abpytools.analysis package

Submodules

abpytools.analysis.amino_acid_freq module

class abpytools.analysis.amino_acid_freq.AminoAcidFreq(antibody_objects=None, path=None, region='CDR3', load=False)[source]

Bases: abpytools.features.regions.ChainDomains

plot(sort_by='name', normalize=True, display_count=True, plot_path='./', plot_name='AminoAcidFrequency.png', notebook_plot=True)[source]
plot_helper(ax, colors, title, keys, position, data, previous)[source]

abpytools.analysis.analysis_helper_functions module

abpytools.analysis.analysis_helper_functions.calculate_scores(matrix, seq_1, seq_2, substitution_matrix, gap_penalty)[source]
abpytools.analysis.analysis_helper_functions.init_score_matrix(seq_1, seq_2, indel)[source]
  • score matrix initialisation with two sequences
  • pure python, i.e. no numpy
Example init_score_matrix(‘SEND’, ‘AND’, -1):
[[0, -1, -2],
[-1, 0, 0], [-2, 0, 0], [-3, 0, 0]]
abpytools.analysis.analysis_helper_functions.load_alignment_algorithm(algorithm)[source]
abpytools.analysis.analysis_helper_functions.load_substitution_matrix(substitution_matrix)[source]
abpytools.analysis.analysis_helper_functions.needleman_wunsch(seq_1, seq_2, substitution_matrix, indel=-1)[source]
abpytools.analysis.analysis_helper_functions.switch_interactive_mode(save=False)[source]
abpytools.analysis.analysis_helper_functions.traceback(traceback_matrix, seq_1, seq_2)[source]

abpytools.analysis.cdr_length module

class abpytools.analysis.cdr_length.CDRLength(path=None, antibody_objects=None, verbose=True, show_progressbar=True, n_threads=10)[source]

Bases: abpytools.features.regions.ChainDomains

plot_cdr(only_cdr3=True, save=False, plot_path='./', plot_name='CDR_length', plot_title=None, hist=True, ax=None, **kwargs)[source]

abpytools.analysis.cluster module

abpytools.analysis.distance module

class abpytools.analysis.distance.DistancePlot(antibody_objects=None, path=None)[source]

Bases: abpytools.core.chain_collection.ChainCollection

plot_dendrogram(feature='chou', distance_metric='cosine_distance', save=False, ax=None, labels=None, multiprocessing=False, **kwargs)[source]
plot_heatmap(feature='chou', distance_metric='cosine_distance', save=False, ax=None, labels=None, multiprocessing=False, file_name='./heatmap.png', **kwargs)[source]

abpytools.analysis.distance_metrics module

abpytools.analysis.distance_metrics.cosine_distance(u, v)[source]

returns the cosine distance between vectors u and v :param u: :param v: :return:

abpytools.analysis.distance_metrics.cosine_similarity(u, v)[source]

returns the cosine similarity between vectors u and v :param u: :param v: :return:

abpytools.analysis.distance_metrics.euclidean_distance(u, v)[source]

returns the euclidean distance :param u: :param v: :return:

abpytools.analysis.distance_metrics.hamming_distance(seq1, seq2)[source]

returns the hamming distance between two sequences :param seq1: :param seq2: :return:

abpytools.analysis.distance_metrics.levenshtein_distance(seq1, seq2)[source]
Parameters:
  • seq1 –
  • seq2 –
Returns:
abpytools.analysis.distance_metrics.manhattan_distance(u, v)[source]

returns the Manhattan distance :param u: :param v: :return:

abpytools.analysis.distance_metrics.norm(u, v, degree=2)[source]

abpytools.analysis.distance_metrics_ module

abpytools.analysis.distance_metrics_.cosine_distance_()
Args:
u: v:

Returns:

abpytools.analysis.distance_metrics_.hamming_distance_()
Args:
seq1: seq2:

Returns:

abpytools.analysis.distance_metrics_.levenshtein_distance_()
Args:
seq1: seq2:

Returns:

abpytools.analysis.sequence_alignment module

class abpytools.analysis.sequence_alignment.SequenceAlignment(target, collection, algorithm, substitution_matrix)[source]

Bases: object

Sequence alignment with two chain objects

align_sequences(**kwargs)[source]
aligned_sequences
print_aligned_sequences()[source]
score
target_sequence

Module contents