Python numpy.fromfunction方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromfunction [as 別名]
def getArrayRegion(self, arr, img=None, axes=(0, 1), **kwds):
        """
        Return the result of ROI.getArrayRegion() masked by the elliptical shape
        of the ROI. Regions outside the ellipse are set to 0.
        """
        # Note: we could use the same method as used by PolyLineROI, but this
        # implementation produces a nicer mask.
        arr = ROI.getArrayRegion(self, arr, img, axes, **kwds)
        if arr is None or arr.shape[axes[0]] == 0 or arr.shape[axes[1]] == 0:
            return arr
        w = arr.shape[axes[0]]
        h = arr.shape[axes[1]]

        ## generate an ellipsoidal mask
        mask = np.fromfunction(lambda x,y: (((x+0.5)/(w/2.)-1)**2+ ((y+0.5)/(h/2.)-1)**2)**0.5 < 1, (w, h))
        
        # reshape to match array axes
        if axes[0] > axes[1]:
            mask = mask.T
        shape = [(n if i in axes else 1) for i,n in enumerate(arr.shape)]
        mask = mask.reshape(shape)
        
        return arr * mask 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromfunction [as 別名]
def fromfunction(function, shape, **kwargs):
    """ Constructs an array by executing a function over each coordinate.

    This is currently equivalent to :func:`numpy.fromfunction`
    wrapped by :func:`chainerx.array`, given the device argument.

    Note:
        Keywords other than ``dtype`` and ``device`` are passed to
        ```function```.

    .. seealso:: :func:`numpy.fromfunction`

    """
    dtype = kwargs.pop('dtype', float)
    device = kwargs.pop('device', None)
    return chainerx.array(
        numpy.fromfunction(
            function, shape, dtype=dtype, **kwargs),
        device=device)


# TODO(hvy): Optimize with pre-allocated memory using count for non-native
# devices. 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromfunction [as 別名]
def permute_graph(graph, label, permutation):
  """Permutes the graph and labels based on permutation.

  Args:
    graph: np.ndarray adjacency matrix.
    label: list of labels of same length as graph dimensions.
    permutation: a permutation list of ints of same length as graph dimensions.

  Returns:
    np.ndarray where vertex permutation[v] is vertex v from the original graph
  """
  # vertex permutation[v] in new graph is vertex v in the old graph
  forward_perm = zip(permutation, list(range(len(permutation))))
  inverse_perm = [x[1] for x in sorted(forward_perm)]
  edge_fn = lambda x, y: graph[inverse_perm[x], inverse_perm[y]] == 1
  new_matrix = np.fromfunction(np.vectorize(edge_fn),
                               (len(label), len(label)),
                               dtype=np.int8)
  new_label = [label[inverse_perm[i]] for i in range(len(label))]
  return new_matrix, new_label 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromfunction [as 別名]
def permute_graph(graph, label, permutation):
    """Permutes the graph and labels based on permutation.

    Args:
      graph: np.ndarray adjacency matrix.
      label: list of labels of same length as graph dimensions.
      permutation: a permutation list of ints of same length as graph dimensions.

    Returns:
      np.ndarray where vertex permutation[v] is vertex v from the original graph
    """
    # vertex permutation[v] in new graph is vertex v in the old graph
    forward_perm = zip(permutation, list(range(len(permutation))))
    inverse_perm = [x[1] for x in sorted(forward_perm)]
    edge_fn = lambda x, y: graph[inverse_perm[x], inverse_perm[y]] == 1
    new_matrix = np.fromfunction(np.vectorize(edge_fn),
                                 (len(label), len(label)),
                                 dtype=np.int8)
    new_label = [label[inverse_perm[i]] for i in range(len(label))]
    return new_matrix, new_label 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromfunction [as 別名]
def test_nearest_masked_swath_target(self):
        """Test that a masked array works as a target."""
        data = np.fromfunction(lambda y, x: y * x, (50, 10))
        lons = np.fromfunction(lambda y, x: 3 + x, (50, 10))
        lats = np.fromfunction(lambda y, x: 75 - y, (50, 10))
        mask = np.ones_like(lons, dtype=np.bool)
        mask[::2, ::2] = False
        swath_def = geometry.SwathDefinition(
            lons=np.ma.masked_array(lons, mask=mask),
            lats=np.ma.masked_array(lats, mask=False)
        )
        res = kd_tree.resample_nearest(swath_def, data.ravel(),
                                       swath_def, 50000, segments=3)
        cross_sum = res.sum()
        # expected = 12716  # if masks aren't respected
        expected = 12000
        self.assertEqual(cross_sum, expected) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromfunction [as 別名]
def test_gauss_multi(self):
        data = np.fromfunction(lambda y, x: (y + x) * 10 ** -6, (5000, 100))
        lons = np.fromfunction(
            lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
        lats = np.fromfunction(
            lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
        swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
        data_multi = np.column_stack((data.ravel(), data.ravel(),
                                      data.ravel()))
        with catch_warnings(UserWarning) as w:
            res = kd_tree.resample_gauss(swath_def, data_multi,
                                         self.area_def, 50000, [25000, 15000, 10000], segments=1)
            self.assertFalse(len(w) != 1)
            self.assertFalse(('Possible more' not in str(w[0].message)))
        cross_sum = res.sum()
        expected = 1461.8429990248171
        self.assertAlmostEqual(cross_sum, expected) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromfunction [as 別名]
def test_gauss_multi_mp(self):
        data = np.fromfunction(lambda y, x: (y + x) * 10 ** -6, (5000, 100))
        lons = np.fromfunction(
            lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
        lats = np.fromfunction(
            lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
        swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
        data_multi = np.column_stack((data.ravel(), data.ravel(),
                                      data.ravel()))
        with catch_warnings(UserWarning) as w:
            res = kd_tree.resample_gauss(swath_def, data_multi,
                                         self.area_def, 50000, [
                                             25000, 15000, 10000],
                                         nprocs=2, segments=1)
            self.assertFalse(len(w) != 1)
            self.assertFalse(('Possible more' not in str(w[0].message)))
        cross_sum = res.sum()
        expected = 1461.8429990248171
        self.assertAlmostEqual(cross_sum, expected) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromfunction [as 別名]
def test_gauss_multi_mp_segments(self):
        data = np.fromfunction(lambda y, x: (y + x) * 10 ** -6, (5000, 100))
        lons = np.fromfunction(
            lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
        lats = np.fromfunction(
            lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
        swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
        data_multi = np.column_stack((data.ravel(), data.ravel(),
                                      data.ravel()))
        with catch_warnings(UserWarning) as w:
            res = kd_tree.resample_gauss(swath_def, data_multi,
                                         self.area_def, 50000, [
                                             25000, 15000, 10000],
                                         nprocs=2, segments=1)
            self.assertFalse(len(w) != 1)
            self.assertFalse('Possible more' not in str(w[0].message))
        cross_sum = res.sum()
        expected = 1461.8429990248171
        self.assertAlmostEqual(cross_sum, expected) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromfunction [as 別名]
def test_masked_gauss(self):
        data = np.ones((50, 10))
        data[:, 5:] = 2
        lons = np.fromfunction(lambda y, x: 3 + x, (50, 10))
        lats = np.fromfunction(lambda y, x: 75 - y, (50, 10))
        swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
        mask = np.ones((50, 10))
        mask[:, :5] = 0
        masked_data = np.ma.array(data, mask=mask)
        res = kd_tree.resample_gauss(swath_def, masked_data.ravel(),
                                     self.area_def, 50000, 25000, segments=1)
        expected_mask = np.fromfile(os.path.join(os.path.dirname(__file__),
                                                 'test_files',
                                                 'mask_test_mask.dat'),
                                    sep=' ').reshape((800, 800))
        expected_data = np.fromfile(os.path.join(os.path.dirname(__file__),
                                                 'test_files',
                                                 'mask_test_data.dat'),
                                    sep=' ').reshape((800, 800))
        expected = expected_data.sum()
        cross_sum = res.data.sum()

        self.assertTrue(np.array_equal(expected_mask, res.mask))
        self.assertAlmostEqual(cross_sum, expected, places=3) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromfunction [as 別名]
def test_masked_full(self):
        data = np.ones((50, 10))
        data[:, 5:] = 2
        mask = np.ones((50, 10))
        mask[:, :5] = 0
        masked_data = np.ma.array(data, mask=mask)
        lons = np.fromfunction(lambda y, x: 3 + x, (50, 10))
        lats = np.fromfunction(lambda y, x: 75 - y, (50, 10))
        swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
        res = kd_tree.resample_nearest(swath_def,
                                       masked_data.ravel(
                                       ), self.area_def, 50000,
                                       fill_value=None, segments=1)
        expected_fill_mask = np.fromfile(os.path.join(os.path.dirname(__file__),
                                                      'test_files',
                                                      'mask_test_full_fill.dat'),
                                         sep=' ').reshape((800, 800))
        fill_mask = res.mask

        self.assertTrue(np.array_equal(fill_mask, expected_fill_mask)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromfunction [as 別名]
def initializeGL(self):
        
        ## Generate texture for rendering points
        w = 64
        def fn(x,y):
            r = ((x-w/2.)**2 + (y-w/2.)**2) ** 0.5
            return 255 * (w/2. - np.clip(r, w/2.-1.0, w/2.))
        pData = np.empty((w, w, 4))
        pData[:] = 255
        pData[:,:,3] = np.fromfunction(fn, pData.shape[:2])
        #print pData.shape, pData.min(), pData.max()
        pData = pData.astype(np.ubyte)
        
        if getattr(self, "pointTexture", None) is None:
            self.pointTexture = glGenTextures(1)
        glActiveTexture(GL_TEXTURE0)
        glEnable(GL_TEXTURE_2D)
        glBindTexture(GL_TEXTURE_2D, self.pointTexture)
        glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA, pData.shape[0], pData.shape[1], 0, GL_RGBA, GL_UNSIGNED_BYTE, pData)
        
        self.shader = shaders.getShaderProgram('pointSprite')
        
    #def getVBO(self, name):
        #if name not in self.vbo:
            #self.vbo[name] = vbo.VBO(getattr(self, name).astype('f'))
        #return self.vbo[name]
        
    #def setupGLState(self):
        #"""Prepare OpenGL state for drawing. This function is called immediately before painting."""
        ##glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA)  ## requires z-sorting to render properly.
        #glBlendFunc(GL_SRC_ALPHA, GL_ONE)
        #glEnable( GL_BLEND )
        #glEnable( GL_ALPHA_TEST )
        #glDisable( GL_DEPTH_TEST )
        
        ##glEnable( GL_POINT_SMOOTH )

        ##glHint(GL_POINT_SMOOTH_HINT, GL_NICEST)
        ##glPointParameterfv(GL_POINT_DISTANCE_ATTENUATION, (0, 0, -1e-3))
        ##glPointParameterfv(GL_POINT_SIZE_MAX, (65500,))
        ##glPointParameterfv(GL_POINT_SIZE_MIN, (0,)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromfunction [as 別名]
def __call__(self, sample):
        if 'flow' not in sample.keys():
            return sample

        applied_angle = random.uniform(-self.angle,self.angle)
        diff = random.uniform(-self.diff_angle,self.diff_angle)
        angle1 = applied_angle - diff/2
        angle2 = applied_angle + diff/2
        angle1_rad = angle1*np.pi/180

        h, w = sample['leftImage'].shape[-2:]

        def rotate_flow(k, i, j):
            return -k*(j-w/2)*(diff*np.pi/180) + (1-k)*(i-h/2)*(diff*np.pi/180)

        rotate_flow_map = np.fromfunction(rotate_flow, sample['flow'].shape)
        sample['flow'] += rotate_flow_map

        sample['leftImage'] = ndimage.interpolation.rotate(sample['leftImage'], angle1, axes=(-2, -1), reshape=self.reshape, order=self.order)
        sample['rightImage'] = ndimage.interpolation.rotate(sample['rightImage'], angle2, axes=(-2, -1), reshape=self.reshape, order=self.order)
        sample['flow'] = ndimage.interpolation.rotate(sample['flow'], angle1, axes=(-2, -1), reshape=self.reshape, order=self.order)
        # flow vectors must be rotated too! careful about Y flow which is upside down, clockwise
        flow = np.copy(sample['flow'])
        sample['flow'][0,:,:] = np.cos(angle1_rad)*flow[0,:,:] + np.sin(angle1_rad)*flow[1,:,:]
        sample['flow'][1,:,:] = -np.sin(angle1_rad)*flow[0,:,:] + np.cos(angle1_rad)*flow[1,:,:]

        return sample 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromfunction [as 別名]
def objective_function(self, config, budget=108):
        if self.multi_fidelity is False:
            assert budget == 108

        bitlist = [0] * (VERTICES * (VERTICES - 1) // 2)
        for i in range(MAX_EDGES):
            bitlist[config["edge_%d" % i]] = 1
        out = 0
        for bit in bitlist:
            out = (out << 1) | bit

        matrix = np.fromfunction(graph_util.gen_is_edge_fn(out),
                                 (VERTICES, VERTICES),
                                 dtype=np.int8)
        # if not graph_util.is_full_dag(matrix) or graph_util.num_edges(matrix) > MAX_EDGES:
        if graph_util.num_edges(matrix) > MAX_EDGES:
            self.record_invalid(config, 1, 1, 0)
            return 1, 0

        labeling = [config["op_node_%d" % i] for i in range(5)]
        labeling = ['input'] + list(labeling) + ['output']
        model_spec = api.ModelSpec(matrix, labeling)
        try:
            data = self.dataset.query(model_spec, epochs=budget)
        except api.OutOfDomainError:
            self.record_invalid(config, 1, 1, 0)
            return 1, 0

        self.record_valid(config, data, model_spec)

        return 1 - data["validation_accuracy"], data["training_time"] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromfunction [as 別名]
def fromfunction(shape, dimensions, type=None, typecode=None, dtype=None):
    dtype = type2dtype(typecode, type, dtype, 1)
    return np.fromfunction(shape, dimensions, dtype=dtype) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromfunction [as 別名]
def fromfunction(args, dimensions):
    return np.fromfunction(args, dimensions, dtype=int)