Commit a272d7c5 authored by Martin Reinecke's avatar Martin Reinecke

more renamings

parent 914f053a
Pipeline #64989 passed with stages
in 8 minutes and 50 seconds
......@@ -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",
......
......@@ -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)
......
......@@ -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)
......
......@@ -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
......
......@@ -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
......
......@@ -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)
......
......@@ -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):
......
......@@ -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.
"""
......
......@@ -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):
......
......@@ -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))
......
......@@ -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,
......
......@@ -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)
......@@ -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)
......
......@@ -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
......
......@@ -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)
......
......@@ -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()))
......
......@@ -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)
......
......@@ -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):
......
......@@ -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])
......
......@@ -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)
......@@ -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):
......
......@@ -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:
......
......@@ -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:
......
......@@ -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)
......
......@@ -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))
......
......@@ -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())
......
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