pitchscapes.keyfinding.KeyEstimator

class pitchscapes.keyfinding.KeyEstimator(profiles='albrecht', scoring_func=<function euclidean_distance>, normalise_counts=True, normalise_profiles=True)

Bases: object

Compute key estimates. Custom parameters can be provided on initialisation.

Public Data Attributes:

profiles

Public Methods:

score(counts, profiles[, scoring_func, ...])	Compute scores for the given count statistics and profiles.
argsort_scores(scores)
argmin_scores(scores)	Compute the best match (minimal score).
__init__([profiles, scoring_func, ...])	Defaults to use :type profiles: :param profiles: a string in KeyEstimator.profiles or profiles of shape Nx12 (for N profiles) :type scoring_func: :param scoring_func: scoring function (default Euclidean distance) :type normalise_counts: :param normalise_counts: whether to normalise counts before computing scores (default: True) :type normalise_profiles: :param normalise_profiles: whether to normalise profiles before computing scores (default: True)
get_estimate(counts)	Get best key estimate for given counts.
get_score(counts)	Get scores for given counts.

---

__annotations__ = {}

__dict__ = mappingproxy({'__module__': 'pitchscapes.keyfinding', '__doc__': '\n    Compute key estimates. Custom parameters can be provided on initialisation.\n    ', 'profiles': {'metadata': {'major': [0.16728176182048993, 0.02588894338557735, 0.1171404498466961, 0.0356495908305276, 0.11123089694868556, 0.09004464377374272, 0.0382659859401006, 0.16751964441157266, 0.03408440554363338, 0.09272715883619839, 0.03950089152204068, 0.08066562714073418], 'minor': [0.1556462295111984, 0.03462755036633632, 0.09903157107324087, 0.1044334346698825, 0.048477125346135526, 0.10397079256257924, 0.034020023696857145, 0.15515432119981398, 0.07456760938348618, 0.05334836234206381, 0.08307367815831439, 0.05364930169009233]}, 'bayes': {'major': [0.19965448155346477, 0.008990613278155785, 0.12994202213313824, 0.01242816337359103, 0.12638864214266632, 0.08545131087389726, 0.0263734257934788, 0.20655914875192785, 0.013393451149384972, 0.08825555654425607, 0.01458718179430356, 0.08797600261173529], 'minor': [0.17930236689047313, 0.017073963004507538, 0.10822708838359425, 0.11953464160572477, 0.025539463722006557, 0.11184032733554274, 0.020187990226227148, 0.181798715257532, 0.07218923098556179, 0.0341394852891066, 0.08257135177438128, 0.047595375525342154]}, 'krumhansl': {'major': [0.15195022732711172, 0.0533620483369227, 0.08327351040918879, 0.05575496530270399, 0.10480976310122037, 0.09787030390045463, 0.06030150753768843, 0.1241923905240488, 0.05719071548217276, 0.08758076094759511, 0.05479779851639147, 0.06891600861450106], 'minor': [0.14221523253201526, 0.06021118849696697, 0.07908335205571781, 0.12087171422152324, 0.05841383958660975, 0.07930802066951245, 0.05706582790384183, 0.1067175915524601, 0.08941810829027184, 0.06043585711076162, 0.07503931700741405, 0.07121995057290496]}, 'temperley': {'major': [0.12987012987012989, 0.05194805194805195, 0.09090909090909091, 0.05194805194805195, 0.1168831168831169, 0.1038961038961039, 0.05194805194805195, 0.1168831168831169, 0.05194805194805195, 0.09090909090909091, 0.03896103896103896, 0.1038961038961039], 'minor': [0.12987012987012989, 0.05194805194805195, 0.09090909090909091, 0.1168831168831169, 0.05194805194805195, 0.1038961038961039, 0.05194805194805195, 0.1168831168831169, 0.09090909090909091, 0.05194805194805195, 0.03896103896103896, 0.1038961038961039]}, 'albrecht': {'major': [0.23800000000000004, 0.006000000000000002, 0.11100000000000003, 0.006000000000000002, 0.13700000000000004, 0.09400000000000003, 0.016000000000000004, 0.21400000000000005, 0.009000000000000001, 0.08000000000000002, 0.008000000000000002, 0.08100000000000002], 'minor': [0.22044088176352702, 0.0060120240480961915, 0.10420841683366731, 0.12324649298597193, 0.019038076152304607, 0.10320641282565128, 0.012024048096192383, 0.21442885771543083, 0.06212424849699398, 0.022044088176352703, 0.06112224448897795, 0.05210420841683366]}}, 'score': <staticmethod(<function KeyEstimator.score>)>, 'argsort_scores': <staticmethod(<function KeyEstimator.argsort_scores>)>, 'argmin_scores': <staticmethod(<function KeyEstimator.argmin_scores>)>, '__init__': <function KeyEstimator.__init__>, 'get_estimate': <function KeyEstimator.get_estimate>, 'get_score': <function KeyEstimator.get_score>, '__dict__': <attribute '__dict__' of 'KeyEstimator' objects>, '__weakref__': <attribute '__weakref__' of 'KeyEstimator' objects>, '__annotations__': {}})

__init__(profiles='albrecht', scoring_func=<function euclidean_distance>, normalise_counts=True, normalise_profiles=True)

Defaults to use :type profiles: :param profiles: a string in KeyEstimator.profiles or profiles of shape Nx12 (for N profiles) :type scoring_func: :param scoring_func: scoring function (default Euclidean distance) :type normalise_counts: :param normalise_counts: whether to normalise counts before computing scores (default: True) :type normalise_profiles: :param normalise_profiles: whether to normalise profiles before computing scores (default: True)

__module__ = 'pitchscapes.keyfinding'

__weakref__

list of weak references to the object (if defined)

static argmin_scores(scores)

Compute the best match (minimal score). :type scores: :param scores: scores as computed by score function; shape MxNxK (data points x profiles x transpositions) :return: array with indices of best match; shape Nx2 (first index is the profile index; second the transposition)

static argsort_scores(scores)

get_estimate(counts)

Get best key estimate for given counts. Shortcut for using KeyEstimator.argmin_scores with given defaults. :type counts: :param counts: counts to compute scores from :return: estimates as return by KeyEstimator.argmin_scores

get_score(counts)

Get scores for given counts. Shotcut to using KeyEstimator.score with given defaults. :type counts: :param counts: counts to compute :return: scores as returned by KeyEstimator.score

profiles = {'albrecht': {'major': [0.23800000000000004, 0.006000000000000002, 0.11100000000000003, 0.006000000000000002, 0.13700000000000004, 0.09400000000000003, 0.016000000000000004, 0.21400000000000005, 0.009000000000000001, 0.08000000000000002, 0.008000000000000002, 0.08100000000000002], 'minor': [0.22044088176352702, 0.0060120240480961915, 0.10420841683366731, 0.12324649298597193, 0.019038076152304607, 0.10320641282565128, 0.012024048096192383, 0.21442885771543083, 0.06212424849699398, 0.022044088176352703, 0.06112224448897795, 0.05210420841683366]}, 'bayes': {'major': [0.19965448155346477, 0.008990613278155785, 0.12994202213313824, 0.01242816337359103, 0.12638864214266632, 0.08545131087389726, 0.0263734257934788, 0.20655914875192785, 0.013393451149384972, 0.08825555654425607, 0.01458718179430356, 0.08797600261173529], 'minor': [0.17930236689047313, 0.017073963004507538, 0.10822708838359425, 0.11953464160572477, 0.025539463722006557, 0.11184032733554274, 0.020187990226227148, 0.181798715257532, 0.07218923098556179, 0.0341394852891066, 0.08257135177438128, 0.047595375525342154]}, 'krumhansl': {'major': [0.15195022732711172, 0.0533620483369227, 0.08327351040918879, 0.05575496530270399, 0.10480976310122037, 0.09787030390045463, 0.06030150753768843, 0.1241923905240488, 0.05719071548217276, 0.08758076094759511, 0.05479779851639147, 0.06891600861450106], 'minor': [0.14221523253201526, 0.06021118849696697, 0.07908335205571781, 0.12087171422152324, 0.05841383958660975, 0.07930802066951245, 0.05706582790384183, 0.1067175915524601, 0.08941810829027184, 0.06043585711076162, 0.07503931700741405, 0.07121995057290496]}, 'metadata': {'major': [0.16728176182048993, 0.02588894338557735, 0.1171404498466961, 0.0356495908305276, 0.11123089694868556, 0.09004464377374272, 0.0382659859401006, 0.16751964441157266, 0.03408440554363338, 0.09272715883619839, 0.03950089152204068, 0.08066562714073418], 'minor': [0.1556462295111984, 0.03462755036633632, 0.09903157107324087, 0.1044334346698825, 0.048477125346135526, 0.10397079256257924, 0.034020023696857145, 0.15515432119981398, 0.07456760938348618, 0.05334836234206381, 0.08307367815831439, 0.05364930169009233]}, 'temperley': {'major': [0.12987012987012989, 0.05194805194805195, 0.09090909090909091, 0.05194805194805195, 0.1168831168831169, 0.1038961038961039, 0.05194805194805195, 0.1168831168831169, 0.05194805194805195, 0.09090909090909091, 0.03896103896103896, 0.1038961038961039], 'minor': [0.12987012987012989, 0.05194805194805195, 0.09090909090909091, 0.1168831168831169, 0.05194805194805195, 0.1038961038961039, 0.05194805194805195, 0.1168831168831169, 0.09090909090909091, 0.05194805194805195, 0.03896103896103896, 0.1038961038961039]}}

static score(counts, profiles, scoring_func=<function euclidean_distance>, normalise_counts=True, normalise_profiles=True)

Compute scores for the given count statistics and profiles. :type counts: :param counts: array-like of shape MxK with count statistics; M is the number of independent statistics to compute scores for and K the number of categories, for instance, 12 for pitch-classes. :type profiles: :param profiles: array-like of shape NxK with profiles to match the count statistics against; N is the number of different profiles and K has to be of the same as for the counts. :type scoring_func: :param scoring_func: the scoring function taking two arrays…. :type normalise_counts: :param normalise_counts: whether to normalise counts before computing scores :type normalise_profiles: :param normalise_profiles: whether to normalise profiles before computing scores :return: array of shape MxNxK with scores for all statistics, profiles, and transpositions