Grid¶

class hcipy.field.Grid(coords, weights=None)¶

Bases: object

A set of points on some coordinate system.

Parameters

coordsCoords: The actual definition of the coordinate values.
weightsarray_like or None: The interval size, area, volume or hypervolume of each point, depending on the number of dimensions. If this is None (default), the weights will be attempted to be calculated on the fly when needed.

Attributes

coords: The coordinate values for each dimension.

Attributes Summary

`delta`	The spacing between points in regularly-spaced grid.
`dims`	The number of elements in each dimension for a separated grid.
`is_regular`	True if the grid is regularly-spaced, False otherwise.
`is_separated`	True if the grid is separated, False otherwise.
`is_unstructured`	True if the grid is unstructured, False otherwise.
`ndim`	The number of dimensions.
`points`	A list of points of this grid.
`regular_coords`	The tuple (delta, dims, zero) for a regularly-spaced grid.
`separated_coords`	A list of coordinates for each dimension in a separated grid.
`shape`	The shape of a reshaped `numpy.ndarray` using this grid.
`size`	The number of points in this grid.
`weights`	The interval size, area, volume or hypervolume of each point, depending on the number of dimensions.
`zero`	The zero point of a regularly-spaced grid.

Methods Summary

`as_`(system)	Convert the grid to the new coordinate system system.
`closest_to`(p)	Get the index of the point closest to point p.
`copy`()	Create a copy.
`empty`([tensor_shape, dtype])	Create an empty Field from this Grid.
`from_dict`(tree)	Make a Grid from a dictionary, previously created by to_dict().
`is_`(system)	Check if the coordinate system is system.
`ones`([tensor_shape, dtype])	Create a field of ones from this Grid.
`reverse`()	Reverse the order of the points in-place.
`reversed`()	Make a copy of the grid with the order of the points reversed.
`rotate`(angle[, axis])	Rotate the grid in-place.
`rotated`(angle[, axis])	A rotated copy of this grid.
`scale`(scale)	Scale the grid in-place.
`scaled`(scale)	A scaled copy of this grid.
`shift`(shift)	Shift the grid in-place.
`shifted`(shift)	A shifted copy of this grid.
`subset`(criterium)	Construct a subset of the current sampling, based on criterium.
`to_dict`()	Convert the object to a dictionary for serialization.
`zeros`([tensor_shape, dtype])	Create a field of zeros from this Grid.

Attributes Documentation

delta¶

The spacing between points in regularly-spaced grid.

Raises

ValueError: If the grid is not regular.

dims¶

The number of elements in each dimension for a separated grid.

Raises

ValueError: If the grid is not separated.

is_regular¶: True if the grid is regularly-spaced, False otherwise.

is_separated¶: True if the grid is separated, False otherwise.

is_unstructured¶: True if the grid is unstructured, False otherwise.

ndim¶: The number of dimensions.

points¶

A list of points of this grid.

This can be used for easier iteration:

for p in grid.points:
        print(p)

regular_coords¶

The tuple (delta, dims, zero) for a regularly-spaced grid.

Raises

ValueError: If the grid is not regular.

separated_coords¶

A list of coordinates for each dimension in a separated grid.

Raises

ValueError: If the grid is not separated.

shape¶

The shape of a reshaped numpy.ndarray using this grid.

Raises

ValueError: If the grid is not separated.

size¶: The number of points in this grid.

weights¶

The interval size, area, volume or hypervolume of each point, depending on the number of dimensions.

The weights are attempted to be calculated on the fly if not set. If this fails, a warning is emitted and all points will be given an equal weight of one.

zero¶

The zero point of a regularly-spaced grid.

Raises

ValueError: If the grid is not regular.

Methods Documentation

as_(system)¶

Convert the grid to the new coordinate system system.

If the grid is already in the right coordinate system, this function doesn’t do anything.

Parameters

systemstr: The name of the coordinate system to check for.

Returns

Grid: A new Grid in the required coordinate system.

Raises

ValueError: If the conversion to the coordinate system system isn’t known.

closest_to(p)¶

Get the index of the point closest to point p.

Point p is assumed to have the same coordinate system as the grid itself.

Parameters

parray_like: The point at which to search for.

Returns

int: The index of the closest point.

copy()¶: Create a copy.

empty(tensor_shape=None, dtype=None)¶

Create an empty Field from this Grid.

Parameters

tensor_shapearray_like or None: The shape of the tensors in the to be created field. If this is None, a scalar field will be created.
dtypedata-type: The numpy data-type with which to create the field.

Returns

Field: A empty field.

classmethod from_dict(tree)¶

Make a Grid from a dictionary, previously created by to_dict().

Parameters

treedictionary: The dictionary from which to make a new Grid object.

Returns

Grid: The created object.

Raises

ValueError: If the dictionary is not formatted correctly.

is_(system)¶

Check if the coordinate system is system.

Parameters

systemstr: The name of the coordinate system to check for.

Returns

bool: If the coordinate system of the grid is equal to system.

ones(tensor_shape=None, dtype=None)¶

Create a field of ones from this Grid.

Parameters

tensor_shapearray_like or None: The shape of the tensors in the to be created field. If this is None, a scalar field will be created.
dtypedata-type: The numpy data-type with which to create the field.

Returns

Field: A ones field.

reverse()¶

Reverse the order of the points in-place.

Returns

Grid: Itself to allow for chaining these transformations.

reversed()¶

Make a copy of the grid with the order of the points reversed.

Returns

Grid: The reversed grid.

rotate(angle, axis=None)¶

Rotate the grid in-place.

Parameters

anglescalar: The angle in radians.
axisndarray or None: The axis of rotation. For two-dimensional grids, it is ignored. For three-dimensional grids it is required.

Returns

Grid: Itself to allow for chaining these transformations.

rotated(angle, axis=None)¶

A rotated copy of this grid.

Parameters

anglescalar: The angle in radians.
axisndarray or None: The axis of rotation. For two-dimensional grids, it is ignored. For three-dimensional grids it is required.

Returns

Grid: The rotated grid.

scale(scale)¶

Scale the grid in-place.

Parameters

scalearray_like: The factor with which to scale the grid.

Returns

Grid: Itself to allow for chaining these transformations.

scaled(scale)¶

A scaled copy of this grid.

Parameters

scalearray_like: The factor with which to scale the grid.

Returns

Grid: The scaled grid.

shift(shift)¶

Shift the grid in-place.

Parameters

shiftarray_like: The amount with which to shift the grid.

Returns

Grid: Itself to allow for chaining these transformations.

shifted(shift)¶

A shifted copy of this grid.

Parameters

shiftarray_like: The amount with which to shift the grid.

Returns

Grid: The scaled grid.

subset(criterium)¶

Construct a subset of the current sampling, based on criterium.

Parameters

criteriumfunction or array_like: The criterium used to select points. A function will be evaluated for every point. Otherwise, this must be a boolean array of integer array, used for slicing the points.

Returns

Grid: A new grid with UnstructuredCoords that includes only the points for which the criterium was true.

to_dict()¶

Convert the object to a dictionary for serialization.

Returns

dictionary: The created dictionary.

zeros(tensor_shape=None, dtype=None)¶

Create a field of zeros from this Grid.

Parameters

tensor_shapearray_like or None: The shape of the tensors in the to be created field. If this is None, a scalar field will be created.
dtypedata-type: The numpy data-type with which to create the field.

Returns

Field: A zeros field.