GridCNStepLayer

class bapsf_motion.motion_builder.layers.regular_grid.GridCNStepLayer(ds: Dataset, center: List[float], npoints: List[int], step_size: List[float], skip_ds_add: bool = False)

Bases: GridLayer

Class for defining a regularly spaced grid. The grid is configured by defining its center, the number of points npoints along each dimension, and the step size step_size between points.

layer type: 'grid_CNStep'

Parameters:

ds (DataSet) – The xarray Dataset the motion builder configuration is constructed in.
center (array_like) – An array-like object containing the center coordinates of the grid. Size (N, ) where N is the dimensionality of the motion space.
npoints (array_like) – An array-like object containing the number of points in the grid along each dimension. Size (N, ) where N is the dimensionality of the motion space. Each entry indicates the number of points used along the associated axis. For example, a 2D space npoints would look like [Nx, Ny].
step_size (array_like) – An array-like object containing the step size between grid points along each dimension. Size (N, ) where N is the dimensionality of the motion space. Each entry indicates the step size for the associated axis. For example, a 2D space step_size would look like [dx, dy].
skip_ds_add (bool) – If True, then skip generating the DataArray corresponding to the motion points and adding it to the Dataset. This keyword is provided to facilitate functionality of composite layers. (DEFAULT: False)

Examples

Note

The following examples include examples for direct instantiation, as well as configuration passing at the MotionGroup and RunManager levels.

Assume we have a 2D motion space and want to define a grid of points centered at (0, 10) with 21 points along each dimension, and a step size of 1 along each dimension. This would look like:

ly = GridCNStepLayer(
    ds,
    center=[0, 10],
    npoints=[21, 21],
    step_size=[.1, .1],
)

ly = layer_factory(
    ds,
    ly_type = "grid_CNStep",
    **{
        "center": [0, 10],
        "npoints": [21, 21],
        "step_size": [.1, .1],
    },
)

[...motion_builder.layers]
type = "grid_CNStep"
center = [0, 10]
npoints = [21, 21]
step_size = [0.1, 0.1]

config["motion_builder"]["layers"] = {
    "type": "grid_CNStep",
    "center": [0, 10],
    "npoints": [21, 21],
    "step_size": [.1, .1],
}

Attributes Summary

`base_name`	Base name for associated items in the `Dataset`.
`center`	Coordinates for the center of the grid.
`config`	Dictionary containing the full configuration of the motion layer.
`dimensionality`	The designed dimensionality of the point layer.
`inputs`	A dictionary of the configuration inputs passed during layer instantiation.
`item`	The representative motion builder item in the `Dataset`.
`layer_type`	String naming the motion layer type.
`limits`	An array of min and max pairs representing the range along each motion space dimensions that the point layer resides in.
`mask`	A \(N\)-D `DataArray` representing a boolean mask of the motion space.
`mask_name`	Name of the `mask` item it the `Dataset`.
`mask_resolution`	Tuple containing the spatial resolution of each dimension of the motion space (i.e. grid spacing in each dimension).
`mspace_coords`	Dictionary-like container of motion space coordinates.
`mspace_dims`	Tuple of motion space dimension names.
`mspace_ndims`	Dimensionality of the motion space.
`name`	Name of the motion builder item in the `Dataset`.
`name_pattern`	The naming pattern for motion builder items in the `Dataset`.
`npoints`	The number of points used along each dimension of the motion space.
`points`	The `DataArray` associate with the layer.
`step_size`	Grid point steps size along each spatial dimension.
`steps`	The number of points used along each dimension of the motion space.
`composed_layers`	List of dependent motion layers used to make this more complex motion layer.

Methods Summary

`drop_vars`(names, *[, errors])	Drop variables from this dataset.
`regenerate_point_matrix`()	Re-generated the motion layer, i.e. `points`.

Attributes Documentation

base_name: Base name for associated items in the Dataset.

center: Coordinates for the center of the grid.

config: Dictionary containing the full configuration of the motion layer.

dimensionality: The designed dimensionality of the point layer. If -1, then the exclusion does not have a fixed dimensionality, and it can morph to the associated motion space.

inputs: A dictionary of the configuration inputs passed during layer instantiation.

item: The representative motion builder item in the Dataset.

layer_type: String naming the motion layer type. This is unique among all subclasses of BaseLayer.

limits: An array of min and max pairs representing the range along each motion space dimensions that the point layer resides in. Shape (N, 2) where N is the dimensionality of the motion space.

mask: A \(N\)-D DataArray representing a boolean mask of the motion space. The mask is True where a probe drive is allowed to move, and False otherwise.

mask_name: Name of the mask item it the Dataset.

mask_resolution: Tuple containing the spatial resolution of each dimension of the motion space (i.e. grid spacing in each dimension).

mspace_coords: Dictionary-like container of motion space coordinates. Keys are given by mspace_dims. Quick access to coords of mask.

mspace_dims: Tuple of motion space dimension names. Quick access to dims of mask.

mspace_ndims: Dimensionality of the motion space. Synonymous with the number of axes of the probe drive.

name: Name of the motion builder item in the Dataset.

name_pattern: The naming pattern for motion builder items in the Dataset.

npoints: The number of points used along each dimension of the motion space. Shape (N, ) where N is the dimensionality of the motion space.

points: The DataArray associate with the layer. If the layer has not been generated, then it will be done automatically.

step_size: Grid point steps size along each spatial dimension.

steps

The number of points used along each dimension of the motion space. Shape (N, ) where N is the dimensionality of the motion space.

Deprecated since v0.2.4

composed_layers: List[BaseLayer]: List of dependent motion layers used to make this more complex motion layer.

Methods Documentation

drop_vars(names: str, *, errors: Literal['raise', 'ignore'] = 'raise')

Drop variables from this dataset.

Parameters:

names (Hashable or iterable of Hashable or Callable) – Name(s) of variables to drop. If a Callable, this object is passed as its only argument and its result is used.
errors ({"raise", "ignore"}, default: "raise") – If ‘raise’, raises a ValueError error if any of the variable passed are not in the dataset. If ‘ignore’, any given names that are in the dataset are dropped and no error is raised.

Examples

>>> dataset = xr.Dataset(
...     {
...         "temperature": (
...             ["time", "latitude", "longitude"],
...             [[[25.5, 26.3], [27.1, 28.0]]],
...         ),
...         "humidity": (
...             ["time", "latitude", "longitude"],
...             [[[65.0, 63.8], [58.2, 59.6]]],
...         ),
...         "wind_speed": (
...             ["time", "latitude", "longitude"],
...             [[[10.2, 8.5], [12.1, 9.8]]],
...         ),
...     },
...     coords={
...         "time": pd.date_range("2023-07-01", periods=1),
...         "latitude": [40.0, 40.2],
...         "longitude": [-75.0, -74.8],
...     },
... )
>>> dataset
<xarray.Dataset> Size: 136B
Dimensions:      (time: 1, latitude: 2, longitude: 2)
Coordinates:
  * time         (time) datetime64[us] 8B 2023-07-01
  * latitude     (latitude) float64 16B 40.0 40.2
  * longitude    (longitude) float64 16B -75.0 -74.8
Data variables:
    temperature  (time, latitude, longitude) float64 32B 25.5 26.3 27.1 28.0
    humidity     (time, latitude, longitude) float64 32B 65.0 63.8 58.2 59.6
    wind_speed   (time, latitude, longitude) float64 32B 10.2 8.5 12.1 9.8

Drop the ‘humidity’ variable

>>> dataset.drop_vars(["humidity"])
<xarray.Dataset> Size: 104B
Dimensions:      (time: 1, latitude: 2, longitude: 2)
Coordinates:
  * time         (time) datetime64[us] 8B 2023-07-01
  * latitude     (latitude) float64 16B 40.0 40.2
  * longitude    (longitude) float64 16B -75.0 -74.8
Data variables:
    temperature  (time, latitude, longitude) float64 32B 25.5 26.3 27.1 28.0
    wind_speed   (time, latitude, longitude) float64 32B 10.2 8.5 12.1 9.8

Drop the ‘humidity’, ‘temperature’ variables

>>> dataset.drop_vars(["humidity", "temperature"])
<xarray.Dataset> Size: 72B
Dimensions:     (time: 1, latitude: 2, longitude: 2)
Coordinates:
  * time        (time) datetime64[us] 8B 2023-07-01
  * latitude    (latitude) float64 16B 40.0 40.2
  * longitude   (longitude) float64 16B -75.0 -74.8
Data variables:
    wind_speed  (time, latitude, longitude) float64 32B 10.2 8.5 12.1 9.8

Drop all indexes

>>> dataset.drop_vars(lambda x: x.indexes)
<xarray.Dataset> Size: 96B
Dimensions:      (time: 1, latitude: 2, longitude: 2)
Dimensions without coordinates: time, latitude, longitude
Data variables:
    temperature  (time, latitude, longitude) float64 32B 25.5 26.3 27.1 28.0
    humidity     (time, latitude, longitude) float64 32B 65.0 63.8 58.2 59.6
    wind_speed   (time, latitude, longitude) float64 32B 10.2 8.5 12.1 9.8

Attempt to drop non-existent variable with errors=”ignore”

>>> dataset.drop_vars(["pressure"], errors="ignore")
<xarray.Dataset> Size: 136B
Dimensions:      (time: 1, latitude: 2, longitude: 2)
Coordinates:
  * time         (time) datetime64[us] 8B 2023-07-01
  * latitude     (latitude) float64 16B 40.0 40.2
  * longitude    (longitude) float64 16B -75.0 -74.8
Data variables:
    temperature  (time, latitude, longitude) float64 32B 25.5 26.3 27.1 28.0
    humidity     (time, latitude, longitude) float64 32B 65.0 63.8 58.2 59.6
    wind_speed   (time, latitude, longitude) float64 32B 10.2 8.5 12.1 9.8

Attempt to drop non-existent variable with errors=”raise”

>>> dataset.drop_vars(["pressure"], errors="raise")
Traceback (most recent call last):
ValueError: These variables cannot be found in this dataset: ['pressure']

Raises:: ValueError – Raised if you attempt to drop a variable which is not present, and the kwarg errors='raise'.
Returns:: dropped
Return type:: Dataset