From e7fd7bd1555eff38d9b27145ccd0d7b200aedf04 Mon Sep 17 00:00:00 2001
From: theos <theo.steininger@ultimanet.de>
Date: Sat, 2 Jul 2016 01:46:41 +0200
Subject: [PATCH] Renamed field to Field; removed space; point_space is now
called Space.
---
__init__.py | 12 +-
demos/demo_excaliwir.py | 8 +-
demos/demo_wf1.py | 8 +-
demos/demo_wf2.py | 6 +-
demos/demo_wf3.py | 2 +-
nifty_field.py => field.py | 40 ++-
lm/nifty_lm.py | 22 +-
lm/nifty_power_conversion_lm.py | 6 +-
nifty_paradict.py | 178 ----------
nifty_simple_math.py | 2 +-
nifty_utilities.py | 1 +
operators/nifty_minimization.py | 6 +-
operators/nifty_operators.py | 58 ++--
operators/nifty_probing.py | 25 +-
rg/nifty_power_conversion_rg.py | 6 +-
rg/nifty_rg.py | 16 +-
setup.py | 2 +-
space.py | 595 +-------------------------------
test/test_nifty_field.py | 25 +-
test/test_nifty_spaces.py | 4 +-
20 files changed, 136 insertions(+), 886 deletions(-)
rename nifty_field.py => field.py (97%)
diff --git a/__init__.py b/__init__.py
index e4acf586f..40ca99bae 100644
--- a/__init__.py
+++ b/__init__.py
@@ -38,17 +38,19 @@ from config import about,\
from d2o import distributed_data_object, d2o_librarian
from nifty_cmaps import ncmap
-from nifty_field import field
-from nifty_core import space,\
- point_space
+from field import Field
+from space import Space
+
+# this line exists for compatibility reasons
+# TODO: Remove this once the transition to field types is done.
+from space import Space as point_space
from nifty_random import random
from nifty_simple_math import *
from nifty_utilities import *
from nifty_paradict import space_paradict,\
- point_space_paradict,\
- nested_space_paradict
+ nested_space_paradict
from field_types import FieldType,\
FieldArray
diff --git a/demos/demo_excaliwir.py b/demos/demo_excaliwir.py
index 670963086..d94e714c7 100644
--- a/demos/demo_excaliwir.py
+++ b/demos/demo_excaliwir.py
@@ -112,7 +112,7 @@ class problem(object):
Parameters
----------
- newspace : {scalar, list, array, field, function}, *optional*
+ newspace : {scalar, list, array, Field, function}, *optional*
Assumed power spectrum (default: k ** -2).
"""
@@ -135,7 +135,7 @@ class problem(object):
Parameters
----------
- newspace : {scalar, list, array, field, function}, *optional*
+ newspace : {scalar, list, array, Field, function}, *optional*
Initial power spectrum (default: k ** -2).
q : {scalar, list, array}, *optional*
Spectral scale parameter of the assumed inverse-Gamme prior
@@ -210,7 +210,7 @@ class problem(object):
self.s.plot(title="signal", save="img/signal.png")
try:
- d_ = field(self.z, val=self.d.val, target=self.k)
+ d_ = Field(self.z, val=self.d.val, target=self.k)
d_.plot(title="data", vmin=self.s.min(), vmax=self.s.max(),
save="img/data.png")
except:
@@ -240,7 +240,7 @@ class problem(object):
self.s.plot(title="signal")
## plot data
try:
- d_ = field(self.z, val=self.d.val, target=self.k)
+ d_ = Field(self.z, val=self.d.val, target=self.k)
d_.plot(title="data", vmin=self.s.min(), vmax=self.s.max())
except:
pass
diff --git a/demos/demo_wf1.py b/demos/demo_wf1.py
index 2097828eb..372b41db4 100644
--- a/demos/demo_wf1.py
+++ b/demos/demo_wf1.py
@@ -72,7 +72,7 @@ if __name__ == "__main__":
# some noise variance; e.g., signal-to-noise ratio of 1
N = diagonal_operator(d_space, diag=s.var(), bare=True) # define noise covariance
- n = N.get_random_field(domain=d_space) # generate noise
+ n = N.get_random_Field(domain=d_space) # generate noise
d = R(s) + n # compute data
@@ -84,7 +84,7 @@ if __name__ == "__main__":
#m = D(j, tol=1E-8, limii=20, note=True, force=True)
ident = identity(x_space)
- #xi = field(x_space, random='gau', target=k_space)
+ #xi = Field(x_space, random='gau', target=k_space)
m = D(j, W=S, tol=1E-8, limii=100, note=True)
@@ -101,11 +101,11 @@ if __name__ == "__main__":
mono=False, save = 'power_plot_s.png', nbin=1000, log=True,
vmax = 100, vmin=10e-7)
- d_ = field(x_space, val=d.val, target=k_space)
+ d_ = Field(x_space, val=d.val, target=k_space)
d_.plot(title="data", vmin=s.min(), vmax=s.max(), save = 'plot_d.png')
- n_ = field(x_space, val=n.val, target=k_space)
+ n_ = Field(x_space, val=n.val, target=k_space)
n_.plot(title="data", vmin=s.min(), vmax=s.max(), save = 'plot_n.png')
diff --git a/demos/demo_wf2.py b/demos/demo_wf2.py
index cd12d2418..8b1c56096 100644
--- a/demos/demo_wf2.py
+++ b/demos/demo_wf2.py
@@ -103,15 +103,15 @@ if __name__ == "__main__":
return energy(x), gradient(x)
- m = field(x_space, codomain=k_space) # reconstruct map
+ m = Field(x_space, codomain=k_space) # reconstruct map
#with PyCallGraph(output=graphviz, config=config):
m, convergence = steepest_descent(eggs=eggs, note=True)(m, tol=1E-3, clevel=3)
- m = field(x_space, codomain=k_space)
+ m = Field(x_space, codomain=k_space)
m, convergence = steepest_descent_new(energy, gradient, note=True)(m, tol=1E-3, clevel=3)
#s.plot(title="signal") # plot signal
- #d_ = field(x_space, val=d.val, target=k_space)
+ #d_ = Field(x_space, val=d.val, target=k_space)
#d_.plot(title="data", vmin=s.min(), vmax=s.max()) # plot data
#m.plot(title="reconstructed map", vmin=s.min(), vmax=s.max()) # plot map
diff --git a/demos/demo_wf3.py b/demos/demo_wf3.py
index e8cda52dc..a97e48e87 100644
--- a/demos/demo_wf3.py
+++ b/demos/demo_wf3.py
@@ -79,7 +79,7 @@ if __name__ == "__main__":
vminmax = {"vmin":1.5 * s.val.min(), "vmax":1.5 * s.val.max()}
s.plot(title="signal", **vminmax) # plot signal
- d_ = field(x_space, val=d.val, target=k_space)
+ d_ = Field(x_space, val=d.val, target=k_space)
d_.plot(title="data", **vminmax) # plot data
m.plot(title="reconstructed map", error=D.diag(bare=True), **vminmax) # plot map
D.plot(title="information propagator", bare=True) # plot information propagator
diff --git a/nifty_field.py b/field.py
similarity index 97%
rename from nifty_field.py
rename to field.py
index 0a56875d3..5856db45a 100644
--- a/nifty_field.py
+++ b/field.py
@@ -9,10 +9,10 @@ from nifty.config import about, \
nifty_configuration as gc, \
dependency_injector as gdi
-from nifty.field_types import Field_type,\
- Field_array
+from nifty.field_types import FieldType,\
+ FieldArray
-from nifty.nifty_core import space
+from nifty.space import Space
import nifty.nifty_utilities as utilities
from nifty_random import random
@@ -20,7 +20,7 @@ from nifty_random import random
POINT_DISTRIBUTION_STRATEGIES = DISTRIBUTION_STRATEGIES['global']
-class field(object):
+class Field(object):
"""
.. ____ __ __ __
.. / _/ /__/ / / / /
@@ -133,7 +133,7 @@ class field(object):
"""
# If the given val was a field, try to cast it accordingly to the given
# domain and codomain, etc...
- if isinstance(val, field):
+ if isinstance(val, Field):
self._init_from_field(f=val,
domain=domain,
codomain=codomain,
@@ -273,7 +273,7 @@ class field(object):
elif not isinstance(domain, tuple):
domain = (domain,)
for d in domain:
- if not isinstance(d, space):
+ if not isinstance(d, Space):
raise TypeError(about._errors.cstring(
"ERROR: Given domain contains something that is not a "
"nifty.space."))
@@ -286,7 +286,7 @@ class field(object):
raise ValueError(about._errors.cstring(
"ERROR: domain and codomain do not have the same length."))
for (cd, d) in zip(codomain, domain):
- if not isinstance(cd, space):
+ if not isinstance(cd, Space):
raise TypeError(about._errors.cstring(
"ERROR: Given codomain contains something that is not a"
"nifty.space."))
@@ -302,9 +302,9 @@ class field(object):
elif not isinstance(field_type, tuple):
field_type = (field_type,)
for ft in field_type:
- if not isinstance(ft, Field_type):
+ if not isinstance(ft, FieldType):
raise TypeError(about._errors.cstring(
- "ERROR: Given object is not a nifty.Field_type."))
+ "ERROR: Given object is not a nifty.FieldType."))
return field_type
def _build_codomain(self, domain):
@@ -417,7 +417,7 @@ class field(object):
kwargs == {}):
new_field = self._fast_copy_empty()
else:
- new_field = field(domain=domain, codomain=codomain, dtype=dtype,
+ new_field = Field(domain=domain, codomain=codomain, dtype=dtype,
comm=comm, datamodel=datamodel,
field_type=field_type, **kwargs)
return new_field
@@ -543,7 +543,7 @@ class field(object):
Whether the method should raise a warning if information is
lost during casting (default: False).
"""
- if isinstance(x, field):
+ if isinstance(x, Field):
x = x.get_val()
if dtype is None:
@@ -695,7 +695,7 @@ class field(object):
return None
# Case 2: x is a field
- elif isinstance(x, field):
+ elif isinstance(x, Field):
for ind, sp in enumerate(self.domain):
assert sp == x.domain[ind]
@@ -1281,8 +1281,8 @@ class field(object):
def _binary_helper(self, other, op='None', inplace=False):
# if other is a field, make sure that the domains match
- if isinstance(other, field):
- other = field(domain=self.domain,
+ if isinstance(other, Field):
+ other = Field(domain=self.domain,
val=other,
codomain=self.codomain,
copy=False)
@@ -1326,11 +1326,13 @@ class field(object):
'mean': lambda y: getattr(y, 'mean')(axis=axis),
'std': lambda y: getattr(y, 'std')(axis=axis),
'var': lambda y: getattr(y, 'var')(axis=axis),
- 'argmin_nonflat': lambda y: getattr(y, 'argmin_nonflat')(
- axis=axis),
+ 'argmin_nonflat': lambda y: getattr(y,
+ 'argmin_nonflat')(
+ axis=axis),
'argmin': lambda y: getattr(y, 'argmin')(axis=axis),
- 'argmax_nonflat': lambda y: getattr(y, 'argmax_nonflat')(
- axis=axis),
+ 'argmax_nonflat': lambda y: getattr(y,
+ 'argmax_nonflat')(
+ axis=axis),
'argmax': lambda y: getattr(y, 'argmax')(axis=axis),
'conjugate': lambda y: getattr(y, 'conjugate')(),
'sum': lambda y: getattr(y, 'sum')(axis=axis),
@@ -1451,6 +1453,6 @@ class field(object):
return self._binary_helper(other, op='gt')
-class EmptyField(field):
+class EmptyField(Field):
def __init__(self):
pass
diff --git a/lm/nifty_lm.py b/lm/nifty_lm.py
index cf02f2fe4..2f0f44568 100644
--- a/lm/nifty_lm.py
+++ b/lm/nifty_lm.py
@@ -41,10 +41,9 @@ from matplotlib.ticker import LogFormatter as lf
from d2o import STRATEGIES as DISTRIBUTION_STRATEGIES
-from nifty.nifty_core import space,\
- point_space
+from nifty.nifty_core import Space
-from nifty.nifty_field import field
+from nifty.field import Field
from nifty.config import about,\
nifty_configuration as gc,\
@@ -63,7 +62,7 @@ GL_DISTRIBUTION_STRATEGIES = DISTRIBUTION_STRATEGIES['global']
HP_DISTRIBUTION_STRATEGIES = DISTRIBUTION_STRATEGIES['global']
-class lm_space(point_space):
+class lm_space(Space):
"""
.. __
.. / /
@@ -277,7 +276,7 @@ class lm_space(point_space):
mol[self.paradict['lmax'] + 1:] = 2 # redundant: (l,m) and (l,-m)
return mol
- def _complement_cast(self, x, axis=None, **kwargs):
+ def complement_cast(self, x, axis=None, **kwargs):
if axis is None:
lmax = self.paradict['lmax']
complexity_mask = x[:lmax+1].iscomplex()
@@ -912,7 +911,7 @@ class lm_space(point_space):
return l, m
-class gl_space(point_space):
+class gl_space(Space):
"""
.. __
.. / /
@@ -1256,12 +1255,13 @@ class gl_space(point_space):
p=np.float32(power),
nlat=nlat, nlon=nlon,
overwrite=False)
- return self.cast(np_result)
+ # return self.cast(np_result)
+ return np_result
def get_weight(self, power=1):
# TODO: Check if this function is compatible to the rest of nifty
# TODO: Can this be done more efficiently?
- dummy = self.dtype(1)
+ dummy = self.dtype.type(1)
weighted_dummy = self.calc_weight(dummy, power=power)
return weighted_dummy / dummy
@@ -1571,7 +1571,7 @@ class gl_space(point_space):
fig.canvas.draw()
-class hp_space(point_space):
+class hp_space(Space):
"""
.. __
.. / /
@@ -2082,11 +2082,11 @@ class hp_space(point_space):
if(isinstance(other, tuple)):
other = list(other)
for ii in xrange(len(other)):
- if(isinstance(other[ii], field)):
+ if(isinstance(other[ii], Field)):
other[ii] = other[ii].power(**kwargs)
else:
other[ii] = self.enforce_power(other[ii])
- elif(isinstance(other, field)):
+ elif(isinstance(other, Field)):
other = [other.power(**kwargs)]
else:
other = [self.enforce_power(other)]
diff --git a/lm/nifty_power_conversion_lm.py b/lm/nifty_power_conversion_lm.py
index d30d9f52b..e7daa8491 100644
--- a/lm/nifty_power_conversion_lm.py
+++ b/lm/nifty_power_conversion_lm.py
@@ -23,7 +23,7 @@
import numpy as np
from numpy import pi
from nifty.config import about
-from nifty.nifty_field import field
+from nifty.field import Field
from nifty.nifty_simple_math import sqrt, exp, log
@@ -71,7 +71,7 @@ def power_backward_conversion_lm(k_space,p,mean=None):
p[0] = 4*pi*mean**2
klen = k_space.get_power_indices()[0]
- C_0_Omega = field(k_space,val=0)
+ C_0_Omega = Field(k_space,val=0)
C_0_Omega.val[:len(klen)] = p*sqrt(2*klen+1)/sqrt(4*pi)
C_0_Omega = C_0_Omega.transform()
@@ -130,7 +130,7 @@ def power_forward_conversion_lm(k_space,p,mean=0):
"""
m = mean
klen = k_space.get_power_indices()[0]
- C_0_Omega = field(k_space,val=0)
+ C_0_Omega = Field(k_space,val=0)
C_0_Omega.val[:len(klen)] = p*sqrt(2*klen+1)/sqrt(4*pi)
C_0_Omega = C_0_Omega.transform()
diff --git a/nifty_paradict.py b/nifty_paradict.py
index cff00ab08..809a6b774 100644
--- a/nifty_paradict.py
+++ b/nifty_paradict.py
@@ -48,22 +48,6 @@ class space_paradict(object):
return result_hash
-class point_space_paradict(space_paradict):
-
- def __setitem__(self, key, arg):
- if key is not 'num':
- raise ValueError(about._errors.cstring(
- "ERROR: Unsupported point_space parameter"))
- if not np.isscalar(arg):
- raise ValueError(about._errors.cstring(
- "ERROR: 'num' parameter must be scalar. Got: " + str(arg)))
- if abs(arg) != arg:
- raise ValueError(about._errors.cstring(
- "ERROR: 'num' parameter must be positive. Got: " + str(arg)))
- temp = np.int(arg)
- self.parameters.__setitem__(key, temp)
-
-
class rg_space_paradict(space_paradict):
def __init__(self, shape, complexity, zerocenter):
@@ -218,165 +202,3 @@ class hp_space_paradict(space_paradict):
raise ValueError(about._errors.cstring(
"ERROR: invalid parameter ( nside <> 2**n )."))
self.parameters.__setitem__(key, temp)
-
-###################
-#
-#
-# class _space(object):
-# def __init__(self):
-# self.paradict = space_paradict(default=123)
-# #self.para = [1,2,3]
-#
-# @property
-# def para(self):
-# return self.paradict['default']
-# #return self.distributed_val
-#
-# @para.setter
-# def para(self, x):
-# self.paradict['default'] = x
-#
-###################
-###################
-#
-#
-#
-# class _point_space(object):
-# def __init__(self):
-# self.paradict = point_space_paradict()
-# self.para = [10]
-#
-# @property
-# def para(self):
-# temp = np.array([self.paradict['num']], dtype=int)
-# return temp
-# #return self.distributed_val
-#
-# @para.setter
-# def para(self, x):
-# self.paradict['num'] = x
-#
-###################
-###################
-#
-#
-# class _rg_space(object):
-# def __init__(self):
-# self.paradict = rg_space_paradict(num=[10,100,200])
-#
-# @property
-# def para(self):
-# temp = np.array(self.paradict['num'] + \
-# [self.paradict['hermitian']] + \
-# self.paradict['zerocenter'], dtype=int)
-# return temp
-#
-#
-# @para.setter
-# def para(self, x):
-# self.paradict['num'] = x[:(np.size(x)-1)//2]
-# self.paradict['zerocenter'] = x[(np.size(x)+1)//2:]
-# self.paradict['complexity'] = x[(np.size(x)-1)//2]
-#
-###################
-###################
-#
-# class _nested_space(object):
-# def __init__(self):
-# self.paradict = nested_space_paradict(ndim=10)
-# for i in range(10):
-# self.paradict[i] = [1+i, 2+i, 3+i]
-#
-# @property
-# def para(self):
-# temp = []
-# for i in range(self.paradict.ndim):
-# temp = np.append(temp, self.paradict[i])
-# return temp
-#
-# @para.setter
-# def para(self, x):
-# dict_iter = 0
-# x_iter = 0
-# while dict_iter < self.paradict.ndim:
-# temp = x[x_iter:x_iter+len(self.paradict[dict_iter])]
-# self.paradict[dict_iter] = temp
-# x_iter = x_iter+len(self.paradict[dict_iter])
-# dict_iter += 1
-#
-###################
-###################
-#
-# class _lm_space(object):
-# def __init__(self):
-# self.paradict = lm_space_paradict(lmax = 10)
-#
-# @property
-# def para(self):
-# temp = np.array([self.paradict['lmax'],
-# self.paradict['mmax']], dtype=int)
-# return temp
-#
-#
-# @para.setter
-# def para(self, x):
-# self.paradict['lmax'] = x[0]
-# self.paradict['mmax'] = x[1]
-#
-#
-###################
-###################
-#
-# class _gl_space(object):
-# def __init__(self):
-# self.paradict = gl_space_paradict(nlat = 10)
-#
-# @property
-# def para(self):
-# temp = np.array([self.paradict['nlat'],
-# self.paradict['nlon']], dtype=int)
-# return temp
-#
-#
-# @para.setter
-# def para(self, x):
-# self.paradict['nlat'] = x[0]
-# self.paradict['nlon'] = x[1]
-#
-#
-###################
-###################
-#
-#
-# class _hp_space(object):
-# def __init__(self):
-# self.paradict = hp_space_paradict(nside=16)
-#
-# @property
-# def para(self):
-# temp = np.array([self.paradict['nside']], dtype=int)
-# return temp
-#
-#
-# @para.setter
-# def para(self, x):
-# self.paradict['nside'] = x[0]
-#
-#
-#
-###################
-###################
-#
-#
-#
-# if __name__ == '__main__':
-# myspace = _space()
-# print myspace.para
-# print myspace.paradict.parameters.items()
-# myspace.para = [4,5,6]
-# print myspace.para
-# print myspace.paradict.parameters.items()
-#
-# myspace.paradict.parameters['default'] = [1,4,7]
-# print myspace.para
-# print myspace.paradict.parameters.items()
diff --git a/nifty_simple_math.py b/nifty_simple_math.py
index 7481fd94c..785fcfb36 100644
--- a/nifty_simple_math.py
+++ b/nifty_simple_math.py
@@ -22,7 +22,7 @@
##-----------------------------------------------------------------------------
import numpy as np
-#from nifty.nifty_core import field
+#from nifty.field import Field
from nifty.config import about
diff --git a/nifty_utilities.py b/nifty_utilities.py
index f56707a72..5f402a7b4 100644
--- a/nifty_utilities.py
+++ b/nifty_utilities.py
@@ -5,6 +5,7 @@ from itertools import product
from nifty.config import about
+
def get_slice_list(shape, axes):
"""
Helper function which generates slice list(s) to traverse over all
diff --git a/operators/nifty_minimization.py b/operators/nifty_minimization.py
index be20ce478..ff85e2a48 100644
--- a/operators/nifty_minimization.py
+++ b/operators/nifty_minimization.py
@@ -40,7 +40,7 @@ from __future__ import division
#from nifty_core import *
import numpy as np
from nifty.config import notification, about
-from nifty.nifty_field import field
+from nifty.field import Field
from nifty.nifty_simple_math import vdot
@@ -555,7 +555,7 @@ class steepest_descent(object):
has converged or not.
"""
- if(not isinstance(x0,field)):
+ if(not isinstance(x0,Field)):
raise TypeError(about._errors.cstring("ERROR: invalid input."))
self.x = x0
@@ -844,7 +844,7 @@ class quasi_newton_minimizer(object):
has converged or not.
"""
- if not isinstance(x0, field):
+ if not isinstance(x0, Field):
raise TypeError(about._errors.cstring("ERROR: invalid input."))
self.x = x0
diff --git a/operators/nifty_operators.py b/operators/nifty_operators.py
index f11726e2b..567641c05 100644
--- a/operators/nifty_operators.py
+++ b/operators/nifty_operators.py
@@ -22,9 +22,9 @@
from __future__ import division
import numpy as np
from nifty.config import about
-from nifty.nifty_field import field
-from nifty.nifty_core import space, \
- point_space
+from nifty.field import Field
+from nifty.space import Space
+
from nifty_minimization import conjugate_gradient
from nifty_probing import trace_prober,\
inverse_trace_prober,\
@@ -227,7 +227,7 @@ class operator(object):
def _briefing(self, x, domain, codomain, inverse):
# make sure, that the result_field of the briefing lives in the
# given domain and codomain
- result_field = field(domain=domain, val=x, codomain=codomain,
+ result_field = Field(domain=domain, val=x, codomain=codomain,
copy=False)
# weight if necessary
@@ -238,7 +238,7 @@ class operator(object):
def _debriefing(self, x, y, target, cotarget, inverse):
# The debriefing takes care that the result field lives in the same
# fourier-type domain as the input field
- assert(isinstance(y, field))
+ assert(isinstance(y, Field))
# weight if necessary
if (not self.imp) and (not target.discrete) and inverse:
@@ -1402,7 +1402,7 @@ class diagonal_operator(operator):
diag_val = self.domain.calc_weight(self.val, power=-1)
else:
diag_val = self.val
- diag = field(self.domain, codomain=self.codomain, val=diag_val)
+ diag = Field(self.domain, codomain=self.codomain, val=diag_val)
else:
diag = super(diagonal_operator, self).diag(bare=bare,
domain=domain,
@@ -1490,7 +1490,7 @@ class diagonal_operator(operator):
power=-1)
else:
inverse_diag_val = inverse_val
- inverse_diag = field(self.domain, codomain=self.codomain,
+ inverse_diag = Field(self.domain, codomain=self.codomain,
val=inverse_diag_val)
else:
@@ -1578,7 +1578,7 @@ class diagonal_operator(operator):
Random field.
"""
- temp_field = field(domain=self.domain,
+ temp_field = Field(domain=self.domain,
codomain=self.codomain,
random='gau',
std=nifty_simple_math.sqrt(
@@ -1592,7 +1592,7 @@ class diagonal_operator(operator):
else:
codomain = domain.get_codomain()
- return field(domain=domain, val=temp_field, codomain=codomain)
+ return Field(domain=domain, val=temp_field, codomain=codomain)
# if domain.harmonic != self.domain.harmonic:
# temp_field = temp_field.transform(new_domain=domain)
@@ -1802,7 +1802,7 @@ class power_operator(diagonal_operator):
"""
# Set the domain
- if not isinstance(domain, space):
+ if not isinstance(domain, Space):
raise TypeError(about._errors.cstring(
"ERROR: The given domain is not a nifty space."))
self.domain = domain
@@ -2173,7 +2173,7 @@ class projection_operator(operator):
"""
# Check the domain
- if not isinstance(domain, space):
+ if not isinstance(domain, Space):
raise TypeError(about._errors.cstring(
"ERROR: The supplied domain is not a nifty space instance."))
self.domain = domain
@@ -2275,7 +2275,7 @@ class projection_operator(operator):
raise TypeError(about._errors.cstring("ERROR: Invalid bands."))
if bands_was_scalar:
- new_field = fx * (self.assign == bands[0])
+ new_field = x * (self.assign == bands[0])
else:
# build up the projection results
# prepare the projector-carrier
@@ -2367,12 +2367,13 @@ class projection_operator(operator):
# Case 1: x is a field
# -> Compute the diagonal of the corresponding vecvec-operator:
# x * x^dagger
- if isinstance(x, field):
+ if isinstance(x, Field):
# check if field is in the same signal/harmonic space as the
# domain of the projection operator
if self.domain != x.domain:
# TODO: check if a exception should be raised, or if
# one should try to fix stuff.
+ pass
# x = x.transform(new_domain=self.domain)
vecvec = vecvec_operator(val=x)
return self.pseudo_tr(x=vecvec, axis=axis, **kwargs)
@@ -2386,6 +2387,7 @@ class projection_operator(operator):
if self.domain != working_field.domain:
# TODO: check if a exception should be raised, or if
# one should try to fix stuff.
+ pass
# working_field = working_field.transform(new_domain=self.domain)
# Case 3: x is something else
@@ -2472,13 +2474,13 @@ class vecvec_operator(operator):
-------
None
"""
- if isinstance(val, field):
+ if isinstance(val, Field):
if domain is None:
domain = val.domain
if codomain is None:
codomain = val.codomain
- if not isinstance(domain, space):
+ if not isinstance(domain, Space):
raise TypeError(about._errors.cstring("ERROR: invalid input."))
self.domain = domain
@@ -2489,7 +2491,7 @@ class vecvec_operator(operator):
self.target = self.domain
self.cotarget = self.codomain
- self.val = field(domain=self.domain,
+ self.val = Field(domain=self.domain,
codomain=self.codomain,
val=val)
@@ -2778,7 +2780,7 @@ class response_operator(operator):
-------
None
"""
- if not isinstance(domain, space):
+ if not isinstance(domain, Space):
raise TypeError(about._errors.cstring(
"ERROR: The domain must be a space instance."))
self.domain = domain
@@ -2838,12 +2840,13 @@ class response_operator(operator):
if target is None:
# set target
- target = point_space(assignments,
+ # TODO: Fix the target spaces
+ target = Space(assignments,
dtype=self.domain.dtype,
datamodel=self.domain.datamodel)
else:
# check target
- if not isinstance(target, space):
+ if not isinstance(target, Space):
raise TypeError(about._errors.cstring(
"ERROR: Given target is not a nifty space"))
elif not target.discrete:
@@ -2915,7 +2918,7 @@ class response_operator(operator):
val = y.flatten(inplace=True)
except TypeError:
val = y.flatten()
- return field(self.target,
+ return Field(self.target,
val=val,
codomain=self.cotarget)
@@ -2928,14 +2931,14 @@ class response_operator(operator):
y *= self.mask
y = self.domain.calc_smooth(y, sigma=self.sigma)
- return field(self.domain,
+ return Field(self.domain,
val=y,
codomain=self.codomain)
def _briefing(self, x, domain, codomain, inverse):
# make sure, that the result_field of the briefing lives in the
# given domain and codomain
- result_field = field(domain=domain, val=x, codomain=codomain,
+ result_field = Field(domain=domain, val=x, codomain=codomain,
copy=False)
# weight if necessary
@@ -2947,7 +2950,7 @@ class response_operator(operator):
def _debriefing(self, x, y, target, cotarget, inverse):
# The debriefing takes care that the result field lives in the same
# fourier-type domain as the input field
- assert(isinstance(y, field))
+ assert(isinstance(y, Field))
# weight if necessary
if (not self.imp) and (not target.discrete) and \
@@ -3066,7 +3069,7 @@ class invertible_operator(operator):
the operator class can use (default: None).
"""
- if not isinstance(domain, space):
+ if not isinstance(domain, Space):
raise TypeError(about._errors.cstring("ERROR: invalid input."))
self.domain = domain
if self.domain.check_codomain(codomain):
@@ -3085,7 +3088,6 @@ class invertible_operator(operator):
self.target = self.domain
self.cotarget = self.codomain
-
def _multiply(self, x, force=False, W=None, spam=None, reset=None,
note=False, x0=None, tol=1E-4, clevel=1, limii=None,
**kwargs):
@@ -3600,8 +3602,8 @@ class propagator_operator_old(operator):
def _briefing(self, x): # > prepares x for `multiply`
# inspect x
- if not isinstance(x, field):
- return (field(self.domain, codomain=self.codomain,
+ if not isinstance(x, Field):
+ return (Field(self.domain, codomain=self.codomain,
val=x),
False)
# check x.domain
@@ -3619,7 +3621,7 @@ class propagator_operator_old(operator):
if x_ is None:
return None
# inspect x
- elif isinstance(x, field):
+ elif isinstance(x, Field):
# repair ...
if in_codomain == True and x.domain != self.codomain:
x_ = x_.transform(new_domain=x.domain) # ... domain
diff --git a/operators/nifty_probing.py b/operators/nifty_probing.py
index 1c8c23b5c..d86745b7b 100644
--- a/operators/nifty_probing.py
+++ b/operators/nifty_probing.py
@@ -22,13 +22,11 @@
from __future__ import division
from nifty.config import about
-from nifty.nifty_core import space
-from nifty.nifty_field import field
+from nifty.space import Space
+from nifty.field import Field
from nifty.nifty_utilities import direct_vdot
-
-
class prober(object):
"""
.. __ __
@@ -185,7 +183,7 @@ class prober(object):
raise ValueError(about._errors.cstring(
"ERROR: invalid input: No function given or not callable."))
# check given domain
- if domain is None or not isinstance(domain, space):
+ if domain is None or not isinstance(domain, Space):
raise ValueError(about._errors.cstring(
"ERROR: invalid input: given domain is not a nifty space"))
@@ -211,7 +209,7 @@ class prober(object):
"of the operator!"))
# Check 2.3 extract domain
- if domain is None or not isinstance(domain, space):
+ if domain is None or not isinstance(domain, Space):
if (function in [operator.inverse_times,
operator.adjoint_times]):
try:
@@ -293,7 +291,7 @@ class prober(object):
each component
"""
- return field(self.domain,
+ return Field(self.domain,
codomain=self.codomain,
random=self.random)
@@ -353,11 +351,10 @@ class prober(object):
else:
about.infos.cflush("\n")
-
- if self.varQ == True:
+ if self.varQ:
if num == 1:
about.warnings.cprint(
- "WARNING: Only one probe available -> infinite variance.")
+ "WARNING: Only one probe available -> infinite variance.")
return (sum_of_probes, None)
else:
var = 1/(num-1)*(sum_of_squares - 1/num*(sum_of_probes**2))
@@ -365,9 +362,7 @@ class prober(object):
else:
return sum_of_probes*(1./num)
-
-
- def print_progress(self, num): # > prints progress status by in upto 10 dots
+ def print_progress(self, num): # > prints progress status upto 10 dots
tenths = 1+(10*num//self.nrun)
about.infos.cflush(("\b")*10+('.')*tenths+(' ')*(10-tenths))
"""
@@ -379,7 +374,7 @@ class prober(object):
return self.probing(zipped[1],probe)
"""
- def probe(self): # > performs the probing operations one after another
+ def probe(self): # > performs the probing operations one after another
# initialize the variables
sum_of_probes = 0
sum_of_squares = 0
@@ -479,7 +474,7 @@ class inverse_trace_prober(_specialized_prober):
def _probing_function(self, probe):
return direct_vdot(probe.conjugate(),
- self.operator.inverse_times(probe))
+ self.operator.inverse_times(probe))
class diagonal_prober(_specialized_prober):
diff --git a/rg/nifty_power_conversion_rg.py b/rg/nifty_power_conversion_rg.py
index 3fc4883fb..8255195f5 100644
--- a/rg/nifty_power_conversion_rg.py
+++ b/rg/nifty_power_conversion_rg.py
@@ -22,7 +22,7 @@
import numpy as np
from nifty.config import about
-from nifty.nifty_field import field
+from nifty.field import Field
def power_backward_conversion_rg(k_space, p, mean=None, bare=True):
@@ -88,7 +88,7 @@ def power_backward_conversion_rg(k_space, p, mean=None, bare=True):
p_val = pindex.apply_scalar_function(lambda x: spec[x],
dtype=spec.dtype.type)
- power_field = field(k_space, val=p_val, zerocenter=True).transform()
+ power_field = Field(k_space, val=p_val, zerocenter=True).transform()
power_field += (mean**2)
if power_field.min() < 0:
@@ -164,7 +164,7 @@ def power_forward_conversion_rg(k_space, p, mean=0, bare=True):
dtype=spec.dtype.type)
# S_x is a field
- S_x = field(k_space, val=S_val, zerocenter=True).transform()
+ S_x = Field(k_space, val=S_val, zerocenter=True).transform()
# s_0 is a scalar
s_0 = k_space.calc_weight(S_val, power=1).sum()
diff --git a/rg/nifty_rg.py b/rg/nifty_rg.py
index 4ce2bfc77..c3330d28a 100644
--- a/rg/nifty_rg.py
+++ b/rg/nifty_rg.py
@@ -43,8 +43,8 @@ from matplotlib.ticker import LogFormatter as lf
from d2o import STRATEGIES as DISTRIBUTION_STRATEGIES
-from nifty.nifty_core import point_space
-from nifty.nifty_field import field
+from nifty.space import Space
+from nifty.field import Field
import nifty_fft
from nifty.config import about,\
@@ -59,7 +59,7 @@ MPI = gdi[gc['mpi_module']]
RG_DISTRIBUTION_STRATEGIES = DISTRIBUTION_STRATEGIES['global']
-class rg_space(point_space):
+class rg_space(Space):
"""
.. _____ _______
.. / __/ / _ /
@@ -153,7 +153,7 @@ class rg_space(point_space):
-------
None
"""
- self._cache_dict = {'check_codomain':{}}
+ self._cache_dict = {'check_codomain': {}}
self.paradict = rg_space_paradict(shape=shape,
complexity=complexity,
zerocenter=zerocenter)
@@ -252,7 +252,7 @@ class rg_space(point_space):
def shape(self):
return tuple(self.paradict['shape'])
- def _complement_cast(self, x, axis=None, hermitianize=True):
+ def complement_cast(self, x, axis=None, hermitianize=True):
if axis is None:
if x is not None and hermitianize and self.paradict['complexity']\
== 1 and not x.hermitian:
@@ -1274,7 +1274,7 @@ class rg_space(point_space):
"""
about.warnings.cflush(
"WARNING: _enforce_values is deprecated function. Please use self.cast")
- if(isinstance(x, field)):
+ if(isinstance(x, Field)):
if(self == x.domain):
if(self.dtype is not x.domain.dtype):
raise TypeError(about._errors.cstring("ERROR: inequal data types ( '" + str(
@@ -1429,12 +1429,12 @@ class rg_space(point_space):
if(isinstance(other, tuple)):
other = list(other)
for ii in xrange(len(other)):
- if(isinstance(other[ii], field)):
+ if(isinstance(other[ii], Field)):
other[ii] = other[ii].power(**kwargs)
else:
other[ii] = self.enforce_power(
other[ii], size=np.size(xaxes), kindex=xaxes)
- elif(isinstance(other, field)):
+ elif(isinstance(other, Field)):
other = [other.power(**kwargs)]
else:
other = [self.enforce_power(
diff --git a/setup.py b/setup.py
index 755fcb5ee..b0b6a8df0 100644
--- a/setup.py
+++ b/setup.py
@@ -34,7 +34,7 @@ setup(name="ift_nifty",
url="http://www.mpa-garching.mpg.de/ift/nifty/",
packages=["nifty", "nifty.demos", "nifty.rg", "nifty.lm",
"nifty.operators", "nifty.dummys", "nifty.field_types",
- "nifty.config"],
+ "nifty.config", "nifty.power"],
package_dir={"nifty": ""},
zip_safe=False,
dependency_links=[
diff --git a/space.py b/space.py
index b7286cfe7..39461f8d8 100644
--- a/space.py
+++ b/space.py
@@ -144,594 +144,15 @@ from __future__ import division
import numpy as np
import pylab as pl
-from d2o import distributed_data_object,\
- STRATEGIES as DISTRIBUTION_STRATEGIES
+from d2o import STRATEGIES as DISTRIBUTION_STRATEGIES
-from nifty_paradict import space_paradict,\
- point_space_paradict
+from nifty_paradict import space_paradict
from nifty.config import about
-from nifty_random import random
-
POINT_DISTRIBUTION_STRATEGIES = DISTRIBUTION_STRATEGIES['global']
-#class space(object):
-# """
-# .. _______ ______ ____ __ _______ _______
-# .. / _____/ / _ | / _ / / ____/ / __ /
-# .. /_____ / / /_/ / / /_/ / / /____ / /____/
-# .. /_______/ / ____/ \______| \______/ \______/ class
-# .. /__/
-#
-# NIFTY base class for spaces and their discretizations.
-#
-# The base NIFTY space class is an abstract class from which other
-# specific space subclasses, including those preimplemented in NIFTY
-# (e.g. the regular grid class) must be derived.
-#
-# Parameters
-# ----------
-# dtype : numpy.dtype, *optional*
-# Data type of the field values for a field defined on this space
-# (default: numpy.float64).
-# datamodel :
-#
-# See Also
-# --------
-# point_space : A class for unstructured lists of numbers.
-# rg_space : A class for regular cartesian grids in arbitrary dimensions.
-# hp_space : A class for the HEALPix discretization of the sphere
-# [#]_.
-# gl_space : A class for the Gauss-Legendre discretization of the sphere
-# [#]_.
-# lm_space : A class for spherical harmonic components.
-# nested_space : A class for product spaces.
-#
-# References
-# ----------
-# .. [#] K.M. Gorski et al., 2005, "HEALPix: A Framework for
-# High-Resolution Discretization and Fast Analysis of Data
-# Distributed on the Sphere", *ApJ* 622..759G.
-# .. [#] M. Reinecke and D. Sverre Seljebotn, 2013, "Libsharp - spherical
-# harmonic transforms revisited";
-# `arXiv:1303.4945 <http://www.arxiv.org/abs/1303.4945>`_
-#
-# Attributes
-# ----------
-# para : {single object, list of objects}
-# This is a freeform list of parameters that derivatives of the space
-# class can use.
-# dtype : numpy.dtype
-# Data type of the field values for a field defined on this space.
-# discrete : bool
-# Whether the space is inherently discrete (true) or a discretization
-# of a continuous space (false).
-# vol : numpy.ndarray
-# An array of pixel volumes, only one component if the pixels all
-# have the same volume.
-# """
-#
-# def __init__(self):
-# """
-# Sets the attributes for a space class instance.
-#
-# Parameters
-# ----------
-# dtype : numpy.dtype, *optional*
-# Data type of the field values for a field defined on this space
-# (default: numpy.float64).
-# datamodel :
-#
-# Returns
-# -------
-# None
-# """
-# self.paradict = space_paradict()
-#
-# @property
-# def para(self):
-# return self.paradict['default']
-#
-# @para.setter
-# def para(self, x):
-# self.paradict['default'] = x
-#
-# def __hash__(self):
-# return hash(())
-#
-# def _identifier(self):
-# """
-# _identiftier returns an object which contains all information needed
-# to uniquely idetnify a space. It returns a (immutable) tuple which
-# therefore can be compared.
-# """
-# return tuple(sorted(vars(self).items()))
-#
-# def __eq__(self, x):
-# if isinstance(x, type(self)):
-# return self._identifier() == x._identifier()
-# else:
-# return False
-#
-# def __ne__(self, x):
-# return not self.__eq__(x)
-#
-# def __len__(self):
-# return int(self.dim)
-#
-# def copy(self):
-# return space(para=self.para,
-# dtype=self.dtype)
-#
-# def getitem(self, data, key):
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'getitem'."))
-#
-# def setitem(self, data, update, key):
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'getitem'."))
-#
-# def apply_scalar_function(self, x, function, inplace=False):
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'apply_scalar_function'."))
-#
-# @property
-# def shape(self):
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'shape'."))
-#
-# @property
-# def dim(self):
-# """
-# Computes the dimension of the space, i.e.\ the number of pixels.
-#
-# Parameters
-# ----------
-# split : bool, *optional*
-# Whether to return the dimension split up, i.e. the numbers of
-# pixels in each direction, or not (default: False).
-#
-# Returns
-# -------
-# dim : {int, numpy.ndarray}
-# Dimension(s) of the space.
-# """
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'dim'."))
-#
-# @property
-# def dof(self):
-# """
-# Computes the number of degrees of freedom of the space.
-#
-# Returns
-# -------
-# dof : int
-# Number of degrees of freedom of the space.
-# """
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'dof'."))
-#
-# @property
-# def dof_split(self):
-# """
-# Computes the number of degrees of freedom of the space.
-#
-# Returns
-# -------
-# dof : int
-# Number of degrees of freedom of the space.
-# """
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'dof_split'."))
-#
-# def complement_cast(self, x, axis=None):
-# return x
-#
-# # TODO: Move enforce power into power_indices class
-# def enforce_power(self, spec, **kwargs):
-# """
-# Provides a valid power spectrum array from a given object.
-#
-# Parameters
-# ----------
-# spec : {scalar, list, numpy.ndarray, nifty.field, function}
-# Fiducial power spectrum from which a valid power spectrum is to
-# be calculated. Scalars are interpreted as constant power
-# spectra.
-#
-# Returns
-# -------
-# spec : numpy.ndarray
-# Valid power spectrum.
-#
-# Other parameters
-# ----------------
-# size : int, *optional*
-# Number of bands the power spectrum shall have (default: None).
-# kindex : numpy.ndarray, *optional*
-# Scale of each band.
-# codomain : nifty.space, *optional*
-# A compatible codomain for power indexing (default: None).
-# log : bool, *optional*
-# Flag specifying if the spectral binning is performed on
-# logarithmic
-# scale or not; if set, the number of used bins is set
-# automatically (if not given otherwise); by default no binning
-# is done (default: None).
-# nbin : integer, *optional*
-# Number of used spectral bins; if given `log` is set to
-# ``False``;
-# integers below the minimum of 3 induce an automatic setting;
-# by default no binning is done (default: None).
-# binbounds : {list, array}, *optional*
-# User specific inner boundaries of the bins, which are preferred
-# over the above parameters; by default no binning is done
-# (default: None).
-# vmin : {scalar, list, ndarray, field}, *optional*
-# Lower limit of the uniform distribution if ``random == "uni"``
-# (default: 0).
-#
-# """
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'enforce_power'."))
-#
-# def check_codomain(self, codomain):
-# """
-# Checks whether a given codomain is compatible to the space or not.
-#
-# Parameters
-# ----------
-# codomain : nifty.space
-# Space to be checked for compatibility.
-#
-# Returns
-# -------
-# check : bool
-# Whether or not the given codomain is compatible to the space.
-# """
-# if codomain is None:
-# return False
-# else:
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'check_codomain'."))
-#
-# def get_codomain(self, **kwargs):
-# """
-# Generates a compatible codomain to which transformations are
-# reasonable, usually either the position basis or the basis of
-# harmonic eigenmodes.
-#
-# Parameters
-# ----------
-# coname : string, *optional*
-# String specifying a desired codomain (default: None).
-# cozerocenter : {bool, numpy.ndarray}, *optional*
-# Whether or not the grid is zerocentered for each axis or not
-# (default: None).
-# conest : list, *optional*
-# List of nested spaces of the codomain (default: None).
-# coorder : list, *optional*
-# Permutation of the list of nested spaces (default: None).
-#
-# Returns
-# -------
-# codomain : nifty.space
-# A compatible codomain.
-# """
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'get_codomain'."))
-#
-# def get_random_values(self, **kwargs):
-# """
-# Generates random field values according to the specifications given
-# by the parameters.
-#
-# Returns
-# -------
-# x : numpy.ndarray
-# Valid field values.
-#
-# Other parameters
-# ----------------
-# random : string, *optional*
-# Specifies the probability distribution from which the random
-# numbers are to be drawn.
-# Supported distributions are:
-#
-# - "pm1" (uniform distribution over {+1,-1} or {+1,+i,-1,-i}
-# - "gau" (normal distribution with zero-mean and a given
-# standard deviation or variance)
-# - "syn" (synthesizes from a given power spectrum)
-# - "uni" (uniform distribution over [vmin,vmax[)
-#
-# (default: None).
-# dev : float, *optional*
-# Standard deviation (default: 1).
-# var : float, *optional*
-# Variance, overriding `dev` if both are specified
-# (default: 1).
-# spec : {scalar, list, numpy.ndarray, nifty.field, function},
-# *optional*
-# Power spectrum (default: 1).
-# pindex : numpy.ndarray, *optional*
-# Indexing array giving the power spectrum index of each band
-# (default: None).
-# kindex : numpy.ndarray, *optional*
-# Scale of each band (default: None).
-# codomain : nifty.space, *optional*
-# A compatible codomain with power indices (default: None).
-# log : bool, *optional*
-# Flag specifying if the spectral binning is performed on
-# logarithmic
-# scale or not; if set, the number of used bins is set
-# automatically (if not given otherwise); by default no binning
-# is done (default: None).
-# nbin : integer, *optional*
-# Number of used spectral bins; if given `log` is set to
-# ``False``;
-# integers below the minimum of 3 induce an automatic setting;
-# by default no binning is done (default: None).
-# binbounds : {list, array}, *optional*
-# User specific inner boundaries of the bins, which are preferred
-# over the above parameters; by default no binning is done
-# (default: None).
-# vmin : {scalar, list, ndarray, field}, *optional*
-# Lower limit of the uniform distribution if ``random == "uni"``
-# (default: 0).
-# vmin : float, *optional*
-# Lower limit for a uniform distribution (default: 0).
-# vmax : float, *optional*
-# Upper limit for a uniform distribution (default: 1).
-# """
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'get_random_values'."))
-#
-# def calc_weight(self, x, power=1):
-# """
-# Weights a given array of field values with the pixel volumes (not
-# the meta volumes) to a given power.
-#
-# Parameters
-# ----------
-# x : numpy.ndarray
-# Array to be weighted.
-# power : float, *optional*
-# Power of the pixel volumes to be used (default: 1).
-#
-# Returns
-# -------
-# y : numpy.ndarray
-# Weighted array.
-# """
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'calc_weight'."))
-#
-# def get_weight(self, power=1):
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'get_weight'."))
-#
-# def calc_norm(self, x, q):
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'norm'."))
-#
-# def dot_contraction(self, x, axes):
-# """
-# Computes the discrete inner product of two given arrays of field
-# values.
-#
-# Parameters
-# ----------
-# x : numpy.ndarray
-# First array
-# y : numpy.ndarray
-# Second array
-#
-# Returns
-# -------
-# dot : scalar
-# Inner product of the two arrays.
-# """
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'dot'."))
-#
-# def calc_transform(self, x, codomain=None, **kwargs):
-# """
-# Computes the transform of a given array of field values.
-#
-# Parameters
-# ----------
-# x : numpy.ndarray
-# Array to be transformed.
-# codomain : nifty.space, *optional*
-# codomain space to which the transformation shall map
-# (default: self).
-#
-# Returns
-# -------
-# Tx : numpy.ndarray
-# Transformed array
-#
-# Other parameters
-# ----------------
-# iter : int, *optional*
-# Number of iterations performed in specific transformations.
-# """
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'calc_transform'."))
-#
-# def calc_smooth(self, x, sigma=0, **kwargs):
-# """
-# Smoothes an array of field values by convolution with a Gaussian
-# kernel.
-#
-# Parameters
-# ----------
-# x : numpy.ndarray
-# Array of field values to be smoothed.
-# sigma : float, *optional*
-# Standard deviation of the Gaussian kernel, specified in units
-# of length in position space (default: 0).
-#
-# Returns
-# -------
-# Gx : numpy.ndarray
-# Smoothed array.
-#
-# Other parameters
-# ----------------
-# iter : int, *optional*
-# Number of iterations (default: 0).
-# """
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'calc_smooth'."))
-#
-# def calc_power(self, x, **kwargs):
-# """
-# Computes the power of an array of field values.
-#
-# Parameters
-# ----------
-# x : numpy.ndarray
-# Array containing the field values of which the power is to be
-# calculated.
-#
-# Returns
-# -------
-# spec : numpy.ndarray
-# Power contained in the input array.
-#
-# Other parameters
-# ----------------
-# pindex : numpy.ndarray, *optional*
-# Indexing array assigning the input array components to
-# components of the power spectrum (default: None).
-# kindex : numpy.ndarray, *optional*
-# Scale corresponding to each band in the power spectrum
-# (default: None).
-# rho : numpy.ndarray, *optional*
-# Number of degrees of freedom per band (default: None).
-# codomain : nifty.space, *optional*
-# A compatible codomain for power indexing (default: None).
-# log : bool, *optional*
-# Flag specifying if the spectral binning is performed on
-# logarithmic
-# scale or not; if set, the number of used bins is set
-# automatically (if not given otherwise); by default no binning
-# is done (default: None).
-# nbin : integer, *optional*
-# Number of used spectral bins; if given `log` is set to
-# ``False``;
-# integers below the minimum of 3 induce an automatic setting;
-# by default no binning is done (default: None).
-# binbounds : {list, array}, *optional*
-# User specific inner boundaries of the bins, which are preferred
-# over the above parameters; by default no binning is done
-# (default: None).
-# vmin : {scalar, list, ndarray, field}, *optional*
-# Lower limit of the uniform distribution if ``random == "uni"``
-# (default: 0).
-#
-# """
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'calc_power'."))
-#
-# def calc_real_Q(self, x):
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'calc_real_Q'."))
-#
-# def calc_bincount(self, x, weights=None, minlength=None):
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'calc_bincount'."))
-#
-# def get_plot(self, x, **kwargs):
-# """
-# Creates a plot of field values according to the specifications
-# given by the parameters.
-#
-# Parameters
-# ----------
-# x : numpy.ndarray
-# Array containing the field values.
-#
-# Returns
-# -------
-# None
-#
-# Other parameters
-# ----------------
-# title : string, *optional*
-# Title of the plot (default: "").
-# vmin : float, *optional*
-# Minimum value to be displayed (default: ``min(x)``).
-# vmax : float, *optional*
-# Maximum value to be displayed (default: ``max(x)``).
-# power : bool, *optional*
-# Whether to plot the power contained in the field or the field
-# values themselves (default: False).
-# unit : string, *optional*
-# Unit of the field values (default: "").
-# norm : string, *optional*
-# Scaling of the field values before plotting (default: None).
-# cmap : matplotlib.colors.LinearSegmentedColormap, *optional*
-# Color map to be used for two-dimensional plots (default: None).
-# cbar : bool, *optional*
-# Whether to show the color bar or not (default: True).
-# other : {single object, tuple of objects}, *optional*
-# Object or tuple of objects to be added, where objects can be
-# scalars, arrays, or fields (default: None).
-# legend : bool, *optional*
-# Whether to show the legend or not (default: False).
-# mono : bool, *optional*
-# Whether to plot the monopole or not (default: True).
-# save : string, *optional*
-# Valid file name where the figure is to be stored, by default
-# the figure is not saved (default: False).
-# error : {float, numpy.ndarray, nifty.field}, *optional*
-# Object indicating some confidence interval to be plotted
-# (default: None).
-# kindex : numpy.ndarray, *optional*
-# Scale corresponding to each band in the power spectrum
-# (default: None).
-# codomain : nifty.space, *optional*
-# A compatible codomain for power indexing (default: None).
-# log : bool, *optional*
-# Flag specifying if the spectral binning is performed on
-# logarithmic
-# scale or not; if set, the number of used bins is set
-# automatically (if not given otherwise); by default no binning
-# is done (default: None).
-# nbin : integer, *optional*
-# Number of used spectral bins; if given `log` is set to
-# ``False``;
-# integers below the minimum of 3 induce an automatic setting;
-# by default no binning is done (default: None).
-# binbounds : {list, array}, *optional*
-# User specific inner boundaries of the bins, which are preferred
-# over the above parameters; by default no binning is done
-# (default: None).
-# vmin : {scalar, list, ndarray, field}, *optional*
-# Lower limit of the uniform distribution if ``random == "uni"``
-# (default: 0).
-# iter : int, *optional*
-# Number of iterations (default: 0).
-#
-# """
-# raise NotImplementedError(about._errors.cstring(
-# "ERROR: no generic instance method 'get_plot'."))
-#
-# def __repr__(self):
-# string = ""
-# string += str(type(self)) + "\n"
-# string += "paradict: " + str(self.paradict) + "\n"
-# return string
-#
-# def __str__(self):
-# return self.__repr__()
-
-
class Space(object):
"""
.. __ __
@@ -767,7 +188,7 @@ class Space(object):
Pixel volume of the :py:class:`point_space`, which is always 1.
"""
- def __init__(self, num, dtype=np.dtype('float')):
+ def __init__(self, dtype=np.dtype('float'), **kwargs):
"""
Sets the attributes for a point_space class instance.
@@ -783,7 +204,7 @@ class Space(object):
None.
"""
self._cache_dict = {'check_codomain': {}}
- self.paradict = point_space_paradict(num=num)
+ self.paradict = space_paradict(**kwargs)
# parse dtype
dtype = np.dtype(dtype)
@@ -832,8 +253,7 @@ class Space(object):
return tuple(sorted(temp))
def copy(self):
- return point_space(num=self.paradict['num'],
- dtype=self.dtype)
+ return Space(dtype=self.dtype, **self.paradict.parameters)
def getitem(self, data, key):
return data[key]
@@ -937,6 +357,9 @@ class Space(object):
mol = self.cast(1, dtype=np.dtype('float'))
return self.calc_weight(mol, power=1)
+ def complement_cast(self, x, axis=None):
+ return x
+
def enforce_power(self, spec, **kwargs):
"""
Raises an error since the power spectrum is ill-defined for point
@@ -1041,7 +464,7 @@ class Space(object):
if codomain is None:
return False
- if not isinstance(codomain, space):
+ if not isinstance(codomain, Space):
raise TypeError(about._errors.cstring(
"ERROR: invalid input. The given input is not a nifty space."))
diff --git a/test/test_nifty_field.py b/test/test_nifty_field.py
index 92c496ea8..e30768d34 100644
--- a/test/test_nifty_field.py
+++ b/test/test_nifty_field.py
@@ -18,7 +18,7 @@ from nifty import space, \
hp_space, \
gl_space
-from nifty.nifty_field import field
+from nifty.field import Field
from nifty.nifty_core import POINT_DISTRIBUTION_STRATEGIES
from nifty.rg.nifty_rg import RG_DISTRIBUTION_STRATEGIES, \
@@ -118,6 +118,7 @@ def generate_data(space):
a = np.arange(space.dim).reshape(space.shape)
return distributed_data_object(a)
+
###############################################################################
###############################################################################
@@ -137,7 +138,7 @@ class Test_field_init(unittest.TestCase):
s = rg_space(shape=shape, zerocenter=zerocenter,
complexity=complexity, distances=distances,
harmonic=harmonic, fft_module=fft_module)
- f = field(domain=(s,), dtype=s.dtype, datamodel=datamodel)
+ f = Field(domain=(s,), dtype=s.dtype, datamodel=datamodel)
assert (f.domain[0] is s)
assert (s.check_codomain(f.codomain[0]))
assert (s.shape == f.shape)
@@ -151,11 +152,12 @@ class Test_field_init2(unittest.TestCase):
def test_successfull_init_and_attributes(self, name, num, datamodel):
s = generate_space_with_size(name, num)
d = generate_data(s)
- f = field(val=d, domain=(s,), dtype=s.dtype, datamodel=datamodel)
+ f = Field(val=d, domain=(s,), dtype=s.dtype, datamodel=datamodel)
assert (f.domain[0] is s)
assert (s.check_codomain(f.codomain[0]))
assert (s.shape == f.shape)
+
class Test_field_multiple_rg_init(unittest.TestCase):
@parameterized.expand(
itertools.product([(1,)],
@@ -175,13 +177,14 @@ class Test_field_multiple_rg_init(unittest.TestCase):
s2 = rg_space(shape=shape, zerocenter=zerocenter,
complexity=complexity, distances=distances,
harmonic=harmonic, fft_module=fft_module)
- f = field(domain=(s1, s2), dtype=s1.dtype, datamodel=datamodel)
+ f = Field(domain=(s1, s2), dtype=s1.dtype, datamodel=datamodel)
assert (f.domain[0] is s1)
assert (f.domain[1] is s2)
assert (s1.check_codomain(f.codomain[0]))
assert (s2.check_codomain(f.codomain[1]))
assert (s1.shape + s2.shape == f.shape)
+
class Test_field_multiple_init(unittest.TestCase):
@parameterized.expand(
itertools.product(point_like_spaces, point_like_spaces, [4]),
@@ -189,14 +192,14 @@ class Test_field_multiple_init(unittest.TestCase):
def test_multiple_space_init(self, space1, space2, shape):
s1 = generate_space_with_size(space1, shape)
s2 = generate_space_with_size(space2, shape)
- f = field(domain=(s1, s2))
+ f = Field(domain=(s1, s2))
assert (f.domain[0] is s1)
assert (f.domain[1] is s2)
assert (s1.check_codomain(f.codomain[0]))
assert (s2.check_codomain(f.codomain[1]))
assert (s1.shape + s2.shape == f.shape)
- s3 = generate_space_with_size('hp_space',shape)
- f = field(domain=(s1, s2, s3))
+ s3 = generate_space_with_size('hp_space', shape)
+ f = Field(domain=(s1, s2, s3))
assert (f.domain[0] is s1)
assert (f.domain[1] is s2)
assert (f.domain[2] is s3)
@@ -220,10 +223,10 @@ class Test_axis(unittest.TestCase):
s = generate_space_with_size(name, num)
d = generate_data(s)
a = d.get_full_data()
- f = field(val=d, domain=(s,), dtype=s.dtype, datamodel=datamodel)
- if op in ['argmin','argmax']:
+ f = Field(val=d, domain=(s,), dtype=s.dtype, datamodel=datamodel)
+ if op in ['argmin', 'argmax']:
assert_almost_equal(getattr(f, op)(),
- getattr(np, op)(a), decimal=4)
+ getattr(np, op)(a), decimal=4)
else:
assert_almost_equal(getattr(f, op)(axis=axis),
getattr(np, op)(a, axis=axis), decimal=4)
@@ -246,7 +249,7 @@ class Test_binary_operation(unittest.TestCase):
s = generate_space_with_size(name, num)
d = generate_data(s)
a = d.get_full_data()
- f = field(val=d, domain=(s,), dtype=s.dtype, datamodel=datamodel)
+ f = Field(val=d, domain=(s,), dtype=s.dtype, datamodel=datamodel)
d2 = d[::-1]
a2 = np.copy(a[::-1])
if op[0] in ['iadd','isub','imul','idiv']:
diff --git a/test/test_nifty_spaces.py b/test/test_nifty_spaces.py
index bb37e19e5..f59f2b9ff 100644
--- a/test/test_nifty_spaces.py
+++ b/test/test_nifty_spaces.py
@@ -9,7 +9,7 @@ import unittest
import itertools
import numpy as np
-from nifty.nifty_field import field
+from nifty.field import Field
from d2o import distributed_data_object
@@ -478,7 +478,7 @@ class Test_Point_Space(unittest.TestCase):
num = 10
a = np.arange(num,).astype(dtype)
s = point_space(num, dtype)
- f = field(s, val=a)
+ f = Field(s, val=a)
d = distributed_data_object(a, dtype=dtype)
--
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