data_structures

`static_interpolation.data_structures`

Classes:

Name	Description
`ImageLayout`	Defines multi-panel image layout.
`AGIPD_1MLayout`	ImageLayout for the AGIPD_1M detector.
`JUNGFRAU_4MLayout`	ImageLayout for the JUNGFRAU_4M detector.
`SamplingGrid`	Defines sampling point collections.

`ImageLayout` [source] `dataclass`

Defines multi-panel image layout. ,implements data standardization routines.

Attributes:

Name	Type	Description
`n_panels`	`int)`	Number of panels
`num_x`	`int`	Number of data pixels in x-direction
`num_y`	`int)`	Number of data pixels in y-direction
`num_x_logical`	`int)`	Number of logical pixels in x-direction
`num_y_logical`	`int)`	Number of logical pixels in y-direction

Methods:

Name	Description
`from_shape`	ImageLayout constructor using a shape tuple.
`normalize`	Normalizes input data shape to (n_images,n_panels,num_x,num_y)
`ravel`	Ravel the image dimensions n_panel,num_x,num_y.
`convert_logical_to_data_ids`	Converts a set of logical indices into actual data indices.

`data_shape` `property`

tuple[int,int,int]: Data shape as tuple (n_panels,num_x,num_y)

`logical_shape` `property`

tuple[int,int,int]: Data shape as tuple (n_panels,num_x,num_y)

`from_shape(data_shape, logical_shape=None)` [source] `classmethod`

ImageLayout constructor using a shape tuple.

Parameters:

Name	Type	Description	Default
`data_shape`	`tuple[int, ...]`	Shape tuple of length 2 or 3. Tuples of length 2 are interpreted as single panel shape, only specifying num_x and num_y.	required

Returns: ImageLayout: instance

`normalize(data)` [source]

Normalizes input data shape to (n_images,n_panels,num_x,num_y) setting n_images and/or n_panels to 1 if necessary. Forces output data array to be c_contiguous. Possible intput shapes are: 1. (num_x,num_y) -> (1,1,num_x,num_y) 2. self.n_panels=1 and (n_images,num_x,num_y) -> (n_image,1,num_x,num_y) 3. self.n_panels!=1 and (n_panels,num_x,num_y -> (1,n_panels,num_x,num_y) 4. (n_image,n_panels,num_x,num_y) -> (n_image,n_panels,num_x,num_y)

Parameters:

Name	Type	Description	Default
`data`	`NDArray`	Input data set.	required

Returns: NDArray: reshaped version of data (No copy if input was C contiguous )

`ravel(data)` [source]

Ravel the image dimensions n_panel,num_x,num_y. (n_images,n_panels,num_x,num_y) -> (n_images,N) with N = n_panelsnum_xnum_y Args: data (NDArray): Input data set Returns: NDArray: Reshaped data set

`convert_logical_to_data_ids(logical_ids)` [source]

Converts a set of logical indices into actual data indices. This Routine simply does the identity map. Subclasses are supposed to overide this method.

Parameters:

Name	Type	Description	Default
`logical_ids`	`NDArray`	Array of indices with values in 0 to n_panelsnum_x_logicalnum_y_logical	required

Returns: NDArray: Corresponding Data indices

`AGIPD_1MLayout` [source] `dataclass`

Bases: ImageLayout

ImageLayout for the AGIPD_1M detector.

Attributes:

Name	Type	Description
`n_panels`	`int)`	Number of panels = 16
`num_x`	`int`	Number of data pixels in x-direction = 512
`num_y`	`int)`	Number of data pixels in y-direction = 128
`num_x_logical`	`int)`	Number of logical pixels in x-direction = 526
`num_y_logical`	`int)`	Number of logical pixels in y-direction = 128

Methods:

Name	Description
`convert_logical_to_data_ids`	Converts a set of logical AGIPD indices into actual data indices taking the double width pixel at

`convert_logical_to_data_ids(logical_ids)` [source]

Converts a set of logical AGIPD indices into actual data indices taking the double width pixel at asic boundaries into account, see https://doi.org/10.1107/S1600577518016077.

Parameters:

Name	Type	Description	Default
`logical_ids`	`NDArray`	Array of indices with values in 0 to 1077248	required

Returns: NDArray: Corresponding Data indices

`JUNGFRAU_4MLayout` [source] `dataclass`

Bases: ImageLayout

ImageLayout for the JUNGFRAU_4M detector.

Attributes:

Name	Type	Description
`n_panels`	`int)`	Number of panels = 16
`num_x`	`int`	Number of data pixels in x-direction = 512
`num_y`	`int)`	Number of data pixels in y-direction = 1024
`num_x_logical`	`int)`	Number of logical pixels in x-direction = 514
`num_y_logical`	`int)`	Number of logical pixels in y-direction = 1030

Methods:

Name	Description
`convert_logical_to_data_ids`	Converts a set of logical JUNGFRAU indices into actual data indices taking the double width/height and quadruple pixels at inner asic boundaries into account, see https://doi.org/10.1107/S1600577526000342.

`convert_logical_to_data_ids(logical_ids)` [source]

Converts a set of logical JUNGFRAU indices into actual data indices taking the double width/height and quadruple pixels at inner asic boundaries into account, see https://doi.org/10.1107/S1600577526000342.

X: –256–+1 | +1–256–
Y: –256–+1 | +1–256–+1 | +1–256–+1 | +1–256–

Parameters:

Name	Type	Description	Default
`logical_ids`	`NDArray`	Array of indices with values in 0 to 1077248	required

Returns: NDArray: Corresponding Data indices

`SamplingGrid` [source] `dataclass`

Defines sampling point collections. Attributes: points (NDArray): Has standard shape (n_panels,…,2). n_panels (int): Number of panels.

Methods:

Name	Description
`__post_init__`	Forces self.points to be C-contiguous and checks points shape.
`from_shared_points`	SamplingGrid constructor for panel-independent points of shape (…, 2).
`from_uniform_polar`	Constructor of SamplingGrid for a uniform polar grid.
`ravel`	Returns self.points in the standard shape (n_panels,N,2)

Attributes:

Name	Type	Description
`out_shape`	`tuple[int, ...]`	Returns the expected interpolation output shape.

`out_shape` `property`

Returns the expected interpolation output shape. Follows the premis that panels are not allowed to overlap. So the output shape is self.points.shape[1:-1].

Returns:

Name	Type	Description
`tuple`	`(int, ...)`	Shape of interpolation output

`__post_init__()` [source]

Forces self.points to be C-contiguous and checks points shape.

`from_shared_points(points, n_panels)` [source] `classmethod`

SamplingGrid constructor for panel-independent points of shape (…, 2).

`from_uniform_polar(n_panels, shape, max_radius, endpoint=False)` [source] `classmethod`

Constructor of SamplingGrid for a uniform polar grid.

Parameters:

Name	Type	Description	Default
`n_panels`	`int`	number of panels	required
`shape`	`tuple(int, int)`	shape of the uniform polar grid (number_of_radial_points,number_of_angular_points)	required
`max_radius`	`float`	Maximal radial distance	required
`endpoint`	`bool`	Whether max_radius or max_radius*(number_of_radial_points-1)/number_of_radial_points is the last radial point.	`False`

`ravel()` [source]

Returns self.points in the standard shape (n_panels,N,2) Returns: NDArray: reshaped version of self.points

data_structures

static_interpolation.data_structures

ImageLayout [source] dataclass

data_shape property

logical_shape property

from_shape(data_shape, logical_shape=None) [source] classmethod

normalize(data) [source]

ravel(data) [source]

convert_logical_to_data_ids(logical_ids) [source]

AGIPD_1MLayout [source] dataclass

convert_logical_to_data_ids(logical_ids) [source]

JUNGFRAU_4MLayout [source] dataclass

convert_logical_to_data_ids(logical_ids) [source]

SamplingGrid [source] dataclass

out_shape property

__post_init__() [source]

from_shared_points(points, n_panels) [source] classmethod

from_uniform_polar(n_panels, shape, max_radius, endpoint=False) [source] classmethod

ravel() [source]

`static_interpolation.data_structures`

`ImageLayout` [source] `dataclass`

`data_shape` `property`

`logical_shape` `property`

`from_shape(data_shape, logical_shape=None)` [source] `classmethod`

`normalize(data)` [source]

`ravel(data)` [source]

`convert_logical_to_data_ids(logical_ids)` [source]

`AGIPD_1MLayout` [source] `dataclass`

`convert_logical_to_data_ids(logical_ids)` [source]

`JUNGFRAU_4MLayout` [source] `dataclass`

`convert_logical_to_data_ids(logical_ids)` [source]

`SamplingGrid` [source] `dataclass`

`out_shape` `property`

`__post_init__()` [source]

`from_shared_points(points, n_panels)` [source] `classmethod`

`from_uniform_polar(n_panels, shape, max_radius, endpoint=False)` [source] `classmethod`

`ravel()` [source]