Help on module S3DataUtils: NAME S3DataUtils - Utils Functions involving usage of DataFrame FUNCTIONS create_FunctionFrame(fs: int, Ns: int, Ss: int) -> pandas.core.frame.DataFrame Takes Sampling Frequency and returns a DataFrame with function vectors of frequencies predict_fs(fs: int, Ns: int, Ss: int, reg: sklearn.linear_model.base.LinearRegression) -> numpy.ndarray Returns predicted signal of given frequency Ss is sample rate Fs is natural frequency Ns is number of samples train_S3(FuncFrame: pandas.core.frame.DataFrame, sig: numpy.ndarray) -> sklearn.linear_model.base.LinearRegression Function That trains FuncFrame on input signal Returns: LinearRegression FILE /mnt/4427FDEE206BF5AE/Documents/codes/S3_Smart_Sampling_Synthesiser/S3DataUtils.py Help on module S3GuiMain: NAME S3GuiMain - # -*- coding: utf-8 -*- CLASSES builtins.object Ui_MainWindow class Ui_MainWindow(builtins.object) | Methods defined here: | | retranslateUi(self, MainWindow) | | setupUi(self, MainWindow) | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) FILE /mnt/4427FDEE206BF5AE/Documents/codes/S3_Smart_Sampling_Synthesiser/S3GuiMain.py Help on module S3Synth: NAME S3Synth - Synthesiser Class of S3 CLASSES builtins.object S3Synth class S3Synth(builtins.object) | S3Synth(wavecoef_: numpy.ndarray, transpo=1, mul=1) | | Main Synth Class that manages backend of Synthesiser | | Methods defined here: | | __init__(self, wavecoef_: numpy.ndarray, transpo=1, mul=1) | Initialize self. See help(type(self)) for accurate signature. | | out(self) | Sends the synth's signal to the audio output and return the object itself. | | sig(self) | Returns the synth's signal for future processing. | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) FUNCTIONS random(...) method of random.Random instance random() -> x in the interval [0, 1). FILE /mnt/4427FDEE206BF5AE/Documents/codes/S3_Smart_Sampling_Synthesiser/S3Synth.py Help on module S3SignalUtils: NAME S3SignalUtils - Utils function related to signals for S3 FUNCTIONS cos(fs: float, Ns: int, Ss: int) -> numpy.ndarray Returns a Cosine wave of Sample rate Ss with Ns number of samples and Sample Frequency Fs filt_bp(sig: numpy.ndarray, Ss: int, Cfs0: int, Cfs1: None, order=5) -> numpy.ndarray return a filtered signal; band pass filter filt_hp(sig: numpy.ndarray, Ss: int, Cfs: int, Cfs1: None, order=5) -> numpy.ndarray return a filtered signal; high pass filter filt_lp(sig: numpy.ndarray, Ss: int, Cfs: int, Cfs1: None, order=5) -> numpy.ndarray return a filtered signal; low pass filter sawtooth(fs: float, Ns: int, Ss: int) -> numpy.ndarray Returns a Sawtooth wave of Sample rate Ss with Ns number of samples and Sample Frequency Fs sigin(wavname: str) -> Tuple[int, numpy.ndarray] Functions that reads wave file and return sample rate and signal as np.array sin(fs: float, Ns: int, Ss: int) -> numpy.ndarray Returns a Sine wave of Sample rate Ss with Ns number of samples and Sample Frequency Fs triangle(fs: float, Ns: int, Ss: int) -> numpy.ndarray Returns a Triangle wave of Sample rate Ss with Ns number of samples and Sample Frequency Fs DATA Tuple = typing.Tuple FILE /mnt/4427FDEE206BF5AE/Documents/codes/S3_Smart_Sampling_Synthesiser/S3SignalUtils.py Help on module S3Utils: NAME S3Utils - Utils Functions for S3 Synthesiser App FUNCTIONS create_env(sig: numpy.ndarray, Fs: float, Ss: int, Ns: int) -> numpy.ndarray return envelope of signal create_partial_envelope(sig: numpy.ndarray, Fs: float, Ss: int) -> numpy.ndarray Creates a partial envelope using min and max of in one cycle. find_Ns(Freq: float, Ss: int) -> int Finds the Ns for Training Phase find_maxsig(sig: numpy.ndarray, Ns: int) -> numpy.ndarray returns part of signal where its in constant sustain freq_calc(sig: numpy.ndarray, Ss: int) -> float Calculates the average frequency of the input signal (of a recorded note) freq_from_HPS(sig, fs) Estimate frequency using harmonic product spectrum (HPS) freq_from_autocorr(sig, fs) Estimate frequency using autocorrelation freq_from_crossings(sig, fs) Estimate frequency by counting zero crossings freq_from_fft(sig, fs) Estimate frequency from peak of FFT get_note(freq: float) -> Tuple[float, str] Returns the Note (and its Natural Frequency) corresponding to input frequency make_natural_env(env: numpy.ndarray, Ns: int) -> numpy.ndarray Returns an envelope in natural time for the signal by upsampling and uniforming partial envelope make_octaves() -> numpy.ndarray Creates Octaves with their corresponding frequency time(...) time() -> floating point number Return the current time in seconds since the Epoch. Fractions of a second may be present if the system clock provides them. DATA Tuple = typing.Tuple log = FILE /mnt/4427FDEE206BF5AE/Documents/codes/S3_Smart_Sampling_Synthesiser/S3Utils.py Help on module S3SynthMain: NAME S3SynthMain CLASSES builtins.object S3App class S3App(builtins.object) | Class to manage interface of S3 Synthesiser | | Methods defined here: | | __init__(self) | Initialize self. See help(type(self)) for accurate signature. | | load_file(self, file_path: str) | Loads a Sample into the synthesiser | | load_trainedsynth(self) | Loads all properties of S3 trains S3 and initialises S3Synth | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) FUNCTIONS main() Driver code FILE /mnt/4427FDEE206BF5AE/Documents/codes/S3_Smart_Sampling_Synthesiser/S3SynthMain.py