Commit d7aaaea0 authored by Martin Reinecke's avatar Martin Reinecke

Merge branch 'inverse_gamma_models' into 'NIFTy_5'

Inverse gamma models

See merge request ift/nifty-dev!75
parents cd89b1aa 63f9fdbc
......@@ -102,6 +102,6 @@ if __name__ == '__main__':
# Plot results
result_sky =
ift.plot(result_sky, title='Reconstruction')
ift.plot(GR.adjoint(data), title='Data')
ift.plot_finish(name='getting_started_2.png', xsize=16, ysize=16)
......@@ -82,7 +82,7 @@ from .plotting.plot import plot, plot_finish
from .library.amplitude_model import make_amplitude_model
from .library.gaussian_energy import GaussianEnergy
from .library.los_response import LOSResponse
from .library.point_sources import PointSources
from .library.inverse_gamma_model import InverseGammaModel
from .library.poissonian_energy import PoissonianEnergy
from .library.wiener_filter_curvature import WienerFilterCurvature
from .library.correlated_fields import (make_correlated_field,
......@@ -30,28 +30,35 @@ from ..sugar import makeOp
from ..utilities import memo
class PointSources(Model):
def __init__(self, position, alpha, q):
super(PointSources, self).__init__(position)
class InverseGammaModel(Model):
def __init__(self, position, alpha, q, key):
super(InverseGammaModel, self).__init__(position)
self._alpha = alpha
self._q = q
self._key = key
def make(cls, actual_position, alpha, q, key):
pos = cls.inverseIG(actual_position, alpha, q)
mf = MultiField.from_dict({key: pos})
return cls(mf, alpha, q, key)
def at(self, position):
return self.__class__(position, self._alpha, self._q)
return self.__class__(position, self._alpha, self._q, self._key)
def value(self):
points = self.position['points'].local_data
points = self.position[self._key].local_data
points = np.clip(points, None, 8.2)
points = Field.from_local_data(self.position['points'].domain, points)
points = Field.from_local_data(self.position[self._key].domain, points)
return self.IG(points, self._alpha, self._q)
def jacobian(self):
u = self.position['points'].local_data
u = self.position[self._key].local_data
inner = norm.pdf(u)
outer_inv = invgamma.pdf(invgamma.ppf(norm.cdf(u),
......@@ -60,19 +67,18 @@ class PointSources(Model):
outer_inv = np.clip(outer_inv, 1e-20, None)
outer = 1/outer_inv
grad = Field.from_local_data(self.position['points'].domain,
grad = Field.from_local_data(self.position[self._key].domain,
grad = makeOp(MultiField.from_dict({"points": grad},
grad = makeOp(MultiField.from_dict({self._key: grad},
return SelectionOperator(, 'points')*grad
return SelectionOperator(, self._key)*grad
def IG(field, alpha, q):
foo = invgamma.ppf(norm.cdf(field.local_data), alpha, scale=q)
return Field.from_local_data(field.domain, foo)
# MR FIXME: why does this take an np.ndarray instead of a Field?
def inverseIG(u, alpha, q):
res = norm.ppf(invgamma.cdf(u, alpha, scale=q))
return res
res = norm.ppf(invgamma.cdf(u.local_data, alpha, scale=q))
return Field.from_local_data(u.domain, res)
......@@ -151,8 +151,8 @@ class Model_Tests(unittest.TestCase):
{'points': S.draw_sample()})
alpha = 1.5
q = 0.73
model = ift.PointSources(pos, alpha, q)
# FIXME All those cdfs and ppfs are not that accurate
model = ift.InverseGammaModel(pos, alpha, q, 'points')
# FIXME All those cdfs and ppfs are not very accurate
ift.extra.check_value_gradient_consistency(model, tol=1e-5)
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