Merge branch 'new_gridder' into 'NIFTy_5'

New gridder (again!)

See merge request !324

Dockerfile

@@ -10,11 +10,11 @@ RUN apt-get update && apt-get install -y \
     # Testing dependencies
     python3-pytest-cov jupyter \
     # Optional NIFTy dependencies
-    libfftw3-dev python3-mpi4py python3-matplotlib \
+    python3-mpi4py python3-matplotlib \
   # more optional NIFTy dependencies
-  && pip3 install pyfftw \
   && pip3 install git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git \
   && pip3 install git+https://gitlab.mpcdf.mpg.de/ift/nifty_gridder.git \
+  && pip3 install git+https://gitlab.mpcdf.mpg.de/mtr/pypocketfft.git \
   && pip3 install jupyter \
   && rm -rf /var/lib/apt/lists/*
 

README.md

@@ -47,9 +47,9 @@ Installation
 
 - [Python 3](https://www.python.org/) (3.5.x or later)
 - [SciPy](https://www.scipy.org/)
+- [pypocketfft](https://gitlab.mpcdf.mpg.de/mtr/pypocketfft)
 
 Optional dependencies:
-- [pyFFTW](https://pypi.python.org/pypi/pyFFTW) for faster Fourier transforms
 - [pyHealpix](https://gitlab.mpcdf.mpg.de/ift/pyHealpix) (for harmonic
     transforms involving domains on the sphere)
 - [nifty_gridder](https://gitlab.mpcdf.mpg.de/ift/nifty_gridder) (for radio
@@ -73,28 +73,12 @@ NIFTy5 and its mandatory dependencies can be installed via:
 
     sudo apt-get install git python3 python3-pip python3-dev
     pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/nifty.git@NIFTy_5
+    pip3 install --user git+https://gitlab.mpcdf.mpg.de/mtr/pypocketfft
 
 Plotting support is added via:
 
     sudo apt-get install python3-matplotlib
 
-NIFTy uses Numpy's FFT implementation by default. For large problems FFTW may be
-used because of its higher performance. It can be installed via:
-
-    sudo apt-get install libfftw3-dev
-    pip3 install --user pyfftw
-
-To enable FFTW usage in NIFTy, call
-
-    nifty5.fft.enable_fftw()
-
-at the beginning of your code.
-
-(Note: If you encounter problems related to `pyFFTW`, make sure that you are
-using a pip-installed `pyFFTW` package. Unfortunately, some distributions are
-shipping an incorrectly configured `pyFFTW` package, which does not cooperate
-with the installed `FFTW3` libraries.)
-
 Support for spherical harmonic transforms is added via:
 
     pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git

demos/bench_gridder.py

@@ -5,7 +5,6 @@ import numpy as np
 
 import nifty5 as ift
 
-ift.fft.enable_fftw()
 np.random.seed(40)
 
 N0s, a0s, b0s, c0s = [], [], [], []

docs/source/installation.rst

@@ -9,28 +9,12 @@ NIFTy5 and its mandatory dependencies can be installed via::
 
     sudo apt-get install git python3 python3-pip python3-dev
     pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/nifty.git@NIFTy_5
+    pip3 install --user git+https://gitlab.mpcdf.mpg.de/mtr/pypocketfft
 
 Plotting support is added via::
 
     sudo apt-get install python3-matplotlib
 
-NIFTy uses Numpy's FFT implementation by default. For large problems FFTW may be
-used because of its higher performance. It can be installed via::
-
-    sudo apt-get install libfftw3-dev
-    pip3 install --user pyfftw
-
-To enable FFTW usage in NIFTy, call::
-
-    nifty5.fft.enable_fftw()
-
-at the beginning of your code.
-
-(Note: If you encounter problems related to `pyFFTW`, make sure that you are
-using a pip-installed `pyFFTW` package. Unfortunately, some distributions are
-shipping an incorrectly configured `pyFFTW` package, which does not cooperate
-with the installed `FFTW3` libraries.)
-
 Support for spherical harmonic transforms is added via::
 
     pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git

nifty5/extra.py

@@ -17,8 +17,10 @@
 
 import numpy as np
 
+from .domain_tuple import DomainTuple
 from .field import Field
 from .linearization import Linearization
+from .multi_domain import MultiDomain
 from .operators.linear_operator import LinearOperator
 from .sugar import from_random
 
@@ -70,12 +72,20 @@ def _full_implementation(op, domain_dtype, target_dtype, atol, rtol,
 def _check_linearity(op, domain_dtype, atol, rtol):
     fld1 = from_random("normal", op.domain, dtype=domain_dtype)
     fld2 = from_random("normal", op.domain, dtype=domain_dtype)
-    alpha = np.random.random()
+    alpha = np.random.random()  # FIXME: this can break badly with MPI!
     val1 = op(alpha*fld1+fld2)
     val2 = alpha*op(fld1)+op(fld2)
     _assert_allclose(val1, val2, atol=atol, rtol=rtol)
 
 
+def _domain_check(op):
+    for dd in [op.domain, op.target]:
+        if not isinstance(dd, (DomainTuple, MultiDomain)):
+            raise TypeError(
+                'The domain and the target of an operator need to',
+                'be instances of either DomainTuple or MultiDomain.')
+
+
 def consistency_check(op, domain_dtype=np.float64, target_dtype=np.float64,
                       atol=0, rtol=1e-7, only_r_linear=False):
     """
@@ -109,6 +119,7 @@ def consistency_check(op, domain_dtype=np.float64, target_dtype=np.float64,
     """
     if not isinstance(op, LinearOperator):
         raise TypeError('This test tests only linear operators.')
+    _domain_check(op)
     _check_linearity(op, domain_dtype, atol, rtol)
     _full_implementation(op, domain_dtype, target_dtype, atol, rtol,
                          only_r_linear)
@@ -162,6 +173,7 @@ def check_jacobian_consistency(op, loc, tol=1e-8, ntries=100):
     tol : float
         Tolerance for the check.
     """
+    _domain_check(op)
     for _ in range(ntries):
         lin = op(Linearization.make_var(loc))
         loc2, lin2 = _get_acceptable_location(op, loc, lin)

nifty5/fft.py

@@ -17,107 +17,52 @@
 
 from .utilities import iscomplextype
 import numpy as np
+import pypocketfft
 
+_nthreads = 1
 
-_use_fftw = False
-_fftw_prepped = False
-_fft_extra_args = {}
 
+def nthreads():
+    return _nthreads
 
-def enable_fftw():
-    global _use_fftw
-    _use_fftw = True
 
+def set_nthreads(nthr):
+    global _nthreads
+    _nthreads = nthr
 
-def disable_fftw():
-    global _use_fftw
-    _use_fftw = False
 
-
-def _init_pyfftw():
-    global _fft_extra_args, _fftw_prepped
-    if not _fftw_prepped:
-        import pyfftw
-        from pyfftw.interfaces.numpy_fft import fftn, rfftn, ifftn
-        pyfftw.interfaces.cache.enable()
-        pyfftw.interfaces.cache.set_keepalive_time(1000.)
-        # Optional extra arguments for the FFT calls
-        # if exact reproducibility is needed,
-        # set "planner_effort" to "FFTW_ESTIMATE"
-        import os
-        nthreads = int(os.getenv("OMP_NUM_THREADS", "1"))
-        _fft_extra_args = dict(planner_effort='FFTW_ESTIMATE',
-                               threads=nthreads)
-        _fftw_prepped = True
+# FIXME this should not be necessary ... no one should call a complex FFT
+# with a float array.
+def _make_complex(a):
+    if a.dtype in (np.complex64, np.complex128):
+        return a
+    if a.dtype == np.float64:
+        return a.astype(np.complex128)
+    if a.dtype == np.float32:
+        return a.astype(np.complex64)
+    raise NotImplementedError
 
 
 def fftn(a, axes=None):
-    if _use_fftw:
-        from pyfftw.interfaces.numpy_fft import fftn
-        _init_pyfftw()
-        return fftn(a, axes=axes, **_fft_extra_args)
-    else:
-        return np.fft.fftn(a, axes=axes)
+    return pypocketfft.fftn(_make_complex(a), axes=axes, nthreads=_nthreads)
 
 
 def rfftn(a, axes=None):
-    if _use_fftw:
-        from pyfftw.interfaces.numpy_fft import rfftn
-        _init_pyfftw()
-        return rfftn(a, axes=axes, **_fft_extra_args)
-    else:
-        return np.fft.rfftn(a, axes=axes)
+    return pypocketfft.rfftn(a, axes=axes, nthreads=_nthreads)
 
 
 def ifftn(a, axes=None):
-    if _use_fftw:
-        from pyfftw.interfaces.numpy_fft import ifftn
-        _init_pyfftw()
-        return ifftn(a, axes=axes, **_fft_extra_args)
+    # FIXME this is a temporary fix and can be done more elegantly
+    if axes is None:
+        fct = 1./a.size
     else:
-        return np.fft.ifftn(a, axes=axes)
+        fct = 1./np.prod(np.take(a.shape, axes))
+    return pypocketfft.ifftn(_make_complex(a), axes=axes, fct=fct,
+                             nthreads=_nthreads)
 
 
 def hartley(a, axes=None):
-    # Check if the axes provided are valid given the shape
-    if axes is not None and \
-            not all(axis < len(a.shape) for axis in axes):
-        raise ValueError("Provided axes do not match array shape")
-    if iscomplextype(a.dtype):
-        raise TypeError("Hartley transform requires real-valued arrays.")
-
-    tmp = rfftn(a, axes=axes)
-
-    def _fill_array(tmp, res, axes):
-        if axes is None:
-            axes = tuple(range(tmp.ndim))
-        lastaxis = axes[-1]
-        ntmplast = tmp.shape[lastaxis]
-        slice1 = (slice(None),)*lastaxis + (slice(0, ntmplast),)
-        np.add(tmp.real, tmp.imag, out=res[slice1])
-
-        def _fill_upper_half(tmp, res, axes):
-            lastaxis = axes[-1]
-            nlast = res.shape[lastaxis]
-            ntmplast = tmp.shape[lastaxis]
-            nrem = nlast - ntmplast
-            slice1 = [slice(None)]*lastaxis + [slice(ntmplast, None)]
-            slice2 = [slice(None)]*lastaxis + [slice(nrem, 0, -1)]
-            for i in axes[:-1]:
-                slice1[i] = slice(1, None)
-                slice2[i] = slice(None, 0, -1)
-            slice1 = tuple(slice1)
-            slice2 = tuple(slice2)
-            np.subtract(tmp[slice2].real, tmp[slice2].imag, out=res[slice1])
-            for i, ax in enumerate(axes[:-1]):
-                dim1 = (slice(None),)*ax + (slice(0, 1),)
-                axes2 = axes[:i] + axes[i+1:]
-                _fill_upper_half(tmp[dim1], res[dim1], axes2)
-
-        _fill_upper_half(tmp, res, axes)
-        return res
-
-    return _fill_array(tmp, np.empty_like(a), axes)
+    return pypocketfft.hartley2(a, axes=axes, nthreads=_nthreads)
 
 
 # Do a real-to-complex forward FFT and return the _full_ output array

nifty5/library/gridder.py

@@ -26,7 +26,8 @@ from ..sugar import from_global_data, makeDomain
 
 
 class GridderMaker(object):
-    def __init__(self, domain, eps=1e-15):
+    def __init__(self, domain, eps=2e-13):
+        from nifty_gridder import get_w
         domain = makeDomain(domain)
         if (len(domain) != 1 or not isinstance(domain[0], RGSpace) or
                 not len(domain.shape) == 2):
@@ -34,20 +35,17 @@ class GridderMaker(object):
         nu, nv = domain.shape
         if nu % 2 != 0 or nv % 2 != 0:
             raise ValueError("dimensions must be even")
-        rat = 3 if eps < 1e-11 else 2
-        nu2, nv2 = rat*nu, rat*nv
-
-        nspread = int(-np.log(eps)/(np.pi*(rat-1)/(rat-.5)) + .5) + 1
-        nu2 = max([nu2, 2*nspread])
-        nv2 = max([nv2, 2*nspread])
-        r2lamb = rat*rat*nspread/(rat*(rat-.5))
+        nu2, nv2 = 2*nu, 2*nv
+        w = get_w(eps)
+        nsafe = (w+1)//2
+        nu2 = max([nu2, 2*nsafe])
+        nv2 = max([nv2, 2*nsafe])
 
         oversampled_domain = RGSpace(
             [nu2, nv2], distances=[1, 1], harmonic=False)
 
-        self._nspread = nspread
-        self._r2lamb = r2lamb
-        self._rest = _RestOperator(domain, oversampled_domain, r2lamb)
+        self._eps = eps
+        self._rest = _RestOperator(domain, oversampled_domain, eps)
 
     def getReordering(self, uv):
         from nifty_gridder import peanoindex
@@ -55,7 +53,7 @@ class GridderMaker(object):
         return peanoindex(uv, nu2, nv2)
 
     def getGridder(self, uv):
-        return RadioGridder(self._rest.domain, self._nspread, self._r2lamb, uv)
+        return RadioGridder(self._rest.domain, self._eps, uv)
 
     def getRest(self):
         return self._rest
@@ -65,22 +63,22 @@ class GridderMaker(object):
 
 
 class _RestOperator(LinearOperator):
-    def __init__(self, domain, oversampled_domain, r2lamb):
+    def __init__(self, domain, oversampled_domain, eps):
+        from nifty_gridder import correction_factors
         self._domain = makeDomain(oversampled_domain)
         self._target = domain
         nu, nv = domain.shape
         nu2, nv2 = oversampled_domain.shape
 
+        fu = correction_factors(nu2, nu//2+1, eps)
+        fv = correction_factors(nv2, nv//2+1, eps)
         # compute deconvolution operator
         rng = np.arange(nu)
         k = np.minimum(rng, nu-rng)
-        c = np.pi*r2lamb/nu2**2
-        self._deconv_u = np.roll(np.exp(c*k**2), -nu//2).reshape((-1, 1))
+        self._deconv_u = np.roll(fu[k], -nu//2).reshape((-1, 1))
         rng = np.arange(nv)
         k = np.minimum(rng, nv-rng)
-        c = np.pi*r2lamb/nv2**2
-        self._deconv_v = np.roll(
-            np.exp(c*k**2)/r2lamb, -nv//2).reshape((1, -1))
+        self._deconv_v = np.roll(fv[k], -nv//2).reshape((1, -1))
         self._capability = self.TIMES | self.ADJOINT_TIMES
 
     def apply(self, x, mode):
@@ -105,24 +103,20 @@ class _RestOperator(LinearOperator):
 
 
 class RadioGridder(LinearOperator):
-    def __init__(self, target, nspread, r2lamb, uv):
+    def __init__(self, target, eps, uv):
         self._domain = DomainTuple.make(
             UnstructuredDomain((uv.shape[0],)))
         self._target = DomainTuple.make(target)
         self._capability = self.TIMES | self.ADJOINT_TIMES
-        self._nspread, self._r2lamb = int(nspread), float(r2lamb)
+        self._eps = float(eps)
         self._uv = uv  # FIXME: should we write-protect this?
 
     def apply(self, x, mode):
-        from nifty_gridder import (to_grid, to_grid_post,
-                                   from_grid, from_grid_pre)
+        from nifty_gridder import to_grid, from_grid
         self._check_input(x, mode)
-        nu2, nv2 = self._target.shape
-        x = x.to_global_data()
         if mode == self.TIMES:
-            res = to_grid(self._uv, x, nu2, nv2, self._nspread, self._r2lamb)
-            res = to_grid_post(res)
+            nu2, nv2 = self._target.shape
+            res = to_grid(self._uv, x.to_global_data(), nu2, nv2, self._eps)
         else:
-            x = from_grid_pre(x)
-            res = from_grid(self._uv, x, nu2, nv2, self._nspread, self._r2lamb)
+            res = from_grid(self._uv, x.to_global_data(), self._eps)
         return from_global_data(self._tgt(mode), res)

nifty5/operators/diagonal_operator.py

@@ -158,7 +158,7 @@ class DiagonalOperator(EndomorphicOperator):
     def process_sample(self, samp, from_inverse):
         if (self._complex or (self._diagmin < 0.) or
                 (self._diagmin == 0. and from_inverse)):
-                    raise ValueError("operator not positive definite")
+            raise ValueError("operator not positive definite")
         if from_inverse:
             res = samp.local_data/np.sqrt(self._ldiag)
         else:

nifty5/operators/scaling_operator.py

@@ -87,7 +87,7 @@ class ScalingOperator(EndomorphicOperator):
         fct = self._factor
         if (fct.imag != 0. or fct.real < 0. or
                 (fct.real == 0. and from_inverse)):
-                    raise ValueError("operator not positive definite")
+            raise ValueError("operator not positive definite")
         return 1./np.sqrt(fct) if from_inverse else np.sqrt(fct)
 
 #     def process_sample(self, samp, from_inverse):

nifty5/plot.py

@@ -349,11 +349,10 @@ def _plot2D(f, ax, **kwargs):
         rgb = _rgb_data(f.to_global_data())
         have_rgb = True
 
-    label = kwargs.pop("label", None)
-
     foo = kwargs.pop("norm", None)
     norm = {} if foo is None else {'norm': foo}
-    aspect = kwargs.pop("aspect", None)
+
+    foo = kwargs.pop("aspect", None)
     aspect = {} if foo is None else {'aspect': foo}
 
     ax.set_title(kwargs.pop("title", ""))
@@ -424,7 +423,7 @@ def _plot(f, ax, **kwargs):
     if len(f) == 0:
         raise ValueError("need something to plot")
     if not isinstance(f[0], Field):
-            raise TypeError("incorrect data type")
+        raise TypeError("incorrect data type")
     dom1 = f[0].domain
     if (len(dom1) == 1 and
         (isinstance(dom1[0], PowerSpace) or

test/test_operators/test_adjoint.py

@@ -37,6 +37,8 @@ _pow_spaces = [ift.PowerSpace(ift.RGSpace((17, 38), harmonic=True))]
 pmp = pytest.mark.parametrize
 dtype = list2fixture([np.float64, np.complex128])
 
+np.random.seed(42)
+
 
 @pmp('sp', _p_RG_spaces)
 def testLOSResponse(sp, dtype):
@@ -75,14 +77,14 @@ def testLinearInterpolator():
 def testRealizer(sp):
     op = ift.Realizer(sp)
     ift.extra.consistency_check(op, np.complex128, np.float64,
-    only_r_linear=True)
+                                only_r_linear=True)
 
 
 @pmp('sp', _h_spaces + _p_spaces + _pow_spaces)
 def testConjugationOperator(sp):
     op = ift.ConjugationOperator(sp)
     ift.extra.consistency_check(op, np.complex128, np.complex128,
-    only_r_linear=True)
+                                only_r_linear=True)
 
 
 @pmp('args', [(ift.RGSpace(10, harmonic=True), 4, 0), (ift.RGSpace(

test/test_operators/test_fft_operator.py

@@ -34,22 +34,10 @@ def _get_rtol(tp):
 pmp = pytest.mark.parametrize
 dtype = list2fixture([np.float64, np.float32, np.complex64, np.complex128])
 op = list2fixture([ift.HartleyOperator, ift.FFTOperator])
-fftw = list2fixture([False, True])
-
-
-def test_switch():
-    ift.fft.enable_fftw()
-    assert_(ift.fft._use_fftw is True)
-    ift.fft.disable_fftw()
-    assert_(ift.fft._use_fftw is False)
-    ift.fft.enable_fftw()
-    assert_(ift.fft._use_fftw is True)
 
 
 @pmp('d', [0.1, 1, 3.7])
-def test_fft1D(d, dtype, op, fftw):
-    if fftw:
-        ift.fft.enable_fftw()
+def test_fft1D(d, dtype, op):
     dim1 = 16
     tol = _get_rtol(dtype)
     a = ift.RGSpace(dim1, distances=d)
@@ -69,16 +57,16 @@ def test_fft1D(d, dtype, op, fftw):
         domain=a, random_type='normal', std=7, mean=3, dtype=dtype)
     out = fft.inverse_times(fft.times(inp))
     assert_allclose(inp.local_data, out.local_data, rtol=tol, atol=tol)
-    ift.fft.disable_fftw()
 
 
 @pmp('dim1', [12, 15])
 @pmp('dim2', [9, 12])
 @pmp('d1', [0.1, 1, 3.7])
 @pmp('d2', [0.4, 1, 2.7])
-def test_fft2D(dim1, dim2, d1, d2, dtype, op, fftw):
-    if fftw:
-        ift.fft.enable_fftw()
+@pmp('nthreads', [0, 1, 2, 3, 4])
+def test_fft2D(dim1, dim2, d1, d2, dtype, op, nthreads):
+    ift.fft.set_nthreads(nthreads)
+    assert_(ift.fft.nthreads() == nthreads)
     tol = _get_rtol(dtype)
     a = ift.RGSpace([dim1, dim2], distances=[d1, d2])
     b = ift.RGSpace(
@@ -97,13 +85,11 @@ def test_fft2D(dim1, dim2, d1, d2, dtype, op, fftw):
         domain=a, random_type='normal', std=7, mean=3, dtype=dtype)
     out = fft.inverse_times(fft.times(inp))
     assert_allclose(inp.local_data, out.local_data, rtol=tol, atol=tol)
-    ift.fft.disable_fftw()
+    ift.fft.set_nthreads(1)
 
 
 @pmp('index', [0, 1, 2])
-def test_composed_fft(index, dtype, op, fftw):
-    if fftw:
-        ift.fft.enable_fftw()
+def test_composed_fft(index, dtype, op):
     tol = _get_rtol(dtype)
     a = [a1, a2,
          a3] = [ift.RGSpace((32,)),
@@ -115,7 +101,6 @@ def test_composed_fft(index, dtype, op, fftw):
         domain=(a1, a2, a3), random_type='normal', std=7, mean=3, dtype=dtype)
     out = fft.inverse_times(fft.times(inp))
     assert_allclose(inp.local_data, out.local_data, rtol=tol, atol=tol)
-    ift.fft.disable_fftw()
 
 
 @pmp('space', [
@@ -123,9 +108,7 @@ def test_composed_fft(index, dtype, op, fftw):
     ift.RGSpace((15, 27), distances=(.7, .33), harmonic=True),
     ift.RGSpace(73, distances=0.5643)
 ])
-def test_normalisation(space, dtype, op, fftw):
-    if fftw:
-        ift.fft.enable_fftw()
+def test_normalisation(space, dtype, op):
     tol = 10*_get_rtol(dtype)
     cospace = space.get_default_codomain()
     fft = op(space, cospace)
@@ -138,4 +121,3 @@ def test_normalisation(space, dtype, op, fftw):
     assert_allclose(
         inp.to_global_data()[zero_idx], out.integrate(), rtol=tol, atol=tol)
     assert_allclose(out.local_data, out2.local_data, rtol=tol, atol=tol)
-    ift.fft.disable_fftw()

test/test_operators/test_nft.py

@@ -17,7 +17,7 @@
 
 import numpy as np
 import pytest
-from numpy.testing import assert_allclose
+from numpy.testing import assert_allclose, assert_
 
 import nifty5 as ift
 
@@ -26,15 +26,20 @@ np.random.seed(40)
 pmp = pytest.mark.parametrize
 
 
+def _l2error(a, b):
+    return np.sqrt(np.sum(np.abs(a-b)**2)/np.sum(np.abs(a)**2))
+
+
+@pmp('eps', [1e-2, 1e-4, 1e-7, 1e-10, 1e-11, 1e-12, 2e-13])
 @pmp('nu', [12, 128])
 @pmp('nv', [4, 12, 128])
 @pmp('N', [1, 10, 100])
-def test_gridding(nu, nv, N):
+def test_gridding(nu, nv, N, eps):
     uv = np.random.rand(N, 2) - 0.5
     vis = np.random.randn(N) + 1j*np.random.randn(N)
 
     # Nifty
-    GM = ift.GridderMaker(ift.RGSpace((nu, nv)))
+    GM = ift.GridderMaker(ift.RGSpace((nu, nv)), eps=eps)
     # re-order for performance
     idx = GM.getReordering(uv)
     uv, vis = uv[idx], vis[idx]
@@ -48,17 +53,17 @@ def test_gridding(nu, nv, N):
     dft = pynu*0.
     for i in range(N):
         dft += (vis[i]*np.exp(2j*np.pi*(x*uv[i, 0] + y*uv[i, 1]))).real
-    assert_allclose(dft, pynu)
+    assert_(_l2error(dft, pynu) < eps)
 
 
-@pmp('eps', [1e-2, 1e-6, 1e-15])
+@pmp('eps', [1e-2, 1e-6, 2e-13])
 @pmp('nu', [12, 128])
 @pmp('nv', [4, 12, 128])
 @pmp('N', [1, 10, 100])
 def test_build(nu, nv, N, eps):
     dom = ift.RGSpace([nu, nv])
     uv = np.random.rand(N, 2) - 0.5
-    GM = ift.GridderMaker(dom)
+    GM = ift.GridderMaker(dom, eps=eps)
     # re-order for performance
     idx = GM.getReordering(uv)
     uv = uv[idx]

test/test_operators/test_simplification.py

@@ -23,23 +23,23 @@ import nifty5 as ift
 
 def test_simplification():
     from nifty5.operators.operator import _ConstantOperator
-    f1 = ift.Field.full(ift.RGSpace(10),2.)
+    f1 = ift.Field.full(ift.RGSpace(10), 2.)
     op = ift.FFTOperator(f1.domain)
     _, op2 = op.simplify_for_constant_input(f1)
     assert_equal(isinstance(op2, _ConstantOperator), True)
     assert_allclose(op(f1).local_data, op2(f1).local_data)