AdaptiveFilter

class sofia_redux.scan.filters.adaptive_filter.AdaptiveFilter(integration=None, data=None)

Bases: VariedFilter

Initialize an integration adaptive filter.

The adaptive filter is an abstract class where each channel has an individual frequency response (see VariedFilter).

Parameters:

integrationIntegration, optional

datanumpy.ndarray (float), optional

An array of shape (nt, n_channels) where nt is the nearest power of 2 integer above the number of integration frames. i.e., if n_frames=5, nt=8, or if n_frames=13, nt=16. If not provided will be set to frame_data * frame_relative_weight.

Attributes Summary

channel_dependent_attributes

Return attributes that are dependent on the parent channels.

Methods Summary

accumulate_profiles([channels])	Accumulates the channel profiles into a single profile.
calc_point_response([channels])	Return the point response of the source profile.
count_parms([channels])	Return the rejection filter sum above the high pass frequency.
get_config_name()	Return the configuration name.
get_id()	Return the filter ID.
get_profile()	Return the adaptive filter profile.
get_valid_profiles([channels])	Return the valid channel profiles.
post_filter_channels([channels])	Performs the post-filtering channels steps.
resample(old_profile, new_profile)	ResamplePolynomial the old profile to a new profile.
response_at(fch)	Return the response at a given frequency channel(s).
set_integration(integration)	Set the varied filter integration.
set_size(nf)	Set the number of frequencies in the adaptive filter.
update_source_profile()	Update the filter source profile.

Attributes Documentation

channel_dependent_attributes

Return attributes that are dependent on the parent channels.

This is required during a slim operation (reducing number of channels).

Returns:

set (str)

Methods Documentation

accumulate_profiles(channels=None)

Accumulates the channel profiles into a single profile.

Parameters:

channelsChannelGroup, optional

The channel group for which to accumulate profiles.

The accumulated profile is the multiplication of each channel profile.

Returns:

None

calc_point_response(channels=None)

Return the point response of the source profile.

channelsChannelGroup, optional

The channel group for which to calculate the point response. The default is all filtering channels.

The point response is given as:

sum(profile <below hi-pass>) + sum(profile * response <above hi-pass>) divided by the source_norm attribute.

Returns:

responsenumpy.ndarray (float)

The point response for each channel of shape (n_channels,).

count_parms(channels=None)

Return the rejection filter sum above the high pass frequency.

channelsChannelGroup, optional

The channel group for which to determine dependents. The default is all filtering channels.

Returns:

dependentsnumpy.ndarray (float)

An array of shape (n_channels,).

abstract get_config_name()

Return the configuration name.

Returns:

config_namestr

abstract get_id()

Return the filter ID.

Returns:

filter_idstr

get_profile()

Return the adaptive filter profile.

Returns:

profilenumpy.ndarray (float)

get_valid_profiles(channels=None)

Return the valid channel profiles.

Parameters:

channelsChannelGroup, optional

The channel group for which to get profiles. The default is all filtering channels.

Returns:

channel_profilesnumpy.ndarray (float)

post_filter_channels(channels=None)

Performs the post-filtering channels steps.

Parameters:

channelsChannelGroup, optional

The channel group for which to perform the post-filtering step. If not supplied, defaults to the filtering channels.

Returns:

None

resample(old_profile, new_profile)

ResamplePolynomial the old profile to a new profile.

Parameters:

old_profilenumpy.ndarray (float)

An array of shape (n_channels, n1) containing the current profile.

new_profilenumpy.ndarray (float)

The new array of shape (n_channels, n2) to populate.

Returns:

None

response_at(fch)

Return the response at a given frequency channel(s).

Parameters:

fchint or numpy.ndarray (int or bool) or slice

The frequency channel or channels in question.

Returns:

responsenumpy.ndarray (float)

The response array of shape (n_channels,) or (n_channels, fch.size).

set_integration(integration)

Set the varied filter integration.

Sets the padding of the FFT filter, the number of frequencies, the frequency spacing, and retrieves the channels from the integration if necessary.

Parameters:

integrationIntegration

Returns:

None

set_size(nf)

Set the number of frequencies in the adaptive filter.

Parameters:

nfint

The number of frequencies in the adaptive filter.

Returns:

None

update_source_profile()

Update the filter source profile.

Returns:

None