o siB@sPdZddlZddlZddlZddlmZdddZdd Zdd d Z d dZ dS)aT The goal of a tempo estimation algorithm is to automatically detect the tempo of a piece of music, measured in beats per minute (BPM). See http://www.music-ir.org/mirex/wiki/2014:Audio_Tempo_Estimation for a description of the task and evaluation criteria. Conventions ----------- Reference and estimated tempi should be positive, and provided in ascending order as a numpy array of length 2. The weighting value from the reference must be a float in the range [0, 1]. Metrics ------- * :func:`mir_eval.tempo.detection`: Relative error, hits, and weighted precision of tempo estimation. N)utilTcCsh|jdkr tdtt|rt|dkr td|d|r0t|dkr2td|dSdS)aCheck that there are two non-negative tempi. For a reference value, at least one tempo has to be greater than zero. Parameters ---------- tempi : np.ndarray length-2 array of tempo, in bpm reference : bool indicates a reference value z"tempi must have exactly two valuesrztempi=z must be non-negative numbersz8reference tempi={} must have one value greater than zeroN)size ValueErrornpallisfiniteanyformat)tempi referencerB/home/ubuntu/.local/lib/python3.10/site-packages/mir_eval/tempo.pyvalidate_tempis rcCs4t|ddt|dd|dks|dkrtddS)a\Check that the input annotations to a metric look like valid tempo annotations. Parameters ---------- reference_tempi : np.ndarray reference tempo values, in bpm reference_weight : float perceptual weight of slow vs fast in reference estimated_tempi : np.ndarray estimated tempo values, in bpm T)r Frrz)Reference weight must lie in range [0, 1]N)rr)reference_tempireference_weightestimated_tempirrrvalidate4s  r{Gz?c Cst||||dks|dkrtd||dkrtdddg}t|D]\}}|dkrDt|}tt |||}||k||<q&||dd||d} t t |} t t|} | | | fS)aDCompute the tempo detection accuracy metric. Parameters ---------- reference_tempi : np.ndarray, shape=(2,) Two non-negative reference tempi reference_weight : float > 0 The relative strength of ``reference_tempi[0]`` vs ``reference_tempi[1]``. estimated_tempi : np.ndarray, shape=(2,) Two non-negative estimated tempi. tol : float in [0, 1]: The maximum allowable deviation from a reference tempo to count as a hit. ``|est_t - ref_t| <= tol * ref_t`` (Default value = 0.08) Returns ------- p_score : float in [0, 1] Weighted average of recalls: ``reference_weight * hits[0] + (1 - reference_weight) * hits[1]`` one_correct : bool True if at least one reference tempo was correctly estimated both_correct : bool True if both reference tempi were correctly estimated Raises ------ ValueError If the input tempi are ill-formed If the reference weight is not in the range [0, 1] If ``tol < 0`` or ``tol > 1``. rrz2invalid tolerance {}: must lie in the range [0, 1]gz:A tolerance of 0.0 may not lead to the results you expect.Fg?) rrr warningswarn enumeratefloatrminabsboolmax) rrrtolhitsiref_tf_ref_trelative_errorp_score one_correct both_correctrrr detectionIs$ %   r'cKs6t}tjt|||fi|\|d<|d<|d<|S)aCompute all metrics for the given reference and estimated annotations. Parameters ---------- reference_tempi : np.ndarray, shape=(2,) Two non-negative reference tempi reference_weight : float > 0 The relative strength of ``reference_tempi[0]`` vs ``reference_tempi[1]``. estimated_tempi : np.ndarray, shape=(2,) Two non-negative estimated tempi. **kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- scores : dict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. zP-scorez One-correctz Both-correct) collections OrderedDictr filter_kwargsr')rrrkwargsscoresrrrevaluatesr-)T)r) __doc__rnumpyrr(rrrr'r-rrrrs    A