Finish tests for all Space classes

test/common.py

 import d2o
 import numpy as np
 from nose_parameterized import parameterized
-from nifty import RGSpace, LMSpace, HPSpace, GLSpace
+from nifty import RGSpace, LMSpace, HPSpace, GLSpace, PowerSpace
+from nifty.config import dependency_injector as di
 from string import strip
 
 
 def pretty_str(obj):
     if type(obj) == list:
         return " ".join(pretty_str(x) for x in obj)
+    if type(obj) == tuple:
+        return " ".join(pretty_str(x) for x in obj)
     if type(obj) == RGSpace:
         return type(obj).__name__
     elif type(obj) == LMSpace:
@@ -16,6 +19,8 @@ def pretty_str(obj):
         return type(obj).__name__
     elif type(obj) == GLSpace:
         return type(obj).__name__
+    elif type(obj) == PowerSpace:
+        return type(obj).__name__
     elif isinstance(obj, d2o.distributed_data_object):
         return 'd2o'
     elif type(obj) == dict:
@@ -40,3 +45,16 @@ def custom_name_func(testcase_func, param_num, param):
 def expand(*args, **kwargs):
     return parameterized.expand(*args, testcase_func_name=custom_name_func,
                                 **kwargs)
+
+
+def generate_spaces():
+    spaces = [RGSpace(4), PowerSpace(RGSpace((4, 4), harmonic=True))]
+
+    if 'healpy' in di:
+        spaces.append(HPSpace(4))
+    if 'libsharp_wrapper_gl' in di:
+        spaces.append(GLSpace(4))
+    if 'healpy' in di or 'libsharp_wrapper_gl' in di:
+        spaces.append(LMSpace(5))
+
+    return spaces

test/data/gen_lm_space_data.py

+#!/usr/bin/env python
+from __future__ import division
+import numpy as np
+
+# deterministic
+np.random.seed(42)
+
+
+def distance_array_helper(index_arr, lmax):
+    if index_arr <= lmax:
+        index_half = index_arr
+    else:
+        if (index_arr - lmax) % 2 == 0:
+            index_half = (index_arr + lmax) / 2
+        else:
+            index_half = (index_arr + lmax + 1) / 2
+
+    m = (
+            np.ceil(((2 * lmax + 1) - np.sqrt((2 * lmax + 1)**2 -
+                    8 * (index_half - lmax))) / 2)
+        ).astype(int)
+
+    return index_half - m * (2 * lmax + 1 - m) // 2
+
+
+# for LMSpace(5)
+da_0 = [distance_array_helper(idx, 5) for idx in np.arange(36)]
+
+# random input for weight
+w_0_x = np.random.rand(32, 16, 6)
+
+# random input for hermitian
+h_0_res_real = np.random.rand(32, 16, 6).astype(np.complex128)
+h_0_res_imag = np.random.rand(32, 16, 6).astype(np.complex128)
+h_0_x = h_0_res_real + h_0_res_imag * 1j
+
+# write everything to disk
+np.savez('lm_space', da_0=da_0, w_0_x=w_0_x, w_0_res=w_0_x, h_0_x=h_0_x,
+         h_0_res_real=h_0_res_real, h_0_res_imag=h_0_res_imag)

test/data/gen_power_space_data.py

+#!/usr/bin/env python
+from __future__ import division
+import numpy as np
+
+# deterministic
+np.random.seed(42)
+
+# for now directly the kindex
+# RGSpace((4, 4), harmonic=True)
+da_0 = np.array([0, 1.0, 1.41421356, 2., 2.23606798, 2.82842712])
+
+# power 1
+w_0_x = np.random.rand(32, 16, 6)
+# RGSpace((4, 4), harmonic=True)
+# using rho directly
+weight_0 = np.array([1, 4, 4, 2, 4, 1])
+weight_0 = weight_0.reshape([1, 1, 6])
+w_0_res = w_0_x * weight_0
+
+
+# write everything to disk
+np.savez('power_space',
+         da_0=da_0,
+         w_0_x=w_0_x,
+         w_0_res=w_0_res)

test/data/gen_rg_space_data.py

+#!/usr/bin/env python
+from __future__ import division
+import numpy as np
+
+# deterministic
+np.random.seed(42)
+
+# for RGSpace(shape=(4, 4), distances=None, zerocenter=[False, False])
+cords_0 = np.ogrid[0:4, 0:4]
+da_0 = ((cords_0[0] - 4 // 2) * 0.25)**2
+da_0 = np.fft.fftshift(da_0)
+temp = ((cords_0[1] - 4 // 2) * 0.25)**2
+temp = np.fft.fftshift(temp)
+da_0 = da_0 + temp
+da_0 = np.sqrt(da_0)
+# for RGSpace(shape=(4, 4), distances=None, zerocenter=[True, True])
+da_1 = ((cords_0[0] - 4 // 2) * 0.25)**2
+temp = ((cords_0[1] - 4 // 2) * 0.25)**2
+da_1 = da_1 + temp
+da_1 = np.sqrt(da_1)
+# for RGSpace(shape=(4, 4), distances=(12, 12), zerocenter=[True, True])
+da_2 = ((cords_0[0] - 4 // 2) * 12)**2
+temp = ((cords_0[1] - 4 // 2) * 12)**2
+da_2 = da_2 + temp
+da_2 = np.sqrt(da_2)
+
+# power 1
+w_0_x = np.random.rand(32, 12, 6)
+# for RGSpace(shape=(11,11), distances=None, harmonic=False)
+w_0_res = w_0_x * (1/11 * 1/11)
+# for RGSpace(shape=(11, 11), distances=(1.3,1.3), harmonic=False)
+w_1_res = w_0_x * (1.3 * 1.3)
+# for RGSpace(shape=(11,11), distances=None, harmonic=True)
+w_2_res = w_0_x * (1.0 * 1.0)
+# for RGSpace(shape=(11,11), distances=(1.3, 1,3), harmonic=True)
+w_3_res = w_0_x * (1.3 * 1.3)
+
+# hermitianization
+h_0_x = np.array([
+    [0.88250339+0.12102381j,  0.54293435+0.7345584j, 0.87057998+0.20515315j,
+        0.16602950+0.09396132j],
+    [0.83853902+0.17974696j,  0.79735933+0.37104425j, 0.22057732+0.9498977j,
+        0.14329183+0.47899678j],
+    [0.96934284+0.3792878j, 0.13118669+0.45643055j, 0.16372149+0.48235714j,
+        0.66141537+0.20383357j],
+    [0.49168197+0.77572178j, 0.09570420+0.14219071j, 0.69735595+0.33017333j,
+        0.83692452+0.18544449j]])
+h_0_res_real = np.array([
+    [0.88250339+0.j, 0.35448193+0.32029854j, 0.87057998+0.j,
+        0.35448193-0.32029854j],
+    [0.66511049-0.29798741j, 0.81714193+0.09279988j, 0.45896664+0.30986218j,
+        0.11949801+0.16840303j],
+    [0.96934284+0.j, 0.39630103+0.12629849j, 0.16372149+0.j,
+        0.39630103-0.12629849j],
+    [0.66511049+0.29798741j, 0.11949801-0.16840303j, 0.45896664-0.30986218j,
+        0.81714193-0.09279988j]])
+h_0_res_imag = np.array([
+    [0.12102381+0.j, 0.41425986-0.18845242j, 0.20515315+0.j,
+        0.41425986+0.18845242j],
+    [0.47773437-0.17342852j, 0.27824437+0.0197826j, 0.64003551+0.23838932j,
+        0.31059374-0.02379381j],
+    [0.37928780+0.j, 0.33013206+0.26511434j, 0.48235714+0.j,
+        0.33013206-0.26511434j],
+    [0.47773437+0.17342852j, 0.31059374+0.02379381j, 0.64003551-0.23838932j,
+        0.27824437-0.0197826j]])
+
+h_1_x = np.array([
+    [[0.23987021+0.41617749j, 0.34605012+0.55462234j, 0.07947035+0.73360723j,
+        0.22853748+0.39275304j],
+     [0.90254910+0.02107809j, 0.28195470+0.56031588j, 0.23004043+0.33873536j,
+         0.56398377+0.68913034j],
+     [0.81897406+0.2050369j, 0.88724852+0.8137488j, 0.84645004+0.0059284j,
+         0.14950377+0.50013099j]],
+    [[0.93491597+0.73251066j, 0.74764790+0.11539037j, 0.48090736+0.04352568j,
+        0.49363732+0.97233093j],
+     [0.72761881+0.74636216j, 0.46390134+0.4343401j, 0.88436859+0.79415269j,
+         0.67027606+0.85498234j],
+     [0.86318727+0.19076379j, 0.36859448+0.89842333j, 0.73407193+0.85091112j,
+         0.44187657+0.08936409j]]
+    ])
+h_1_res_real = np.array([
+    [[0.23987021+0.j, 0.28729380+0.08093465j, 0.07947035+0.j,
+        0.28729380-0.08093465j],
+     [0.90254910+0.j, 0.42296924-0.06440723j, 0.23004043+0.j,
+         0.42296924+0.06440723j],
+     [0.81897406+0.j, 0.51837614+0.1568089j, 0.84645004+0.j,
+         0.51837614-0.1568089j]],
+    [[0.93491597+0.j, 0.62064261-0.42847028j, 0.48090736+0.j,
+        0.62064261+0.42847028j],
+     [0.72761881+0.j, 0.56708870-0.21032112j, 0.88436859+0.j,
+         0.56708870+0.21032112j],
+     [0.86318727+0.j, 0.40523552+0.40452962j, 0.73407193+0.j,
+         0.40523552-0.40452962j]]
+    ])
+h_1_res_imag = np.array([
+    [[0.41617749+0.j, 0.47368769-0.05875632j, 0.73360723+0.j,
+        0.47368769+0.05875632j],
+     [0.02107809+0.j, 0.62472311+0.14101454j, 0.33873536+0.j,
+         0.62472311-0.14101454j],
+     [0.20503690+0.j, 0.65693990-0.36887238j, 0.00592840+0.j,
+         0.65693990+0.36887238j]],
+    [[0.73251066+0.j, 0.54386065-0.12700529j, 0.04352568+0.j,
+        0.54386065+0.12700529j],
+     [0.74636216+0.j, 0.64466122+0.10318736j, 0.79415269+0.j,
+         0.64466122-0.10318736j],
+     [0.19076379+0.j, 0.49389371+0.03664104j, 0.85091112+0.j,
+         0.49389371-0.03664104j]]
+    ])
+
+# write everything to disk
+np.savez('rg_space',
+         da_0=da_0,
+         da_1=da_1,
+         da_2=da_2,
+         w_0_x=w_0_x,
+         w_0_res=w_0_res,
+         w_1_res=w_1_res,
+         w_2_res=w_2_res,
+         w_3_res=w_3_res,
+         h_0_x=h_0_x,
+         h_0_res_real=h_0_res_real,
+         h_0_res_imag=h_0_res_imag,
+         h_1_x=h_1_x,
+         h_1_res_real=h_1_res_real,
+         h_1_res_imag=h_1_res_imag)

test/test_serialization.py

+import unittest
+
+from numpy.testing import assert_equal
+from keepers import Repository
+from test.common import expand, generate_spaces
+
+
+class SpaceSerializationTests(unittest.TestCase):
+    # variable to hold the repository
+    _repo = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls._repo = Repository('test.h5')
+
+    @expand([[space] for space in generate_spaces()])
+    def test_serialization(self, space):
+        self._repo.add(space, 'space')
+        self._repo.commit()
+
+        assert_equal(space, self._repo.get('space'))

test/test_spaces/test_gl_space.py

@@ -38,7 +38,7 @@ def get_weight_configs():
     return [
         [npzfile['w_0_x'], 1, None, False, npzfile['w_0_res']],
         [npzfile['w_0_x'], 1, None, True, npzfile['w_0_res']],
-        [npzfile['w_1_x'], 1, None, True, npzfile['w_1_res']],
+        [npzfile['w_1_x'], 1, (2,), True, npzfile['w_1_res']],
         ]
 
 

test/test_spaces/test_interface.py

@@ -2,25 +2,12 @@ import unittest
 import numpy as np
 
 from itertools import product
-from d2o import distributed_data_object
 from types import LambdaType
 from numpy.testing import assert_, assert_raises, assert_equal
-from nifty import RGSpace, LMSpace, GLSpace, HPSpace
+from nose.plugins.skip import SkipTest
+from nifty import LMSpace, GLSpace, HPSpace
 from nifty.config import dependency_injector as di
-from test.common import expand
-
-
-def generate_spaces():
-    spaces = [RGSpace(4)]
-
-    if 'healpy' in di:
-        spaces.append(HPSpace(4))
-    if 'libsharp_wrapper_gl' in di:
-        spaces.append(GLSpace(4))
-    if 'healpy' in di or 'libsharp_wrapper_gl' in di:
-        spaces.append(LMSpace(5))
-
-    return spaces
+from test.common import expand, generate_spaces
 
 
 class SpaceInterfaceTests(unittest.TestCase):
@@ -53,6 +40,12 @@ class SpaceInterfaceTests(unittest.TestCase):
         ['get_fft_smoothing_kernel_function', 2.0, LambdaType],
         ]))
     def test_method_ret_type(self, space, method_expected_type):
+        # handle exceptions here
+        try:
+            getattr(
+                space, method_expected_type[0])(*method_expected_type[1:-1])
+        except NotImplementedError:
+            raise SkipTest
 
         assert_equal(
             type(getattr(
@@ -61,3 +54,10 @@ class SpaceInterfaceTests(unittest.TestCase):
             ),
             method_expected_type[-1]
         )
+
+    @expand([[space] for space in generate_spaces()])
+    def test_copy(self, space):
+        # make sure it's a deep copy
+        assert_(space is not space.copy())
+        # make sure contents are the same
+        assert_equal(space, space.copy())

test/test_spaces/test_lm_space.py

 import unittest
 import numpy as np
 
-from numpy.testing import assert_, assert_equal, assert_raises
+from numpy.testing import assert_, assert_equal, assert_raises,\
+        assert_almost_equal
 from nose.plugins.skip import SkipTest
+from d2o import distributed_data_object
 from nifty import LMSpace
 from nifty.config import dependency_injector as di
 from test.common import expand
 
 # [lmax, dtype, expected]
-INIT_CONFIGS = [
+CONSTRUCTOR_CONFIGS = [
         [5, None, {
             'lmax': 5,
             'mmax': 5,
@@ -33,20 +35,40 @@ INIT_CONFIGS = [
     ]
 
 
+def get_distance_array_configs():
+    npzfile = np.load('test/data/lm_space.npz')
+    return [[5, None, npzfile['da_0']]]
+
+
+def get_weight_configs():
+    npzfile = np.load('test/data/lm_space.npz')
+    return [
+        [npzfile['w_0_x'], 1, None, False, npzfile['w_0_res']],
+        [npzfile['w_0_x'], 1, None, True, npzfile['w_0_res']]
+        ]
+
+
+def get_hermitian_configs():
+    npzfile = np.load('test/data/lm_space.npz')
+    return [
+        [npzfile['h_0_x'], npzfile['h_0_res_real'], npzfile['h_0_res_imag']]
+    ]
+
+
 class LMSpaceIntefaceTests(unittest.TestCase):
     @expand([['lmax', int],
             ['mmax', int],
             ['dim', int]])
-    def test_properties(self, attribute, expected_type):
+    def test_property_ret_type(self, attribute, expected_type):
         try:
-            x = LMSpace(7)
+            l = LMSpace(7)
         except ImportError:
             raise SkipTest
-        assert_(isinstance(getattr(x, attribute), expected_type))
+        assert_(isinstance(getattr(l, attribute), expected_type))
 
 
 class LMSpaceFunctionalityTests(unittest.TestCase):
-    @expand(INIT_CONFIGS)
+    @expand(CONSTRUCTOR_CONFIGS)
     def test_constructor(self, lmax, dtype, expected):
         if 'libsharp_wrapper_gl' not in di or 'healpy' not in di:
             raise SkipTest
@@ -59,11 +81,34 @@ class LMSpaceFunctionalityTests(unittest.TestCase):
                 for key, value in expected.iteritems():
                     assert_equal(getattr(l, key), value)
 
-    def test_hermitian_decomposition(self):
-        pass
+    @expand(get_hermitian_configs())
+    def test_hermitian_decomposition(self, x, real, imag):
+        if 'libsharp_wrapper_gl' not in di or 'healpy' not in di:
+            raise SkipTest
+        else:
+            l = LMSpace(5)
+            assert_almost_equal(
+                l.hermitian_decomposition(distributed_data_object(x))[0],
+                real)
+            assert_almost_equal(
+                l.hermitian_decomposition(distributed_data_object(x))[1],
+                imag)
 
-    def test_weight(self):
-        pass
+    @expand(get_weight_configs())
+    def test_weight(self, x, power, axes, inplace, expected):
+        if 'libsharp_wrapper_gl' not in di or 'healpy' not in di:
+            raise SkipTest
+        else:
+            l = LMSpace(5)
+            res = l.weight(x, power, axes, inplace)
+            assert_almost_equal(res, expected)
+            if inplace:
+                assert_(x is res)
 
-    def test_distance_array(self):
-        pass
+    @expand(get_distance_array_configs())
+    def test_distance_array(self, lmax, dtype, expected):
+        if 'libsharp_wrapper_gl' not in di or 'healpy' not in di:
+            raise SkipTest
+        else:
+            l = LMSpace(lmax, dtype)
+            assert_almost_equal(l.get_distance_array('not').data, expected)

test/test_spaces/test_power_space.py

+from __future__ import division
+
+import unittest
+import numpy as np
+
+from d2o import distributed_data_object
+from numpy.testing import assert_, assert_equal, assert_almost_equal,\
+        assert_raises
+from nifty import PowerSpace, RGSpace, LMSpace, Space
+from types import NoneType
+from test.common import expand
+
+# [harmonic_domain, distribution_strategy,
+#  log, nbin, binbounds, dtype, expected]
+CONSTRUCTOR_CONFIGS = [
+    [1, 'not', False, None, None, None, {'error': ValueError}],
+    [RGSpace((8,)), 'not', False, None, None, None, {'error': ValueError}],
+    [RGSpace((8,), harmonic=True), 'not', False, None, None, None, {
+        'harmonic': True,
+        'shape': (5,),
+        'dim': 5,
+        'total_volume': 8.0,
+        'harmonic_domain': RGSpace((8,), harmonic=True),
+        'log': False,
+        'nbin': None,
+        'binbounds': None,
+        'pindex': distributed_data_object([0, 1, 2, 3, 4, 3, 2, 1]),
+        'kindex': np.array([0., 1., 2., 3., 4.]),
+        'rho': np.array([1, 2, 2, 2, 1]),
+        'pundex': np.array([0, 1, 2, 3, 4]),
+        'k_array': np.array([0., 1., 2., 3., 4., 3., 2., 1.]),
+        'dtype': np.dtype('float64')
+        }],
+    [RGSpace((8,), harmonic=True), 'not', True, None, None, None, {
+        'harmonic': True,
+        'shape': (2,),
+        'dim': 2,
+        'total_volume': 8.0,
+        'harmonic_domain': RGSpace((8,), harmonic=True),
+        'log': True,
+        'nbin': None,
+        'binbounds': None,
+        'pindex': distributed_data_object([0, 1, 1, 1, 1, 1, 1, 1]),
+        'kindex': np.array([0., 2.28571429]),
+        'rho': np.array([1, 7]),
+        'pundex': np.array([0, 1]),
+        'k_array': np.array([0., 2.28571429, 2.28571429, 2.28571429,
+                             2.28571429, 2.28571429, 2.28571429, 2.28571429]),
+        'dtype': np.dtype('float64')
+        }],
+    ]
+
+
+def get_distance_array_configs():
+    npzfile = np.load('test/data/power_space.npz')
+    return [
+        [RGSpace((4, 4), harmonic=True),  npzfile['da_0']],
+        ]
+
+
+def get_weight_configs():
+    npzfile = np.load('test/data/power_space.npz')
+    return [
+        [RGSpace((4, 4), harmonic=True),
+            npzfile['w_0_x'], 1, (2,), False, npzfile['w_0_res']],
+        [RGSpace((4, 4), harmonic=True),
+            npzfile['w_0_x'], 1, (2,), True, npzfile['w_0_res']],
+        ]
+
+
+class PowerSpaceInterfaceTest(unittest.TestCase):
+    @expand([
+        ['harmonic_domain', Space],
+        ['log', bool],
+        ['nbin', (int, NoneType)],
+        ['binbounds', (list, NoneType)],
+        ['pindex', distributed_data_object],
+        ['kindex', np.ndarray],
+        ['rho', np.ndarray],
+        ['pundex', np.ndarray],
+        ['k_array', distributed_data_object],
+        ])
+    def test_property_ret_type(self, attribute, expected_type):
+        r = RGSpace((4, 4), harmonic=True)
+        p = PowerSpace(r)
+        assert_(isinstance(getattr(p, attribute), expected_type))
+
+
+class PowerSpaceFunctionalityTest(unittest.TestCase):
+    @expand(CONSTRUCTOR_CONFIGS)
+    def test_constructor(self, harmonic_domain, distribution_strategy, log,
+                         nbin, binbounds, dtype, expected):
+        if 'error' in expected:
+            with assert_raises(expected['error']):
+                PowerSpace(harmonic_domain=harmonic_domain,
+                           distribution_strategy=distribution_strategy,
+                           log=log, nbin=nbin, binbounds=binbounds,
+                           dtype=dtype)
+        else:
+            p = PowerSpace(harmonic_domain=harmonic_domain,
+                           distribution_strategy=distribution_strategy,
+                           log=log, nbin=nbin, binbounds=binbounds,
+                           dtype=dtype)
+            for key, value in expected.iteritems():
+                if isinstance(value, np.ndarray):
+                    assert_almost_equal(getattr(p, key), value)
+                else:
+                    assert_equal(getattr(p, key), value)
+
+    @expand(get_distance_array_configs())
+    def test_distance_array(self, harmonic_domain, expected):
+        p = PowerSpace(harmonic_domain=harmonic_domain)
+        assert_almost_equal(p.get_distance_array('not'), expected)
+
+    @expand(get_weight_configs())
+    def test_weight(self, harmonic_domain, x, power, axes,
+                    inplace, expected):
+        p = PowerSpace(harmonic_domain=harmonic_domain)
+        res = p.weight(x, power, axes, inplace)
+        assert_almost_equal(res, expected)
+        if inplace:
+            assert_(x is res)

test/test_spaces/test_rg_space.py

@@ -3,12 +3,12 @@ from __future__ import division
 import unittest
 import numpy as np
 
-from numpy.testing import assert_, assert_equal
+from numpy.testing import assert_, assert_equal, assert_almost_equal
 from nifty import RGSpace
 from test.common import expand
 
 # [shape, zerocenter, distances, harmonic, dtype, expected]
-INIT_CONFIGS = [
+CONSTRUCTOR_CONFIGS = [
         [(8,), False, None, False, None,
             {
                 'shape': (8,),
@@ -73,27 +73,79 @@ INIT_CONFIGS = [
     ]
 
 
+def get_distance_array_configs():
+    npzfile = np.load('test/data/rg_space.npz')
+    return [
+        [(4, 4),  None, [False, False], npzfile['da_0']],
+        [(4, 4),  None, [True, True], npzfile['da_1']],
+        [(4, 4),  (12, 12), [True, True], npzfile['da_2']]
+        ]
+
+
+def get_weight_configs():
+    npzfile = np.load('test/data/rg_space.npz')
+    return [
+        [(11, 11), None, False,
+            npzfile['w_0_x'], 1, None, False, npzfile['w_0_res']],
+        [(11, 11), None, False,
+            npzfile['w_0_x'], 1, None, True, npzfile['w_0_res']],
+        [(11, 11), (1.3, 1.3), False,
+            npzfile['w_0_x'], 1, None, False, npzfile['w_1_res']],
+        [(11, 11), (1.3, 1.3), False,
+            npzfile['w_0_x'], 1, None, True, npzfile['w_1_res']],
+        [(11, 11), None, True,
+            npzfile['w_0_x'], 1, None, False, npzfile['w_2_res']],