distancematrix.consumer.radius_profile

Module Contents

Classes

RadiusProfile0	Consumer that calculates (common-k) radius profiles.
RadiusProfile	Consumer that calculates (common-k) radius profiles.

class distancematrix.consumer.radius_profile.RadiusProfile0(track_indices)

Bases: distancematrix.consumer.abstract_consumer.AbstractConsumer

Consumer that calculates (common-k) radius profiles.

The (common-k) radius profile tracks the distance between each subsequence and its k-th best match. It can be used to find subsequences with at least k repetitions (so called common motifs).

This class has been optimised for finding matches without ignoring trivial matches. In other words, it is not possible to define an exclusion zone for the matches.

initialise(self, dims, query_subseq, series_subseq)

Initialise this consumer.

Parameters

• dims – the number of dimensions (data channels) this consumer will receive

• query_subseq – the number of query subsequences (rows in the distance matrix)

• series_subseq – the number of series subsequences (column in the distance matrix)

Returns

None

abstract process_diagonal(self, diag, values)

Method called when a diagonal of the distance matrix is calculated.

The number of values on the diagonal might be less than the diagonal of the full matrix profile, this can occur when not enough data is available yet to calculate the entire distance matrix (typically for streaming when not enough data is available to fill the entire foreseen space).

Parameters

• diagonal_index – index of the diagonal in range ]-num_query_subseq, num_series_subseq[, the main diagonal has index 0

• values – array of shape (num_dimensions, num_values_on_diagonal) containing the distances

Returns

None

process_column(self, column_index, values)

Method called when a column of the distance matrix is calculated.

The number of values on the column might be less than the column of the full matrix profile, this can occur when not enough data is available yet to calculate the entire distance matrix (typically for streaming when not enough data is available to fill the entire foreseen space).

Parameters

• column_index – index of the column, in range [0, series_subseq[

• values – array of shape (num_dimensions, num_values_on_column) containing the distances

Returns

None

class distancematrix.consumer.radius_profile.RadiusProfile(track_indices: Union[int, List[int]], exclude_distance: int)

Bases: distancematrix.consumer.abstract_consumer.AbstractConsumer

Consumer that calculates (common-k) radius profiles.

The (common-k) radius profile tracks the distance between each subsequence and its k-th best match. It can be used to find subsequences with at least k repetitions (so called common motifs).

initialise(self, dims, query_subseq, series_subseq)

Initialise this consumer.

Parameters

• dims – the number of dimensions (data channels) this consumer will receive

• query_subseq – the number of query subsequences (rows in the distance matrix)

• series_subseq – the number of series subsequences (column in the distance matrix)

Returns

None

abstract process_diagonal(self, diag, values)

Method called when a diagonal of the distance matrix is calculated.

The number of values on the diagonal might be less than the diagonal of the full matrix profile, this can occur when not enough data is available yet to calculate the entire distance matrix (typically for streaming when not enough data is available to fill the entire foreseen space).

Parameters

• diagonal_index – index of the diagonal in range ]-num_query_subseq, num_series_subseq[, the main diagonal has index 0

• values – array of shape (num_dimensions, num_values_on_diagonal) containing the distances

Returns

None

process_column(self, column_index, values)

Method called when a column of the distance matrix is calculated.

The number of values on the column might be less than the column of the full matrix profile, this can occur when not enough data is available yet to calculate the entire distance matrix (typically for streaming when not enough data is available to fill the entire foreseen space).

Parameters

• column_index – index of the column, in range [0, series_subseq[

• values – array of shape (num_dimensions, num_values_on_column) containing the distances

Returns

None