gl_space.py 6.65 KB
Newer Older
csongor's avatar
csongor committed
1
2
from __future__ import division

Jait Dixit's avatar
Jait Dixit committed
3
import itertools
csongor's avatar
csongor committed
4
5
import numpy as np

6
7
import d2o
from d2o import STRATEGIES as DISTRIBUTION_STRATEGIES
8
from keepers import Versionable
csongor's avatar
csongor committed
9

10
from nifty.spaces.space import Space
11
from nifty.config import nifty_configuration as gc,\
12
                         dependency_injector as gdi
13
import nifty.nifty_utilities as utilities
csongor's avatar
csongor committed
14
15
16
17
18

gl = gdi.get('libsharp_wrapper_gl')

GL_DISTRIBUTION_STRATEGIES = DISTRIBUTION_STRATEGIES['global']

19

20
class GLSpace(Versionable, Space):
csongor's avatar
csongor committed
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
    """
        ..                 __
        ..               /  /
        ..     ____ __  /  /
        ..   /   _   / /  /
        ..  /  /_/  / /  /_
        ..  \___   /  \___/  space class
        .. /______/

        NIFTY subclass for Gauss-Legendre pixelizations [#]_ of the two-sphere.

        Parameters
        ----------
        nlat : int
            Number of latitudinal bins, or rings.
        nlon : int, *optional*
            Number of longitudinal bins (default: ``2*nlat - 1``).
        dtype : numpy.dtype, *optional*
            Data type of the field values (default: numpy.float64).

        See Also
        --------
        hp_space : A class for the HEALPix discretization of the sphere [#]_.
        lm_space : A class for spherical harmonic components.

        Notes
        -----
        Only real-valued fields on the two-sphere are supported, i.e.
        `dtype` has to be either numpy.float64 or numpy.float32.

        References
        ----------
        .. [#] M. Reinecke and D. Sverre Seljebotn, 2013, "Libsharp - spherical
               harmonic transforms revisited";
               `arXiv:1303.4945 <http://www.arxiv.org/abs/1303.4945>`_
        .. [#] 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.

        Attributes
        ----------
        para : numpy.ndarray
            One-dimensional array containing the two numbers `nlat` and `nlon`.
        dtype : numpy.dtype
            Data type of the field values.
        discrete : bool
            Whether or not the underlying space is discrete, always ``False``
            for spherical spaces.
        vol : numpy.ndarray
            An array containing the pixel sizes.
    """

73
74
    # ---Overwritten properties and methods---

75
    def __init__(self, nlat=2, nlon=None, dtype=np.dtype('float')):
csongor's avatar
csongor committed
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
        """
            Sets the attributes for a gl_space class instance.

            Parameters
            ----------
            nlat : int
                Number of latitudinal bins, or rings.
            nlon : int, *optional*
                Number of longitudinal bins (default: ``2*nlat - 1``).
            dtype : numpy.dtype, *optional*
                Data type of the field values (default: numpy.float64).

            Returns
            -------
            None

            Raises
            ------
            ImportError
                If the libsharp_wrapper_gl module is not available.
            ValueError
                If input `nlat` is invaild.

        """
        # check imports
        if not gc['use_libsharp']:
102
103
            raise ImportError(
                "libsharp_wrapper_gl not available or not loaded.")
104
105

        super(GLSpace, self).__init__(dtype)
csongor's avatar
csongor committed
106

107
108
        self._nlat = self._parse_nlat(nlat)
        self._nlon = self._parse_nlon(nlon)
csongor's avatar
csongor committed
109

110
    # ---Mandatory properties and methods---
csongor's avatar
csongor committed
111

112
113
114
    @property
    def harmonic(self):
        return False
csongor's avatar
csongor committed
115
116
117

    @property
    def shape(self):
118
        return (np.int((self.nlat * self.nlon)),)
csongor's avatar
csongor committed
119

120
    @property
121
    def dim(self):
122
        return np.int((self.nlat * self.nlon))
123
124
125
126

    @property
    def total_volume(self):
        return 4 * np.pi
127

128
129
130
131
132
    def copy(self):
        return self.__class__(nlat=self.nlat,
                              nlon=self.nlon,
                              dtype=self.dtype)

Jait Dixit's avatar
Jait Dixit committed
133
    def weight(self, x, power=1, axes=None, inplace=False):
134
        axes = utilities.cast_axis_to_tuple(axes, length=1)
135

136
137
        nlon = self.nlon
        nlat = self.nlat
138
139

        weight = np.array(list(itertools.chain.from_iterable(
140
141
            itertools.repeat(x ** power, nlon)
            for x in gl.vol(nlat))))
Jait Dixit's avatar
Jait Dixit committed
142
143
144

        if axes is not None:
            # reshape the weight array to match the input shape
145
            new_shape = np.ones(len(x.shape), dtype=np.int)
Jait Dixit's avatar
Jait Dixit committed
146
147
148
149
150
151
152
            for index in range(len(axes)):
                new_shape[index] = len(weight)
            weight = weight.reshape(new_shape)

        if inplace:
            x *= weight
            result_x = x
csongor's avatar
csongor committed
153
        else:
Jait Dixit's avatar
Jait Dixit committed
154
            result_x = x * weight
csongor's avatar
csongor committed
155

Jait Dixit's avatar
Jait Dixit committed
156
        return result_x
157

158
    def get_distance_array(self, distribution_strategy):
159
160
        dists = d2o.arange(start=0, stop=self.shape[0],
                           distribution_strategy=distribution_strategy)
161

162
        dists = dists.apply_scalar_function(
163
            lambda x: self._distance_array_helper(divmod(x, self.nlon)),
164
            dtype=np.float)
165
166
167

        return dists

theos's avatar
theos committed
168
    def _distance_array_helper(self, qr_tuple):
169
170
        lat = qr_tuple[0]*(np.pi/(self.nlat-1))
        lon = qr_tuple[1]*(2*np.pi/(self.nlon-1))
171
172
173
        numerator = np.sqrt(np.sin(lat)**2 +
                            (np.sin(lon) * np.cos(lat))**2)
        denominator = np.cos(lon) * np.cos(lat)
174

theos's avatar
theos committed
175
        return np.arctan(numerator / denominator)
176

177
    def get_fft_smoothing_kernel_function(self, sigma):
Jait Dixit's avatar
Jait Dixit committed
178
        if sigma is None:
179
            sigma = np.sqrt(2) * np.pi
Jait Dixit's avatar
Jait Dixit committed
180
181

        return lambda x: np.exp((-0.5 * x**2) / sigma**2)
182

183
184
185
186
187
188
189
190
191
192
193
194
195
    # ---Added properties and methods---

    @property
    def nlat(self):
        return self._nlat

    @property
    def nlon(self):
        return self._nlon

    def _parse_nlat(self, nlat):
        nlat = int(nlat)
        if nlat < 2:
196
197
            raise ValueError(
                "nlat must be a positive number.")
198
        elif nlat % 2 != 0:
199
200
            raise ValueError(
                "nlat must be a multiple of 2.")
201
202
203
204
205
206
207
208
        return nlat

    def _parse_nlon(self, nlon):
        if nlon is None:
            nlon = 2 * self.nlat - 1
        else:
            nlon = int(nlon)
            if nlon != 2 * self.nlat - 1:
209
210
                self.logger.warn("nlon was set to an unrecommended value: "
                                 "nlon <> 2*nlat-1.")
211
        return nlon
212
213
214
215

    # ---Serialization---

    def _to_hdf5(self, hdf5_group):
Jait Dixit's avatar
Jait Dixit committed
216
217
        hdf5_group['nlat'] = self.nlat
        hdf5_group['nlon'] = self.nlon
218
        hdf5_group['dtype'] = self.dtype.name
Jait Dixit's avatar
Jait Dixit committed
219

220
221
222
223
224
        return None

    @classmethod
    def _from_hdf5(cls, hdf5_group, loopback_get):
        result = cls(
Jait Dixit's avatar
Jait Dixit committed
225
226
            nlat=hdf5_group['nlat'][()],
            nlon=hdf5_group['nlon'][()],
227
            dtype=np.dtype(hdf5_group['dtype'][()])
Jait Dixit's avatar
Jait Dixit committed
228
229
            )

230
        return result