Python numpy.in1d方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import in1d [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import in1d [as 別名]
def _fit(self, classif, X, a, r, p, rs):
        obs_take = np.in1d(a, self.tree.node_comparisons[classif][0])
        X_node = X[obs_take, :]
        a_node = a[obs_take]
        r_node = r[obs_take]
        p_node = p[obs_take]
        
        r_more_onehalf = r_node >= .5
        y = (  np.in1d(a_node, self.tree.node_comparisons[classif][2])  ).astype('uint8')
        
        y_node = y.copy()
        y_node[r_more_onehalf] = 1. - y[r_more_onehalf]
        w_node = (.5 - r_node) / p_node
        w_node[r_more_onehalf] = (  (r_node - .5) / p_node  )[r_more_onehalf]
        w_node = w_node * (w_node.shape[0] / np.sum(w_node))
        
        n_pos = y_node.sum()
        if y_node.shape[0] == 0:
            self._oracles[classif] = _RandomPredictor(_check_random_state(rs[classif]))
        elif n_pos == y_node.shape[0]:
            self._oracles[classif] = _OnePredictor()
        elif n_pos == 0:
            self._oracles[classif] = _ZeroPredictor()
        else:
            self._oracles[classif].fit(X_node, y_node, sample_weight = w_node) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import in1d [as 別名]
def read_file(file, participant):
    """Utility function
    """
    # Get signal
    data = pd.DataFrame({"ECG": wfdb.rdsamp(file[:-4])[0][:, 0]})
    data["Participant"] = "MIT-Arrhythmia_%.2i" %(participant)
    data["Sample"] = range(len(data))
    data["Sampling_Rate"] = 360
    data["Database"] = "MIT-Arrhythmia-x" if "x_mitdb" in file else "MIT-Arrhythmia"

    # getting annotations
    anno = wfdb.rdann(file[:-4], 'atr')
    anno = np.unique(anno.sample[np.in1d(anno.symbol, ['N', 'L', 'R', 'B', 'A', 'a', 'J', 'S', 'V', 'r', 'F', 'e', 'j', 'n', 'E', '/', 'f', 'Q', '?'])])
    anno = pd.DataFrame({"Rpeaks": anno})
    anno["Participant"] = "MIT-Arrhythmia_%.2i" %(participant)
    anno["Sampling_Rate"] = 360
    anno["Database"] = "MIT-Arrhythmia-x" if "x_mitdb" in file else "MIT-Arrhythmia"

    return data, anno 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import in1d [as 別名]
def _get_roi(center, radius, mesh, mesh_surface=[5, 1005], min_cos=.1):
    ''' Defines the region of interest of a given radius. Around a given center. Only
    node with normals pointing in the given direction are
    considered. Returns a list of triangles with at least 1 element in the ROI'''
    center = np.array(center, dtype=float)
    nodes_in_surface = _get_nodes_in_surface(mesh, mesh_surface)
    if len(nodes_in_surface) == 0:
        raise ValueError('Could not find surface {0} in mesh'.format(mesh_surface))
    distances = np.linalg.norm(center - mesh.nodes[nodes_in_surface], axis=1)
    normals = mesh.nodes_normals()[nodes_in_surface]
    center_normal = normals[np.argmin(distances)]
    in_roi = nodes_in_surface[(distances <= radius) *
                              (center_normal.dot(normals.T) > min_cos)]
    tr_in_roi = np.any(
        np.in1d(mesh.elm[mesh.elm.triangles, :3], in_roi).reshape(-1, 3), axis=1)
    roi_nodes, roi_triangles_reordering = \
        np.unique(mesh.elm[mesh.elm.triangles[tr_in_roi], :3], return_inverse=True)
    return mesh.elm.triangles[tr_in_roi], roi_nodes, roi_triangles_reordering.reshape(-1,
                                                                                      3) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import in1d [as 別名]
def _move_point(new_position, to_be_moved, nodes, triangles, min_angle=0.25,
                edge_list=None, kdtree=None):
    '''Moves one point to the new_position. The angle of the patch should not become less
    than min_angle (in radians) '''
    # Certify that the new_position is inside the patch
    tr = _triangle_with_points(np.atleast_2d(new_position), triangles, nodes,
                               edge_list=edge_list, kdtree=kdtree)
    tr_with_node = np.where(np.any(triangles == to_be_moved, axis=1))[0]
    patch = triangles[tr_with_node]
    if not np.in1d(tr, tr_with_node):
        return None, None
    new_nodes = np.copy(nodes)
    position = nodes[to_be_moved]
    d = new_position - position
    # Start from the full move and go back
    for t in np.linspace(0, 1, num=10)[::-1]:
        new_nodes[to_be_moved] = position + t*d
        angle = np.min(_calc_triangle_angles(new_nodes[patch]))
        if angle > min_angle:
            break
    # Return the new node list and the minimum angle in the patch
    return new_nodes, angle 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import in1d [as 別名]
def apply_to_solution(self, x, dof_map):
        ''' Applies the dirichlet BC to a solution
        Parameters:
        -------
        x: numpy array
            Righ-hand side
        dof_map: dofMap
            Mapping of node indexes to rows and columns in A and b

        Returns:
        ------
        x: numpy array
            Righ-hand side, modified
        dof_map: dofMap
            Mapping of node indexes to rows and columns in A and b, modified
        '''
        if np.any(np.in1d(self.nodes, dof_map.inverse)):
            raise ValueError('Found DOFs already defined')
        dof_inverse = np.hstack((dof_map.inverse, self.nodes))
        x = np.atleast_2d(x)
        if x.shape[0] < x.shape[1]:
            x = x.T
        x = np.vstack((x, np.tile(self.values, (x.shape[1], 1)).T))
        return x, dofMap(dof_inverse) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import in1d [as 別名]
def test_join_mesh_data(self, sphere3_msh):
        m1 = sphere3_msh.crop_mesh([3, 1003])
        m1.nodedata = [mesh_io.NodeData(value=m1.nodes.node_coord)]
        m1.elmdata = [m1.elements_baricenters()]
        m2 = sphere3_msh.crop_mesh([5, 1005])
        m2.nodedata = [mesh_io.NodeData(value=m2.nodes.node_number)]
        m2.elmdata = [mesh_io.ElementData(value=m2.elm.tag1)]
        m = m1.join_mesh(m2)
        assert np.all(np.isclose(m.nodedata[0].value[:m1.nodes.nr],m1.nodes.node_coord))
        assert np.all(np.isnan(m.nodedata[0].value[m1.nodes.nr:]))

        v = m.elmdata[0].value
        ref = m1.elmdata[0].value
        assert np.all(np.isclose(v[np.in1d(m.elm.tag1, [3, 1003])], ref))
        assert np.all(np.isnan(v[~np.in1d(m.elm.tag1, [3, 1003])]))

        assert np.all(np.isclose(m.nodedata[1].value[m1.nodes.nr:],m2.nodes.node_number))
        assert np.all(np.isnan(m.nodedata[1].value[:m1.nodes.nr]))

        v = m.elmdata[1].value
        ref = m2.elmdata[0].value
        assert np.all(np.isclose(v[np.in1d(m.elm.tag1, [5, 1005])], ref))
        assert np.all(np.isnan(v[~np.in1d(m.elm.tag1, [5, 1005])])) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import in1d [as 別名]
def group_years(years, interval=3):
    """ Return integers representing sequential groupings of years

    Note: years specified must be sorted

    Args:
        years (np.ndarray): the year corresponding to each EVI value
        interval (int, optional): number of years to group together
            (default: 3)

    Returns:
        np.ndarray: integers representing sequential year groupings

    """
    n_groups = math.ceil((years.max() - years.min()) / interval)
    if n_groups <= 1:
        return np.zeros_like(years, dtype=np.uint16)
    splits = np.array_split(np.arange(years.min(), years.max() + 1), n_groups)

    groups = np.zeros_like(years, dtype=np.uint16)
    for i, s in enumerate(splits):
        groups[np.in1d(years, s)] = i

    return groups 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import in1d [as 別名]
def __iter__(self):
        n = self.n
        n_folds = self.n_folds

        fold_sizes = (n // n_folds) * np.ones(n_folds, dtype=np.int)
        fold_sizes[:n % n_folds] += 1
        current = 0

        for fold_size in fold_sizes:
            start, stop = current, current + fold_size

            test_i = np.in1d(self.indices[:, 0], self.labels[start:stop])
            train_i = np.in1d(self.indices[:, 0], self.labels[stop:])

            yield ((self.indices[test_i, 1], self.indices[test_i, 2]),
                   (self.indices[train_i, 1], self.indices[train_i, 2]))
            current = stop 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import in1d [as 別名]
def _label_roi(self):
        """ Internal method to label region of interest image
        """
        labeled, n = scipy.ndimage.label(self.roi)

        labels = np.unique(labeled)
        self.labels = labels[~np.in1d(labels, self.mask_values)]
        self.n = self.labels.size

        n_samples = (~np.in1d(self.roi, self.mask_values)).sum()
        self.indices = np.zeros((n_samples, 3), dtype=np.int)
        _start = 0

        for l in self.labels:
            _n = (labeled == l).sum()
            _row, _col = np.where(labeled == l)
            self.indices[_start:_start + _n, 0] = l
            self.indices[_start:_start + _n, 1] = _row
            self.indices[_start:_start + _n, 2] = _col
            _start += _n

        if self.shuffle:
            self.rng.shuffle(self.labels) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import in1d [as 別名]
def plot_TS(dates, y, seasons):
    """ Create a standard timeseries plot

    Args:
        dates (iterable): sequence of datetime
        y (np.ndarray): variable to plot
        seasons (bool): Plot seasonal symbology
    """
    # Plot data
    if seasons:
        months = np.array([d.month for d in dates])
        for season_months, color, alpha in SEASONS.values():
            season_idx = np.in1d(months, season_months)
            plt.plot(dates[season_idx], y[season_idx], marker='o',
                     mec=color, mfc=color, alpha=alpha, ls='')
    else:
        plt.scatter(dates, y, c='k', marker='o', edgecolors='none', s=35)
    plt.xlabel('Date') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import in1d [as 別名]
def ensure_resampled_obs_ids_in_df(resampled_obs_ids, orig_obs_id_array):
    """
    Checks whether all ids in `resampled_obs_ids` are in `orig_obs_id_array`.
    Raises a helpful ValueError if not.

    Parameters
    ----------
    resampled_obs_ids : 1D ndarray of ints.
        Should contain the observation ids of the observational units that will
        be used in the current bootstrap sample.
    orig_obs_id_array : 1D ndarray of ints.
        Should countain the observation ids of the observational units in the
        original dataframe containing the data for this model.

    Returns
    -------
    None.
    """
    if not np.in1d(resampled_obs_ids, orig_obs_id_array).all():
        msg =\
            "All values in `resampled_obs_ids` MUST be in `orig_obs_id_array`."
        raise ValueError(msg)
    return None 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import in1d [as 別名]
def clear_dust(data, min_size=10):
  """Removes small objects from a segmentation array.

  Replaces objects smaller than `min_size` with 0 (background).

  Args:
    data: numpy array of segment IDs
    min_size: minimum size in voxels of an object to be retained

  Returns:
    the data array (modified in place)
  """
  ids, sizes = np.unique(data, return_counts=True)
  small = ids[sizes < min_size]
  small_mask = np.in1d(data.flat, small).reshape(data.shape)
  data[small_mask] = 0
  return data 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import in1d [as 別名]
def subset(self, query_ids, name=None):
        """
        This method return a subset of the dataset according to the query_ids
        parameter.

        Parameters
        ----------
        query_ids : numpy 1d array of int
            It is a ndarray with the query_ids to select
        name : str
            The name to give to the dataset

        Returns
        -------
        datasets : rankeval.dataset.Dataset
            The resulting dataset with only the query_ids requested
        """
        qid_map = self.get_qids_dataset()
        mask = np.in1d(qid_map, query_ids)

        return Dataset(self.X[mask], self.y[mask],
                       qid_map[mask], name=name) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import in1d [as 別名]
def _update_material_types(self):

        # What do the materials (in air/sediment terms) look like now?
        if self.Model.mesh.dim == 3:
            material_flags = self._determine_particle_state()
        if self.Model.mesh.dim == 2:
            material_flags = self._determine_particle_state_2D()

        # If any materials changed state, update the Underworld material types
        mi = self.Model.materialField.data

        # convert air to sediment
        for air_material in self.airIndex:
            sedimented_mask = np.logical_and(np.in1d(mi, air_material), material_flags)
            mi[sedimented_mask] = self.sedimentIndex

        # convert sediment to air
        for air_material in self.airIndex:
            eroded_mask = np.logical_and(~np.in1d(mi, air_material), ~material_flags)
            mi[eroded_mask] = self.airIndex[0]