Python numpy.alltrue函数代码示例

    def testIter(self):
        # Override testIter.

        index = self.cls(texts, self.w2v_model)
        for sims in index:
            self.assertTrue(numpy.alltrue(sims >= 0.0))
            self.assertTrue(numpy.alltrue(sims <= 1.0))

 def test_local_max(self):
     bd = BlobDetection(self.img)
     bd._one_octave(shrink=False, refine=False, n_5=False)
     self.assert_(numpy.alltrue(_blob.local_max(bd.dogs, bd.cur_mask, False) == \
                                      local_max(bd.dogs, bd.cur_mask, False)), "max test, 3x3x3")
     self.assert_(numpy.alltrue(_blob.local_max(bd.dogs, bd.cur_mask, True) == \
                                      local_max(bd.dogs, bd.cur_mask, True)), "max test, 3x5x5")

    def testWeightedModelMatrix(self):

        linearModel = LinearModel(
            self.regressorList, self.regressorNames, self.covMatrixObserv2, regressorsAreWeighted=True
        )
        self.assertTrue(np.alltrue(linearModel.designMatrix() == self.unweightedDesignMatrix))

        linearModel = LinearModel(
            self.regressorList, self.regressorNames, self.covMatrixObserv2, regressorsAreWeighted=False
        )
        self.assertFalse(np.alltrue(linearModel.designMatrix() == self.unweightedDesignMatrix))

        expectedWeightedDesignMatrix = np.array(
            [
                [3.16227766e00, 4.73643073e-15],
                [4.23376727e-01, 2.79508497e00],
                [-2.67843095e-01, 3.79299210e00],
                [-5.45288776e-01, 4.39760881e00],
                [-6.78144517e-01, 4.84809047e00],
                [-7.46997591e-01, 5.21965041e00],
                [-7.83412990e-01, 5.54374745e00],
                [-8.01896636e-01, 5.83605564e00],
                [-8.09868157e-01, 6.10538773e00],
                [-8.11425366e-01, 6.35716086e00],
            ]
        )

        self.assertTrue(
            np.allclose(linearModel.designMatrix(), expectedWeightedDesignMatrix, rtol=1.0e-6, atol=1.0e-08)
        )

 def set_weights(self, weight_dict):
     """Update weights with a dictionary keyed by test_mi, whose values are
     either:
      (1) dicts of feature -> scalar weight.
      (2) a scalar which will apply to all features of that model interface
     Features and model interfaces must correspond to those declared for the
     context.
     """
     for test_mi, fs in weight_dict.items():
         try:
             flist = list(self.metric_features[test_mi]['features'].keys())
         except KeyError:
             raise AssertionError("Invalid test model interface")
         if isinstance(fs, common._num_types):
             feat_dict = {}.fromkeys(flist, fs)
         elif isinstance(fs, dict):
             assert npy.alltrue([isinstance(w, common._num_types) for \
                         w in fs.values()]), "Invalid scalar weight"
             assert npy.alltrue([f in flist for f in fs.keys()]), \
                    "Invalid features given for this test model interface"
             feat_dict = fs
         for f, w in feat_dict.items():
             self.feat_weights[(test_mi, f)] = w
             # update weight value
             start_ix, end_ix = self.weight_index_mapping[test_mi][f]
             self.weights[start_ix:end_ix] = w

 def nested_equal(self, val1, val2):
     """Test for equality in a nested list or ndarray
     """
     if isinstance(val1, list):
         for (subval1, subval2) in zip(val1, val2):
             if isinstance(subval1, list):
                 self.nested_equal(subval1, subval2)
             elif isinstance(subval1, np.ndarray):
                 try:
                     np.allclose(subval1, subval2)
                 except NotImplementedError:
                     import sys
                     print >> sys.stderr, '****', subval1, subval1.size
                     print >> sys.stderr, subval2, subval2.shape
                     print >> sys.stderr, '******\n'
             else:
                 self.assertEqual(subval1, subval2)
     elif isinstance(val1, np.ndarray):
         np.allclose(val1, np.array(val2))
     elif isinstance(val1, basestring):
         self.assertEqual(val1, val2)
     else:
         try:
             assert (np.alltrue(np.isnan(val1)) and
                     np.alltrue(np.isnan(val2)))
         except (AssertionError, NotImplementedError):
             self.assertEqual(val1, val2)

def test_capacitor():
    """Verify simple capacitance model"""

    class Capacitor(Behavioural):
        instparams = [Parameter(name="c", desc="Capacitance", unit="F")]

        @staticmethod
        def analog(plus, minus):
            b = Branch(plus, minus)
            return (Contribution(b.I, dtt(c * b.V)),)

    C = sympy.Symbol("C")

    cap = Capacitor(c=C)

    v1, v2 = sympy.symbols(("v1", "v2"))

    assert cap.i([v1, v2]) == [0, 0]

    assert cap.q([v1, v2]) == [C * (v1 - v2), -C * (v1 - v2)]

    assert np.alltrue(cap.C([v1, v2]) == np.array([[C, -C], [-C, C]]))

    assert np.alltrue(cap.G([v1, v2]) == np.zeros((2, 2)))

    assert np.alltrue(cap.CY([v1, v2]) == np.zeros((2, 2)))

def pixfun_inv_c():

    if not numpy_available:
        return 'skip'

    filename = 'data/pixfun_inv_c.vrt'
    ds = gdal.OpenShared(filename, gdal.GA_ReadOnly)
    if ds is None:
        gdaltest.post_reason('Unable to open "%s" dataset.' % filename)
        return 'fail'
    data = ds.GetRasterBand(1).ReadAsArray()

    reffilename = 'data/cint_sar.tif'
    refds = gdal.Open(reffilename)
    if refds is None:
        gdaltest.post_reason('Unable to open "%s" dataset.' % reffilename)
        return 'fail'
    refdata = refds.GetRasterBand(1).ReadAsArray()
    refdata = refdata.astype('complex')
    delta = data - 1./refdata

    if not numpy.alltrue(abs(delta.real) < 1e-13):
        return 'fail'
    if not numpy.alltrue(abs(delta.imag) < 1e-13):
        return 'fail'

    return 'success'

    def test_1d_weight_array(self):
        """"""
        sample_size = 5
        # check the individual gridcells
        # This is a stochastic model, so it may legitimately fail occassionally.
        index1 = where(self.households.get_attribute("lucky"))[0]
        index2 = where(self.gridcells.get_attribute("filter"))[0]
        weight=self.gridcells.get_attribute("weight")
        for icc in [0,1]: #include_chosen_choice?
            #icc = sample([0,1],1)
            sampler_ret = weighted_sampler().run(dataset1=self.households, dataset2=self.gridcells, index1=index1,
                            index2=index2, sample_size=sample_size, weight="weight",include_chosen_choice=icc)
            # get results
            sampled_index = sampler_ret.get_2d_index()
            chosen_choices = UNPLACED_ID * ones(index1.size, dtype="int32") 
            where_chosen = where(sampler_ret.get_attribute("chosen_choice"))
            chosen_choices[where_chosen[0]]=where_chosen[1]

            sample_results = sampled_index, chosen_choices
            sampled_index = sample_results[0]
            self.assertEqual(sampled_index.shape, (index1.size, sample_size))
            if icc:
                placed_agents_index = self.gridcells.try_get_id_index(
                                        self.households.get_attribute("grid_id")[index1],UNPLACED_ID)
                chosen_choice_index = resize(array([UNPLACED_ID], dtype="int32"), index1.shape)
                w = where(chosen_choices>=0)[0]
                # for 64 bit machines, need to coerce the type to int32 -- on a
                # 32 bit machine the astype(int32) doesn't do anything
                chosen_choice_index[w] = sampled_index[w, chosen_choices[w]].astype(int32)
                self.assert_( alltrue(equal(placed_agents_index, chosen_choice_index)) )
                sampled_index = sampled_index[:,1:]
            
            self.assert_( alltrue(lookup(sampled_index.ravel(), index2, index_if_not_found=UNPLACED_ID)!=UNPLACED_ID) )
            self.assert_( all(not_equal(weight[sampled_index], 0.0)) )

 def __contains__(self, point):
     """
     :param Point point: the box of the problem
     """
     l = np.alltrue(point.x >= self.box[:, 0])
     u = np.alltrue(point.x <= self.box[:, 1])
     return l and u

def test_bitsShaders():
    #a dict of dicts for expected vals
    for mode in ['bits++', 'mono++', 'color++']:
        bits.mode=mode
        for finalVal in [255.0, 1024, 65535]:
            thisExpected = expectedVals[mode][finalVal]

            #print bits.mode, finalVal
            intended = np.linspace(0.0,1,256)*255.0/finalVal
            stim.image = np.resize(intended,[256,256])*2-1 #NB psychopy uses -1:1

            stim.draw()
            #fr = np.array(win._getFrame('back').transpose(Image.ROTATE_270))
            #print 'pre r', fr[0:10,-1,0], fr[250:256,-1,0]
            #print 'pre g', fr[0:10,-1,1], fr[250:256,-1,0]
            win.flip()
            fr = np.array(win._getFrame('front').transpose(Image.ROTATE_270))
            if not _travisTesting:
                assert np.alltrue(thisExpected['lowR'] == fr[0:10,-1,0])
                assert np.alltrue(thisExpected['lowG'] == fr[0:10,-1,1])
                assert np.alltrue(thisExpected['highR'] == fr[250:256,-1,0])
                assert np.alltrue(thisExpected['highG'] == fr[250:256,-1,1])

            if not _travisTesting:
                print 'R', repr(fr[0:10,-1,0]), repr(fr[250:256,-1,0])
                print 'G', repr(fr[0:10,-1,1]), repr(fr[250:256,-1,0])

    def test_call_with_normalisation_precision(self):
        '''The normalisation should use a double precision scaling.
        '''
        # Should be the case for double inputs...
        _input_array = empty_aligned((256, 512), dtype='complex128', n=16)

        self.fft()
        ifft = FFTW(self.output_array, _input_array,
                direction='FFTW_BACKWARD')

        ref_output = ifft(normalise_idft=False).copy()/numpy.float64(ifft.N)
        test_output = ifft(normalise_idft=True).copy()

        self.assertTrue(numpy.alltrue(ref_output == test_output))

        # ... and single inputs.
        _input_array = empty_aligned((256, 512), dtype='complex64', n=16)

        ifft = FFTW(numpy.array(self.output_array, _input_array.dtype),
                    _input_array,
                    direction='FFTW_BACKWARD')

        ref_output = ifft(normalise_idft=False).copy()/numpy.float64(ifft.N)
        test_output = ifft(normalise_idft=True).copy()

        self.assertTrue(numpy.alltrue(ref_output == test_output))

    def test_integrate(self):
        h = 1.
        x0 = np.array([0, 0, 1.])
        dt, vu0, uu = .01, .01, .5
        self.s.electrode("rf").rf = uu*h**2
        t, x, v = [], [], []
        for ti, xi, vi in self.s.trajectory(
                x0, np.array([0, 0, vu0*uu*h]), axis=(1, 2),
                t1=20*2*np.pi, dt=dt*2*np.pi):
            t.append(ti)
            x.append(xi)
            v.append(vi)

        t = np.array(t)
        x = np.array(x)
        v = np.array(v)

        self.assertEqual(np.alltrue(utils.norm(x, axis=1)<3), True)
        self.assertEqual(np.alltrue(utils.norm(v, axis=1)<1), True)

        avg = int(1/dt)
        kin = (((x[:-avg]-x[avg:])/(2*np.pi))**2).sum(axis=-1)/2*4 # 4?
        pot = self.s.potential(np.array([x0[0]+0*x[:,0], x[:,0], x[:,1]]).T)
        pot = pot[avg/2:-avg/2]
        t = t[avg/2:-avg/2]
        do_avg = lambda ar: ar[:ar.size/avg*avg].reshape(
                (-1, avg)).mean(axis=-1)
        t, kin, pot = map(do_avg, (t, kin, pot))

        self.assertEqual(np.alltrue(np.std(kin+pot)/np.mean(kin+pot)<.01),
                True)

def test_multib0_dsi():
    data, gtab = dsi_voxels()
    # Create a new data-set with a b0 measurement:
    new_data = np.concatenate([data, data[..., 0, None]], -1)
    new_bvecs = np.concatenate([gtab.bvecs, np.zeros((1, 3))])
    new_bvals = np.concatenate([gtab.bvals, [0]])
    new_gtab = gradient_table(new_bvals, new_bvecs)
    ds = DiffusionSpectrumModel(new_gtab)
    sphere = get_sphere('repulsion724')
    dsfit = ds.fit(new_data)
    pdf = dsfit.pdf()
    dsfit.odf(sphere)
    assert_equal(new_data.shape[:-1] + (17, 17, 17), pdf.shape)
    assert_equal(np.alltrue(np.isreal(pdf)), True)

    # And again, with one more b0 measurement (two in total):
    new_data = np.concatenate([data, data[..., 0, None]], -1)
    new_bvecs = np.concatenate([gtab.bvecs, np.zeros((1, 3))])
    new_bvals = np.concatenate([gtab.bvals, [0]])
    new_gtab = gradient_table(new_bvals, new_bvecs)
    ds = DiffusionSpectrumModel(new_gtab)
    dsfit = ds.fit(new_data)
    pdf = dsfit.pdf()
    dsfit.odf(sphere)
    assert_equal(new_data.shape[:-1] + (17, 17, 17), pdf.shape)
    assert_equal(np.alltrue(np.isreal(pdf)), True)

def test_capacitor():
    """Verify simple capacitance model"""
    
    class Capacitor(Behavioural):
         instparams = [Parameter(name='c', desc='Capacitance', unit='F')]
         @staticmethod
         def analog(plus, minus):
             b = Branch(plus, minus)
             return Contribution(b.I, ddt(c * b.V)),
         
    C = sympy.Symbol('C')

    cap = Capacitor(c=C)
    
    v1,v2 = sympy.symbols(('v1', 'v2'))

    assert cap.i([v1,v2]) == [0, 0]

    assert cap.q([v1,v2]) == [C*(v1-v2), -C*(v1-v2)]

    assert np.alltrue(cap.C([v1,v2]) == 
                       np.array([[C, -C], [-C, C]]))

    assert np.alltrue(cap.G([v1,v2]) == np.zeros((2,2)))

    assert np.alltrue(cap.CY([v1,v2]) == np.zeros((2,2)))

    def test_array_alpha(self):
        if not arraytype:
            self.fail("no array package installed")
        if arraytype == 'numeric':
            # This is known to fail with Numeric (differing values for
            # get_rgb and array element for 16 bit surfaces).
            return

        palette = [(0, 0, 0, 0),
                   (10, 50, 100, 255),
                   (60, 120, 240, 130),
                   (64, 128, 255, 0),
                   (255, 128, 0, 65)]
        targets = [self._make_src_surface(8, palette=palette),
                   self._make_src_surface(16, palette=palette),
                   self._make_src_surface(16, palette=palette, srcalpha=True),
                   self._make_src_surface(24, palette=palette),
                   self._make_src_surface(32, palette=palette),
                   self._make_src_surface(32, palette=palette, srcalpha=True)]

        for surf in targets:
            p = palette
            if surf.get_bitsize() == 16:
                p = [surf.unmap_rgb(surf.map_rgb(c)) for c in p]
            arr = pygame.surfarray.array_alpha(surf)
            if surf.get_masks()[3]:
                for (x, y), i in self.test_points:
                    self.failUnlessEqual(arr[x, y], p[i][3],
                                         ("%i != %i, posn: (%i, %i), "
                                          "bitsize: %i" %
                                          (arr[x, y], p[i][3],
                                           x, y,
                                           surf.get_bitsize())))
            else:
                self.failUnless(alltrue(arr == 255))

        # No per-pixel alpha when blanket alpha is None.
        for surf in targets:
            blacket_alpha = surf.get_alpha()
            surf.set_alpha(None)
            arr = pygame.surfarray.array_alpha(surf)
            self.failUnless(alltrue(arr == 255),
                            "bitsize: %i, flags: %i" %
                            (surf.get_bitsize(), surf.get_flags()))
            surf.set_alpha(blacket_alpha)

        # Bug for per-pixel alpha surface when blanket alpha 0.
        for surf in targets:
            blanket_alpha = surf.get_alpha()
            surf.set_alpha(0)
            arr = pygame.surfarray.array_alpha(surf)
            if surf.get_masks()[3]:
                self.failIf(alltrue(arr == 255),
                            "bitsize: %i, flags: %i" %
                            (surf.get_bitsize(), surf.get_flags()))
            else:
                self.failUnless(alltrue(arr == 255),
                                "bitsize: %i, flags: %i" %
                                (surf.get_bitsize(), surf.get_flags()))
            surf.set_alpha(blanket_alpha)