power_space.py 9.65 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
Theo Steininger's avatar
Theo Steininger committed
13
#
Martin Reinecke's avatar
Martin Reinecke committed
14
# Copyright(C) 2013-2018 Max-Planck-Society
Theo Steininger's avatar
Theo Steininger committed
15
16
17
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.
Theo Steininger's avatar
Theo Steininger committed
18

19
from __future__ import absolute_import, division, print_function
Philipp Arras's avatar
Philipp Arras committed
20

21
import numpy as np
Philipp Arras's avatar
Philipp Arras committed
22

Martin Reinecke's avatar
Martin Reinecke committed
23
from .. import dobj
Philipp Arras's avatar
Philipp Arras committed
24
25
from ..compat import *
from .structured_domain import StructuredDomain
Theo Steininger's avatar
Theo Steininger committed
26
27


Martin Reinecke's avatar
Martin Reinecke committed
28
class PowerSpace(StructuredDomain):
Martin Reinecke's avatar
Martin Reinecke committed
29
    """NIFTy class for spaces of power spectra.
Theo Steininger's avatar
Theo Steininger committed
30

Martin Reinecke's avatar
Martin Reinecke committed
31
    A power space is the result of a projection of a harmonic domain where
Martin Reinecke's avatar
Martin Reinecke committed
32
33
    k-modes of equal length get mapped to one power index.

Theo Steininger's avatar
Theo Steininger committed
34
35
    Parameters
    ----------
Martin Reinecke's avatar
Martin Reinecke committed
36
    harmonic_partner : StructuredDomain
Martin Reinecke's avatar
Martin Reinecke committed
37
38
        The harmonic domain of which this is the power space.
    binbounds : None, or tuple of float (default: None)
Martin Reinecke's avatar
Martin Reinecke committed
39
40
41
        if None:
            There will be as many bins as there are distinct k-vector lengths
            in the harmonic partner space.
Martin Reinecke's avatar
Martin Reinecke committed
42
            The `binbounds` property of the PowerSpace will also be None.
Martin Reinecke's avatar
Martin Reinecke committed
43
44
45
46
        else:
            the bin bounds requested for this PowerSpace. The array
            must be sorted and strictly ascending. The first entry is the right
            boundary of the first bin, and the last entry is the left boundary
Martin Reinecke's avatar
Martin Reinecke committed
47
48
49
            of the last bin, i.e. thee will be `len(binbounds)+1` bins in
            total, with the first and last bins reaching to -+infinity,
            respectively.
Theo Steininger's avatar
Theo Steininger committed
50
    """
51

52
    _powerIndexCache = {}
Martin Reinecke's avatar
Martin Reinecke committed
53
    _needed_for_hash = ["_harmonic_partner", "_binbounds"]
54

Martin Reinecke's avatar
Martin Reinecke committed
55
    @staticmethod
Martin Reinecke's avatar
PEP8    
Martin Reinecke committed
56
    def linear_binbounds(nbin, first_bound, last_bound):
Martin Reinecke's avatar
Martin Reinecke committed
57
58
        """Produces linearly spaced bin bounds.

Martin Reinecke's avatar
Martin Reinecke committed
59
60
        Parameters
        ----------
Martin Reinecke's avatar
Martin Reinecke committed
61
        nbin : int
Martin Reinecke's avatar
Martin Reinecke committed
62
            the number of bins
Martin Reinecke's avatar
Martin Reinecke committed
63
        first_bound, last_bound : float
Martin Reinecke's avatar
Martin Reinecke committed
64
65
66
            the k values for the right boundary of the first bin and the left
            boundary of the last bin, respectively. They are given in length
            units of the harmonic partner space.
Martin Reinecke's avatar
Martin Reinecke committed
67
68
69
70
71
72
73
74

        Returns
        -------
        numpy.ndarray(numpy.float64)
            binbounds array with nbin-1 entries with
            binbounds[0]=first_bound and binbounds[-1]=last_bound and the
            remaining values equidistantly spaced (in linear scale) between
            these two.
Martin Reinecke's avatar
Martin Reinecke committed
75
76
        """
        nbin = int(nbin)
77
78
        if nbin < 3:
            raise ValueError("nbin must be at least 3")
Martin Reinecke's avatar
PEP8    
Martin Reinecke committed
79
        return np.linspace(float(first_bound), float(last_bound), nbin-1)
Martin Reinecke's avatar
Martin Reinecke committed
80
81

    @staticmethod
Martin Reinecke's avatar
PEP8    
Martin Reinecke committed
82
    def logarithmic_binbounds(nbin, first_bound, last_bound):
Martin Reinecke's avatar
Martin Reinecke committed
83
84
        """Produces logarithmically spaced bin bounds.

Martin Reinecke's avatar
Martin Reinecke committed
85
86
        Parameters
        ----------
Martin Reinecke's avatar
Martin Reinecke committed
87
        nbin : int
Martin Reinecke's avatar
Martin Reinecke committed
88
            the number of bins
Martin Reinecke's avatar
Martin Reinecke committed
89
        first_bound, last_bound : float
Martin Reinecke's avatar
Martin Reinecke committed
90
91
92
            the k values for the right boundary of the first bin and the left
            boundary of the last bin, respectively. They are given in length
            units of the harmonic partner space.
Martin Reinecke's avatar
Martin Reinecke committed
93
94
95
96
97
98
99
100

        Returns
        -------
        numpy.ndarray(numpy.float64)
            binbounds array with nbin-1 entries with
            binbounds[0]=first_bound and binbounds[-1]=last_bound and the
            remaining values equidistantly spaced (in natural logarithmic
            scale) between these two.
Martin Reinecke's avatar
Martin Reinecke committed
101
        """
Martin Reinecke's avatar
Martin Reinecke committed
102
        nbin = int(nbin)
103
104
        if nbin < 3:
            raise ValueError("nbin must be at least 3")
Martin Reinecke's avatar
Martin Reinecke committed
105
106
107
        return np.logspace(np.log(float(first_bound)),
                           np.log(float(last_bound)),
                           nbin-1, base=np.e)
Martin Reinecke's avatar
Martin Reinecke committed
108

109
110
    @staticmethod
    def useful_binbounds(space, logarithmic, nbin=None):
Martin Reinecke's avatar
Martin Reinecke committed
111
112
        """Produces bin bounds suitable for a given domain.

Martin Reinecke's avatar
Martin Reinecke committed
113
114
        Parameters
        ----------
Martin Reinecke's avatar
Martin Reinecke committed
115
116
117
118
        space : StructuredDomain
            the domain for which the binbounds will be computed.
        logarithmic : bool
            If True bins will have equal size in linear space; otherwise they
Martin Reinecke's avatar
Martin Reinecke committed
119
            will have equal size in logarithmic space.
Martin Reinecke's avatar
Martin Reinecke committed
120
121
122
        nbin : int, optional
            the number of bins
            If None, the highest possible number of bins will be used
Martin Reinecke's avatar
Martin Reinecke committed
123
124
125
126
127
128
129
130

        Returns
        -------
        numpy.ndarray(numpy.float64)
            Binbounds array with `nbin-1` entries, if `nbin` is
            supplied, or the maximum number of entries that does not produce
            empty bins, if `nbin` is not supplied.
            The first and last bin boundary are inferred from `space`.
Martin Reinecke's avatar
Martin Reinecke committed
131
        """
Martin Reinecke's avatar
Martin Reinecke committed
132
        if not (isinstance(space, StructuredDomain) and space.harmonic):
133
134
135
136
137
            raise ValueError("first argument must be a harmonic space.")
        if logarithmic is None and nbin is None:
            return None
        nbin = None if nbin is None else int(nbin)
        logarithmic = bool(logarithmic)
138
        dists = space.get_unique_k_lengths()
139
140
141
142
143
144
145
146
147
148
149
150
        if len(dists) < 3:
            raise ValueError("Space does not have enough unique k lengths")
        lbound = 0.5*(dists[0]+dists[1])
        rbound = 0.5*(dists[-2]+dists[-1])
        dists[0] = lbound
        dists[-1] = rbound
        if logarithmic:
            dists = np.log(dists)
        binsz_min = np.max(np.diff(dists))
        nbin_max = int((dists[-1]-dists[0])/binsz_min)+2
        if nbin is None:
            nbin = nbin_max
151
152
        if nbin < 3:
            raise ValueError("nbin must be at least 3")
153
154
155
156
157
158
159
        if nbin > nbin_max:
            raise ValueError("nbin is too large")
        if logarithmic:
            return PowerSpace.logarithmic_binbounds(nbin, lbound, rbound)
        else:
            return PowerSpace.linear_binbounds(nbin, lbound, rbound)

Martin Reinecke's avatar
Martin Reinecke committed
160
    def __init__(self, harmonic_partner, binbounds=None):
Martin Reinecke's avatar
Martin Reinecke committed
161
        if not (isinstance(harmonic_partner, StructuredDomain) and
Martin Reinecke's avatar
Martin Reinecke committed
162
163
                harmonic_partner.harmonic):
            raise ValueError("harmonic_partner must be a harmonic space.")
Martin Reinecke's avatar
Martin Reinecke committed
164
        if harmonic_partner.scalar_dvol is None:
Martin Reinecke's avatar
Martin Reinecke committed
165
166
            raise ValueError("harmonic partner must have "
                             "scalar volume factors")
167
        self._harmonic_partner = harmonic_partner
Martin Reinecke's avatar
Martin Reinecke committed
168
        pdvol = harmonic_partner.scalar_dvol
169

Martin Reinecke's avatar
Martin Reinecke committed
170
171
        if binbounds is not None:
            binbounds = tuple(binbounds)
172

Martin Reinecke's avatar
Martin Reinecke committed
173
        key = (harmonic_partner, binbounds)
174
        if self._powerIndexCache.get(key) is None:
175
            k_length_array = self.harmonic_partner.get_k_length_array()
Martin Reinecke's avatar
Martin Reinecke committed
176
177
178
179
180
            if binbounds is None:
                tmp = harmonic_partner.get_unique_k_lengths()
                tbb = 0.5*(tmp[:-1]+tmp[1:])
            else:
                tbb = binbounds
Martin Reinecke's avatar
Martin Reinecke committed
181
            locdat = np.searchsorted(tbb, k_length_array.local_data)
Martin Reinecke's avatar
tweaks    
Martin Reinecke committed
182
            temp_pindex = dobj.from_local_data(
183
184
                k_length_array.val.shape, locdat,
                dobj.distaxis(k_length_array.val))
Martin Reinecke's avatar
Martin Reinecke committed
185
            nbin = len(tbb)+1
Martin Reinecke's avatar
Martin Reinecke committed
186
187
            temp_rho = np.bincount(dobj.local_data(temp_pindex).ravel(),
                                   minlength=nbin)
Martin Reinecke's avatar
Martin Reinecke committed
188
            temp_rho = dobj.np_allreduce_sum(temp_rho)
189
190
            if (temp_rho == 0).any():
                raise ValueError("empty bins detected")
Martin Reinecke's avatar
Martin Reinecke committed
191
192
193
            # The explicit conversion to float64 is necessary because bincount
            # sometimes returns its result as an integer array, even when
            # floating-point weights are present ...
194
195
            temp_k_lengths = np.bincount(
                dobj.local_data(temp_pindex).ravel(),
Martin Reinecke's avatar
Martin Reinecke committed
196
                weights=k_length_array.local_data.ravel(),
Martin Reinecke's avatar
Martin Reinecke committed
197
                minlength=nbin).astype(np.float64, copy=False)
Martin Reinecke's avatar
Martin Reinecke committed
198
            temp_k_lengths = dobj.np_allreduce_sum(temp_k_lengths) / temp_rho
Martin Reinecke's avatar
Martin Reinecke committed
199
200
            temp_k_lengths.flags.writeable = False
            dobj.lock(temp_pindex)
Martin Reinecke's avatar
Martin Reinecke committed
201
            temp_dvol = temp_rho*pdvol
Martin Reinecke's avatar
Martin Reinecke committed
202
            temp_dvol.flags.writeable = False
Martin Reinecke's avatar
Martin Reinecke committed
203
204
            self._powerIndexCache[key] = (binbounds, temp_pindex,
                                          temp_k_lengths, temp_dvol)
205

Martin Reinecke's avatar
Martin Reinecke committed
206
        (self._binbounds, self._pindex, self._k_lengths, self._dvol) = \
207
208
            self._powerIndexCache[key]

209
    def __repr__(self):
Martin Reinecke's avatar
Martin Reinecke committed
210
211
        return ("PowerSpace(harmonic_partner={}, binbounds={})"
                .format(self.harmonic_partner, self._binbounds))
212

213
214
    @property
    def harmonic(self):
Martin Reinecke's avatar
Martin Reinecke committed
215
        """bool : Always False for this class."""
216
        return False
217

218
219
    @property
    def shape(self):
Martin Reinecke's avatar
Martin Reinecke committed
220
        return self.k_lengths.shape
221

222
    @property
Martin Reinecke's avatar
Martin Reinecke committed
223
    def size(self):
224
225
        return self.shape[0]

Martin Reinecke's avatar
Martin Reinecke committed
226
    @property
227
    def scalar_dvol(self):
Martin Reinecke's avatar
Martin Reinecke committed
228
229
        return None

Martin Reinecke's avatar
Martin Reinecke committed
230
    @property
Martin Reinecke's avatar
Martin Reinecke committed
231
232
    def dvol(self):
        return self._dvol
233

234
    @property
235
    def harmonic_partner(self):
Martin Reinecke's avatar
Martin Reinecke committed
236
        """StructuredDomain : the harmonic domain associated with `self`."""
237
        return self._harmonic_partner
238
239

    @property
Martin Reinecke's avatar
Martin Reinecke committed
240
    def binbounds(self):
Martin Reinecke's avatar
Martin Reinecke committed
241
242
243
244
        """None or tuple of float : inner bin boundaries

        The boundaries between bins, starting with the right boundary of the
        first bin, up to the left boundary of the last bin.
Martin Reinecke's avatar
Martin Reinecke committed
245
246

        `None` is used to indicate natural binning.
Martin Reinecke's avatar
Martin Reinecke committed
247
        """
Martin Reinecke's avatar
Martin Reinecke committed
248
        return self._binbounds
249
250
251

    @property
    def pindex(self):
Martin Reinecke's avatar
Martin Reinecke committed
252
253
254
        """data_object : bin indices

        Bin index for every pixel in the harmonic partner.
Theo Steininger's avatar
Theo Steininger committed
255
        """
256
257
258
        return self._pindex

    @property
Martin Reinecke's avatar
Martin Reinecke committed
259
    def k_lengths(self):
Martin Reinecke's avatar
Martin Reinecke committed
260
        """numpy.ndarray(float) : k-vector length for each bin."""
Martin Reinecke's avatar
Martin Reinecke committed
261
        return self._k_lengths