Python numpy.all() Examples

def test_region_init():
    region = Region(
        name='test',
        description='region description',
        west_bound=0.,
        east_bound=5,
        south_bound=0,
        north_bound=90.,
        do_land_mask=True
    )
    assert region.name == 'test'
    assert region.description == 'region description'
    assert isinstance(region.mask_bounds, tuple)
    assert len(region.mask_bounds) == 1
    assert isinstance(region.mask_bounds[0], BoundsRect)
    assert np.all(region.mask_bounds[0] ==
                  (Longitude(0.), Longitude(5), 0, 90.))
    assert region.do_land_mask is True 

def test_bounds(self):
        """
        Test that out-of-bounds coordinates return NaN reddening, and that
        in-bounds coordinates do not return NaN reddening.
        """

        for mode in (['random_sample', 'random_sample_per_pix',
                      'median', 'samples', 'mean']):
            # Draw random coordinates, both above and below dec = -30 degree line
            n_pix = 1000
            ra = -180. + 360.*np.random.random(n_pix)
            dec = -75. + 90.*np.random.random(n_pix)    # 45 degrees above/below
            c = coords.SkyCoord(ra, dec, frame='icrs', unit='deg')

            ebv_calc = self._bayestar(c, mode=mode)

            nan_below = np.isnan(ebv_calc[dec < -35.])
            nan_above = np.isnan(ebv_calc[dec > -25.])
            pct_nan_above = np.sum(nan_above) / float(nan_above.size)

            # print r'{:s}: {:.5f}% nan above dec=-25 deg.'.format(mode, 100.*pct_nan_above)

            self.assertTrue(np.all(nan_below))
            self.assertTrue(pct_nan_above < 0.05) 

def test_module_input_grads():
    a = mx.sym.Variable('a', __layout__='NC')
    b = mx.sym.Variable('b', __layout__='NC')
    c = mx.sym.Variable('c', __layout__='NC')

    c = a + 2 * b + 3 * c
    net = mx.mod.Module(c, data_names=['b', 'c', 'a'], label_names=None,
                        context=[mx.cpu(0), mx.cpu(1)])
    net.bind(data_shapes=[['b', (5, 5)], ['c', (5, 5)], ['a', (5, 5)]],
             label_shapes=None, inputs_need_grad=True)
    net.init_params()

    net.forward(data_batch=mx.io.DataBatch(data=[nd.ones((5, 5)),
                                                 nd.ones((5, 5)),
                                                 nd.ones((5, 5))]))
    net.backward(out_grads=[nd.ones((5, 5))])
    input_grads = net.get_input_grads()
    b_grad = input_grads[0].asnumpy()
    c_grad = input_grads[1].asnumpy()
    a_grad = input_grads[2].asnumpy()
    assert np.all(a_grad == 1), a_grad
    assert np.all(b_grad == 2), b_grad
    assert np.all(c_grad == 3), c_grad 

def annToRLE(self, ann):
        """
        Convert annotation which can be polygons, uncompressed RLE to RLE.
        :return: binary mask (numpy 2D array)
        """
        t = self.imgs[ann['image_id']]
        h, w = t['height'], t['width']
        segm = ann['segmentation']
        if type(segm) == list:
            # polygon -- a single object might consist of multiple parts
            # we merge all parts into one mask rle code
            # rles = maskUtils.frPyObjects(segm, h, w)
            # rle = maskUtils.merge(rles)
            raise NotImplementedError("maskUtils disabled!")
        elif type(segm['counts']) == list:
            # uncompressed RLE
            # rle = maskUtils.frPyObjects(segm, h, w)
            raise NotImplementedError("maskUtils disabled!")
        else:
            # rle
            rle = ann['segmentation']
        return rle 

def find_match(self, pred, gt):
    '''
    Match component to balls.
    '''
    batch_size, n_frames_input, n_components, _ = pred.shape
    diff = pred.reshape(batch_size, n_frames_input, n_components, 1, 2) - \
               gt.reshape(batch_size, n_frames_input, 1, n_components, 2)
    diff = np.sum(np.sum(diff ** 2, axis=-1), axis=1)
    # Direct indices
    indices = np.argmin(diff, axis=2)
    ambiguous = np.zeros(batch_size, dtype=np.int8)
    for i in range(batch_size):
      _, counts = np.unique(indices[i], return_counts=True)
      if not np.all(counts == 1):
        ambiguous[i] = 1
    return indices, ambiguous 

def getImgIds(self, imgIds=[], catIds=[]):
        '''
        Get img ids that satisfy given filter conditions.
        :param imgIds (int array) : get imgs for given ids
        :param catIds (int array) : get imgs with all given cats
        :return: ids (int array)  : integer array of img ids
        '''
        imgIds = imgIds if type(imgIds) == list else [imgIds]
        catIds = catIds if type(catIds) == list else [catIds]

        if len(imgIds) == len(catIds) == 0:
            ids = self.imgs.keys()
        else:
            ids = set(imgIds)
            for i, catId in enumerate(catIds):
                if i == 0 and len(ids) == 0:
                    ids = set(self.catToImgs[catId])
                else:
                    ids &= set(self.catToImgs[catId])
        return list(ids) 

def test_sparse_nd_setitem():
    def check_sparse_nd_setitem(stype, shape, dst):
        x = mx.nd.zeros(shape=shape, stype=stype)
        x[:] = dst
        dst_nd = mx.nd.array(dst) if isinstance(dst, (np.ndarray, np.generic)) else dst
        assert np.all(x.asnumpy() == dst_nd.asnumpy() if isinstance(dst_nd, NDArray) else dst)

    shape = rand_shape_2d()
    for stype in ['row_sparse', 'csr']:
        # ndarray assignment
        check_sparse_nd_setitem(stype, shape, rand_ndarray(shape, 'default'))
        check_sparse_nd_setitem(stype, shape, rand_ndarray(shape, stype))
        # numpy assignment
        check_sparse_nd_setitem(stype, shape, np.ones(shape))
    # scalar assigned to row_sparse NDArray
    check_sparse_nd_setitem('row_sparse', shape, 2) 

def test_module_reshape():
    data = mx.sym.Variable('data')
    sym = mx.sym.FullyConnected(data, num_hidden=20, name='fc')

    dshape = (7, 20)
    mod = mx.mod.Module(sym, ('data',), None, context=[mx.cpu(0), mx.cpu(1)])
    mod.bind(data_shapes=[('data', dshape)])
    mod.init_params()
    mod.init_optimizer(optimizer_params={'learning_rate': 1})

    mod.forward(mx.io.DataBatch(data=[mx.nd.ones(dshape)],
                                label=None))
    mod.backward([mx.nd.ones(dshape)])
    mod.update()
    assert mod.get_outputs()[0].shape == dshape
    assert (mod.get_params()[0]['fc_bias'].asnumpy() == -1).all()

    dshape = (14, 20)
    mod.reshape(data_shapes=[('data', dshape)])
    mod.forward(mx.io.DataBatch(data=[mx.nd.ones(dshape)],
                                label=None))
    mod.backward([mx.nd.ones(dshape)])
    mod.update()
    assert mod.get_outputs()[0].shape == dshape
    assert (mod.get_params()[0]['fc_bias'].asnumpy() == -3).all() 

def map_values(values, pos, target_pos, dtype=None, nan=dat.CPG_NAN):
    """Maps `values` array at positions `pos` to `target_pos`.

    Inserts `nan` for uncovered positions.
    """
    assert len(values) == len(pos)
    assert np.all(pos == np.sort(pos))
    assert np.all(target_pos == np.sort(target_pos))

    values = values.ravel()
    pos = pos.ravel()
    target_pos = target_pos.ravel()
    idx = np.in1d(pos, target_pos)
    pos = pos[idx]
    values = values[idx]
    if not dtype:
        dtype = values.dtype
    target_values = np.empty(len(target_pos), dtype=dtype)
    target_values.fill(nan)
    idx = np.in1d(target_pos, pos).nonzero()[0]
    assert len(idx) == len(values)
    assert np.all(target_pos[idx] == pos)
    target_values[idx] = values
    return target_values 

def get_constraint_value(self, applyMultiframePrior=True):
        """
        Compute all partial Structure Factor (SQs).

        :Parameters:
            #. applyMultiframePrior (boolean): Whether to apply subframe weight
               and prior to the total. This will only have an effect when used
               frame is a subframe and in case subframe weight and prior is
               defined.

        :Returns:
            #. SQs (dictionary): The SQs dictionnary, where keys are the
               element wise intra and inter molecular SQs and values are
               the computed SQs.
        """
        if self.data is None:
            LOGGER.warn("data must be computed first using 'compute_data' method.")
            return {}
        return self._get_constraint_value(self.data, applyMultiframePrior=applyMultiframePrior) 

def _project_to_map(map, vertex, wt=None, ignore_points_outside_map=False):
  """Projects points to map, returns how many points are present at each
  location."""
  num_points = np.zeros((map.size[1], map.size[0]))
  vertex_ = vertex[:, :2] - map.origin
  vertex_ = np.round(vertex_ / map.resolution).astype(np.int)
  if ignore_points_outside_map:
    good_ind = np.all(np.array([vertex_[:,1] >= 0, vertex_[:,1] < map.size[1],
                                vertex_[:,0] >= 0, vertex_[:,0] < map.size[0]]),
                      axis=0)
    vertex_ = vertex_[good_ind, :]
    if wt is not None:
      wt = wt[good_ind, :]
  if wt is None:
    np.add.at(num_points, (vertex_[:, 1], vertex_[:, 0]), 1)
  else:
    assert(wt.shape[0] == vertex.shape[0]), \
      'number of weights should be same as vertices.'
    np.add.at(num_points, (vertex_[:, 1], vertex_[:, 0]), wt)
  return num_points 

def raw_valid_fn_vec(self, xyt):
    """Returns if the given set of nodes is valid or not."""
    height = self.traversible.shape[0]
    width = self.traversible.shape[1]
    x = np.round(xyt[:,[0]]).astype(np.int32)
    y = np.round(xyt[:,[1]]).astype(np.int32)
    is_inside = np.all(np.concatenate((x >= 0, y >= 0,
                                       x < width, y < height), axis=1), axis=1)
    x = np.minimum(np.maximum(x, 0), width-1)
    y = np.minimum(np.maximum(y, 0), height-1)
    ind = np.ravel_multi_index((y,x), self.traversible.shape)
    is_traversible = self.traversible.ravel()[ind]

    is_valid = np.all(np.concatenate((is_inside[:,np.newaxis], is_traversible),
                                     axis=1), axis=1)
    return is_valid 

def test_one_hot():
    """Check if one_hot returns correct label matrices."""
    # Generate label vector
    y = np.hstack((np.ones((10,))*0,
                   np.ones((10,))*1,
                   np.ones((10,))*2))

    # Map to matrix
    Y, labels = one_hot(y)

    # Check for only 0's and 1's
    assert len(np.setdiff1d(np.unique(Y), [0, 1])) == 0

    # Check for correct labels
    assert np.all(labels == np.unique(y))

    # Check correct shape of matrix
    assert Y.shape[0] == y.shape[0]
    assert Y.shape[1] == len(labels) 

def valid_fn_vec(self, pqr):
    """Returns if the given set of nodes is valid or not."""
    xyt = self.to_actual_xyt_vec(np.array(pqr))
    height = self.traversible.shape[0]
    width = self.traversible.shape[1]
    x = np.round(xyt[:,[0]]).astype(np.int32)
    y = np.round(xyt[:,[1]]).astype(np.int32)
    is_inside = np.all(np.concatenate((x >= 0, y >= 0,
                                       x < width, y < height), axis=1), axis=1)
    x = np.minimum(np.maximum(x, 0), width-1)
    y = np.minimum(np.maximum(y, 0), height-1)
    ind = np.ravel_multi_index((y,x), self.traversible.shape)
    is_traversible = self.traversible.ravel()[ind]

    is_valid = np.all(np.concatenate((is_inside[:,np.newaxis], is_traversible),
                                     axis=1), axis=1)
    return is_valid 

def is_pos_def(self, A):
        """
        Check for positive definiteness.

        Parameters
        ---------
        A : array
            square symmetric matrix.

        Returns
        -------
        bool
            whether matrix is positive-definite.
            Warning! Returns false for arrays containing inf or NaN.


        """
        # Check for valid numbers
        if np.any(np.isnan(A)) or np.any(np.isinf(A)):
            return False

        else:
            return np.all(np.real(np.linalg.eigvals(A)) > 0) 

def image_to_cortex(self, image,
                        surface='midgray', hemi=None, affine=Ellipsis, method=None, fill=0,
                        dtype=None, weights=None):
        '''
        sub.image_to_cortex(image) is equivalent to the tuple
          (sub.lh.from_image(image), sub.rh.from_image(image)).
        sub.image_to_cortex(image, surface) uses the given surface (see also cortex.surface).
        '''
        if hemi is None: hemi = 'both'
        hemi = hemi.lower()
        if hemi in ['both', 'lr', 'all', 'auto']:
            return tuple(
                [self.image_to_cortex(image, surface=surface, hemi=h, affine=affine,
                                      method=method, fill=fill, dtype=dtype, weights=weights)
                 for h in ['lh', 'rh']])
        else:
            hemi = getattr(self, hemi)
            return hemi.from_image(image, surface=surface, affine=affine,
                                   method=method, fill=fill, dtype=dtype, weights=weights) 

def add_diagonal_edges(g, nodes, sz_x, sz_y, edge_len):
  offset = [sz_x+1, sz_x-1]
  for o in offset:
    s = np.arange(nodes.shape[0]-o-1)
    t = s + o
    ind = np.all(np.abs(nodes[s,:] - nodes[t,:]) == np.array([[1,1]]), axis=1)
    s = s[ind][:,np.newaxis]
    t = t[ind][:,np.newaxis]
    st = np.concatenate((s,t), axis=1)
    for i in range(st.shape[0]):
      e = g.add_edge(st[i,0], st[i,1], add_missing=False)
      g.ep['wts'][e] = edge_len 

def test_predicted_scores_are_within_range(self):
    ocr_model = self.create_model()

    _, _, scores = ocr_model.char_predictions(self.fake_logits)
    with self.test_session() as sess:
      scores_np = sess.run(scores)

    values_in_range = (scores_np >= 0.0) & (scores_np <= 1.0)
    self.assertTrue(
        np.all(values_in_range),
        msg=('Scores contains out of the range values %s' %
             scores_np[np.logical_not(values_in_range)])) 

def simple_summaries(summarize_ops, summarize_names, mode, to_aggregate=False,
                     scope_name='summary'):

  if type(to_aggregate) != list:
    to_aggregate = [to_aggregate for _ in summarize_ops]
  
  summary_key = '{:s}_summaries'.format(mode)
  print_summary_key = '{:s}_print_summaries'.format(mode)
  prefix=' [{:s}]: '.format(mode)
  
  # Default ops for things that dont need to be aggregated.
  if not np.all(to_aggregate):
    for op, name, to_agg in zip(summarize_ops, summarize_names, to_aggregate):
      if not to_agg:
        add_scalar_summary_op(op, name, summary_key, print_summary_key, prefix)
    summary_ops = tf.summary.merge_all(summary_key)
    print_summary_ops = tf.summary.merge_all(print_summary_key)
  else:
    summary_ops = tf.no_op()
    print_summary_ops = tf.no_op()
 
  # Default ops for things that dont need to be aggregated.
  if np.any(to_aggregate):
    additional_return_ops = [[summarize_ops[i] 
                              for i, x in enumerate(to_aggregate )if x]]
    arop_summary_iters = [-1]
    s_names = ['{:s}/{:s}'.format(scope_name, summarize_names[i]) 
               for i, x in enumerate(to_aggregate) if x]
    fn = lambda outputs, global_step, output_dir, metric_summary, N: \
      accum_val_ops(outputs, s_names, global_step, output_dir, metric_summary,
                    N)
    arop_eval_fns = [fn]
  else:
    additional_return_ops = []
    arop_summary_iters = []
    arop_eval_fns = []
  return summary_ops, print_summary_ops, additional_return_ops, \
    arop_summary_iters, arop_eval_fns 

def _label_nodes_with_room_id(xyt, room_dims):
  # Label the room with the ID into things.
  node_room_id = -1*np.ones((xyt.shape[0], 1))
  dims = room_dims['dims']
  for x, name in enumerate(room_dims['names']):
    all_ = np.concatenate((xyt[:,[0]] >= dims[x,0],
                           xyt[:,[0]] <= dims[x,3],
                           xyt[:,[1]] >= dims[x,1],
                           xyt[:,[1]] <= dims[x,4]), axis=1)
    node_room_id[np.all(all_, axis=1), 0] = x
  return node_room_id 

def rng_target_dist_field(batch_size, gtG, rng, max_dist, max_dist_to_compute,
                          nodes=None, compute_path=False):
  # Sample a single node, compute distance to all nodes less than max_dist,
  # sample nodes which are a particular distance away.
  dists = []; pred_maps = []; paths = []; start_node_ids = []
  end_node_ids = rng.choice(gtG.num_vertices(), size=(batch_size,),
                            replace=False).tolist()

  for i in range(batch_size):
    dist, pred_map = gt.topology.shortest_distance(
        gt.GraphView(gtG, reversed=True), source=gtG.vertex(end_node_ids[i]),
        target=None, max_dist=max_dist_to_compute, pred_map=True)
    dist = np.array(dist.get_array())
    pred_map = np.array(pred_map.get_array())
    dists.append(dist)
    pred_maps.append(pred_map)

    # Randomly sample nodes which are withing max_dist
    near_ids = np.where(dist <= max_dist)[0]
    start_node_id = rng.choice(near_ids, size=(1,), replace=False)[0]
    start_node_ids.append(start_node_id)

    path = None
    if compute_path:
      path = get_path_ids(start_node_ids[i], end_node_ids[i], pred_map)
    paths.append(path)

  return start_node_ids, end_node_ids, dists, pred_maps, paths 

def test_sparse_nd_greater_equal():
    for stype in ['row_sparse', 'csr']:
        shape = rand_shape_2d()
        x = mx.nd.zeros(shape=shape, stype=stype)
        y = sparse_nd_ones(shape, stype)
        z = x >= y
        assert (z.asnumpy() == np.zeros(shape)).all()
        z = y >= 0
        assert (z.asnumpy() == np.ones(shape)).all()
        z = 0 >= y
        assert (z.asnumpy() == np.zeros(shape)).all()
        z = y >= 1
        assert (z.asnumpy() == np.ones(shape)).all() 

def test_save_load():
    def dict_equ(a, b):
        assert set(a) == set(b)
        for k in a:
            assert (a[k].asnumpy() == b[k].asnumpy()).all()

    sym = mx.sym.Variable('data')
    sym = mx.sym.FullyConnected(sym, num_hidden=100)

    # single device
    mod = mx.mod.Module(sym, ('data',))
    mod.bind(data_shapes=[('data', (10, 10))])
    mod.init_params()
    mod.init_optimizer(optimizer_params={'learning_rate':0.1, 'momentum':0.9})
    mod.update()
    mod.save_checkpoint('test', 0, save_optimizer_states=True)

    mod2 = mx.mod.Module.load('test', 0, load_optimizer_states=True, data_names=('data',))
    mod2.bind(data_shapes=[('data', (10, 10))])
    mod2.init_optimizer(optimizer_params={'learning_rate':0.1, 'momentum':0.9})
    assert mod._symbol.tojson() == mod2._symbol.tojson()
    dict_equ(mod.get_params()[0], mod2.get_params()[0])
    dict_equ(mod._updater.states, mod2._updater.states)

    # multi device
    mod = mx.mod.Module(sym, ('data',), context=[mx.cpu(0), mx.cpu(1)])
    mod.bind(data_shapes=[('data', (10, 10))])
    mod.init_params()
    mod.init_optimizer(optimizer_params={'learning_rate':0.1, 'momentum':0.9})
    mod.update()
    mod.save_checkpoint('test', 0, save_optimizer_states=True)

    mod2 = mx.mod.Module.load('test', 0, load_optimizer_states=True, data_names=('data',))
    mod2.bind(data_shapes=[('data', (10, 10))])
    mod2.init_optimizer(optimizer_params={'learning_rate':0.1, 'momentum':0.9})
    assert mod._symbol.tojson() == mod2._symbol.tojson()
    dict_equ(mod.get_params()[0], mod2.get_params()[0])
    dict_equ(mod._kvstore._updater.states, mod2._updater.states) 

def get_constraint_original_value(self):
        """
        Compute all partial Pair Distribution Functions (PDFs).

        :Returns:
            #. PDFs (dictionary): The PDFs dictionnary, where keys are the
               element wise intra and inter molecular PDFs and values are
               the computed PDFs.
        """
        if self.originalData is None:
            LOGGER.warn("originalData must be computed first using 'compute_data' method.")
            return {}
        return self._get_constraint_value(self.originalData) 

def check_experimental_data(self, experimentalData):
        """
        Check whether experimental data is correct.

        :Parameters:
            #. experimentalData (object): Experimental data to check.

        :Returns:
            #. result (boolean): Whether it is correct or not.
            #. message (str): Checking message that explains whats's
               wrong with the given data.
        """
        if not isinstance(experimentalData, np.ndarray):
            return False, "experimentalData must be a numpy.ndarray"
        if experimentalData.dtype.type is not FLOAT_TYPE:
            return False, "experimentalData type must be %s"%FLOAT_TYPE
        if len(experimentalData.shape) !=2:
            return False, "experimentalData must be of dimension 2"
        if experimentalData.shape[1] !=2:
            return False, "experimentalData must have only 2 columns"
        # check distances order
        inOrder = (np.array(sorted(experimentalData[:,0]), dtype=FLOAT_TYPE)-experimentalData[:,0])<=PRECISION
        if not np.all(inOrder):
            return False, "experimentalData distances are not sorted in order"
        if experimentalData[0][0]<0:
            return False, "experimentalData distances min value is found negative"
        bin  = experimentalData[1,0] -experimentalData[0,0]
        bins = experimentalData[1:,0]-experimentalData[0:-1,0]
        for b in bins:
            if np.abs(b-bin)>PRECISION:
                return False, "experimentalData distances bins are found not coherent"
        # data format is correct
        return True, "" 

def set_window_function(self, windowFunction, frame=None):
        """
        Set convolution window function.

        :Parameters:
             #. windowFunction (None, numpy.ndarray): The window function to
                convolute with the computed pair distribution function of the
                system prior to comparing it with the experimental data. In
                general, the experimental pair distribution function G(r) shows
                artificial wrinkles, among others the main reason is because
                G(r) is computed by applying a sine Fourier transform to the
                experimental structure factor S(q). Therefore window function is
                used to best imitate the numerical artefacts in the experimental
                data.
             #. frame (None, string): Target frame name. If None, engine used
                frame is used. If multiframe is given, all subframes will be
                targeted. If subframe is given, all other multiframe subframes
                will be targeted.
        """
        if windowFunction is not None:
            assert isinstance(windowFunction, np.ndarray), LOGGER.error("windowFunction must be a numpy.ndarray")
            assert windowFunction.dtype.type is FLOAT_TYPE, LOGGER.error("windowFunction type must be %s"%FLOAT_TYPE)
            assert len(windowFunction.shape) == 1, LOGGER.error("windowFunction must be of dimension 1")
            assert len(windowFunction) <= self.experimentalData.shape[0], LOGGER.error("windowFunction length must be smaller than experimental data")
            # normalize window function
            windowFunction /= np.sum(windowFunction)
        # check window size
        # set windowFunction
        self.__windowFunction = windowFunction
        # dump to repository
        usedIncluded, frame, allFrames = get_caller_frames(engine=self.engine,
                                                           frame=frame,
                                                           subframeToAll=True,
                                                           caller="%s.%s"%(self.__class__.__name__,inspect.stack()[0][3]) )
        if usedIncluded:
            self.__windowFunction = windowFunction
        for frm in allFrames:
            self._dump_to_repository({'_PairDistributionConstraint__windowFunction': self.__windowFunction}, frame=frm) 

def get_constraint_original_value(self):
        """
        Compute all partial Pair Distribution Functions (PDFs).

        :Returns:
            #. PDFs (dictionary): The PDFs dictionnary, where keys are the
               element wise intra and inter molecular PDFs and values are the
               computed PDFs.
        """
        if self.originalData is None:
            LOGGER.warn("originalData must be computed first using 'compute_data' method.")
            return {}
        return self._get_constraint_value(self.originalData) 

def check_experimental_data(self, experimentalData):
        """
        Check whether experimental data is correct.

        :Parameters:
            #. experimentalData (object): The experimental data to check.

        :Returns:
            #. result (boolean): Whether it is correct or not.
            #. message (str): Checking message that explains whats's wrong
               with the given data
        """
        if not isinstance(experimentalData, np.ndarray):
            return False, "experimentalData must be a numpy.ndarray"
        if experimentalData.dtype.type is not FLOAT_TYPE:
            return False, "experimentalData type must be %s"%FLOAT_TYPE
        if len(experimentalData.shape) !=2:
            return False, "experimentalData must be of dimension 2"
        if experimentalData.shape[1] !=2:
            return False, "experimentalData must have only 2 columns"
        # check distances order
        inOrder = (np.array(sorted(experimentalData[:,0]), dtype=FLOAT_TYPE)-experimentalData[:,0])<=PRECISION
        if not np.all(inOrder):
            return False, "experimentalData distances are not sorted in order"
        if experimentalData[0][0]<0:
            return False, "experimentalData distances min value is found negative"
        # data format is correct
        return True, "" 

def image_clear(img, fill=0):
    '''
    image_clear(img) yields a duplicate of the given image img but with all voxel values set to 0.
    image_clear(img, fill) sets all voxels to the given fill value.
    '''
    img = image_copy(img)
    img.dataobj[...] = fill
    return img 

def unaddress(self, data, surface=0.5):
        '''
        cortex.unaddress(address) yields the (3 x n) coordinate matrix of the given addresses (or,
          if address is singular, the 3D vector) in the given cortex. If the address is a 2D instead
          of a 3D address, then the mid-gray position is returned by default.

        The following options may be given:
          * surface (default: 0.5) specifies the surface to use for 2D addresses; this should be
            either 'white', 'pial', 'midgray', or a real number in the range [0,1] where 0 is the
            white surface and 1 is the pial surface.
        '''
        (faces, coords) = address_data(data, 3, surface=surface)
        (bc, ds) = (coords[:2], coords[2])
        faces = self.tess.index(faces)
        (wx, px) = (self.white_surface.coordinates, self.pial_surface.coordinates)
        if all(len(np.shape(x)) > 1 for x in (faces, coords)):
            (wtx, ptx) = [
                np.transpose([sx[:,ff] if ff[0] >= 0 else null for ff in faces.T], (2,1,0))
                for null in [np.full((3, wx.shape[0]), np.nan)]
                for sx   in (wx, px)]
        elif faces == -1:
            return np.full(selfx.shape[0], np.nan)
        else:
            (wtx, ptx) = [sx[:,faces].T for sx in (wx, px)]
        (wu, pu) = [geo.barycentric_to_cartesian(tx, bc) for tx in (wtx, ptx)]
        return wu*ds + pu*(1 - ds)