SingleColorChannelData

class sofia_redux.scan.channels.channel_data.single_color_channel_data.SingleColorChannelData(channels=None)

Bases: ColorArrangementData

Initialize single color channel data.

The single color channel data defines each channel as a single color pixel on a camera detector arranged by row and column, with a focal plane position. Each pixel may defined as having no relation to other pixels on the same detector (independent=True), or that they do share some commonality (independent=False) such as overlapping sensitivities to the source response.

Parameters:

channels

Attributes Summary

default_field_types

Return the default values for the channel attributes.

Methods Summary

calculate_overlap_values(overlap_distances, ...)	Calculates the overlap values based on overlap distances.
find_row_col_overlap_indices(radius, rows, cols)	Find which pixels overlap with others within a given radius.
geometric_cols()	Return the number of geometric columns in the detector array.
geometric_rows()	Return the number of geometric rows in the detector array.
get_geometric_overlap_indices(radius)	Return a cross-array indicating overlapping indices from data.
get_mapping_pixels([indices, name, ...])	Creates and returns mapping pixels.
get_overlap_distances(overlap_indices)	Calculates the overlap distances.
get_overlap_indices(radius)	Return a cross-array indicating overlapping indices.
get_pixel_count()	Return the number of pixels in the channels.
get_pixels()	Return the pixels in the arrangement.
get_positional_overlap_indices(position, radius)	Return a cross-array indicating overlapping indices from positions.
get_rcp_string([indices])	Return a string representation for the RCP of ALL channels.

Attributes Documentation

default_field_types

Return the default values for the channel attributes.

These are used during the initial creation of the data arrays to populate each of the attribute arrays with a default value. The field types are returned as a dictionary {attribute (str): value}. If a simple initialized value is supplied, that attribute of the data will default to a numpy array filled with that value. Other options are:

- (value, shape): will create arrays of shape (n, shape,) filled with
  value.
- type or class: will create empty arrays of the given type

Special handling are provided for units.Quantity, units.Unit and the sofia_redux.scan.coordinate_systems.coordinate.Coordinate classes. For the coordinate type classes, a field value also may be provided as (class, value, shape) to create filled coordinates of shape (n, shape,) with the value. Here, n is the size of the data.

Returns:

defaultsdict

A dictionary of the form {name (str): value}.

Methods Documentation

calculate_overlap_values(overlap_distances, point_size)

Calculates the overlap values based on overlap distances.

The overlap values are stored in the overlaps attribute values. This should be a csr_sparse matrix of shape (n_channels, n_channels) where overlaps[i, j] gives the overlap value between channels i and j.

Parameters:

overlap_distancesscipy.sparse.csr.csr_matrix (float)

A Compressed Sparse Row (CSR) matrix of shape (channels, channels) where distances[i, j] gives the distance between channels i and j. Although these are float values, they should be converted be in units of point_size.

point_sizeastropy.units.Quantity

The point size for calculating the overlaps. Typically, the beam FWHM.

Returns:

None

find_row_col_overlap_indices(radius, rows, cols)

Find which pixels overlap with others within a given radius.

Parameters:

radiusunits.Quantity

The radius defining the maximum overlapping radius. Should be provided in the same units as the SI pixel size of the parent channels.

rowsnumpy.ndarray (int or float)

The row indices of the channels for which to determine overlaps.

colsnumpy.ndarray (int or float)

The column indices of the channels for which to determine overlaps.

Returns:

overlapscsr_matrix

abstract geometric_cols()

Return the number of geometric columns in the detector array.

Returns:

colsint

abstract geometric_rows()

Return the number of geometric rows in the detector array.

Returns:

rowsint

get_geometric_overlap_indices(radius)

Return a cross-array indicating overlapping indices from data.

Overlaps are calculated based on the geometric properties of channels (rows and columns). A maximum radius must be supplied as well as the pixel size indicating the separation between pixels in the x and y directions.

Parameters:

radiusastropy.units.Quantity

The maximum radius about which to include overlaps.

Returns:

overlap_indicesscipy.sparse.csr.csr_matrix (bool)

A Compressed Sparse Row (CSR) matrix of shape (channels, channels) where a True value for overlap_indices[i, j] signals that channel i overlaps with the channel j.

get_mapping_pixels(indices=None, name=None, keep_flag=None, discard_flag=None, match_flag=None)

Creates and returns mapping pixels.

Parameters:

indicesnumpy.ndarray (int), optional

The indices to reference. If not supplied, defaults to all channels.

namestr, optional

The name of the channel group. If not supplied, defaults to the name of the channel data.

discard_flagint or str or ChannelFlagTypes, optional

Flags to discard_flag from the new group.

keep_flagint or str or ChannelFlagTypes, optional

Keep channels with these matching flags.

match_flagint or str or ChannelFlagTypes, optional

Keep only channels with a flag exactly matching this flag.

Returns:

ChannelGroup

A newly created channel group.

get_overlap_distances(overlap_indices)

Calculates the overlap distances.

The overlap distances are stored in the overlaps attribute values. This should be a csr_sparse matrix of shape (n_channels, n_channels) where overlaps[i, j] gives the distance between channel i and channel j.

Parameters:

overlap_indicesscipy.sparse.csr.csr_matrix (bool)

A Compressed Sparse Row (CSR) matrix of shape (channels, channels) where a True value for overlap_indices[i, j] signals that channel i overlaps with the channel j.

Returns:

distances, unitscipy.sparse.csr.csr_matrix, astropy.units.Unit

distances is a Compressed Sparse Row (CSR) matrix of shape (channels, channels) and of float type where distances[i, j] gives the distance between channels i and j. unit gives the distance unit.

get_overlap_indices(radius)

Return a cross-array indicating overlapping indices.

Parameters:

radiusastropy.units.Quantity

The maximum radius about which to include overlaps.

Returns:

overlap_indicesscipy.sparse.csr.csr_matrix (bool)

A Compressed Sparse Row (CSR) matrix of shape (channels, channels) where a True value for overlap_indices[i, j] signals that channel i overlaps with the channel j.

get_pixel_count()

Return the number of pixels in the channels.

Returns:

pixelsint

get_pixels()

Return the pixels in the arrangement.

Returns:

ChannelData

static get_positional_overlap_indices(position, radius)

Return a cross-array indicating overlapping indices from positions.

Given a search radius, find all overlapping positions.

Parameters:

positionnumpy.ndarray (float or int)

An array of orthogonal 2-dimensional coordinates of shape (n, 2). Note that all negative positions will not be included in the overlaps (this is a way of flagging default values).

radiusfloat

The maximum radius about which to include overlaps.

Returns:

overlap_indicesscipy.sparse.csr.csr_matrix (bool)

A Compressed Sparse Row (CSR) matrix of shape (channels, channels) where a True value for overlap_indices[i, j] signals that channel i overlaps with the channel j.

get_rcp_string(indices=None)

Return a string representation for the RCP of ALL channels.

Parameters:

indicesnumpy.ndarray or slice, optional

The channel indices (not fixed) to return. The default is all channels.

Returns:

str