diff --git a/demos/critical_filtering.py b/demos/critical_filtering.py
index 3fef05a59b312239234f49f074096253e73a4b5d..7866f04d3e290f71b5457effbb2eaf69c3f7362e 100644
--- a/demos/critical_filtering.py
+++ b/demos/critical_filtering.py
@@ -53,7 +53,7 @@ if __name__ == "__main__":
d_data = d.val
ift.plot(d, name="data.png")
- IC1 = ift.GradientNormController(verbose=True, iteration_limit=100,
+ IC1 = ift.GradientNormController(name="IC1", iteration_limit=100,
tol_abs_gradnorm=0.1)
minimizer = ift.RelaxedNewton(IC1)
diff --git a/demos/log_normal_wiener_filter.py b/demos/log_normal_wiener_filter.py
index 87563debbfae8a130727a7f2aaa9673ee30fb759..5ab513539d01bb135d5bd95d8c959245cfe73317 100644
--- a/demos/log_normal_wiener_filter.py
+++ b/demos/log_normal_wiener_filter.py
@@ -47,9 +47,8 @@ if __name__ == "__main__":
# Wiener filter
m0 = ift.Field.zeros(harmonic_space)
- ctrl = ift.GradientNormController(verbose=False, tol_abs_gradnorm=0.0001)
- ctrl2 = ift.GradientNormController(verbose=True, tol_abs_gradnorm=0.1,
- name="outer")
+ ctrl = ift.GradientNormController(tol_abs_gradnorm=0.0001)
+ ctrl2 = ift.GradientNormController(tol_abs_gradnorm=0.1, name="outer")
inverter = ift.ConjugateGradient(controller=ctrl)
energy = ift.library.LogNormalWienerFilterEnergy(m0, data, R_harmonic,
N, S, inverter=inverter)
diff --git a/demos/nonlinear_critical_filter.py b/demos/nonlinear_critical_filter.py
index 7feef7420d90a66f0c4903e5aea20e3fb0e65031..9a67ca09ab4ff326f14e3407e63933bb8da889da 100644
--- a/demos/nonlinear_critical_filter.py
+++ b/demos/nonlinear_critical_filter.py
@@ -72,13 +72,12 @@ if __name__ == "__main__":
t0 = ift.Field(p_space, val=-4.)
power0 = Projection.adjoint_times(ift.exp(0.5 * t0))
- IC1 = ift.GradientNormController(verbose=True, iteration_limit=100,
+ IC1 = ift.GradientNormController(name="IC1", iteration_limit=100,
tol_abs_gradnorm=1e-3)
LS = ift.LineSearchStrongWolfe(c2=0.02)
minimizer = ift.RelaxedNewton(IC1, line_searcher=LS)
- ICI = ift.GradientNormController(verbose=False, name="ICI",
- iteration_limit=500,
+ ICI = ift.GradientNormController(iteration_limit=500,
tol_abs_gradnorm=1e-3)
inverter = ift.ConjugateGradient(controller=ICI)
diff --git a/demos/paper_demos/cartesian_wiener_filter.py b/demos/paper_demos/cartesian_wiener_filter.py
index 4fd696d093a656d10289b7347925e6c376fb49c5..9b4665424be2334a6fa34b622085da6f4ed34000 100644
--- a/demos/paper_demos/cartesian_wiener_filter.py
+++ b/demos/paper_demos/cartesian_wiener_filter.py
@@ -86,7 +86,7 @@ if __name__ == "__main__":
# Wiener filter
j = R_harmonic.adjoint_times(N.inverse_times(data))
- ctrl = ift.GradientNormController(verbose=True, tol_abs_gradnorm=0.1)
+ ctrl = ift.GradientNormController(name="inverter", tol_abs_gradnorm=0.1)
inverter = ift.ConjugateGradient(controller=ctrl)
wiener_curvature = ift.library.WienerFilterCurvature(
S=S, N=N, R=R_harmonic, inverter=inverter)
diff --git a/demos/paper_demos/wiener_filter.py b/demos/paper_demos/wiener_filter.py
index ddf5a63a74468b782716bb5b21fc60d327046309..b0d682fcd671bb58b34c5b164eab7625661a5166 100644
--- a/demos/paper_demos/wiener_filter.py
+++ b/demos/paper_demos/wiener_filter.py
@@ -19,7 +19,7 @@ if __name__ == "__main__":
signal_space = ift.RGSpace([N_pixels, N_pixels], distances=L/N_pixels)
harmonic_space = signal_space.get_default_codomain()
- fft = ift.FFTOperator(harmonic_space, target=signal_space)
+ fft = ift.HarmonicTransformOperator(harmonic_space, target=signal_space)
power_space = ift.PowerSpace(
harmonic_space, binbounds=ift.PowerSpace.useful_binbounds(
harmonic_space, logarithmic=True))
@@ -28,40 +28,47 @@ if __name__ == "__main__":
S = ift.create_power_operator(harmonic_space,
power_spectrum=power_spectrum)
mock_power = ift.PS_field(power_space, power_spectrum)
- mock_signal = fft(ift.power_synthesize(mock_power, real_signal=True))
+ mock_signal = ift.power_synthesize(mock_power, real_signal=True)
# Setting up an exemplary response
mask = np.ones(signal_space.shape)
N10 = int(N_pixels/10)
mask[N10*5:N10*9, N10*5:N10*9] = 0.
mask = ift.Field(signal_space, ift.dobj.from_global_data(mask))
- R = ift.ResponseOperator(signal_space, sigma=(response_sigma,),
- exposure=(mask,))
+ R = ift.GeometryRemover(signal_space)
+ R = R*ift.DiagonalOperator(mask)
+ R = R*fft
+ R = R * ift.create_harmonic_smoothing_operator((harmonic_space,),0,response_sigma)
data_domain = R.target[0]
- R_harmonic = R * fft
# Setting up the noise covariance and drawing a random noise realization
- ndiag = ift.Field.full(data_domain, mock_signal.var()/signal_to_noise)
- N = ift.DiagonalOperator(ndiag.weight(1))
+ ndiag = 1e-8*ift.Field.full(data_domain, fft(mock_signal).var()/signal_to_noise)
+ N = ift.DiagonalOperator(ndiag)
noise = ift.Field.from_random(
domain=data_domain, random_type='normal',
std=mock_signal.std()/np.sqrt(signal_to_noise), mean=0)
data = R(mock_signal) + noise
# Wiener filter
- j = R_harmonic.adjoint_times(N.inverse_times(data))
- ctrl = ift.GradientNormController(verbose=True, tol_abs_gradnorm=0.1)
+ j = R.adjoint_times(N.inverse_times(data))
+ ctrl = ift.GradientNormController(name="inverter", tol_abs_gradnorm=1e-6)
inverter = ift.ConjugateGradient(controller=ctrl)
wiener_curvature = ift.library.WienerFilterCurvature(
- S=S, N=N, R=R_harmonic, inverter=inverter)
+ S=S, N=N, R=R, inverter=inverter)
m_k = wiener_curvature.inverse_times(j)
m = fft(m_k)
+ plotdict = {"xlabel": "Pixel index", "ylabel": "Pixel index",
+ "colormap": "Planck-like"}
+ ift.plot(mock_signal, name="mock_signal.png", **plotdict)
+ ift.plot(ift.Field(signal_space, val=data.val),
+ name="data.png", **plotdict)
+ ift.plot(m, name="map.png", **plotdict)
# Probing the uncertainty
class Proby(ift.DiagonalProberMixin, ift.Prober):
pass
- proby = Proby(signal_space, probe_count=1, ncpu=1)
- proby(lambda z: fft(wiener_curvature.inverse_times(fft.inverse_times(z))))
+ proby = Proby(harmonic_space, probe_count=1, ncpu=1)
+ proby(lambda z: wiener_curvature.inverse_times(z))
sm = ift.FFTSmoothingOperator(signal_space, sigma=0.03)
variance = ift.sqrt(sm(proby.diagonal.weight(-1)))
diff --git a/demos/wiener_filter_easy.py b/demos/wiener_filter_easy.py
index 5884b1451991bf4a8671aeda4833e9a3b6711074..09f946326056ebf2cb3d3ebee96c52b91f8c83bc 100644
--- a/demos/wiener_filter_easy.py
+++ b/demos/wiener_filter_easy.py
@@ -54,7 +54,7 @@ if __name__ == "__main__":
# Wiener filter
j = R.adjoint_times(N.inverse_times(d))
- IC = ift.GradientNormController(verbose=True, iteration_limit=500,
+ IC = ift.GradientNormController(name="inverter", iteration_limit=500,
tol_abs_gradnorm=0.1)
inverter = ift.ConjugateGradient(controller=IC)
S_inv = fft.adjoint*Sh.inverse*fft
diff --git a/demos/wiener_filter_via_curvature.py b/demos/wiener_filter_via_curvature.py
index 16ee1acb47cada4981a88f52a64a312d007e6eb9..0e3309b748fe63c700f598af1755cab1d45e4270 100644
--- a/demos/wiener_filter_via_curvature.py
+++ b/demos/wiener_filter_via_curvature.py
@@ -69,7 +69,7 @@ if __name__ == "__main__":
j = R.adjoint_times(N.inverse_times(data))
ctrl = ift.GradientNormController(
- verbose=True, tol_abs_gradnorm=1e-5/(nu.K*(nu.m**dimensionality)))
+ name="inverter", tol_abs_gradnorm=1e-5/(nu.K*(nu.m**dimensionality)))
inverter = ift.ConjugateGradient(controller=ctrl)
wiener_curvature = ift.library.WienerFilterCurvature(
S=S, N=N, R=R, inverter=inverter)
diff --git a/demos/wiener_filter_via_hamiltonian.py b/demos/wiener_filter_via_hamiltonian.py
index eeeff78153da2272308e16ecf28389aecc954e3f..74f5ae90c082dfd8039077e2c19456e54798806b 100644
--- a/demos/wiener_filter_via_hamiltonian.py
+++ b/demos/wiener_filter_via_hamiltonian.py
@@ -50,9 +50,9 @@ if __name__ == "__main__":
# Choosing the minimization strategy
- ctrl = ift.GradientNormController(verbose=True, tol_abs_gradnorm=0.1)
+ ctrl = ift.GradientNormController(name="inverter", tol_abs_gradnorm=0.1)
inverter = ift.ConjugateGradient(controller=ctrl)
- controller = ift.GradientNormController(verbose=True, tol_abs_gradnorm=0.1)
+ controller = ift.GradientNormController(name="min", tol_abs_gradnorm=0.1)
minimizer = ift.RelaxedNewton(controller=controller)
m0 = ift.Field.zeros(h_space)
# Initializing the Wiener Filter energy
diff --git a/nifty4/minimization/gradient_norm_controller.py b/nifty4/minimization/gradient_norm_controller.py
index 2c9c9a4f227b618161487b9e9f73b541332d6858..c06a9988dfd568479178ae8f6fa88e57a469f0a4 100644
--- a/nifty4/minimization/gradient_norm_controller.py
+++ b/nifty4/minimization/gradient_norm_controller.py
@@ -22,15 +22,13 @@ from .. import dobj
class GradientNormController(IterationController):
def __init__(self, tol_abs_gradnorm=None, tol_rel_gradnorm=None,
- convergence_level=1, iteration_limit=None,
- name=None, verbose=None):
+ convergence_level=1, iteration_limit=None, name=None):
super(GradientNormController, self).__init__()
self._tol_abs_gradnorm = tol_abs_gradnorm
self._tol_rel_gradnorm = tol_rel_gradnorm
self._convergence_level = convergence_level
self._iteration_limit = iteration_limit
self._name = name
- self._verbose = verbose
def start(self, energy):
self._itcount = -1
@@ -56,10 +54,8 @@ class GradientNormController(IterationController):
self._ccount = max(0, self._ccount-1)
# report
- if self._verbose:
- msg = ""
- if self._name is not None:
- msg += self._name+":"
+ if self._name is not None:
+ msg = self._name+":"
msg += " Iteration #" + str(self._itcount)
msg += " energy=" + str(energy.value)
msg += " gradnorm=" + str(energy.gradient_norm)
diff --git a/nifty4/minimization/line_search_strong_wolfe.py b/nifty4/minimization/line_search_strong_wolfe.py
index 1a139892bc802c61f6893babc244edb28fd009e9..7987a6271a09f3e06aae74cc5d515e0cabbc56a8 100644
--- a/nifty4/minimization/line_search_strong_wolfe.py
+++ b/nifty4/minimization/line_search_strong_wolfe.py
@@ -209,8 +209,10 @@ class LineSearchStrongWolfe(LineSearch):
alpha_recent = None
phi_recent = None
- assert phi_lo <= phi_0 + self.c1*alpha_lo*phiprime_0
- assert phiprime_lo*(alpha_hi-alpha_lo) < 0.
+ if phi_lo > phi_0 + self.c1*alpha_lo*phiprime_0:
+ raise ValueError("inconsistent data")
+ if phiprime_lo*(alpha_hi-alpha_lo) >= 0.:
+ raise ValueError("inconsistent data")
for i in range(self.max_zoom_iterations):
# assert phi_lo <= phi_0 + self.c1*alpha_lo*phiprime_0
# assert phiprime_lo*(alpha_hi-alpha_lo)<0.
diff --git a/nifty4/operators/harmonic_transform_operator.py b/nifty4/operators/harmonic_transform_operator.py
index c7e368ccb5af9e43d378af94363658f506485f7e..d3d8cf29749f55b39c94ca51ff0afa4b25b84e41 100644
--- a/nifty4/operators/harmonic_transform_operator.py
+++ b/nifty4/operators/harmonic_transform_operator.py
@@ -149,8 +149,9 @@ class HarmonicTransformOperator(LinearOperator):
def _slice_p2h(self, inp):
rr = self.sjob.alm2map_adjoint(inp)
- assert len(rr) == ((self.mmax+1)*(self.mmax+2))//2 + \
- (self.mmax+1)*(self.lmax-self.mmax)
+ if len(rr) != ((self.mmax+1)*(self.mmax+2))//2 + \
+ (self.mmax+1)*(self.lmax-self.mmax):
+ raise ValueError("array length mismatch")
res = np.empty(2*len(rr)-self.lmax-1, dtype=rr[0].real.dtype)
res[0:self.lmax+1] = rr[0:self.lmax+1].real
res[self.lmax+1::2] = np.sqrt(2)*rr[self.lmax+1:].real
@@ -159,8 +160,9 @@ class HarmonicTransformOperator(LinearOperator):
def _slice_h2p(self, inp):
res = np.empty((len(inp)+self.lmax+1)//2, dtype=(inp[0]*1j).dtype)
- assert len(res) == ((self.mmax+1)*(self.mmax+2))//2 + \
- (self.mmax+1)*(self.lmax-self.mmax)
+ if len(res) != ((self.mmax+1)*(self.mmax+2))//2 + \
+ (self.mmax+1)*(self.lmax-self.mmax):
+ raise ValueError("array length mismatch")
res[0:self.lmax+1] = inp[0:self.lmax+1]
res[self.lmax+1:] = np.sqrt(0.5)*(inp[self.lmax+1::2] +
1j*inp[self.lmax+2::2])
diff --git a/nifty4/spaces/power_space.py b/nifty4/spaces/power_space.py
index f5b9d38a05aaa59a5f677f56890fd032264ce3c2..c4ec0b547dbce1325d0efe36d2d68f66c9e6c3ae 100644
--- a/nifty4/spaces/power_space.py
+++ b/nifty4/spaces/power_space.py
@@ -62,7 +62,8 @@ class PowerSpace(Space):
units of the harmonic partner space.
"""
nbin = int(nbin)
- assert nbin >= 3, "nbin must be at least 3"
+ if nbin < 3:
+ raise ValueError("nbin must be at least 3")
return np.linspace(float(first_bound), float(last_bound), nbin-1)
@staticmethod
@@ -81,7 +82,8 @@ class PowerSpace(Space):
units of the harmonic partner space.
"""
nbin = int(nbin)
- assert nbin >= 3, "nbin must be at least 3"
+ if nbin < 3:
+ raise ValueError("nbin must be at least 3")
return np.logspace(np.log(float(first_bound)),
np.log(float(last_bound)),
nbin-1, base=np.e)
@@ -107,7 +109,8 @@ class PowerSpace(Space):
nbin_max = int((dists[-1]-dists[0])/binsz_min)+2
if nbin is None:
nbin = nbin_max
- assert nbin >= 3, "nbin must be at least 3"
+ if nbin < 3:
+ raise ValueError("nbin must be at least 3")
if nbin > nbin_max:
raise ValueError("nbin is too large")
if logarithmic:
@@ -141,16 +144,19 @@ class PowerSpace(Space):
tbb = binbounds
locdat = np.searchsorted(tbb, dobj.local_data(k_length_array.val))
temp_pindex = dobj.from_local_data(
- k_length_array.val.shape, locdat, dobj.distaxis(k_length_array.val))
+ k_length_array.val.shape, locdat,
+ dobj.distaxis(k_length_array.val))
nbin = len(tbb)+1
temp_rho = np.bincount(dobj.local_data(temp_pindex).ravel(),
minlength=nbin)
temp_rho = dobj.np_allreduce_sum(temp_rho)
- assert not (temp_rho == 0).any(), "empty bins detected"
+ if (temp_rho == 0).any():
+ raise ValueError("empty bins detected")
# The explicit conversion to float64 is necessary because bincount
# sometimes returns its result as an integer array, even when
# floating-point weights are present ...
- temp_k_lengths = np.bincount(dobj.local_data(temp_pindex).ravel(),
+ temp_k_lengths = np.bincount(
+ dobj.local_data(temp_pindex).ravel(),
weights=dobj.local_data(k_length_array.val).ravel(),
minlength=nbin).astype(np.float64, copy=False)
temp_k_lengths = dobj.np_allreduce_sum(temp_k_lengths) / temp_rho
diff --git a/test/test_minimization/test_minimizers.py b/test/test_minimization/test_minimizers.py
index 30b54e33477f6a20541d331aeb2b60f0b61cba64..31460d29270c1da5677d465250a7f331e4d72e85 100644
--- a/test/test_minimization/test_minimizers.py
+++ b/test/test_minimization/test_minimizers.py
@@ -41,7 +41,7 @@ class Test_Minimizers(unittest.TestCase):
covariance = ift.DiagonalOperator(covariance_diagonal)
required_result = ift.Field.ones(space, dtype=np.float64)
- IC = ift.GradientNormController(verbose=True,tol_abs_gradnorm=1e-5, iteration_limit=1000)
+ IC = ift.GradientNormController(tol_abs_gradnorm=1e-5, iteration_limit=1000)
try:
minimizer = minimizer_class(controller=IC)
energy = ift.QuadraticEnergy(A=covariance, b=required_result,