From 8122ea7a6cf4b46b9abc43ec9b79ff0cf08237b3 Mon Sep 17 00:00:00 2001
From: Martin Reinecke <martin@mpa-garching.mpg.de>
Date: Tue, 28 Nov 2017 21:11:42 +0100
Subject: [PATCH] cleanup
---
demos/wiener_filter_via_curvature.py | 62 +++++++---
nifty/library/nonlinear_signal_curvature.py | 28 -----
nifty/library/nonlinear_signal_energy.py | 33 ++---
nifty/library/nonlinearities.py | 1 -
nifty/library/response_operators.py | 56 ---------
nifty/plotting/plot.py | 130 +++++++++++++++++---
setup.py | 6 +-
7 files changed, 175 insertions(+), 141 deletions(-)
delete mode 100644 nifty/library/nonlinear_signal_curvature.py
diff --git a/demos/wiener_filter_via_curvature.py b/demos/wiener_filter_via_curvature.py
index 7d4f41593..fad1d3f0c 100644
--- a/demos/wiener_filter_via_curvature.py
+++ b/demos/wiener_filter_via_curvature.py
@@ -1,9 +1,17 @@
+use_nifty2go = True
+
import numpy as np
-import nifty2go as ift
+if use_nifty2go:
+ import nifty2go as ift
+else:
+ import nifty as ift
import numericalunits as nu
if __name__ == "__main__":
# In MPI mode, the random seed for numericalunits must be set by hand
+ if not use_nifty2go:
+ ift.nifty_configuration['default_distribution_strategy'] = 'fftw'
+ ift.nifty_configuration['harmonic_rg_base'] = 'real'
nu.reset_units(43)
dimensionality = 2
np.random.seed(43)
@@ -32,11 +40,14 @@ if __name__ == "__main__":
# Total side-length of the domain
L = 2.*nu.m
# Grid resolution (pixels per axis)
- N_pixels = 512
+ N_pixels = 4096
shape = [N_pixels]*dimensionality
signal_space = ift.RGSpace(shape, distances=L/N_pixels)
- harmonic_space = signal_space.get_default_codomain()
+ if use_nifty2go:
+ harmonic_space = signal_space.get_default_codomain()
+ else:
+ harmonic_space = ift.FFTOperator.get_default_codomain(signal_space)
fft = ift.FFTOperator(harmonic_space, target=signal_space)
power_space = ift.PowerSpace(harmonic_space)
@@ -45,8 +56,12 @@ if __name__ == "__main__":
power_spectrum=power_spectrum)
np.random.seed(43)
- mock_power = ift.PS_field(power_space, power_spectrum)
- mock_harmonic = ift.power_synthesize(mock_power, real_signal=True)
+ if use_nifty2go:
+ mock_power = ift.PS_field(power_space, power_spectrum)
+ mock_harmonic = ift.power_synthesize(mock_power, real_signal=True)
+ else:
+ mock_power = ift.Field(power_space, val=power_spectrum)
+ mock_harmonic = mock_power.power_synthesize(real_signal=True)
mock_harmonic = mock_harmonic.real
mock_signal = fft(mock_harmonic)
@@ -54,11 +69,19 @@ if __name__ == "__main__":
R = ift.ResponseOperator(signal_space, sigma=(response_sigma,),
exposure=(exposure,))
data_domain = R.target[0]
- R_harmonic = ift.ComposedOperator([fft, R])
+ if use_nifty2go:
+ R_harmonic = ift.ComposedOperator([fft, R])
+ else:
+ R_harmonic = ift.ComposedOperator([fft, R], default_spaces=[0, 0])
+
+ if use_nifty2go:
+ N = ift.DiagonalOperator(
+ ift.Field.full(data_domain,
+ mock_signal.var()/signal_to_noise).weight(1))
+ else:
+ ndiag = ift.Field(data_domain, mock_signal.var()/signal_to_noise).weight(1)
+ N = ift.DiagonalOperator(data_domain, ndiag)
- N = ift.DiagonalOperator(
- ift.Field.full(data_domain,
- mock_signal.var()/signal_to_noise).weight(1))
noise = ift.Field.from_random(
domain=data_domain, random_type='normal',
std=mock_signal.std()/np.sqrt(signal_to_noise), mean=0)
@@ -67,12 +90,23 @@ if __name__ == "__main__":
# Wiener filter
j = R_harmonic.adjoint_times(N.inverse_times(data))
- ctrl = ift.GradientNormController(
- verbose=True, tol_abs_gradnorm=1e-4/nu.K/(nu.m**(0.5*dimensionality)))
- wiener_curvature = ift.library.WienerFilterCurvature(S=S, N=N,
- R=R_harmonic)
+ if use_nifty2go:
+ ctrl = ift.GradientNormController(
+ verbose=True, iteration_limit=10, tol_abs_gradnorm=1e-4/nu.K/(nu.m**(0.5*dimensionality)))
+ else:
+ def print_stats(a_energy, iteration): # returns current energy
+ x = a_energy.value
+ print(x, iteration)
+ ctrl = ift.GradientNormController(
+ callback=print_stats, iteration_limit=10, tol_abs_gradnorm=1e-4/nu.K/(nu.m**(0.5*dimensionality)))
+
inverter = ift.ConjugateGradient(controller=ctrl)
- wiener_curvature = ift.InversionEnabler(wiener_curvature, inverter)
+ if use_nifty2go:
+ wiener_curvature = ift.library.WienerFilterCurvature(S=S, N=N,
+ R=R_harmonic)
+ wiener_curvature = ift.InversionEnabler(wiener_curvature, inverter)
+ else:
+ wiener_curvature = ift.library.WienerFilterCurvature(S=S, N=N, R=R_harmonic, inverter=inverter)
m = wiener_curvature.inverse_times(j)
m_s = fft(m)
diff --git a/nifty/library/nonlinear_signal_curvature.py b/nifty/library/nonlinear_signal_curvature.py
deleted file mode 100644
index 29b3698be..000000000
--- a/nifty/library/nonlinear_signal_curvature.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from ..operators.endomorphic_operator import EndomorphicOperator
-
-
-class NonlinearSignalCurvature(EndomorphicOperator):
- def __init__(self, R, N, S, inverter=None):
- self.R = R
- self.N = N
- self.S = S
- # if preconditioner is None:
- # preconditioner = self.S.times
- self._domain = self.S.domain
- super(NonlinearSignalCurvature, self).__init__(inverter=inverter)
-
- @property
- def domain(self):
- return self._domain
-
- @property
- def self_adjoint(self):
- return True
-
- @property
- def unitary(self):
- return False
-
- # ---Added properties and methods---
- def _times(self, x, spaces):
- return self.R.adjoint_times(self.N.inverse_times(self.R(x))) + self.S.inverse_times(x)
diff --git a/nifty/library/nonlinear_signal_energy.py b/nifty/library/nonlinear_signal_energy.py
index a151abda6..9e6aed8e9 100644
--- a/nifty/library/nonlinear_signal_energy.py
+++ b/nifty/library/nonlinear_signal_energy.py
@@ -1,4 +1,4 @@
-from .nonlinear_signal_curvature import NonlinearSignalCurvature
+from .wiener_filter_curvature import WienerFilterCurvature
from .. import Field, exp
from ..utilities import memo
from ..sugar import generate_posterior_sample
@@ -8,33 +8,17 @@ from .response_operators import LinearizedSignalResponse
class NonlinearWienerFilterEnergy(Energy):
- """The Energy for the Gaussian lognormal case.
-
- It describes the situation of linear measurement of a
- lognormal signal with Gaussian noise and Gaussain signal prior.
-
- Parameters
- ----------
- d : Field,
- the data.
- R : Operator,
- The nonlinear response operator, describtion of the measurement process.
- N : EndomorphicOperator,
- The noise covariance in data space.
- S : EndomorphicOperator,
- The prior signal covariance in harmonic space.
- """
-
- def __init__(self, position, d, Instrument, nonlinearity, FFT, power, N, S, inverter=None):
+ def __init__(self, position, d, Instrument, nonlinearity, FFT, power, N, S,
+ inverter=None):
super(NonlinearWienerFilterEnergy, self).__init__(position=position)
- # print "init", position.norm()
self.d = d
self.Instrument = Instrument
self.nonlinearity = nonlinearity
self.FFT = FFT
self.power = power
- self.LinearizedResponse = LinearizedSignalResponse(Instrument, nonlinearity,
- FFT, power, self.position)
+ self.LinearizedResponse = \
+ LinearizedSignalResponse(Instrument, nonlinearity, FFT, power,
+ self.position)
position_map = FFT.adjoint_times(self.power * self.position)
# position_map = (Field(FFT.domain,val=position_map.val.real+0j))
@@ -68,7 +52,6 @@ class NonlinearWienerFilterEnergy(Energy):
@property
@memo
def curvature(self):
- curvature = NonlinearSignalCurvature(R=self.LinearizedResponse,
- N=self.N,
- S=self.S, inverter=self.inverter)
+ curvature = WienerFilterCurvature(R=self.LinearizedResponse,
+ N=self.N, S=self.S)
return InversionEnabler(curvature, inverter=self.inverter)
diff --git a/nifty/library/nonlinearities.py b/nifty/library/nonlinearities.py
index 3a1c78983..bd0e1cbc9 100644
--- a/nifty/library/nonlinearities.py
+++ b/nifty/library/nonlinearities.py
@@ -1,5 +1,4 @@
from numpy import logical_and, where
-
from ... import Field, exp, tanh
diff --git a/nifty/library/response_operators.py b/nifty/library/response_operators.py
index afcbebc05..dbf77fc1e 100644
--- a/nifty/library/response_operators.py
+++ b/nifty/library/response_operators.py
@@ -2,33 +2,6 @@ from .. import exp
from ..operators.linear_operator import LinearOperator
-class AdjointFFTResponse(LinearOperator):
- def __init__(self, FFT, R, default_spaces=None):
- super(AdjointFFTResponse, self).__init__(default_spaces)
- self._domain = FFT.target
- self._target = R.target
- self.R = R
- self.FFT = FFT
-
- def _times(self, x, spaces=None):
- return self.R(self.FFT.adjoint_times(x))
-
- def _adjoint_times(self, x, spaces=None):
- return self.FFT(self.R.adjoint_times(x))
-
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
- @property
- def unitary(self):
- return False
-
-
class LinearizedSignalResponse(LinearOperator):
def __init__(self, Instrument, nonlinearity, FFT, power, m, default_spaces=None):
super(LinearizedSignalResponse, self).__init__(default_spaces)
@@ -94,32 +67,3 @@ class LinearizedPowerResponse(LinearOperator):
@property
def unitary(self):
return False
-
-
-class SignalResponse(LinearOperator):
- def __init__(self, t, FFT, R, default_spaces=None):
- super(SignalResponse, self).__init__(default_spaces)
- self._domain = FFT.target
- self._target = R.target
- self.power = exp(t).power_synthesize(
- mean=1, std=0, real_signal=False)
- self.R = R
- self.FFT = FFT
-
- def _times(self, x, spaces=None):
- return self.R(self.FFT.adjoint_times(self.power * x))
-
- def _adjoint_times(self, x, spaces=None):
- return self.power * self.FFT(self.R.adjoint_times(x))
-
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
- @property
- def unitary(self):
- return False
diff --git a/nifty/plotting/plot.py b/nifty/plotting/plot.py
index d0a9921eb..3c3a16b0b 100644
--- a/nifty/plotting/plot.py
+++ b/nifty/plotting/plot.py
@@ -42,7 +42,7 @@ def _find_closest(A, target):
return idx
-def _makeplot(name):
+def _mpl_makeplot(name):
import matplotlib.pyplot as plt
if dobj.rank != 0:
return
@@ -70,7 +70,7 @@ def _makeplot(name):
raise ValueError("file format not understood")
-def _limit_xy(**kwargs):
+def _mpl_limit_xy(**kwargs):
import matplotlib.pyplot as plt
x1, x2, y1, y2 = plt.axis()
x1 = _get_kw("xmin", x1, **kwargs)
@@ -145,12 +145,11 @@ def _register_cmaps():
def _get_kw(kwname, kwdefault=None, **kwargs):
- if kwargs.get(kwname) is not None:
- return kwargs.get(kwname)
- return kwdefault
+ res = kwargs.get(kwname)
+ return kwdefault if res is None else res
-def plot(f, **kwargs):
+def _mpl_plot(f, **kwargs):
import matplotlib.pyplot as plt
_register_cmaps()
if not isinstance(f, Field):
@@ -176,8 +175,8 @@ def plot(f, **kwargs):
xcoord = np.arange(npoints, dtype=np.float64)*dist
ycoord = dobj.to_global_data(f.val)
plt.plot(xcoord, ycoord)
- _limit_xy(**kwargs)
- _makeplot(kwargs.get("name"))
+ _mpl_limit_xy(**kwargs)
+ _mpl_makeplot(kwargs.get("name"))
return
elif len(dom.shape) == 2:
nx = dom.shape[0]
@@ -195,8 +194,8 @@ def plot(f, **kwargs):
# cax = divider.append_axes("right", size="5%", pad=0.05)
# plt.colorbar(im,cax=cax)
plt.colorbar(im)
- _limit_xy(**kwargs)
- _makeplot(kwargs.get("name"))
+ _mpl_limit_xy(**kwargs)
+ _mpl_makeplot(kwargs.get("name"))
return
elif isinstance(dom, PowerSpace):
xcoord = dom.k_lengths
@@ -205,8 +204,8 @@ def plot(f, **kwargs):
plt.yscale('log')
plt.title('power')
plt.plot(xcoord, ycoord)
- _limit_xy(**kwargs)
- _makeplot(kwargs.get("name"))
+ _mpl_limit_xy(**kwargs)
+ _mpl_makeplot(kwargs.get("name"))
return
elif isinstance(dom, HPSpace):
import pyHealpix
@@ -222,7 +221,7 @@ def plot(f, **kwargs):
plt.imshow(res, vmin=kwargs.get("zmin"), vmax=kwargs.get("zmax"),
cmap=cmap, origin="lower")
plt.colorbar(orientation="horizontal")
- _makeplot(kwargs.get("name"))
+ _mpl_makeplot(kwargs.get("name"))
return
elif isinstance(dom, GLSpace):
import pyHealpix
@@ -239,7 +238,110 @@ def plot(f, **kwargs):
plt.imshow(res, vmin=kwargs.get("zmin"), vmax=kwargs.get("zmax"),
cmap=cmap, origin="lower")
plt.colorbar(orientation="horizontal")
- _makeplot(kwargs.get("name"))
+ _mpl_makeplot(kwargs.get("name"))
return
raise ValueError("Field type not(yet) supported")
+
+
+def _plotly_plot(f, **kwargs):
+ if not isinstance(f, Field):
+ raise TypeError("incorrect data type")
+ if len(f.domain) != 1:
+ raise ValueError("input field must have exactly one domain")
+
+ dom = f.domain[0]
+ fig = plt.figure()
+ ax = fig.add_subplot(1, 1, 1)
+
+ xsize = _get_kw("xsize", 6, **kwargs)
+ ysize = _get_kw("ysize", 6, **kwargs)
+ fig.set_size_inches(xsize, ysize)
+ ax.set_title(_get_kw("title", "", **kwargs))
+ ax.set_xlabel(_get_kw("xlabel", "", **kwargs))
+ ax.set_ylabel(_get_kw("ylabel", "", **kwargs))
+ cmap = _get_kw("colormap", plt.rcParams['image.cmap'], **kwargs)
+ if isinstance(dom, RGSpace):
+ if len(dom.shape) == 1:
+ npoints = dom.shape[0]
+ dist = dom.distances[0]
+ xcoord = np.arange(npoints, dtype=np.float64)*dist
+ ycoord = dobj.to_global_data(f.val)
+ plt.plot(xcoord, ycoord)
+ _mpl_limit_xy(**kwargs)
+ _mpl_makeplot(kwargs.get("name"))
+ return
+ elif len(dom.shape) == 2:
+ nx = dom.shape[0]
+ ny = dom.shape[1]
+ dx = dom.distances[0]
+ dy = dom.distances[1]
+ xc = np.arange(nx, dtype=np.float64)*dx
+ yc = np.arange(ny, dtype=np.float64)*dy
+ im = ax.imshow(dobj.to_global_data(f.val),
+ extent=[xc[0], xc[-1], yc[0], yc[-1]],
+ vmin=kwargs.get("zmin"),
+ vmax=kwargs.get("zmax"), cmap=cmap, origin="lower")
+ # from mpl_toolkits.axes_grid1 import make_axes_locatable
+ # divider = make_axes_locatable(ax)
+ # cax = divider.append_axes("right", size="5%", pad=0.05)
+ # plt.colorbar(im,cax=cax)
+ plt.colorbar(im)
+ _mpl_limit_xy(**kwargs)
+ _mpl_makeplot(kwargs.get("name"))
+ return
+ elif isinstance(dom, PowerSpace):
+ xcoord = dom.k_lengths
+ ycoord = dobj.to_global_data(f.val)
+ plt.xscale('log')
+ plt.yscale('log')
+ plt.title('power')
+ plt.plot(xcoord, ycoord)
+ _mpl_limit_xy(**kwargs)
+ _mpl_makeplot(kwargs.get("name"))
+ return
+ elif isinstance(dom, HPSpace):
+ import pyHealpix
+ xsize = 800
+ res, mask, theta, phi = _mollweide_helper(xsize)
+
+ ptg = np.empty((phi.size, 2), dtype=np.float64)
+ ptg[:, 0] = theta
+ ptg[:, 1] = phi
+ base = pyHealpix.Healpix_Base(int(np.sqrt(f.val.size//12)), "RING")
+ res[mask] = dobj.to_global_data(f.val)[base.ang2pix(ptg)]
+ plt.axis('off')
+ plt.imshow(res, vmin=kwargs.get("zmin"), vmax=kwargs.get("zmax"),
+ cmap=cmap, origin="lower")
+ plt.colorbar(orientation="horizontal")
+ _mpl_makeplot(kwargs.get("name"))
+ return
+ elif isinstance(dom, GLSpace):
+ import pyHealpix
+ xsize = 800
+ res, mask, theta, phi = _mollweide_helper(xsize)
+ ra = np.linspace(0, 2*np.pi, dom.nlon+1)
+ dec = pyHealpix.GL_thetas(dom.nlat)
+ ilat = _find_closest(dec, theta)
+ ilon = _find_closest(ra, phi)
+ ilon = np.where(ilon == dom.nlon, 0, ilon)
+ res[mask] = dobj.to_global_data(f.val)[ilat*dom.nlon + ilon]
+
+ plt.axis('off')
+ plt.imshow(res, vmin=kwargs.get("zmin"), vmax=kwargs.get("zmax"),
+ cmap=cmap, origin="lower")
+ plt.colorbar(orientation="horizontal")
+ _mpl_makeplot(kwargs.get("name"))
+ return
+
+ raise ValueError("Field type not(yet) supported")
+
+
+def plot(f, **kwargs):
+ extension = os.path.splitext(kwargs.get("name"))[1]
+ if extension in [".html"]:
+ _plotly_plot(f, **kwargs)
+ elif extension in [".pdf", ".png"]:
+ _mpl_plot(f, **kwargs)
+ else:
+ raise ValueError("unknown file name extension: " + extension)
diff --git a/setup.py b/setup.py
index 4ddc06ca0..5fa9d4fc4 100644
--- a/setup.py
+++ b/setup.py
@@ -30,10 +30,10 @@ setup(name="nifty2go",
packages=["nifty2go"] + ["nifty2go."+p for p in find_packages("nifty")],
zip_safe=False,
dependency_links=[
- 'git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git@setuptools_test#egg=pyHealpix-0.0.1'],
+ 'git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git#egg=pyHealpix-0.0.1'],
license="GPLv3",
- setup_requires=['future', 'pyHealpix>=0.0.1', 'numpy', 'pyfftw>=0.10.4'],
- install_requires=['future', 'pyHealpix>=0.0.1', 'numpy', 'pyfftw>=0.10.4'],
+ setup_requires=['future', 'numpy'],
+ install_requires=['future', 'numpy'],
classifiers=[
"Development Status :: 4 - Beta",
"Topic :: Utilities",
--
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