def get_iterator(self, missing_data_for_missing_sample, coord_map, coords, data_points, shape, points, output_data):
"""
Iterator to iterate through the points needed to be calculated.
The default iterator, iterates through all the sample points calling :meth:`.Constraint.constrain_points` for
each one.
:param missing_data_for_missing_sample: If true anywhere there is missing data on the sample then final point is
missing; otherwise just use the sample
:param coord_map: Coordinate map - list of tuples of indexes of hyperpoint coord, data coords and output coords
:param coords: The coordinates to map the data onto
:param data_points: The (non-masked) data points
:param shape: Shape of the final data values
:param points: The original points object, these are the points to collocate
:param output_data: Output data set
:return: Iterator which iterates through (sample indices, hyper point and constrained points) to be placed in
these points
"""
from cis.collocation.col_implementations import HyperPoint
if missing_data_for_missing_sample:
iterator = index_iterator_for_non_masked_data(shape, points)
else:
iterator = index_iterator_nditer(shape, output_data[0])
for indices in iterator:
hp_values = [None] * HyperPoint.number_standard_names
for (hpi, ci, shi) in coord_map:
hp_values[hpi] = coords[ci].points[indices[shi]]
hp = HyperPoint(*hp_values)
constrained_points = self.constrain_points(hp, data_points)
yield indices, hp, constrained_points
def get_iterator(self, missing_data_for_missing_sample, coord_map, coords,
data_points, shape, points, output_data):
"""
Iterator to iterate through the points needed to be calculated.
The default iterator, iterates through all the sample points calling :meth:`.Constraint.constrain_points` for
each one.
:param missing_data_for_missing_sample: If true anywhere there is missing data on the sample then final point is
missing; otherwise just use the sample
:param coord_map: Coordinate map - list of tuples of indexes of hyperpoint coord, data coords and output coords
:param coords: The coordinates to map the data onto
:param data_points: The (non-masked) data points
:param shape: Shape of the final data values
:param points: The original points object, these are the points to collocate
:param output_data: Output data set
:return: Iterator which iterates through (sample indices, hyper point and constrained points) to be placed in
these points
"""
from cis.collocation.col_implementations import HyperPoint
if missing_data_for_missing_sample:
iterator = index_iterator_for_non_masked_data(shape, points)
else:
iterator = index_iterator_nditer(shape, output_data[0])
for indices in iterator:
hp_values = [None] * HyperPoint.number_standard_names
for (hpi, ci, shi) in coord_map:
hp_values[hpi] = coords[ci].points[indices[shi]]
hp = HyperPoint(*hp_values)
constrained_points = self.constrain_points(hp, data_points)
yield indices, hp, constrained_points
def get_iterator(self, missing_data_for_missing_sample, coord_map, coords, data_points, shape, points, output_data):
from cis.collocation.col_implementations import HyperPoint
if missing_data_for_missing_sample:
iterator = index_iterator_for_non_masked_data(shape, points)
else:
iterator = index_iterator_nditer(shape, output_data[0])
for indices in iterator:
hp_values = [None] * HyperPoint.number_standard_names
for (hpi, ci, shi) in coord_map:
hp_values[hpi] = coords[ci].points[indices[shi]]
hp = HyperPoint(*hp_values)
constrained_points = self.constrain_points(indices, data_points)
yield indices, hp, constrained_points
def get_iterator(self, missing_data_for_missing_sample, coord_map, coords,
data_points, shape, points, output_data):
from cis.collocation.col_implementations import HyperPoint
if missing_data_for_missing_sample:
iterator = index_iterator_for_non_masked_data(shape, points)
else:
iterator = index_iterator_nditer(shape, output_data[0])
for indices in iterator:
hp_values = [None] * HyperPoint.number_standard_names
for (hpi, ci, shi) in coord_map:
hp_values[hpi] = coords[ci].points[indices[shi]]
hp = HyperPoint(*hp_values)
constrained_points = self.constrain_points(indices, data_points)
yield indices, hp, constrained_points
