diff --git a/demos/Wiener_Filter.ipynb b/demos/Wiener_Filter.ipynb
index 26f5e402851e21ab9fc4e2bab9fe5a88f9a3e730..1754210855c62bbedd755e0db45f734f626cab0e 100644
--- a/demos/Wiener_Filter.ipynb
+++ b/demos/Wiener_Filter.ipynb
@@ -427,12 +427,12 @@
"\n",
"mask = np.full(s_space.shape, 1.)\n",
"mask[l:h] = 0\n",
- "mask = ift.Field.from_arr(s_space, mask)\n",
+ "mask = ift.Field.from_raw(s_space, mask)\n",
"\n",
"R = ift.DiagonalOperator(mask)(HT)\n",
- "n = n.val.copy()\n",
+ "n = n.val_rw()\n",
"n[l:h] = 0\n",
- "n = ift.Field.from_arr(s_space, n)\n",
+ "n = ift.Field.from_raw(s_space, n)\n",
"\n",
"d = R(sh) + n"
]
@@ -501,7 +501,7 @@
"m_data = HT(m).val\n",
"m_var_data = m_var.val\n",
"uncertainty = np.sqrt(m_var_data)\n",
- "d_data = d.val.copy()\n",
+ "d_data = d.val_rw()\n",
"\n",
"# Set lost data to NaN for proper plotting\n",
"d_data[d_data == 0] = np.nan"
@@ -583,12 +583,12 @@
"\n",
"mask = np.full(s_space.shape, 1.)\n",
"mask[l:h,l:h] = 0.\n",
- "mask = ift.Field.from_arr(s_space, mask)\n",
+ "mask = ift.Field.from_raw(s_space, mask)\n",
"\n",
"R = ift.DiagonalOperator(mask)(HT)\n",
- "n = n.val.copy()\n",
+ "n = n.val_rw()\n",
"n[l:h, l:h] = 0\n",
- "n = ift.Field.from_arr(s_space, n)\n",
+ "n = ift.Field.from_raw(s_space, n)\n",
"curv = Curvature(R=R, N=N, Sh=Sh)\n",
"D = curv.inverse\n",
"\n",
diff --git a/demos/bernoulli_demo.py b/demos/bernoulli_demo.py
index 19e82af32ede0fd54febdb17e9abf1077ba991b6..4cd8dbc1d0ee3003e28a3015b0fec1990e8663af 100644
--- a/demos/bernoulli_demo.py
+++ b/demos/bernoulli_demo.py
@@ -63,7 +63,7 @@ if __name__ == '__main__':
mock_position = ift.from_random('normal', harmonic_space)
tmp = p(mock_position).val.astype(np.float64)
data = np.random.binomial(1, tmp)
- data = ift.Field.from_arr(R.target, data)
+ data = ift.Field.from_raw(R.target, data)
# Compute likelihood and Hamiltonian
position = ift.from_random('normal', harmonic_space)
diff --git a/demos/find_amplitude_parameters.py b/demos/find_amplitude_parameters.py
index e96c06615d227c072f6547f73ef0a054769daa21..90cdfd29390173ec8bd9a6e9a14d6bd709b64964 100644
--- a/demos/find_amplitude_parameters.py
+++ b/demos/find_amplitude_parameters.py
@@ -57,7 +57,7 @@ if __name__ == '__main__':
for _ in range(n_samps):
fld = pspec(ift.from_random('normal', pspec.domain))
klengths = fld.domain[0].k_lengths
- ycoord = fld.val.copy()
+ ycoord = fld.val_rw()
ycoord[0] = ycoord[1]
ax.plot(klengths, ycoord, alpha=1)
diff --git a/demos/getting_started_1.py b/demos/getting_started_1.py
index b0277a7504b1baf2e2b55f6e1b22151a2c118132..3698549a02ba2640b895fdf6019824a90c8f7537 100644
--- a/demos/getting_started_1.py
+++ b/demos/getting_started_1.py
@@ -95,7 +95,7 @@ if __name__ == '__main__':
# and harmonic transformaion
# Masking operator to model that parts of the field have not been observed
- mask = ift.Field.from_arr(position_space, mask)
+ mask = ift.Field.from_raw(position_space, mask)
Mask = ift.MaskOperator(mask)
# The response operator consists of
diff --git a/demos/getting_started_2.py b/demos/getting_started_2.py
index 62f2e9351b58f039fbc49febf8eb66f3bf0affd6..17b4e05158567bd61685e3504d40b23774f14096 100644
--- a/demos/getting_started_2.py
+++ b/demos/getting_started_2.py
@@ -40,7 +40,7 @@ def exposure_2d():
exposure[:, x_shape*4//5:x_shape] *= .1
exposure[:, x_shape//2:x_shape*3//2] *= 3.
- return ift.Field.from_arr(position_space, exposure)
+ return ift.Field.from_raw(position_space, exposure)
if __name__ == '__main__':
@@ -95,7 +95,7 @@ if __name__ == '__main__':
mock_position = ift.from_random('normal', domain)
data = lamb(mock_position)
data = np.random.poisson(data.val.astype(np.float64))
- data = ift.Field.from_arr(d_space, data)
+ data = ift.Field.from_raw(d_space, data)
likelihood = ift.PoissonianEnergy(data)(lamb)
# Settings for minimization
diff --git a/demos/polynomial_fit.py b/demos/polynomial_fit.py
index dcc3a47e1de6439335470c5c5b2c40d1baadf168..be51daf0c06cff26f1c7a01f7e306bf4ebe3dd9c 100644
--- a/demos/polynomial_fit.py
+++ b/demos/polynomial_fit.py
@@ -71,7 +71,7 @@ class PolynomialResponse(ift.LinearOperator):
def apply(self, x, mode):
self._check_input(x, mode)
- val = x.val.copy()
+ val = x.val_rw()
if mode == self.TIMES:
# FIXME Use polynomial() here
out = self._mat.dot(val)
diff --git a/nifty6/domains/lm_space.py b/nifty6/domains/lm_space.py
index ad5799bfdea370d80a74b103d5d8369f44906619..d3e9723d03b62344fac28ea42ef32c27687978bb 100644
--- a/nifty6/domains/lm_space.py
+++ b/nifty6/domains/lm_space.py
@@ -87,7 +87,7 @@ class LMSpace(StructuredDomain):
for m in range(1, mmax+1):
ldist[idx:idx+2*(lmax+1-m)] = tmp[2*m:]
idx += 2*(lmax+1-m)
- return Field.from_arr(self, ldist)
+ return Field.from_raw(self, ldist)
def get_unique_k_lengths(self):
return np.arange(self.lmax+1, dtype=np.float64)
@@ -123,7 +123,7 @@ class LMSpace(StructuredDomain):
lm0 = gl.get_default_codomain()
theta = pyHealpix.GL_thetas(gl.nlat)
# evaluate the kernel function at the required thetas
- kernel_sphere = Field.from_arr(gl, func(theta))
+ kernel_sphere = Field.from_raw(gl, func(theta))
# normalize the kernel such that the integral over the sphere is 4pi
kernel_sphere = kernel_sphere * (4 * np.pi / kernel_sphere.integrate())
# compute the spherical harmonic coefficients of the kernel
@@ -131,7 +131,7 @@ class LMSpace(StructuredDomain):
kernel_lm = op.adjoint_times(kernel_sphere.weight(1)).val
# evaluate the k lengths of the harmonic space
k_lengths = self.get_k_length_array().val.astype(np.int)
- return Field.from_arr(self, kernel_lm[k_lengths])
+ return Field.from_raw(self, kernel_lm[k_lengths])
@property
def lmax(self):
diff --git a/nifty6/domains/power_space.py b/nifty6/domains/power_space.py
index f5774e3fd90019710c7ef4be311eaf2f859accde..13b73cdb858a4a44485537b292217170580175e4 100644
--- a/nifty6/domains/power_space.py
+++ b/nifty6/domains/power_space.py
@@ -240,7 +240,7 @@ class PowerSpace(StructuredDomain):
@property
def pindex(self):
- """data_object : bin indices
+ """numpy.ndarray : bin indices
Bin index for every pixel in the harmonic partner.
"""
diff --git a/nifty6/domains/rg_space.py b/nifty6/domains/rg_space.py
index 3301d2e68495ee78d6fb3153e24c6e555441e598..7b41ff75438b385aea45677cee60adbd4b14da62 100644
--- a/nifty6/domains/rg_space.py
+++ b/nifty6/domains/rg_space.py
@@ -97,14 +97,14 @@ class RGSpace(StructuredDomain):
res = np.arange(self.shape[0], dtype=np.float64)
res = np.minimum(res, self.shape[0]-res)*self.distances[0]
if len(self.shape) == 1:
- return Field.from_arr(self, res)
+ return Field.from_raw(self, res)
res *= res
for i in range(1, len(self.shape)):
tmp = np.arange(self.shape[i], dtype=np.float64)
tmp = np.minimum(tmp, self.shape[i]-tmp)*self.distances[i]
tmp *= tmp
res = np.add.outer(res, tmp)
- return Field.from_arr(self, np.sqrt(res))
+ return Field.from_raw(self, np.sqrt(res))
def get_k_length_array(self):
if (not self.harmonic):
diff --git a/nifty6/field.py b/nifty6/field.py
index 1adb5143c8ab60c3ffc65aaaf903519898470838..00cb2923d50ca3b392c3d71a0e11ff36d91d9146 100644
--- a/nifty6/field.py
+++ b/nifty6/field.py
@@ -32,8 +32,8 @@ class Field(object):
----------
domain : DomainTuple
The domain of the new Field.
- val : data_object
- This object's global shape must match the domain shape
+ val : numpy.ndarray
+ This object's shape must match the domain shape
After construction, the object will no longer be writeable!
Notes
@@ -93,7 +93,7 @@ class Field(object):
return Field(domain, val)
@staticmethod
- def from_arr(domain, arr):
+ def from_raw(domain, arr):
"""Returns a Field constructed from `domain` and `arr`.
Parameters
@@ -148,15 +148,19 @@ class Field(object):
@property
def val(self):
- """numpy.ndarray : the data object storing the field's entries.
+ """numpy.ndarray : the array storing the field's entries.
Notes
-----
- This property is intended for low-level, internal use only. Do not use
- from outside of NIFTy's core; there should be better alternatives.
+ The returned array is read-only.
"""
return self._val
+ def val_rw(self):
+ """numpy.ndarray : a copy of the array storing the field's entries.
+ """
+ return self._val.copy()
+
@property
def dtype(self):
"""type : the data type of the field's entries"""
@@ -241,7 +245,7 @@ class Field(object):
Field
The weighted field.
"""
- aout = self.val.copy()
+ aout = self.val_rw()
spaces = utilities.parse_spaces(spaces, len(self._domain))
diff --git a/nifty6/library/correlated_fields.py b/nifty6/library/correlated_fields.py
index a6a8a019bdd06556fc34436f3bb6f8c082ff10b1..ba348cac0365e3cee1c29e76d449520e14612f9b 100644
--- a/nifty6/library/correlated_fields.py
+++ b/nifty6/library/correlated_fields.py
@@ -179,7 +179,7 @@ class _TwoLogIntegrations(LinearOperator):
res[from_third] = (res[from_third] + res[no_border])/2*self._log_vol[extender_sl] + x[first]
res[from_third] = np.cumsum(res[from_third], axis=axis)
else:
- x = x.val.copy()
+ x = x.val_rw()
res = np.zeros(self._domain.shape)
x[from_third] = np.cumsum(x[from_third][reverse], axis=axis)[reverse]
res[first] += x[from_third]
@@ -199,7 +199,7 @@ class _Normalization(Operator):
pd = PowerDistributor(hspace,
power_space=self._domain[space],
space=space)
- mode_multiplicity = pd.adjoint(full(pd.target, 1.)).val.copy()
+ mode_multiplicity = pd.adjoint(full(pd.target, 1.)).val_rw()
zero_mode = (slice(None),)*self._domain.axes[space][0] + (0,)
mode_multiplicity[zero_mode] = 0
self._mode_multiplicity = makeField(self._domain,
diff --git a/nifty6/library/los_response.py b/nifty6/library/los_response.py
index 25e05de1995178fa7f3685213c415d37867af2d8..bd8e65c12f1ff3ce0e67b3e030ea37da38067958 100644
--- a/nifty6/library/los_response.py
+++ b/nifty6/library/los_response.py
@@ -172,12 +172,11 @@ class LOSResponse(LinearOperator):
"getting negative distances")
real_ends = starts + diffs*real_distances
dist = np.array(self.domain[0].distances).reshape((-1, 1))
- localized_pixel_starts = starts/dist + 0.5
- localized_pixel_ends = real_ends/dist + 0.5
+ pixel_starts = starts/dist + 0.5
+ pixel_ends = real_ends/dist + 0.5
- # get the shape of the local data slice
- w_i = _comp_traverse(localized_pixel_starts,
- localized_pixel_ends,
+ w_i = _comp_traverse(pixel_starts,
+ pixel_ends,
self.domain[0].shape,
np.array(self.domain[0].distances),
1./(1./difflen+truncation*sigmas),
@@ -229,6 +228,6 @@ class LOSResponse(LinearOperator):
if mode == self.TIMES:
result_arr = self._smat.matvec(x.val.reshape(-1))
return Field(self._target, result_arr)
- local_input_data = x.val.reshape(-1)
- res = self._smat.rmatvec(local_input_data).reshape(self.domain[0].shape)
+ input_data = x.val.reshape(-1)
+ res = self._smat.rmatvec(input_data).reshape(self.domain[0].shape)
return Field(self._domain, res)
diff --git a/nifty6/linearization.py b/nifty6/linearization.py
index 10141ca6561579b5e1de587e53ffa1e65cd94b11..d62d5b402fb7731874c73cbc5966011192ef25a2 100644
--- a/nifty6/linearization.py
+++ b/nifty6/linearization.py
@@ -321,7 +321,7 @@ class Linearization(object):
tmp = self._val.sinc()
tmp2 = ((np.pi*self._val).cos()-tmp)/self._val
ind = self._val.val == 0
- loc = tmp2.val.copy()
+ loc = tmp2.val_rw()
loc[ind] = 0
tmp2 = Field(tmp.domain, loc)
return self.new(tmp, makeOp(tmp2)(self._jac))
@@ -371,7 +371,7 @@ class Linearization(object):
tmp2 = self._val.sign()
ind = self._val.val == 0
- loc = tmp2.val.copy().astype(float)
+ loc = tmp2.val_rw().astype(float)
loc[ind] = np.nan
tmp2 = Field(tmp.domain, loc)
diff --git a/nifty6/minimization/metric_gaussian_kl_mpi.py b/nifty6/minimization/metric_gaussian_kl_mpi.py
index f0dc1ce58177c417587f2fbe3f6ba92976bb99f1..9de0866af69a9631255cceeb1fe31532514498f9 100644
--- a/nifty6/minimization/metric_gaussian_kl_mpi.py
+++ b/nifty6/minimization/metric_gaussian_kl_mpi.py
@@ -188,7 +188,7 @@ class MetricGaussianKL_MPI(Energy):
for s in self._samples:
tmp = self._hamiltonian(self._lin+s)
if v is None:
- v = tmp.val.val.copy()
+ v = tmp.val.val_rw()
g = tmp.gradient
else:
v += tmp.val.val
diff --git a/nifty6/minimization/scipy_minimizer.py b/nifty6/minimization/scipy_minimizer.py
index fcba4863108dbbd49e5288c702ed80da7955cef1..353006869ca522dc52670c628a2c64687422025b 100644
--- a/nifty6/minimization/scipy_minimizer.py
+++ b/nifty6/minimization/scipy_minimizer.py
@@ -48,7 +48,7 @@ def _toArray(fld):
def _toArray_rw(fld):
if isinstance(fld, Field):
- return fld.val.copy().reshape(-1)
+ return fld.val_rw().reshape(-1)
return _multiToArray(fld)
diff --git a/nifty6/multi_field.py b/nifty6/multi_field.py
index b78caa5a0da725605ee9a4773ad229996a481c4f..66d655505f172bed31d89c530e621de5a714d220 100644
--- a/nifty6/multi_field.py
+++ b/nifty6/multi_field.py
@@ -132,12 +132,17 @@ class MultiField(object):
return MultiField(domain, tuple(Field(dom, val)
for dom in domain._domains))
- def to_global_data(self):
+ @property
+ def val(self):
return {key: val.val
for key, val in zip(self._domain.keys(), self._val)}
+ def val_rw(self):
+ return {key: val.val_rw()
+ for key, val in zip(self._domain.keys(), self._val)}
+
@staticmethod
- def from_global_data(domain, arr):
+ def from_raw(domain, arr):
return MultiField(
domain, tuple(Field(domain[key], arr[key])
for key in domain.keys()))
diff --git a/nifty6/operator_spectrum.py b/nifty6/operator_spectrum.py
index 53336afc6e89c21afd2b37162a48f334f59b0fb1..cb0a8a3ef4207e340e275e9452ae4a07583e09e7 100644
--- a/nifty6/operator_spectrum.py
+++ b/nifty6/operator_spectrum.py
@@ -56,7 +56,7 @@ class _DomRemover(LinearOperator):
def apply(self, x, mode):
self._check_input(x, mode)
self._check_float_dtype(x)
- x = x.to_global_data()
+ x = x.val
if isinstance(self._domain, DomainTuple):
res = x.ravel() if mode == self.TIMES else x.reshape(
self._domain.shape)
diff --git a/nifty6/operators/distributors.py b/nifty6/operators/distributors.py
index ee7e102ef498d6705dc7b8be22b37f3fac57490c..bcd413ad3ebcc4c512f692f8ed5082c0337918a5 100644
--- a/nifty6/operators/distributors.py
+++ b/nifty6/operators/distributors.py
@@ -74,7 +74,7 @@ class DOFDistributor(LinearOperator):
wgt = np.bincount(dofdex.val.ravel(), minlength=nbin)
wgt = wgt*partner.scalar_dvol
else:
- dvol = Field.from_arr(partner, partner.dvol).val
+ dvol = Field.from_raw(partner, partner.dvol).val
wgt = np.bincount(dofdex.val.ravel(),
minlength=nbin, weights=dvol)
# The explicit conversion to float64 is necessary because bincount
@@ -108,7 +108,7 @@ class DOFDistributor(LinearOperator):
oarr = np.zeros(self._hshape, dtype=x.dtype)
oarr = special_add_at(oarr, 1, self._dofdex, arr)
oarr = oarr.reshape(self._domain.shape)
- res = Field.from_arr(self._domain, oarr)
+ res = Field.from_raw(self._domain, oarr)
return res
def _times(self, x):
diff --git a/nifty6/plot.py b/nifty6/plot.py
index 574a54587ae2d93707949910f42d9dc8cb305fcd..8e84cd6742b92f0718e240199e7f201a4fc2ed9e 100644
--- a/nifty6/plot.py
+++ b/nifty6/plot.py
@@ -310,7 +310,7 @@ def _plot1D(f, ax, **kwargs):
plt.yscale(kwargs.pop("yscale", "log"))
xcoord = dom.k_lengths
for i, fld in enumerate(f):
- ycoord = fld.val.copy()
+ ycoord = fld.val_rw()
ycoord[0] = ycoord[1]
plt.plot(xcoord, ycoord, label=label[i],
linewidth=linewidth[i], alpha=alpha[i])
diff --git a/nifty6/probing.py b/nifty6/probing.py
index f99b8d3863d44d9dc312aec4d2d7b9a78604810d..337e239a1d6f5826bea481cf670a1a60a9921cef 100644
--- a/nifty6/probing.py
+++ b/nifty6/probing.py
@@ -144,7 +144,7 @@ def approximation2endo(op, nsamples):
approx = sc.var
dct = approx.to_dict()
for kk in dct:
- foo = dct[kk].to_global_data_rw()
+ foo = dct[kk].val_rw()
foo[foo == 0] = 1
dct[kk] = makeField(dct[kk].domain, foo)
return MultiField.from_dict(dct)
diff --git a/nifty6/sugar.py b/nifty6/sugar.py
index 973b989d0f0cd9f3f7b550583545a93aee316f5c..2a568eac9d75021e5beaad68d60107f092ad430f 100644
--- a/nifty6/sugar.py
+++ b/nifty6/sugar.py
@@ -297,8 +297,8 @@ def makeField(domain, arr):
The newly created random field
"""
if isinstance(domain, (dict, MultiDomain)):
- return MultiField.from_global_data(domain, arr)
- return Field.from_arr(domain, arr)
+ return MultiField.from_raw(domain, arr)
+ return Field.from_raw(domain, arr)
def makeDomain(domain):
diff --git a/test/test_minimizers.py b/test/test_minimizers.py
index 83c8d89864a92971c9512f3201c60ba93a00869b..e0c6641808cea8db0a95ba936ab8cb3fa1bdcfc0 100644
--- a/test/test_minimizers.py
+++ b/test/test_minimizers.py
@@ -131,11 +131,11 @@ def test_rosenbrock(minimizer):
@property
def value(self):
- return rosen(self._position.val.copy())
+ return rosen(self._position.val_rw())
@property
def gradient(self):
- inp = self._position.val.copy()
+ inp = self._position.val_rw()
out = ift.Field(space, rosen_der(inp))
return out
@@ -143,13 +143,13 @@ def test_rosenbrock(minimizer):
def metric(self):
class RBCurv(ift.EndomorphicOperator):
def __init__(self, loc):
- self._loc = loc.val.copy()
+ self._loc = loc.val_rw()
self._capability = self.TIMES
self._domain = space
def apply(self, x, mode):
self._check_input(x, mode)
- inp = x.val.copy()
+ inp = x.val_rw()
out = ift.Field(
space, rosen_hess_prod(self._loc.copy(), inp))
return out
@@ -159,8 +159,8 @@ def test_rosenbrock(minimizer):
return ift.InversionEnabler(RBCurv(self._position), t1)
def apply_metric(self, x):
- inp = x.val.copy()
- pos = self._position.val.copy()
+ inp = x.val_rw()
+ pos = self._position.val_rw()
return ift.Field(space, rosen_hess_prod(pos, inp))
try:
diff --git a/test/test_multi_field.py b/test/test_multi_field.py
index 9c1a7c0815d17c1648899dcea2d189e7d6d9f605..e29fd31873b1d03fcd83e60a195d3dce7d44c946 100644
--- a/test/test_multi_field.py
+++ b/test/test_multi_field.py
@@ -44,7 +44,7 @@ def test_multifield_field_consistency():
def test_dataconv():
f1 = ift.full(dom, 27)
- f2 = ift.makeField(dom, f1.to_global_data())
+ f2 = ift.makeField(dom, f1.val)
for key, val in f1.items():
assert_equal(val.val, f2[key].val)
if "d1" not in f2:
diff --git a/test/test_operators/test_adjoint.py b/test/test_operators/test_adjoint.py
index 0f3b2622a6ae219cbf5c47d5682e13febb1f18e4..c55f6b986c3f76fd250a8134ab15a2e853b95ec7 100644
--- a/test/test_operators/test_adjoint.py
+++ b/test/test_operators/test_adjoint.py
@@ -127,7 +127,7 @@ def testDOFDistributor(sp, dtype):
if sp.size < 4:
return
dofdex = np.arange(sp.size).reshape(sp.shape) % 3
- dofdex = ift.Field.from_arr(sp, dofdex)
+ dofdex = ift.Field.from_raw(sp, dofdex)
op = ift.DOFDistributor(dofdex)
ift.extra.consistency_check(op, dtype, dtype)
@@ -174,7 +174,7 @@ def testMask(sp, dtype):
f = ift.from_random('normal', sp).val
mask = np.zeros_like(f)
mask[f > 0] = 1
- mask = ift.Field.from_arr(sp, mask)
+ mask = ift.Field.from_raw(sp, mask)
# Test MaskOperator
op = ift.MaskOperator(mask)
ift.extra.consistency_check(op, dtype, dtype)
diff --git a/test/test_operators/test_representation.py b/test/test_operators/test_representation.py
index efa87b8486a7892c3daa8989c8994d0c15d7f4dc..b817b18be6e5c60f27cc4391ec92cf57ae8cca4d 100644
--- a/test/test_operators/test_representation.py
+++ b/test/test_operators/test_representation.py
@@ -102,7 +102,7 @@ def testDOFDistributor(sp, dtype):
if sp.size < 4:
return
dofdex = np.arange(sp.size).reshape(sp.shape) % 3
- dofdex = ift.Field.from_arr(sp, dofdex)
+ dofdex = ift.Field.from_raw(sp, dofdex)
_check_repr(ift.DOFDistributor(dofdex))
@@ -140,7 +140,7 @@ def testMask(sp, dtype):
f = ift.from_random('normal', sp).val
mask = np.zeros_like(f)
mask[f > 0] = 1
- mask = ift.Field.from_arr(sp, mask)
+ mask = ift.Field.from_raw(sp, mask)
# Test MaskOperator
_check_repr(ift.MaskOperator(mask))
diff --git a/test/test_operators/test_smoothing_operator.py b/test/test_operators/test_smoothing_operator.py
index 71ce7b825e8347759ad5bcdc3674b0a1b36cda56..3bebbc419f43283c5d34bb086498058bd4001239 100644
--- a/test/test_operators/test_smoothing_operator.py
+++ b/test/test_operators/test_smoothing_operator.py
@@ -56,7 +56,7 @@ def test_times(space, sigma):
op = ift.HarmonicSmoothingOperator(space, sigma=sigma)
fld = np.zeros(space.shape, dtype=np.float64)
fld[0] = 1.
- rand1 = ift.Field.from_arr(space, fld)
+ rand1 = ift.Field.from_raw(space, fld)
tt1 = op.times(rand1)
assert_allclose(1, tt1.sum())