}KgBfUdZddlmZddlZddlZddlmZddlm Z ddl m Z dd l m Z mZmZmZmZmZmZmZdd lmZmZmZmZmZgd Zed dd  ddZed dd  ddZd dd  ddZeddd  ddZeddd  ddZeddd  ddZd dd  ddZedddd ddZedddd ddZedddd ddZdd dd ddZedddd ddZedddd ddZedddd ddZdd dd ddZedd dd!Z edd  dd"Z edd  dd#Z dd  dd$Z edd dd%Z!edd  dd&Z!edd  dd'Z!dd  dd(Z!edd)Z"e dd*Z"e dd+Z" dd,Z"e dd-Z#e dd.Z#e dd/Z# dd0Z#e dd1Z$e dd2Z$e dd3Z$ dd4Z$edd5Z%edd6Z%e dd7Z% dd8Z%edd9dd:Z&edd9dd;Z&edd9 dd<Z&d=d9 dd>Z&eddddd? dd@Z'eddddd? ddAZ'eddddd? ddBZ'e gd?Cd=dDdEd=d? ddFZ'eddGZ(eddHZ(e ddIZ( ddJZ(eddKZ)eddLZ)e ddMZ) ddNZ)eddOZ*eddPZ*e ddQZ* ddRZ*eddSddTZ+eddS ddUZ+eddS ddVZ+dDdS ddWZ+eddSddXZ,eddSddYZ,eddS ddZZ,dDdS dd[Z,eddd\ dd]Z-eddd\ dd^Z-eddd\ dd_Z-d`dad\ ddbZ-eddd\ ddcZ.eddd\ dddZ.eddd\ ddeZ.d`dad\ ddfZ.eddgddhZ/eddg ddiZ/eddg ddjZ/dadg ddkZ/eddg ddlZ0eddg ddmZ0eddg ddnZ0dadg ddoZ0ddpdqddrZ1dad`ds ddtZ2 dd`dudDdv ddwZ3d ddx ddyZ4ddzd d{ dd|Z5edd} dd~Z6edd} ddZ6edd} ddZ6dd} ddZ6edd} ddZ7edd} ddZ7edd} ddZ7dd} ddZ7edd} ddZ8edd} ddZ8edd} ddZ8dd} ddZ8edd} ddZ9edd} ddZ9edd} ddZ9dd} ddZ9dd} d dZ:e6e7e8e9e:e:dZ;dedd ddd ddZ?d ddd ddZ@edddd ddZAedddd ddZAedddd ddZAe gdCd=d=d=d ddZAedddd ddZBedddd ddZBedddd ddZBd=d=d=d ddZBedddd ddZCedddd ddZCedddd ddZCd=d=d=d ddZCedddd ddZDedddd ddZDedddd ddZDe gdCd=d=d=d ddZDedddd ddZEedddd ddZEedddd ddZEd=d=d=d ddZEedddd ddZFedddd d dZFedddd d!dZFd=d=d=d d!dZFedddd d"dZGedddd d#dZGdddDd d$dZGy(%zUnit conversion utilities) annotationsN)notation)ParameterError) vectorize)AnyCallableDictIterableOptionalSizedUnionoverload) _IterableLike _FloatLike_co _SequenceLike_ScalarOrSequence _IntLike_co)*frames_to_samplesframes_to_timesamples_to_framessamples_to_timetime_to_samplestime_to_framesblocks_to_samplesblocks_to_framesblocks_to_time note_to_hz note_to_midi midi_to_hz midi_to_note hz_to_note hz_to_midi hz_to_mel hz_to_octs hz_to_fjs mel_to_hz octs_to_hz A4_to_tuning tuning_to_A4fft_frequenciescqt_frequenciesmel_frequenciestempo_frequenciesfourier_tempo_frequencies A_weighting B_weighting C_weighting D_weighting Z_weightingfrequency_weightingmulti_frequency_weighting samples_like times_likemidi_to_svara_hmidi_to_svara_cnote_to_svara_hnote_to_svara_c hz_to_svara_h hz_to_svara_ci hop_lengthn_fftcyNframesrArBs X/home/alanp/www/video.onchill/myenv/lib/python3.12/site-packages/librosa/core/convert.pyrrAcyrDrErFs rHrrHrJcd}|t|dz}tj||z|zjtS)aConvert frame indices to audio sample indices. Parameters ---------- frames : number or np.ndarray [shape=(n,)] frame index or vector of frame indices hop_length : int > 0 [scalar] number of samples between successive frames n_fft : None or int > 0 [scalar] Optional: length of the FFT window. If given, time conversion will include an offset of ``n_fft // 2`` to counteract windowing effects when using a non-centered STFT. Returns ------- times : number or np.ndarray time (in samples) of each given frame number:: times[i] = frames[i] * hop_length See Also -------- frames_to_time : convert frame indices to time values samples_to_frames : convert sample indices to frame indices Examples -------- >>> y, sr = librosa.load(librosa.ex('choice')) >>> tempo, beats = librosa.beat.beat_track(y=y, sr=sr) >>> beat_samples = librosa.frames_to_samples(beats, sr=sr) rr)intnp asanyarrayastype)rGrArBoffsets rHrrRsAJF UaZ MM& !J . 7 ? ? DDrJ.cyrDrEsamplesrArBs rHrr~rIrJcyrDrErTs rHrrrLrJcyrDrErTs rHrrrLrJcd}|t|dz}tj|}tjtj||z |ztS)aConvert sample indices into STFT frames. Examples -------- >>> # Get the frame numbers for every 256 samples >>> librosa.samples_to_frames(np.arange(0, 22050, 256)) array([ 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 20, 20, 21, 21, 22, 22, 23, 23, 24, 24, 25, 25, 26, 26, 27, 27, 28, 28, 29, 29, 30, 30, 31, 31, 32, 32, 33, 33, 34, 34, 35, 35, 36, 36, 37, 37, 38, 38, 39, 39, 40, 40, 41, 41, 42, 42, 43]) Parameters ---------- samples : int or np.ndarray [shape=(n,)] sample index or vector of sample indices hop_length : int > 0 [scalar] number of samples between successive frames n_fft : None or int > 0 [scalar] Optional: length of the FFT window. If given, time conversion will include an offset of ``- n_fft // 2`` to counteract windowing effects in STFT. .. note:: This may result in negative frame indices. Returns ------- frames : int or np.ndarray [shape=(n,), dtype=int] Frame numbers corresponding to the given times:: frames[i] = floor( samples[i] / hop_length ) See Also -------- samples_to_time : convert sample indices to time values frames_to_samples : convert frame indices to sample indices rrdtype)rNrOrPasarrayfloor)rUrArBrRs rHrrsO^F UaZmmG$G ::bhh& 0Z?@ LLrJ)srrArBcyrDrErGr]rArBs rHrrrJcyrDrEr_s rHrrr`rJcyrDrEr_s rHrrr`rJi"Vc8t|||}t||S)aConvert frame counts to time (seconds). Parameters ---------- frames : np.ndarray [shape=(n,)] frame index or vector of frame indices sr : number > 0 [scalar] audio sampling rate hop_length : int > 0 [scalar] number of samples between successive frames n_fft : None or int > 0 [scalar] Optional: length of the FFT window. If given, time conversion will include an offset of ``n_fft // 2`` to counteract windowing effects when using a non-centered STFT. Returns ------- times : np.ndarray [shape=(n,)] time (in seconds) of each given frame number:: times[i] = frames[i] * hop_length / sr See Also -------- time_to_frames : convert time values to frame indices frames_to_samples : convert frame indices to sample indices Examples -------- >>> y, sr = librosa.load(librosa.ex('choice')) >>> tempo, beats = librosa.beat.beat_track(y=y, sr=sr) >>> beat_times = librosa.frames_to_time(beats, sr=sr) r@r])rr)rGr]rArBrUs rHrrs P :UKG 7r **rJcyrDrEtimesr]rArBs rHrrr`rJcyrDrErfs rHrr)r`rJcyrDrErfs rHrr4r`rJc8t||}t|||S)axConvert time stamps into STFT frames. Parameters ---------- times : np.ndarray [shape=(n,)] time (in seconds) or vector of time values sr : number > 0 [scalar] audio sampling rate hop_length : int > 0 [scalar] number of samples between successive frames n_fft : None or int > 0 [scalar] Optional: length of the FFT window. If given, time conversion will include an offset of ``- n_fft // 2`` to counteract windowing effects in STFT. .. note:: This may result in negative frame indices. Returns ------- frames : np.ndarray [shape=(n,), dtype=int] Frame numbers corresponding to the given times:: frames[i] = floor( times[i] * sr / hop_length ) See Also -------- frames_to_time : convert frame indices to time values time_to_samples : convert time values to sample indices Examples -------- Get the frame numbers for every 100ms >>> librosa.time_to_frames(np.arange(0, 1, 0.1), ... sr=22050, hop_length=512) array([ 0, 4, 8, 12, 17, 21, 25, 30, 34, 38]) rdr@)rr)rgr]rArBrUs rHrr?s ^e+G W5 IIrJrdcyrDrErgr]s rHrrsrJcyrDrErls rHrrxrIrJcyrDrErls rHrrrIrJcXtj||zjtS)aConvert timestamps (in seconds) to sample indices. Parameters ---------- times : number or np.ndarray Time value or array of time values (in seconds) sr : number > 0 Sampling rate Returns ------- samples : int or np.ndarray [shape=times.shape, dtype=int] Sample indices corresponding to values in ``times`` See Also -------- time_to_frames : convert time values to frame indices samples_to_time : convert sample indices to time values Examples -------- >>> librosa.time_to_samples(np.arange(0, 1, 0.1), sr=22050) array([ 0, 2205, 4410, 6615, 8820, 11025, 13230, 15435, 17640, 19845]) )rOrPrQrNrls rHrrs#8 MM% 2 % - -c 22rJcyrDrErUr]s rHrrrmrJcyrDrErrs rHrrrIrJcyrDrErrs rHrrrIrJcDtj|t|z S)a"Convert sample indices to time (in seconds). Parameters ---------- samples : np.ndarray Sample index or array of sample indices sr : number > 0 Sampling rate Returns ------- times : np.ndarray [shape=samples.shape] Time values corresponding to ``samples`` (in seconds) See Also -------- samples_to_frames : convert sample indices to frame indices time_to_samples : convert time values to sample indices Examples -------- Get timestamps corresponding to every 512 samples >>> librosa.samples_to_time(np.arange(0, 22050, 512), sr=22050) array([ 0. , 0.023, 0.046, 0.07 , 0.093, 0.116, 0.139, 0.163, 0.186, 0.209, 0.232, 0.255, 0.279, 0.302, 0.325, 0.348, 0.372, 0.395, 0.418, 0.441, 0.464, 0.488, 0.511, 0.534, 0.557, 0.58 , 0.604, 0.627, 0.65 , 0.673, 0.697, 0.72 , 0.743, 0.766, 0.789, 0.813, 0.836, 0.859, 0.882, 0.906, 0.929, 0.952, 0.975, 0.998]) )rOrPfloatrrs rHrrsF == !E"I --rJcyrDrEblocks block_lengths rHrrrmrJcyrDrErxs rHrrrIrJcyrDrErxs rHrrrIrJc2|tj|zS)aZConvert block indices to frame indices Parameters ---------- blocks : np.ndarray Block index or array of block indices block_length : int > 0 The number of frames per block Returns ------- frames : np.ndarray [shape=samples.shape, dtype=int] The index or indices of frames corresponding to the beginning of each provided block. See Also -------- blocks_to_samples blocks_to_time Examples -------- Get frame indices for each block in a stream >>> filename = librosa.ex('brahms') >>> sr = librosa.get_samplerate(filename) >>> stream = librosa.stream(filename, block_length=16, ... frame_length=2048, hop_length=512) >>> for n, y in enumerate(stream): ... n_frame = librosa.blocks_to_frames(n, block_length=16) rOrPrxs rHrrsF "--/ //rJcyrDrEryrzrAs rHrrrIrJcyrDrErs rHrrrIrJcyrDrErs rHrr%rIrJc6t||}t||S)adConvert block indices to sample indices Parameters ---------- blocks : np.ndarray Block index or array of block indices block_length : int > 0 The number of frames per block hop_length : int > 0 The number of samples to advance between frames Returns ------- samples : np.ndarray [shape=samples.shape, dtype=int] The index or indices of samples corresponding to the beginning of each provided block. Note that these correspond to the *first* sample index in each block, and are not frame-centered. See Also -------- blocks_to_frames blocks_to_time Examples -------- Get sample indices for each block in a stream >>> filename = librosa.ex('brahms') >>> sr = librosa.get_samplerate(filename) >>> stream = librosa.stream(filename, block_length=16, ... frame_length=2048, hop_length=512) >>> for n, y in enumerate(stream): ... n_sample = librosa.blocks_to_samples(n, block_length=16, ... hop_length=512) )rz)rA)rr)ryrzrArGs rHrr,sRf< @F V ;;rJcyrDrEryrzrAr]s rHrrYrIrJcyrDrErs rHrr`rIrJcyrDrErs rHrrgr`rJc8t|||}t||S)aConvert block indices to time (in seconds) Parameters ---------- blocks : np.ndarray Block index or array of block indices block_length : int > 0 The number of frames per block hop_length : int > 0 The number of samples to advance between frames sr : int > 0 The sampling rate (samples per second) Returns ------- times : np.ndarray [shape=samples.shape] The time index or indices (in seconds) corresponding to the beginning of each provided block. Note that these correspond to the time of the *first* sample in each block, and are not frame-centered. See Also -------- blocks_to_frames blocks_to_samples Examples -------- Get time indices for each block in a stream >>> filename = librosa.ex('brahms') >>> sr = librosa.get_samplerate(filename) >>> stream = librosa.stream(filename, block_length=16, ... frame_length=2048, hop_length=512) >>> for n, y in enumerate(stream): ... n_time = librosa.blocks_to_time(n, block_length=16, ... hop_length=512, sr=sr) )rzrArd)rr)ryrzrAr]rUs rHrrrs%^ \jG 7r **rJc yrDrEnotekwargss rHrrrmrJc yrDrErs rHrrrmrJc yrDrErs rHrrrIrJc ,tt|fi|S)a1Convert one or more note names to frequency (Hz) Examples -------- >>> # Get the frequency of a note >>> librosa.note_to_hz('C') array([ 16.352]) >>> # Or multiple notes >>> librosa.note_to_hz(['A3', 'A4', 'A5']) array([ 220., 440., 880.]) >>> # Or notes with tuning deviations >>> librosa.note_to_hz('C2-32', round_midi=False) array([ 64.209]) Parameters ---------- note : str or iterable of str One or more note names to convert **kwargs : additional keyword arguments Additional parameters to `note_to_midi` Returns ------- frequencies : number or np.ndarray [shape=(len(note),)] Array of frequencies (in Hz) corresponding to ``note`` See Also -------- midi_to_hz note_to_midi hz_to_note )r!r rs rHrrsF l4262 33rJ round_midicyrDrErrs rHr r rmrJcyrDrErs rHr r rmrJcyrDrErs rHr r rIrJTc t|ts.tj|Dcgc]}t ||c}Sdddddddd }d dd d d dd d dd }t j j|}|std|d|jdj}tj|jdDcgc]}|| c}}|jd} |jd} | sd} n t| } | sd} nt| dz} d| d zz||z|z| z} |rttj| S| Scc}wcc}w)uConvert one or more spelled notes to MIDI number(s). Notes may be spelled out with optional accidentals or octave numbers. The leading note name is case-insensitive. Sharps are indicated with ``#``, flats may be indicated with ``!`` or ``b``. Parameters ---------- note : str or iterable of str One or more note names. round_midi : bool - If ``True``, midi numbers are rounded to the nearest integer. - If ``False``, allow fractional midi numbers. Returns ------- midi : float or np.array Midi note numbers corresponding to inputs. Raises ------ ParameterError If the input is not in valid note format See Also -------- midi_to_note note_to_hz Examples -------- >>> librosa.note_to_midi('C') 12 >>> librosa.note_to_midi('C#3') 49 >>> librosa.note_to_midi('C♯3') # Using Unicode sharp 49 >>> librosa.note_to_midi('C♭3') # Using Unicode flat 47 >>> librosa.note_to_midi('f4') 65 >>> librosa.note_to_midi('Bb-1') 10 >>> librosa.note_to_midi('A!8') 116 >>> librosa.note_to_midi('G𝄪6') # Double-sharp 93 >>> librosa.note_to_midi('B𝄫6') # Double-flat 93 >>> librosa.note_to_midi('C♭𝄫5') # Triple-flats also work 69 >>> # Lists of notes also work >>> librosa.note_to_midi(['C', 'E', 'G']) array([12, 16, 19]) rrr )CDEFGABr) #b!u♯u𝄪u♭u𝄫u♮zImproper note format: sr accidentaloctavecentsg{Gz? ) isinstancestrrOarrayr rNOTE_REmatchrgroupuppersumrNround) rrn pitch_mapacc_maprpitchorRrr note_values rHr r sxx dC xxNAaJ?NOO      !I   G    " "4 (E 5d1X>?? KK  % % 'E VV\)BC)BAWQZ)BC DF [[ "F KK E V E T!fqj)Ie,<>> librosa.midi_to_note(0) 'C-1' >>> librosa.midi_to_note(37) 'C♯2' >>> librosa.midi_to_note(37, unicode=False) 'C#2' >>> librosa.midi_to_note(-2) 'A♯-2' >>> librosa.midi_to_note(104.7) 'A7' >>> librosa.midi_to_note(104.7, cents=True) 'A7-30' >>> librosa.midi_to_note(np.arange(12, 24))) array(['C0', 'C♯0', 'D0', 'D♯0', 'E0', 'F0', 'F♯0', 'G0', 'G♯0', 'A0', 'A♯0', 'B0'], dtype='>> librosa.midi_to_note(range(12, 24), key='F:min') array(['C0', 'D♭0', 'D0', 'E♭0', 'E0', 'F0', 'G♭0', 'G0', 'A♭0', 'A0', 'B♭0', 'B0'], dtype='H288D>"HS299TH_a889J HrM "D !!$HrM(:Q(>? q*T*+ KrJcyrDrEnotess rHr!r!rmrJcyrDrErs rHr!r!rmrJcyrDrErs rHr!r!rIrJcDddtj|dz dz zzS)axGet the frequency (Hz) of MIDI note(s) Examples -------- >>> librosa.midi_to_hz(36) 65.406 >>> librosa.midi_to_hz(np.arange(36, 48)) array([ 65.406, 69.296, 73.416, 77.782, 82.407, 87.307, 92.499, 97.999, 103.826, 110. , 116.541, 123.471]) Parameters ---------- notes : int or np.ndarray [shape=(n,), dtype=int] midi number(s) of the note(s) Returns ------- frequency : number or np.ndarray [shape=(n,), dtype=float] frequency (frequencies) of ``notes`` in Hz See Also -------- hz_to_midi note_to_hz {@@g@Q@g(@r~rs rHr!r!s'< CR]]51D8D@A BBrJcyrDrE frequenciess rHr$r$rmrJcyrDrErs rHr$r$rmrJcyrDrErs rHr$r$$rIrJcdtjtj|tjdz zdz}|S)aGet MIDI note number(s) for given frequencies Examples -------- >>> librosa.hz_to_midi(60) 34.506 >>> librosa.hz_to_midi([110, 220, 440]) array([ 45., 57., 69.]) Parameters ---------- frequencies : float or np.ndarray [shape=(n,), dtype=float] frequencies to convert Returns ------- note_nums : number or np.ndarray [shape=(n,), dtype=float] MIDI notes to ``frequencies`` See Also -------- midi_to_hz note_to_midi hz_to_note rrErOlog2rP)rrs rHr$r$+s88RWWR]];%?@2775>QRUWWD KrJc yrDrErrs rHr#r#KrmrJc yrDrErs rHr#r#PrmrJc yrDrErs rHr#r#UrIrJc ,tt|fi|S)aConvert one or more frequencies (in Hz) to the nearest note names. Parameters ---------- frequencies : float or iterable of float Input frequencies, specified in Hz **kwargs : additional keyword arguments Arguments passed through to `midi_to_note` Returns ------- notes : str or np.ndarray of str ``notes[i]`` is the closest note name to ``frequency[i]`` (or ``frequency`` if the input is scalar) See Also -------- hz_to_midi midi_to_note note_to_hz Examples -------- Get a single note name for a frequency >>> librosa.hz_to_note(440.0) 'A5' Get multiple notes with cent deviation >>> librosa.hz_to_note([32, 64], cents=True) ['C1-38', 'C2-38'] Get multiple notes, but suppress octave labels >>> librosa.hz_to_note(440.0 * (2.0 ** np.linspace(0, 1, 12)), ... octave=False) ['A', 'A#', 'B', 'C', 'C#', 'D', 'E', 'F', 'F#', 'G', 'G#', 'A'] )r"r$rs rHr#r#\sV  ;/ :6 ::rJhtkcyrDrErrs rHr%r%rmrJcyrDrErs rHr%r%rIrJcyrDrErs rHr%r%rIrJctj|}|r dtjd|dz zz}|Sd}d}||z |z }d}||z |z }tjddz }|jr+||k\}|tj|||z |z z||<|S||k\r|tj||z |z z}|S) aConvert Hz to Mels Examples -------- >>> librosa.hz_to_mel(60) 0.9 >>> librosa.hz_to_mel([110, 220, 440]) array([ 1.65, 3.3 , 6.6 ]) Parameters ---------- frequencies : number or np.ndarray [shape=(n,)] , float scalar or array of frequencies htk : bool use HTK formula instead of Slaney Returns ------- mels : number or np.ndarray [shape=(n,)] input frequencies in Mels See Also -------- mel_to_hz F@?@竪P@@@皙@;@)rOrPlog10logndim) rrmelsf_minf_sp min_log_hz min_log_mellogsteplog_ts rHr%r%s8-- ,K !BHHS;3F-F$GG  E D % 4 'DJ%-KffSkD Gz)!BFF;u+= +J$Kg$UUU K  "RVVK*$<=GG KrJcyrDrErrs rHr(r(rmrJcyrDrErs rHr(r(rmrJcyrDrErs rHr(r(rIrJc`tj|}|rdd|dz zdz zSd}d}|||zz}d}||z |z }tjdd z }|jr+||k\}|tj||||z zz||<|S||k\r|tj|||z zz}|S) aConvert mel bin numbers to frequencies Examples -------- >>> librosa.mel_to_hz(3) 200. >>> librosa.mel_to_hz([1,2,3,4,5]) array([ 66.667, 133.333, 200. , 266.667, 333.333]) Parameters ---------- mels : np.ndarray [shape=(n,)], float mel bins to convert htk : bool use HTK formula instead of Slaney Returns ------- frequencies : np.ndarray [shape=(n,)] input mels in Hz See Also -------- hz_to_mel rg$@rrrrrrr)rOrPrrexp) rrrrfreqsrrrrs rHr(r(s: == D /#566 E D D4K EJ%-KffSkD G yy #!BFF7d5kK6O+P$QQe L  RVVGtk/A$BCC LrJtuningbins_per_octavecyrDrErrrs rHr&r&rIrJcyrDrErs rHr&r&%rLrJcyrDrErs rHr&r&/rLrJrrcdd||z zz}tjtj|t|dz z }|S)aConvert frequencies (Hz) to (fractional) octave numbers. Examples -------- >>> librosa.hz_to_octs(440.0) 4. >>> librosa.hz_to_octs([32, 64, 128, 256]) array([ 0.219, 1.219, 2.219, 3.219]) Parameters ---------- frequencies : number >0 or np.ndarray [shape=(n,)] or float scalar or vector of frequencies tuning : float Tuning deviation from A440 in (fractional) bins per octave. bins_per_octave : int > 0 Number of bins per octave. Returns ------- octaves : number or np.ndarray [shape=(n,)] octave number for each frequency See Also -------- octs_to_hz rr)rOrrPrv)rrrA440octss rHr&r&9sCB 36O34 4Dwwr}}[9U4[2=MNOD KrJcyrDrErrrs rHr)r)`rIrJcyrDrErs rHr)r)grLrJcyrDrErs rHr)r)qrLrJcfdd||z zz}t|dz dtj|zzS)aConvert octaves numbers to frequencies. Octaves are counted relative to A. Examples -------- >>> librosa.octs_to_hz(1) 55. >>> librosa.octs_to_hz([-2, -1, 0, 1, 2]) array([ 6.875, 13.75 , 27.5 , 55. , 110. ]) Parameters ---------- octs : np.ndarray [shape=(n,)] or float octave number for each frequency tuning : float Tuning deviation from A440 in (fractional) bins per octave. bins_per_octave : int > 0 Number of bins per octave. Returns ------- frequencies : number or np.ndarray [shape=(n,)] scalar or vector of frequencies See Also -------- hz_to_octs rrr )rvrOrP)rrrr s rHr)r){s;F 36O34 4D $K"  d(;!; <>> librosa.A4_to_tuning(440.0) 0. Convert a non-A440 frequency to a tuning offset relative to A440 using the default of 12 bins per octave. >>> librosa.A4_to_tuning(432.0) -0.318 Convert two reference pitch frequencies to corresponding tuning estimations at once, but using 24 bins per octave. >>> librosa.A4_to_tuning([440.0, 444.0], bins_per_octave=24) array([ 0., 0.313 ]) Parameters ---------- A4 : float or np.ndarray [shape=(n,), dtype=float] Reference frequency(s) corresponding to A4. bins_per_octave : int > 0 Number of bins per octave. Returns ------- tuning : float or np.ndarray [shape=(n,), dtype=float] Tuning deviation from A440 in (fractional) bins per octave. See Also -------- tuning_to_A4 rr)rrrs rHr*r*s4`)BGGBMM"4E,FQV,WXF MrJcyrDrErs rHr+r+rIrJcyrDrErs rHr+r+rIrJcyrDrErs rHr+r+rIrJc>ddtj||z zzS)a@Convert a tuning deviation (from 0) in fractions of a bin per octave (e.g., ``tuning=-0.1``) to a reference pitch frequency relative to A440. This is useful if you are working in a non-A440 tuning system to determine the reference pitch frequency given a tuning offset and assuming equal temperament. By default, 12 bins per octave are used. This method is the inverse of `A4_to_tuning`. Examples -------- The base case of this method in which a tuning deviation of 0 gets us to our A440 reference pitch. >>> librosa.tuning_to_A4(0.0) 440. Convert a nonzero tuning offset to its reference pitch frequency. >>> librosa.tuning_to_A4(-0.318) 431.992 Convert 3 tuning deviations at once to respective reference pitch frequencies, using 36 bins per octave. >>> librosa.tuning_to_A4([0.1, 0.2, -0.1], bins_per_octave=36) array([ 440.848, 441.698 439.154]) Parameters ---------- tuning : float or np.ndarray [shape=(n,), dtype=float] Tuning deviation from A440 in fractional bins per octave. bins_per_octave : int > 0 Number of bins per octave. Returns ------- A4 : float or np.ndarray [shape=(n,), dtype=float] Reference frequency corresponding to A4. See Also -------- A4_to_tuning rrr~rs rHr+r+s#b 32==0?BC CCrJir]rBcJtjj|d|z S)aAlternative implementation of `np.fft.fftfreq` Parameters ---------- sr : number > 0 [scalar] Audio sampling rate n_fft : int > 0 [scalar] FFT window size Returns ------- freqs : np.ndarray [shape=(1 + n_fft/2,)] Frequencies ``(0, sr/n_fft, 2*sr/n_fft, ..., sr/2)`` Examples -------- >>> librosa.fft_frequencies(sr=22050, n_fft=16) array([ 0. , 1378.125, 2756.25 , 4134.375, 5512.5 , 6890.625, 8268.75 , 9646.875, 11025. ]) r)rd)rOfftrfftfreqrs rHr,r,3s* 66??UcBh? //rJ)rrcxdt||z z}dtjd|t|z z}||z|zS)aCompute the center frequencies of Constant-Q bins. Examples -------- >>> # Get the CQT frequencies for 24 notes, starting at C2 >>> librosa.cqt_frequencies(24, fmin=librosa.note_to_hz('C2')) array([ 65.406, 69.296, 73.416, 77.782, 82.407, 87.307, 92.499, 97.999, 103.826, 110. , 116.541, 123.471, 130.813, 138.591, 146.832, 155.563, 164.814, 174.614, 184.997, 195.998, 207.652, 220. , 233.082, 246.942]) Parameters ---------- n_bins : int > 0 [scalar] Number of constant-Q bins fmin : float > 0 [scalar] Minimum frequency bins_per_octave : int > 0 [scalar] Number of bins per octave tuning : float Deviation from A440 tuning in fractional bins Returns ------- frequencies : np.ndarray [shape=(n_bins,)] Center frequency for each CQT bin rrrY)rvrOarange)n_binsfminrr correctionrs rHr-r-KsH<f  ?@J! !V5)O;K  { **rJg@)r&fmaxrct||}t||}tj|||}t||}|S)a Compute an array of acoustic frequencies tuned to the mel scale. The mel scale is a quasi-logarithmic function of acoustic frequency designed such that perceptually similar pitch intervals (e.g. octaves) appear equal in width over the full hearing range. Because the definition of the mel scale is conditioned by a finite number of subjective psychoaoustical experiments, several implementations coexist in the audio signal processing literature [#]_. By default, librosa replicates the behavior of the well-established MATLAB Auditory Toolbox of Slaney [#]_. According to this default implementation, the conversion from Hertz to mel is linear below 1 kHz and logarithmic above 1 kHz. Another available implementation replicates the Hidden Markov Toolkit [#]_ (HTK) according to the following formula:: mel = 2595.0 * np.log10(1.0 + f / 700.0). The choice of implementation is determined by the ``htk`` keyword argument: setting ``htk=False`` leads to the Auditory toolbox implementation, whereas setting it ``htk=True`` leads to the HTK implementation. .. [#] Umesh, S., Cohen, L., & Nelson, D. Fitting the mel scale. In Proc. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 1, pp. 217-220, 1998. .. [#] Slaney, M. Auditory Toolbox: A MATLAB Toolbox for Auditory Modeling Work. Technical Report, version 2, Interval Research Corporation, 1998. .. [#] Young, S., Evermann, G., Gales, M., Hain, T., Kershaw, D., Liu, X., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., & Woodland, P. The HTK book, version 3.4. Cambridge University, March 2009. See Also -------- hz_to_mel mel_to_hz librosa.feature.melspectrogram librosa.feature.mfcc Parameters ---------- n_mels : int > 0 [scalar] Number of mel bins. fmin : float >= 0 [scalar] Minimum frequency (Hz). fmax : float >= 0 [scalar] Maximum frequency (Hz). htk : bool If True, use HTK formula to convert Hz to mel. Otherwise (False), use Slaney's Auditory Toolbox. Returns ------- bin_frequencies : ndarray [shape=(n_mels,)] Vector of ``n_mels`` frequencies in Hz which are uniformly spaced on the Mel axis. Examples -------- >>> librosa.mel_frequencies(n_mels=40) array([ 0. , 85.317, 170.635, 255.952, 341.269, 426.586, 511.904, 597.221, 682.538, 767.855, 853.173, 938.49 , 1024.856, 1119.114, 1222.042, 1334.436, 1457.167, 1591.187, 1737.532, 1897.337, 2071.84 , 2262.393, 2470.47 , 2697.686, 2945.799, 3216.731, 3512.582, 3835.643, 4188.417, 4573.636, 4994.285, 5453.621, 5955.205, 6502.92 , 7101.009, 7754.107, 8467.272, 9246.028, 10096.408, 11025. ]) r)r%rOlinspacer()n_melsr&r(rmin_melmax_melrhzs rHr.r.qs@V#&G#&G ;;w 0Dt-B IrJ)rAr]ctjt|tj}tj|d<d|z|tj d|zz |dd|S)aCompute the frequencies (in beats per minute) corresponding to an onset auto-correlation or tempogram matrix. Parameters ---------- n_bins : int > 0 The number of lag bins hop_length : int > 0 The number of samples between each bin sr : number > 0 The audio sampling rate Returns ------- bin_frequencies : ndarray [shape=(n_bins,)] vector of bin frequencies measured in BPM. .. note:: ``bin_frequencies[0] = +np.inf`` corresponds to 0-lag Examples -------- Get the tempo frequencies corresponding to a 384-bin (8-second) tempogram >>> librosa.tempo_frequencies(384, sr=22050) array([ inf, 2583.984, 1291.992, ..., 6.782, 6.764, 6.747]) rYrgN@rrN)rOzerosrNfloat64infr$)r%rAr]bin_frequenciess rHr/r/sU<hhs6{"**=OOA)zBIIc64J'JKOAB rJir] win_lengthrAc:t|dzt|z |S)aCompute the frequencies (in beats per minute) corresponding to a Fourier tempogram matrix. Parameters ---------- sr : number > 0 The audio sampling rate win_length : int > 0 The number of frames per analysis window hop_length : int > 0 The number of samples between each bin Returns ------- bin_frequencies : ndarray [shape=(win_length // 2 + 1 ,)] vector of bin frequencies measured in BPM. Examples -------- Get the tempo frequencies corresponding to a 384-bin (8-second) tempogram >>> librosa.fourier_tempo_frequencies(win_length=384, sr=22050) array([ 0. , 0.117, 0.234, ..., 22.266, 22.383, 22.5 ]) <r)r,rvr4s rHr0r0s: b2gj(99 LLrJ)min_dbcyrDrErr8s rHr1r1 rIrJcyrDrEr:s rHr1r1rIrJcyrDrEr:s rHr1r1rIrJgTc tj|dz}tjgddz}ddtj|ddtj|zztj||dzz tj||dzz dtj||dzzz dtj||dzzz zz}||Stj||S) aCompute the A-weighting of a set of frequencies. Parameters ---------- frequencies : scalar or np.ndarray [shape=(n,)] One or more frequencies (in Hz) min_db : float [scalar] or None Clip weights below this threshold. If `None`, no clipping is performed. Returns ------- A_weighting : scalar or np.ndarray [shape=(n,)] ``A_weighting[i]`` is the A-weighting of ``frequencies[i]`` See Also -------- perceptual_weighting frequency_weighting multi_frequency_weighting B_weighting C_weighting D_weighting Examples -------- Get the A-weighting for CQT frequencies >>> import matplotlib.pyplot as plt >>> freqs = librosa.cqt_frequencies(n_bins=108, fmin=librosa.note_to_hz('C1')) >>> weights = librosa.A_weighting(freqs) >>> fig, ax = plt.subplots() >>> ax.plot(freqs, weights) >>> ax.set(xlabel='Frequency (Hz)', ... ylabel='Weighting (log10)', ... title='A-Weighting of CQT frequencies') r)@f W4@goZ@g @4@rrr?rOrPrrmaximumrr8f_sqconstweightss rHr1r1!sP == % ,D HHA Bc IE q bhhtn   ((4%(? # $ ((4%(? # $ a) )  * a) )  *!G~zz&'**rJcyrDrEr:s rHr2r2[rIrJcyrDrEr:s rHr2r2brIrJcyrDrEr:s rHr2r2irIrJc tj|dz}tjgddz}ddtj|ddtj|zztj||dzz tj||dzz dtj||d zzz zz}||Stj||S) aCompute the B-weighting of a set of frequencies. Parameters ---------- frequencies : scalar or np.ndarray [shape=(n,)] One or more frequencies (in Hz) min_db : float [scalar] or None Clip weights below this threshold. If `None`, no clipping is performed. Returns ------- B_weighting : scalar or np.ndarray [shape=(n,)] ``B_weighting[i]`` is the B-weighting of ``frequencies[i]`` See Also -------- perceptual_weighting frequency_weighting multi_frequency_weighting A_weighting C_weighting D_weighting Examples -------- Get the B-weighting for CQT frequencies >>> import matplotlib.pyplot as plt >>> freqs = librosa.cqt_frequencies(n_bins=108, fmin=librosa.note_to_hz('C1')) >>> weights = librosa.B_weighting(freqs) >>> fig, ax = plt.subplots() >>> ax.plot(freqs, weights) >>> ax.set(xlabel='Frequency (Hz)', ... ylabel='Weighting (log10)', ... title='B-Weighting of CQT frequencies') r)r>r?gjc@g(\?r@rg?rrArrCrEs rHr2r2psP == % ,D HH6 73 >E q    ((4%(? # $ ((4%(? # $ a) )  *"Gn7E"**VW*EErJcyrDrEr:s rHr3r3rIrJcyrDrEr:s rHr3r3rIrJcyrDrEr:s rHr3r3rIrJcrtj|dz}tjddgdz}ddtj|dtj|ztj||dzz tj||dzz zz}||Stj||S)aCompute the C-weighting of a set of frequencies. Parameters ---------- frequencies : scalar or np.ndarray [shape=(n,)] One or more frequencies (in Hz) min_db : float [scalar] or None Clip weights below this threshold. If `None`, no clipping is performed. Returns ------- C_weighting : scalar or np.ndarray [shape=(n,)] ``C_weighting[i]`` is the C-weighting of ``frequencies[i]`` See Also -------- perceptual_weighting frequency_weighting multi_frequency_weighting A_weighting B_weighting D_weighting Examples -------- Get the C-weighting for CQT frequencies >>> import matplotlib.pyplot as plt >>> freqs = librosa.cqt_frequencies(n_bins=108, fmin=librosa.note_to_hz('C1')) >>> weights = librosa.C_weighting(freqs) >>> fig, ax = plt.subplots() >>> ax.plot(freqs, weights) >>> ax.set(xlabel='Frequency (Hz)', ylabel='Weighting (log10)', ... title='C-Weighting of CQT frequencies') rr>r?gX9v?r@rrrCrEs rHr3r3sN == % ,D HHi+ , 3E$ q ((4.  ((4%(? # $ ((4%(? # $#Gn7E"**VW*EErJcyrDrEr:s rHr4r4rIrJcyrDrEr:s rHr4r4rIrJcyrDrEr:s rHr4r4rIrJc tj|dz}tjgddz}ddtj|ztj|dz dtj|d|z dz|d|zztj|d|z dz|d |zzz tj|d |zz tj|d |zz zzz}||Stj||S) aCompute the D-weighting of a set of frequencies. Parameters ---------- frequencies : scalar or np.ndarray [shape=(n,)] One or more frequencies (in Hz) min_db : float [scalar] or None Clip weights below this threshold. If `None`, no clipping is performed. Returns ------- D_weighting : scalar or np.ndarray [shape=(n,)] ``D_weighting[i]`` is the D-weighting of ``frequencies[i]`` See Also -------- perceptual_weighting frequency_weighting multi_frequency_weighting A_weighting B_weighting C_weighting Examples -------- Get the D-weighting for CQT frequencies >>> import matplotlib.pyplot as plt >>> freqs = librosa.cqt_frequencies(n_bins=108, fmin=librosa.note_to_hz('C1')) >>> weights = librosa.D_weighting(freqs) >>> fig, ax = plt.subplots() >>> ax.plot(freqs, weights) >>> ax.set(xlabel='Frequency (Hz)', ylabel='Weighting (log10)', ... title='D-Weighting of CQT frequencies') r)g9?gՏ@gfffff>@g@i` g33333q@ir@rArrrrBrrrCrEs rHr4r4sN == % ,D HHN OSV VE bhhtn ((58   XXuQx$1,uQx$> ? ?hha4A-a4?@ AhhuQx$' (hhuQx$' (   G~zz&'**rJcrtjt|}||Stj||S)a#Apply no weighting curve (aka Z-weighting). This function behaves similarly to `A_weighting`, `B_weighting`, etc., but all frequencies are equally weighted. An optional threshold `min_db` can still be used to clip energies. Parameters ---------- frequencies : scalar or np.ndarray [shape=(n,)] One or more frequencies (in Hz) min_db : float [scalar] or None Clip weights below this threshold. If `None`, no clipping is performed. Returns ------- Z_weighting : scalar or np.ndarray [shape=(n,)] ``Z_weighting[i]`` is the Z-weighting of ``frequencies[i]`` See Also -------- perceptual_weighting frequency_weighting multi_frequency_weighting A_weighting B_weighting C_weighting D_weighting )rOr0lenrD)rr8rHs rHr5r5@s3@hhs;'(G ~zz&'**rJ)rrrrZNzGDict[Optional[str], Callable[..., Union[np.floating[Any], np.ndarray]]]WEIGHTING_FUNCTIONS)kindc yrDrErrZrs rHr6r6srIrJc yrDrEr\s rHr6r6zrIrJc yrDrEr\s rHr6r6rIrJrc `t|tr|j}t||fi|S)a2Compute the weighting of a set of frequencies. Parameters ---------- frequencies : scalar or np.ndarray [shape=(n,)] One or more frequencies (in Hz) kind : str in The weighting kind. e.g. `'A'`, `'B'`, `'C'`, `'D'`, `'Z'` **kwargs Additional keyword arguments to A_weighting, B_weighting, etc. Returns ------- weighting : scalar or np.ndarray [shape=(n,)] ``weighting[i]`` is the weighting of ``frequencies[i]`` See Also -------- perceptual_weighting multi_frequency_weighting A_weighting B_weighting C_weighting D_weighting Examples -------- Get the A-weighting for CQT frequencies >>> import matplotlib.pyplot as plt >>> freqs = librosa.cqt_frequencies(n_bins=108, fmin=librosa.note_to_hz('C1')) >>> weights = librosa.frequency_weighting(freqs, kind='A') >>> fig, ax = plt.subplots() >>> ax.plot(freqs, weights) >>> ax.set(xlabel='Frequency (Hz)', ylabel='Weighting (log10)', ... title='A-Weighting of CQT frequencies') )rrrrYr\s rHr6r6s/P$zz| t $[ ;F ;;rJZAC)kindsc ntj|Dcgc]}t|fd|i|c}dScc}w)aCompute multiple weightings of a set of frequencies. Parameters ---------- frequencies : scalar or np.ndarray [shape=(n,)] One or more frequencies (in Hz) kinds : list or tuple or str An iterable of weighting kinds. e.g. `('Z', 'B')`, `'ZAD'`, `'C'` **kwargs : keywords to pass to the weighting function. Returns ------- weighting : scalar or np.ndarray [shape=(len(kinds), n)] ``weighting[i, j]`` is the weighting of ``frequencies[j]`` using the curve determined by ``kinds[i]``. See Also -------- perceptual_weighting frequency_weighting A_weighting B_weighting C_weighting D_weighting Examples -------- Get the A, B, C, D, and Z weightings for CQT frequencies >>> import matplotlib.pyplot as plt >>> freqs = librosa.cqt_frequencies(n_bins=108, fmin=librosa.note_to_hz('C1')) >>> weightings = 'ABCDZ' >>> weights = librosa.multi_frequency_weighting(freqs, kinds=weightings) >>> fig, ax = plt.subplots() >>> for label, w in zip(weightings, weights): ... ax.plot(freqs, w, label=label) >>> ax.set(xlabel='Frequency (Hz)', ylabel='Weighting (log10)', ... title='Weightings of CQT frequencies') >>> ax.legend() rZr)axis)rOstackr6)rrarks rHr7r7s>\ 88EJKU [ ;q ;F ;UKRS Ks2r)r]rArBrcc>t||||}t||}|S)aReturn an array of time values to match the time axis from a feature matrix. Parameters ---------- X : np.ndarray or scalar - If ndarray, X is a feature matrix, e.g. STFT, chromagram, or mel spectrogram. - If scalar, X represents the number of frames. sr : number > 0 [scalar] audio sampling rate hop_length : int > 0 [scalar] number of samples between successive frames n_fft : None or int > 0 [scalar] Optional: length of the FFT window. If given, time conversion will include an offset of ``n_fft // 2`` to counteract windowing effects when using a non-centered STFT. axis : int [scalar] The axis representing the time axis of X. By default, the last axis (-1) is taken. Returns ------- times : np.ndarray [shape=(n,)] ndarray of times (in seconds) corresponding to each frame of X. See Also -------- samples_like : Return an array of sample indices to match the time axis from a feature matrix. Examples -------- Provide a feature matrix input: >>> y, sr = librosa.load(librosa.ex('trumpet')) >>> D = librosa.stft(y) >>> times = librosa.times_like(D, sr=sr) >>> times array([0. , 0.023, ..., 5.294, 5.317]) Provide a scalar input: >>> n_frames = 2647 >>> times = librosa.times_like(n_frames, sr=sr) >>> times array([ 0.00000000e+00, 2.32199546e-02, 4.64399093e-02, ..., 6.13935601e+01, 6.14167800e+01, 6.14400000e+01]) rArBrcrd)r8r)Xr]rArBrcrUtimes rHr9r9s&n15tLG&w26D KrJrgctj|rtj|}n"tj|j|}t |||S)aReturn an array of sample indices to match the time axis from a feature matrix. Parameters ---------- X : np.ndarray or scalar - If ndarray, X is a feature matrix, e.g. STFT, chromagram, or mel spectrogram. - If scalar, X represents the number of frames. hop_length : int > 0 [scalar] number of samples between successive frames n_fft : None or int > 0 [scalar] Optional: length of the FFT window. If given, time conversion will include an offset of ``n_fft // 2`` to counteract windowing effects when using a non-centered STFT. axis : int [scalar] The axis representing the time axis of ``X``. By default, the last axis (-1) is taken. Returns ------- samples : np.ndarray [shape=(n,)] ndarray of sample indices corresponding to each frame of ``X``. See Also -------- times_like : Return an array of time values to match the time axis from a feature matrix. Examples -------- Provide a feature matrix input: >>> y, sr = librosa.load(librosa.ex('trumpet')) >>> X = librosa.stft(y) >>> samples = librosa.samples_like(X) >>> samples array([ 0, 512, ..., 116736, 117248]) Provide a scalar input: >>> n_frames = 2647 >>> samples = librosa.samples_like(n_frames) >>> samples array([ 0, 512, 1024, ..., 1353728, 1354240, 1354752]) r@)rOisscalarr$shaper)rhrArBrcrGs rHr8r8$ sBh {{1~11774=) V % HHrJ)abbrrrcyrDrErSarmrrs rHr:r:_ rrJcyrDrEros rHr:r:k rrJcyrDrEros rHr:r:w rrJrprmrrc8gd}td|D}ttj||z }|r ||dz}n||dz}|rMd|cxkDrdk\rnn|r|ddz|ddz}|S|d z }|Sd |cxkrdkrn|S|r|dd z|ddz}|S|d z }|S) uConvert MIDI numbers to Hindustani svara Parameters ---------- midi : numeric or np.ndarray The MIDI number or numbers to convert Sa : number > 0 MIDI number of the reference Sa. abbr : bool If `True` (default) return abbreviated names ('S', 'r', 'R', 'g', 'G', ...) If `False`, return long-form names ('Sa', 're', 'Re', 'ga', 'Ga', ...) octave : bool If `True`, decorate svara in neighboring octaves with over- or under-dots. If `False`, ignore octave height information. unicode : bool If `True`, use unicode symbols to decorate octave information. If `False`, use low-order ASCII (' and ,) for octave decorations. This only takes effect if `octave=True`. Returns ------- svara : str or np.ndarray of str The svara corresponding to the given MIDI number(s) See Also -------- hz_to_svara_h note_to_svara_h midi_to_svara_c midi_to_note Examples -------- Convert a single midi number: >>> librosa.midi_to_svara_h(65, Sa=60) 'm' The first three svara with Sa at midi number 60: >>> librosa.midi_to_svara_h([60, 61, 62], Sa=60) array(['S', 'r', 'R'], dtype='>> librosa.midi_to_svara_h([60, 61, 62], Sa=67) array(['ṃ', 'Ṃ', 'P̣'], dtype='>> librosa.midi_to_svara_h([60, 61, 62], Sa=67, unicode=False) array(['m,', 'M,', 'P,'], dtype='>> librosa.midi_to_svara_h([72, 73, 74], Sa=60, abbr=False) array(['Ṡa', 'ṙe', 'Ṙe'], dtype='z"midi_to_svara_h.. s3A1Q4srṙrN'̣,)listrNrOr) rrprmrr SVARA_MAPSVARA_MAP_SHORT svara_numsvaras rHr:r: sV I333OBHHTBY'(I  B/)b.) R a8+eABi7 L  L I ! ! L a8+eABi7 L  LrJcyrDrErrprmrrs rHr>r> rrJcyrDrErs rHr>r> rrJcyrDrErs rHr>r> rrJcJt|}t|t||||S)uConvert frequencies (in Hz) to Hindustani svara Note that this conversion assumes 12-tone equal temperament. Parameters ---------- frequencies : positive number or np.ndarray The frequencies (in Hz) to convert Sa : positive number Frequency (in Hz) of the reference Sa. abbr : bool If `True` (default) return abbreviated names ('S', 'r', 'R', 'g', 'G', ...) If `False`, return long-form names ('Sa', 're', 'Re', 'ga', 'Ga', ...) octave : bool If `True`, decorate svara in neighboring octaves with over- or under-dots. If `False`, ignore octave height information. unicode : bool If `True`, use unicode symbols to decorate octave information. If `False`, use low-order ASCII (' and ,) for octave decorations. This only takes effect if `octave=True`. Returns ------- svara : str or np.ndarray of str The svara corresponding to the given frequency/frequencies See Also -------- midi_to_svara_h note_to_svara_h hz_to_svara_c hz_to_note Examples -------- Convert Sa in three octaves: >>> librosa.hz_to_svara_h([261/2, 261, 261*2], Sa=261) ['Ṣ', 'S', 'Ṡ'] Convert one octave worth of frequencies with full names: >>> freqs = librosa.cqt_frequencies(n_bins=12, fmin=261) >>> librosa.hz_to_svara_h(freqs, Sa=freqs[0], abbr=False) ['Sa', 're', 'Re', 'ga', 'Ga', 'ma', 'Ma', 'Pa', 'dha', 'Dha', 'ni', 'Ni'] rs)r$r:)rrprmrrmidiss rHr>r> s+| { #E  *R.tFG rJcyrDrErrprmrrs rHr<r<^ rIrJcyrDrErs rHr<r<e rrJcyrDrErs rHr<r<q rrJcNt|d}t|t||||S)u*Convert western notes to Hindustani svara Note that this conversion assumes 12-tone equal temperament. Parameters ---------- notes : str or iterable of str Notes to convert (e.g., `'C#'` or `['C4', 'Db4', 'D4']` Sa : str Note corresponding to Sa (e.g., `'C'` or `'C5'`). If no octave information is provided, it will default to octave 0 (``C0`` ~= 16 Hz) abbr : bool If `True` (default) return abbreviated names ('S', 'r', 'R', 'g', 'G', ...) If `False`, return long-form names ('Sa', 're', 'Re', 'ga', 'Ga', ...) octave : bool If `True`, decorate svara in neighboring octaves with over- or under-dots. If `False`, ignore octave height information. unicode : bool If `True`, use unicode symbols to decorate octave information. If `False`, use low-order ASCII (' and ,) for octave decorations. This only takes effect if `octave=True`. Returns ------- svara : str or np.ndarray of str The svara corresponding to the given notes See Also -------- midi_to_svara_h hz_to_svara_h note_to_svara_c note_to_midi note_to_hz Examples -------- >>> librosa.note_to_svara_h(['C4', 'G4', 'C5', 'G5'], Sa='C5') ['Ṣ', 'P̣', 'S', 'P'] Frrs)r r:)rrprmrrrs rHr<r<} s.t 5 1E  ,r"fg rJcyrDrErrpmelarmrrs rHr;r; rJcyrDrErs rHr;r; rrJcyrDrErs rHr;r; rrJrprrmrrc&ttj||z }tj|||}||dz}|rMd|cxkDrdk\rnn|r|ddz|ddz}|S|dz }|Sd |cxkrdkrn|S|r|dd z|ddz}|S|d z }|S) aWConvert MIDI numbers to Carnatic svara within a given melakarta raga Parameters ---------- midi : numeric The MIDI numbers to convert Sa : number > 0 MIDI number of the reference Sa. Default: 60 (261.6 Hz, `C4`) mela : int or str The name or index of the melakarta raga abbr : bool If `True` (default) return abbreviated names ('S', 'R1', 'R2', 'G1', 'G2', ...) If `False`, return long-form names ('Sa', 'Ri1', 'Ri2', 'Ga1', 'Ga2', ...) octave : bool If `True`, decorate svara in neighboring octaves with over- or under-dots. If `False`, ignore octave height information. unicode : bool If `True`, use unicode symbols to decorate octave information and subscript numbers. If `False`, use low-order ASCII (' and ,) for octave decorations. Returns ------- svara : str or np.ndarray of str The svara corresponding to the given MIDI number(s) See Also -------- hz_to_svara_c note_to_svara_c mela_to_degrees mela_to_svara list_mela )rmrrrrrrNrrrr)rNrOrr mela_to_svara) rrprrmrrr svara_maprs rHr;r; slBHHTBY'(I&&t$HI i"n %E R a8+eABi7 L  L I ! ! L a8+eABi7 L  LrJcyrDrErrprrmrrs rHr?r?0 rrJcyrDrErs rHr?r?= rrJcyrDrErs rHr?r?J rrJcLt|}t|t|||||S)u]Convert frequencies (in Hz) to Carnatic svara Note that this conversion assumes 12-tone equal temperament. Parameters ---------- frequencies : positive number or np.ndarray The frequencies (in Hz) to convert Sa : positive number Frequency (in Hz) of the reference Sa. mela : int [1, 72] or string The melakarta raga to use. abbr : bool If `True` (default) return abbreviated names ('S', 'R1', 'R2', 'G1', 'G2', ...) If `False`, return long-form names ('Sa', 'Ri1', 'Ri2', 'Ga1', 'Ga2', ...) octave : bool If `True`, decorate svara in neighboring octaves with over- or under-dots. If `False`, ignore octave height information. unicode : bool If `True`, use unicode symbols to decorate octave information. If `False`, use low-order ASCII (' and ,) for octave decorations. This only takes effect if `octave=True`. Returns ------- svara : str or np.ndarray of str The svara corresponding to the given frequency/frequencies See Also -------- note_to_svara_c midi_to_svara_c hz_to_svara_h hz_to_note list_mela Examples -------- Convert Sa in three octaves: >>> librosa.hz_to_svara_c([261/2, 261, 261*2], Sa=261, mela='kanakangi') ['Ṣ', 'S', 'Ṡ'] Convert one octave worth of frequencies using melakarta #36: >>> freqs = librosa.cqt_frequencies(n_bins=12, fmin=261) >>> librosa.hz_to_svara_c(freqs, Sa=freqs[0], mela=36) ['S', 'R₁', 'R₂', 'R₃', 'G₃', 'M₁', 'M₂', 'P', 'D₁', 'D₂', 'D₃', 'N₃'] r)r$r;)rrprrmrrrs rHr?r?W s-F { #E  *R.t$vw rJcyrDrErrprrmrrs rHr=r= rrJcyrDrErs rHr=r= rrJcyrDrErs rHr=r= rrJcPt|d}t|t|||||S)uConvert western notes to Carnatic svara Note that this conversion assumes 12-tone equal temperament. Parameters ---------- notes : str or iterable of str Notes to convert (e.g., `'C#'` or `['C4', 'Db4', 'D4']` Sa : str Note corresponding to Sa (e.g., `'C'` or `'C5'`). If no octave information is provided, it will default to octave 0 (``C0`` ~= 16 Hz) mela : str or int [1, 72] Melakarta raga name or index abbr : bool If `True` (default) return abbreviated names ('S', 'R1', 'R2', 'G1', 'G2', ...) If `False`, return long-form names ('Sa', 'Ri1', 'Ri2', 'Ga1', 'Ga2', ...) octave : bool If `True`, decorate svara in neighboring octaves with over- or under-dots. If `False`, ignore octave height information. unicode : bool If `True`, use unicode symbols to decorate octave information. If `False`, use low-order ASCII (' and ,) for octave decorations. This only takes effect if `octave=True`. Returns ------- svara : str or np.ndarray of str The svara corresponding to the given notes See Also -------- midi_to_svara_c hz_to_svara_c note_to_svara_h note_to_midi note_to_hz list_mela Examples -------- >>> librosa.note_to_svara_h(['C4', 'G4', 'C5', 'D5', 'G5'], Sa='C5', mela=1) ['Ṣ', 'P̣', 'S', 'G₁', 'P'] Frr)r r;)rrprrmrrrs rHr=r= s1~ 5 1E  ,r"DQX rJ)r&unisonrcyrDrErr&rrs rHr'r' r`rJcyrDrErs rHr'r' r`rJc|tj|}|t|dd}tj|r||z }ntj||z }t j |||S)uConvert one or more frequencies (in Hz) from a just intonation scale to notes in FJS notation. Parameters ---------- frequencies : float or iterable of float Input frequencies, specified in Hz fmin : float (optional) The minimum frequency, corresponding to a unison note. If not provided, it will be inferred as `min(frequencies)` unison : str (optional) The name of the unison note. If not provided, it will be inferred as the scientific pitch notation name of `fmin`, that is, `hz_to_note(fmin)` unicode : bool If `True`, then unicode symbols are used for accidentals. If `False`, then low-order ASCII symbols are used for accidentals. Returns ------- notes : str or np.ndarray(dtype=str) ``notes[i]`` is the closest note name to ``frequency[i]`` (or ``frequency`` if the input is scalar) See Also -------- hz_to_note interval_to_fjs Examples -------- Get a single note name for a frequency, relative to A=55 Hz >>> librosa.hz_to_fjs(66, fmin=55, unicode=True) 'C₅' Get notation for a 5-limit frequency set starting at A=55 >>> freqs = librosa.interval_frequencies(24, intervals="ji5", fmin=55) >>> freqs array([ 55. , 58.667, 61.875, 66. , 68.75 , 73.333, 77.344, 82.5 , 88. , 91.667, 99. , 103.125, 110. , 117.333, 123.75 , 132. , 137.5 , 146.667, 154.687, 165. , 176. , 183.333, 198. , 206.25 ]) >>> librosa.hz_to_fjs(freqs, unicode=True) array(['A', 'B♭₅', 'B', 'C₅', 'C♯⁵', 'D', 'D♯⁵', 'E', 'F₅', 'F♯⁵', 'G₅', 'G♯⁵', 'A', 'B♭₅', 'B', 'C₅', 'C♯⁵', 'D', 'D♯⁵', 'E', 'F₅', 'F♯⁵', 'G₅', 'G♯⁵'], dtype=' {{;$& JJ{+d2   # #Ifg NNrJ)rGrrArNrB Optional[int]returnnp.integer[Any])rG_SequenceLike[_IntLike_co]rArNrBrr np.ndarray)rG_ScalarOrSequence[_IntLike_co]rArNrBrr"Union[np.integer[Any], np.ndarray])rUrrArNrBrrr)rUrrArNrBrrr)rUrrArNrBrrr) rGrr]rvrArNrBrrnp.floating[Any]) rGrr]rvrArNrBrrr) rGrr]rvrArNrBrr#Union[np.floating[Any], np.ndarray]) rgrr]rvrArNrBrrr) rg_SequenceLike[_FloatLike_co]r]rvrArNrBrrr) rg _ScalarOrSequence[_FloatLike_co]r]rvrArNrBrrr)rgrr]rvrr)rgrr]rvrr)rgrr]rvrr)rUrr]rvrr)rUrr]rvrr)rUrr]rvrr)ryrrzrNrr)ryrrzrNrr)ryrrzrNrr)ryrrzrNrArNrr)ryrrzrNrArNrr)ryrrzrNrArNrr) ryrrzrNrArNr]rvrr) ryrrzrNrArNr]rvrr) ryrrzrNrArNr]rvrr)rrrr rr)r_IterableLike[str]rr rr)r-Union[str, _IterableLike[str], Iterable[str]]rr rr)rrrboolrzUnion[float, int])rrrrrr)rrrrrzUnion[float, int, np.ndarray])rrrrrUnion[float, np.ndarray]) rrrrrrrrrrrr) rrrrrrrrrrrr) rrrrrrrrrrrUnion[str, np.ndarray])rrrr)rrrr)rrr#Union[np.ndarray, np.floating[Any]])rrrr)rrrr)rrrr)rrrr rr)rrrr rr)rrrr rr)rrrrrr)rrrrrr)rrrrrr)rrrrrr)rrrrrr)rrrrrr)rrrrvrrNrr)rrrrvrrNrr)rrrrvrrNrr)rrrrvrrNrr)rrrrvrrNrr)rrrrvrrNrr)rrrrNrr)rrrrNrr)rrrrNrr)rrrrNrr)rrrrNrr)rrrrNrr)r]rvrBrNrr) r%rNr&rvrrNrrvrr)) r+rNr&rvr(rvrrrr)r%rNrArNr]rvrr)r]rvr5rNrArNrr)rrr8Optional[float]rr)rrr8rrr)rrr8rrr)rrr8rrr)rrrZrrr rr)rrrZrrr rr)rrrZrrr rr)rrraz Iterable[str]rr rr) rhUnion[np.ndarray, float]r]rvrArNrBrrcrNrr) rhrrArNrBrrcrNrr) rrrprrmrrrrrrr) rrrprrmrrrrrrr) rz Union[_FloatLike_co, np.ndarray]rprrmrrrrrrr) rrrprrmrrrrrrr) rrrprrmrrrrrrr) rrrprrmrrrrrrr) rrrprrmrrrrrrr) rrrprrmrrrrrrr) rUnion[str, _IterableLike[str]]rprrmrrrrrrr)rrrprrUnion[int, str]rmrrrrrrr)rrrprrrrmrrrrrrr)rrrprrrrmrrrrrrr)rrvrprvrrrmrrrrrrr)rrrprvrrrmrrrrrrr)rrrprvrrrmrrrrrrr)rrrprrUnion[str, int]rmrrrrrrr)rrrprrrrmrrrrrrr)rrrprrrrmrrrrrrr) rrr&rr Optional[str]rrrr) rrr&rrrrrrr) rrr&rrrrrrr)H__doc__ __future__rrunumpyrOrrutil.exceptionsrutil.decoratorsrtypingr r r r r rrr_typingrrrrr__all__rrrrrrrrrrr r"r!r$r#r%r(r&r)r*r+r,r-r.r/r0r1r2r3r4r5rY__annotations__r6r7r9r8r:r>r<r;r?r=r'rErJrHrs " ,'RRR+ \ .1$ (+:G     &      )E *)E)E  )E ( )EX /23 ),;H     '        +   (   4M +4M4M  4M ( 4Mn             &          *     )   *+ **+ *+ *+  *+ ) *+Z             '          +     (   1J +1J 1J 1J  1J ( 1Jh 9<  8; '05    9<  8; '05       &9<JM    *=@NQ'  *< **<=@*DI    &9<JMSX    *    )  2+ *2+2+ 2+  2+ ) 2+j      7CF(  #4 7#4CF#4(#4L 25  AD  OR 7HL" PTm 7mHLmm`              &          *        9: a *a a  a a  aa;aH      +(  C +C(CB     1(  1(@     1=@  +;1+;=@+;+;\ 9<  >A-7;   BE1;?( CH616;?6(6r 25  AD  ;> *48( A  @C$:=   DG(>A( EG1(1>A1(1h 58 /2   DG (>A   HK ,BE( IK1D ,1DBE1D(1Dh$)t029;C#+ #+#+25#+DI#+#+NQ(+7PUQ Q %Q38QIMQQj'*u# # ##/4##NC3M M&)M=@MMB =@+:   LO-:I   PS1>M( QV7+17+>M7+(7+t =@+:   LO-:I   PS1>M( QV3F13F>M3F(3Fl =@+:   LO-:I   PS1>M( QV1F11F>M1F(1Fh =@+:   LO-:I   PS1>M( QV9+19+>M9+(9+z6:$+$+#2$+$+T         /2),>A   >A-8;MP   BE1 )> >  >  >  >>B                                    "              ?@ G "G G  G  G  GGGAGT                                 )             "F)F F  F  F  FFFR                                    )             "C )C C  C  C  CCCL              -        !  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