ChannelData

class sofia_redux.scan.channels.channel_data.channel_data.ChannelData(channels=None)

Bases: FlaggedData

Initialize channel data.

The channel data is a FlaggedData collection containing parameters relating to each individual channel (pixel or detector) of a given instrument.

Parameters:

channelssofia_redux.scan.channels.channels.Channels

Attributes Summary

configuration	Return the configuration for the channel data.
default_field_types	Return the default values for the channel attributes.
info	Return the instrument information object.
referenced_attributes	Return attributes that should be referenced rather than copied.

Methods Summary

add_dependents(dependents)	Add dependents to channel data.
apply_info(info)	Apply scan/instrument information to the channels.
calculate_overlap_values(overlap_distances, ...)	Calculates the overlap values based on overlap distances.
calculate_overlaps(point_size[, maximum_radius])	Calculates the overlaps between channels.
clear_overlaps()	Remove all overlap values.
flag_channel_list(channel_list)	Flag channels as DEAD from a list of channel ranges/fixed indices.
flag_field(field, specs)	Given a data field name and list of specifications, flag as DEAD.
flag_fields(fields)	Flags elements in various data fields as dead based on data values.
flatten_weights()	Flattens weight according to gain^2.
get_filtering(integration)	Return the filtering for a given integration.
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 arrangement.
get_pixels()	Return the pixels in the arrangement.
get_rcp_string([indices])	Return a string representation for the RCP of ALL channels.
get_typical_gain_magnitude(gains[, ...])	Return the mean gain value of data.
instance_from_instrument_name(name)	Returns a ChannelData instance for a given instrument.
kill_channels([flag])	Given a flag, sets all matching elements to DEAD only flag status.
read_channel_data_file(filename)	Read a channel data file and return the information within.
read_pixel_data(filename)	Read a pixel data file and apply the results.
read_wiring_data(filename)
remove_dependents(dependents)	Remove dependents from channel data.
set_blind_channels(fixed_indices)	Set BLIND flag for elements based on fixed indices.
set_channel_data(index, channel_info)	Set the channel info for a selected index.
set_flag_defaults()	Sets data values based on currently set flags.
set_hardware_gain(info)	Set the hardware gain attribute from the supplied info object.
set_parent(channels)	Set the parent channels of the channel data.
set_uniform_gains([field])	Sets the gain and coupling to 1.0 for all channels.
to_string([indices, frame])	Return a string representation of channels.
validate_pixel_data()	Validates data read from the pixel data file.
validate_weights()	Validates weight data.

Attributes Documentation

configuration

Return the configuration for the channel data.

Returns:

Configuration

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}.

info

Return the instrument information object.

Returns:

Info

referenced_attributes

Return attributes that should be referenced rather than copied.

Returns:

set (str)

Methods Documentation

add_dependents(dependents)

Add dependents to channel data.

Parameters:

dependentsnumpy.ndarray (float)

Returns:

None

apply_info(info)

Apply scan/instrument information to the channels.

Parameters:

infoInfo

Returns:

None

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. These float values should be converted to units of point_size.

point_sizeastropy.units.Quantity

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

Returns:

None

calculate_overlaps(point_size, maximum_radius=2.0)

Calculates the overlaps between channels.

The overlap array (in the overlaps attribute) is a csr_sparse array of shape (n_channels, n_channels) where overlaps[i, j] givens the overlap value of channel j from the channel i.

Parameters:

point_sizeastropy.units.Quantity

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

maximum_radiusfloat, optional

The maximum radius in units of point_size to search for channel overlaps.

Returns:

None

clear_overlaps()

Remove all overlap values.

Returns:

None

flag_channel_list(channel_list)

Flag channels as DEAD from a list of channel ranges/fixed indices.

Parameters:

channel_listlist or str

If provided as a string, elements should be comma delimited. Each element can be an int or string specifying fixed indices to flag. Note that ranges can be specified via ‘min:max’ or ‘min-max’.

Returns:

None

flag_field(field, specs)

Given a data field name and list of specifications, flag as DEAD.

The specifications may define a single value or range of values. Any data value within that range or equal to a specified value will be flagged as dead.

Parameters:

fieldstr

The data field name.

specslist of str

Each element may contain a single value or range of values (marked by lower-upper or lower:upper) that should be flagged as dead.

Returns:

None

flag_fields(fields)

Flags elements in various data fields as dead based on data values.

Parameters:

fieldsdict

A dictionary where keys should define a data field name, and values are lists of str where each element defines a value or range of values (min:max, or min-max) that should be flagged as dead for the given field.

Returns:

None

flatten_weights()

Flattens weight according to gain^2.

Does not include hardware gains.

Returns:

None

get_filtering(integration)

Return the filtering for a given integration.

Parameters:

integrationIntegration

Returns:

filteringnumpy.ndarray (float)

abstract 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:

Group

A newly created channel group.

abstract 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.

abstract get_overlap_indices(radius)

Return a cross-array indicating overlapping indices.

Parameters:

radiusastropy.units.Quantity

The maximum radius about which to include overlaps.

pixel_xy_sizeastropy.units.Quantity (numpy.ndarray)

The pixel (x, y) size.

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.

abstract get_pixel_count()

Return the number of pixels in the arrangement.

Returns:

pixelsint

abstract get_pixels()

Return the pixels in the arrangement.

Returns:

Channels

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

get_typical_gain_magnitude(gains, keep_flag=None, discard_flag=None, match_flag=None)

Return the mean gain value of data.

The mean value may be calculated given a number of flag criteria. The outer 10% of log(1 + |gain|) are excluded from the mean calculation.

Parameters:

gainsnumpy.ndarray (float)

keep_flagint or ChannelFlagTypes, optional

Flag values to keep in the calculation.

discard_flagint or ChannelFlagTypes, optional

Flag values to discard_flag from the calculation.

match_flagint or ChannelFlagTypes, optional

Only matching flag values will be used in the calculation.

Returns:

meanfloat

The mean gain value.

classmethod instance_from_instrument_name(name)

Returns a ChannelData instance for a given instrument.

Parameters:

namestr

The name of the instrument.

Returns:

ChannelData

kill_channels(flag=None)

Given a flag, sets all matching elements to DEAD only flag status.

Parameters:

flagint or ChannelFlagTypes

Returns:

None

abstract read_channel_data_file(filename)

Read a channel data file and return the information within.

Parameters:

filenamestr

The path to a channel data file.

Returns:

channel_infopandas.DataFrame

read_pixel_data(filename)

Read a pixel data file and apply the results.

Parameters:

filenamestr

File path to the pixel data file.

Returns:

None

read_wiring_data(filename)

remove_dependents(dependents)

Remove dependents from channel data.

Parameters:

dependentsnumpy.ndarray (float)

Returns:

None

set_blind_channels(fixed_indices)

Set BLIND flag for elements based on fixed indices.

Will kill (set flag to only DEAD) any previously defined BLIND channels. All new blinded channels will only have the BLIND flag.

Parameters:

fixed_indicesnumpy.ndarray of int

Returns:

None

set_channel_data(index, channel_info)

Set the channel info for a selected index.

Parameters:

indexint

The channel index for which to set new data.

channel_infodict

A dictionary of the form {field: value} where. The attribute field at ‘index’ will be set to value.

Returns:

None

set_flag_defaults()

Sets data values based on currently set flags.

Returns:

None

set_hardware_gain(info)

Set the hardware gain attribute from the supplied info object.

Parameters:

infoInfo

Returns:

None

set_parent(channels)

Set the parent channels of the channel data.

Parameters:

channelsChannels

Returns:

None

set_uniform_gains(field=None)

Sets the gain and coupling to 1.0 for all channels.

Parameters:

fieldstr, optional

If supplied, sets all values of the requested field to unity.

Returns:

None

to_string(indices=None, frame=False)

Return a string representation of channels.

Parameters:

indicesnumpy.ndarray or slice, optional

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

framebool, optional

If True, returns a pd.DataFrame instead of a string representation.

Returns:

str or pd.DataFrame

validate_pixel_data()

Validates data read from the pixel data file.

Returns:

None

validate_weights()

Validates weight data.

Returns:

None