopy.inference import prior, AlphaModel, NmpfitStrategy
from holopy.inference.model import PerfectLensModel
RGB_CHANNEL = 1
HOLOGRAM_SIZE = 100
def bg_correct(raw, bg, df=None):
if df is None:
df = raw.copy()
df[:] = 0
denominator = bg - df
denominator.values = np.clip(denominator.values, 1e-7, np.inf)
holo = (raw - df) / (bg - df)
holo = hp.core.copy_metadata(raw, holo)
return holo
def _get_bounds(hologram, guess_parameters):
sphere_priors, lens_prior = _make_priors(hologram, guess_parameters)
xbounds = (sphere_priors.x.lower_bound, sphere_priors.x.upper_bound)
ybounds = (sphere_priors.y.lower_bound, sphere_priors.y.upper_bound)
zbounds = (sphere_priors.z.lower_bound, sphere_priors.z.upper_bound)
rbounds = (0.05, 10)
nbounds = (1.35, 2.3)
lens_bounds = (lens_prior.lower_bound, lens_prior.upper_bound)
return [xbounds, ybounds, zbounds, rbounds, nbounds, lens_bounds]
# ~~~ fitting
class Fitter(object):
_default_lens_angle = 0.8
_min_lens_angle = 0.0
_max_lens_angle = 1.2
_default_alpha = 1.0
_min_alpha = 0.0
_max_alpha = 2.0
_min_index = 1.33
_max_index = 2.3
_min_radius = 0.05
_max_radius = 5.0
def __init__(self, data, guess, theory='mielens'):
"""for now, this is for the lens model only
Parameters
----------
data : hologram
guess : dict
Must have keys `'n'`, `'r'`, `'z'`. An additional key of
`'lens_angle'` is optional.
"""
self.data = data
self.guess = guess
self.theory = theory
def fit(self):
sphere_priors = self.make_guessed_scatterer()
if self.theory == 'mielens':
lens_prior = self.guess_lens_angle()
model = PerfectLensModel(
sphere_priors, noise_sd=self.data.noise_sd,
lens_angle=lens_prior)
elif self.theory == 'mieonly':
alpha_prior = self.guess_alpha()
model = AlphaModel(
sphere_priors, noise_sd=self.data.noise_sd, alpha=alpha_prior)
optimizer = NmpfitStrategy()
result = optimizer.optimize(model, self.data)
# FIXME this result sometimes leaves the allowed ranges. To get
# result = hp.fitting.fit(model, self.data, minimizer=optimizer)
return result
def evaluate_model(self, params):
scatterer = self.make_scatterer(params)
if self.theory == 'mieonly':
theory = Mie()
else:
theory = MieLens(lens_angle=params['lens_angle'])
if self.theory == 'mielens':
scaling = 1.0
else:
scaling = params['alpha']
model = calc_holo(self.data, scatterer, theory=theory, scaling=scaling)
return model
def evaluate_residuals(self, params):
"""params is a dict-like"""
model = self.evaluate_model(params)
return self.data.values.squeeze() - model.values.squeeze()
def evaluate_chisq(self, params):
residuals = self.evaluate_residuals(params)
return np.sum(residuals**2)
def make_guessed_scatterer(self):
return self.make_scatterer(self.guess)
def make_scatterer(self, params):
center = self._make_center_priors(params)
index = prior.Uniform(
self._min_index, self._max_index, guess=params['n'])
radius = prior.Uniform(
self._min_radius, self._max_radius, guess=params['r'])
scatterer = Sphere(n=index, r=radius, center=center)
return scatterer
def guess_lens_angle(self):
lens_angle = (self.guess['lens_angle'] if 'lens_angle' in self.guess
else self._default_lens_angle)
lens_prior = prior.Uniform(
self._min_lens_angle, self._max_lens_angle, guess=lens_angle)
return lens_prior
def guess_alpha(self):
alpha = (self.guess['alpha'] if 'alpha' in self.guess
else self._default_alpha)
alpha_prior = prior.Uniform(
self._min_alpha, self._max_alpha, guess=alpha)
return alpha_prior
def _make_center_priors(self, params):
image_x_values = self.data.x.values
image_min_x = image_x_values.min()
image_max_x = image_x_values.max()
image_y_values = self.data.y.values
image_min_y = image_y_values.min()
image_max_y = image_y_values.max()
if ('x' not in params) or ('y' not in params):
pixel_spacing = get_spacing(self.data)
image_lower_left = np.array([image_min_x, image_min_y])
center = center_find(self.data) * pixel_spacing + image_lower_left
else:
center = [params['x'], params['y']]
xpar = prior.Uniform(image_min_x, image_max_x, guess=center[0])
ypar = prior.Uniform(image_min_y, image_max_y, guess=center[1])
extents = get_extents(self.data)
extent = max(extents['x'], extents['y'])
zextent = 5
zpar = prior.Uniform(
-extent * zextent, extent * zextent, guess=params['z'])
return xpar, ypar, zpar
def fit_mielens(hologram, guess_parameters):
fitter = Fitter(hologram, guess_parameters, theory='mielens')
return fitter.fit()
def fit_mieonly(hologram, guess_parameters):
fitter = Fitter(hologram, guess_parameters, theory='mieonly')
return fitter.fit()
# ~~~ loading data
class NormalizedDataLoader(object):
def __init__(self, data_filenames, metadata, particle_position,
background_prefix="bg", darkfield_prefix=None):
self.data_filenames = list(data_filenames)
self.metadata = metadata
self.particle_position = particle_position
self.background_prefix = background_prefix
self.darkfield_prefix = darkfield_prefix
self.root_folder = os.path.dirname(self.data_filenames[0])
self._reference_image = hp.load_image(
self.data_filenames[0], channel=RGB_CHANNEL, **self.metadata)
self._background = self._load_background()
self._darkfield = self._load_darkfield()
def load_all_data(self):
return [self._load_data(nm) for nm in self.data_filenames]
def _load_data(self, name): # need metadata, particle_position!
data = hp.load_image(name, channel=RGB_CHANNEL, **self.metadata)
data = bg_correct(data, self._background, self._darkfield)
data = subimage(data, self.particle_position[::-1], HOLOGRAM_SIZE)
data = normalize(data)
return data
def _load_background(self):
names = self._get_filenames_which_contain(self.background_prefix)
background = load_average(
names, refimg=self._reference_image, channel=RGB_CHANNEL)
return background
def _load_darkfield(self):
if self.darkfield_prefix is not None:
names = self._get_filenames_which_contain(self.darkfield_prefix)
darkfield = load_average(
names, refimg=self._reference_image, channel=RGB_CHANNEL)
else:
darkfield = None
return darkfield
def _get_filenames_which_contain(self, prefix):
paths = [os.path.join(self.root_folder, name)
for name in os.listdir(self.root_folder)
if prefix in name]
return paths
def load_bgdivide_crop(
path, metadata, particle_position, bg_prefix="bg", df_prefix=None,
channel=RGB_CHANNEL, size=HOLOGRAM_SIZE):
data = hp.load_image(path, channel=channel, **metadata)
bkg = load_bkg(path, bg_prefix, refimg=data)
dark = None # load_dark(path, df_prefix, refimg=data)
data = bg_correct(data, bkg, dark)
data = subimage(data, particle_position[::-1], size)
data = normalize(data)
return data
def load_bgdivide_crop_all_images(paths, metadata, particle_position, **kwargs):
loader = NormalizedDataLoader(paths, metadata, particle_position, **kwargs)
return loader.load_all_data()
def load_bkg(path, bg_prefix, refimg):
subdir = os.path.dirname(path)
bkg_paths = [
subdir + '/' + pth for pth in os.listdir(subdir) if bg_prefix in pth]
bkg = load_average(bkg_paths, refimg=refimg, channel=RGB_CHANNEL)
return bkg
def load_dark(path, df_prefix, refimg):
return load_bkg(path, df_prefix, refimg) if df_prefix is not None else None
def load_bgdivide_crop_v2(
path, metadata, particle_position, bkg, dark, channel=RGB_CHANNEL,
size=HOLOGRAM_SIZE):
data = hp.load_image(path, channel=channel, **metadata)
data = bg_correct(data, bkg, dark)
data = subimage(data, particle_position[::-1], size)
data = normalize(data)
return data