diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 8acd2e2..a4da04d 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -14,6 +14,37 @@ adheres to `Semantic Versioning `_. - Validation for results from tests in every module (so far many tests are only regarding functionality) +`0.4.0 `_ - +--------------------- +Added +~~~~~~ +- `ImpulseResponse` as a subclass of `Signal`. It handles time windows, coherence + and plotting of those windows. Assertions for expected `ImpulseResponse` instead + of `Signal` were added as well +- new module ``tools`` for computations with primitive data types, added time + smoothing, interpolation of frequency response +- `get_transfer_function` in Filter and FilterBank +- analog-matched biquads in ``filterbanks`` +- `gaussian_kernel` approximation in ``filterbanks`` +- gain parameter functionality for some biquads +- new biquad types (lowpass and highpass first order, inverter) +- new explicit constructors for signal and filter +- pearson correlation as part quality estimator for latency computation +- new scaling parameter in synchrosqueezing of `cwt` +- new parameter in `window_frequency_dependent` + +Bugfix +~~~~~~ +- bugfix in `window_frequency_dependent` when querying a single frequency bin +- corrected plotting of spl when calibrated signal is passed + +Misc +~~~~~~~ +- got rid of signal type attribute. Use now `ImpulseResponse` +- general doc additions and fixes, type annotations +- `fractional_octave_smoothing` performance improved +- renamed some files of code base for consistency + `0.3.9 `_ - --------------------- Added diff --git a/docs/modules.rst b/docs/modules.rst index 56969ff..e681c99 100644 --- a/docs/modules.rst +++ b/docs/modules.rst @@ -17,3 +17,4 @@ The modules and functions of dsptoolbox are listed down below. modules/dsptoolbox.standard_functions modules/dsptoolbox.transfer_functions modules/dsptoolbox.effects + modules/dsptoolbox.tools diff --git a/docs/modules/dsptoolbox.general_tools.rst b/docs/modules/dsptoolbox.general_tools.rst new file mode 100644 index 0000000..0414fff --- /dev/null +++ b/docs/modules/dsptoolbox.general_tools.rst @@ -0,0 +1,7 @@ +Tools (dsptoolbox.tools) +============================== + +.. automodule:: dsptoolbox.tools + :members: + :undoc-members: + :show-inheritance: diff --git a/dsptoolbox/__init__.py b/dsptoolbox/__init__.py index 459eb9e..7a2a848 100644 --- a/dsptoolbox/__init__.py +++ b/dsptoolbox/__init__.py @@ -4,14 +4,12 @@ merge_filterbanks, pad_trim, fractional_delay, - fractional_octave_frequencies, activity_detector, fade, normalize, true_peak_level, resample, load_pkl_object, - erb_frequencies, detrend, rms, CalibrationData, @@ -22,6 +20,7 @@ Filter, FilterBank, Signal, + ImpulseResponse, MultiBandSignal, ) from . import transfer_functions @@ -34,10 +33,12 @@ from . import audio_io from . import beamforming from . import effects +from . import tools __all__ = [ # Basic classes "Signal", + "ImpulseResponse", "MultiBandSignal", "Filter", "FilterBank", @@ -52,9 +53,7 @@ "normalize", "fractional_delay", "true_peak_level", - "erb_frequencies", "load_pkl_object", - "fractional_octave_frequencies", "detrend", "rms", "CalibrationData", @@ -71,6 +70,7 @@ "audio_io", "beamforming", "effects", + "tools", ] -__version__ = "0.3.9" +__version__ = "0.4.0" diff --git a/dsptoolbox/_general_helpers.py b/dsptoolbox/_general_helpers.py index e54ab31..bbc029d 100644 --- a/dsptoolbox/_general_helpers.py +++ b/dsptoolbox/_general_helpers.py @@ -3,21 +3,25 @@ """ import numpy as np +from numpy.typing import NDArray from scipy.signal import ( windows, - convolve as scipy_convolve, + oaconvolve, hilbert, correlate, + lfilter, + lfilter_zi, ) from scipy.fft import fft, ifft -from scipy.interpolate import interp1d +from scipy.interpolate import interp1d, PchipInterpolator from scipy.linalg import toeplitz as toeplitz_scipy +from scipy.stats import pearsonr from os import sep from warnings import warn from scipy.fft import next_fast_len -def _find_nearest(points, vector) -> np.ndarray: +def _find_nearest(points, vector) -> NDArray[np.int_]: """Gives back the indexes with the nearest points in vector Parameters @@ -29,14 +33,14 @@ def _find_nearest(points, vector) -> np.ndarray: Returns ------- - indexes : `np.ndarray` + indexes : `NDArray[np.int_]` Indexes of the points. """ points = np.array(points) if np.ndim(points) == 0: points = points[..., None] - indexes = np.zeros(len(points), dtype=int) + indexes = np.zeros(len(points), dtype=np.int_) for ind, p in enumerate(points): indexes[ind] = np.argmin(np.abs(p - vector)) return indexes @@ -48,7 +52,7 @@ def _calculate_window( window_type: str | tuple | list = "hann", at_start: bool = True, inverse=False, -) -> np.ndarray: +) -> NDArray[np.float64]: """Creates a custom window with given indexes Parameters @@ -70,7 +74,7 @@ def _calculate_window( Returns ------- - window_full: np.ndarray + window_full: NDArray[np.float64] Custom window. """ @@ -112,23 +116,26 @@ def _calculate_window( def _get_normalized_spectrum( f, - spectra: np.ndarray, + spectra: NDArray[np.float64], scaling: str = "amplitude", f_range_hz=[20, 20000], normalize: str | None = None, smoothe: int = 0, phase=False, calibrated_data: bool = False, -) -> tuple[np.ndarray, np.ndarray] | tuple[np.ndarray, np.ndarray, np.ndarray]: +) -> ( + tuple[NDArray[np.float64], NDArray[np.float64]] + | tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]] +): """This function gives a normalized magnitude spectrum in dB with frequency vector for a given range. It is also smoothed. Use `None` for the spectrum without f_range_hz. Parameters ---------- - f : `np.ndarray` + f : NDArray[np.float64] Frequency vector. - spectra : `np.ndarray` + spectra : NDArray[np.float64] Spectrum matrix. scaling : str, optional Information about whether the spectrum is scaled as an amplitude or @@ -153,11 +160,11 @@ def _get_normalized_spectrum( Returns ------- - f : `np.ndarray` + f : NDArray[np.float64] Frequency vector. - mag_spectra : `np.ndarray` + mag_spectra : NDArray[np.float64] Magnitude spectrum matrix. - phase_spectra : `np.ndarray` + phase_spectra : NDArray[np.float64] Phase spectrum matrix, only returned when `phase=True`. Notes @@ -265,11 +272,11 @@ def _find_frequencies_above_threshold( def _pad_trim( - vector: np.ndarray, + vector: NDArray[np.float64], desired_length: int, axis: int = 0, in_the_end: bool = True, -) -> np.ndarray: +) -> NDArray[np.float64]: """Pads (with zeros) or trim (depending on size and desired length).""" throw_axis = False if vector.ndim < 2: @@ -344,12 +351,14 @@ def _compute_number_frames( return n_frames, padding_samples -def _normalize(s: np.ndarray, dbfs: float, mode="peak") -> np.ndarray: +def _normalize( + s: NDArray[np.float64], dbfs: float, mode="peak" +) -> NDArray[np.float64]: """Normalizes a signal. Parameters ---------- - s: `np.ndarray` + s: NDArray[np.float64] Signal to normalize. dbfs: float dbfs value to normalize to. @@ -359,7 +368,7 @@ def _normalize(s: np.ndarray, dbfs: float, mode="peak") -> np.ndarray: Returns ------- - s_out: `np.ndarray` + s_out: NDArray[np.float64] Normalized signal. """ @@ -376,28 +385,28 @@ def _normalize(s: np.ndarray, dbfs: float, mode="peak") -> np.ndarray: return s -def _rms(x: np.ndarray) -> np.ndarray: +def _rms(x: NDArray[np.float64]) -> NDArray[np.float64]: """Root mean square computation.""" return np.sqrt(np.sum(x**2) / len(x)) -def _amplify_db(s: np.ndarray, db: float) -> np.ndarray: +def _amplify_db(s: NDArray[np.float64], db: float) -> NDArray[np.float64]: """Amplify by dB.""" return s * 10 ** (db / 20) def _fade( - s: np.ndarray, + s: NDArray[np.float64], length_seconds: float = 0.1, mode: str = "exp", sampling_rate_hz: int = 48000, at_start: bool = True, -) -> np.ndarray: +) -> NDArray[np.float64]: """Create a fade in signal. Parameters ---------- - s : `np.ndarray` + s : NDArray[np.float64] np.array to be faded. length_seconds : float, optional Length of fade in seconds. Default: 0.1. @@ -412,7 +421,7 @@ def _fade( Returns ------- - s : `np.ndarray` + s : NDArray[np.float64] Faded vector. """ @@ -475,19 +484,19 @@ def _gaussian_window_sigma(window_length: int, alpha: float = 2.5) -> float: def _fractional_octave_smoothing( - vector: np.ndarray, + vector: NDArray[np.float64], num_fractions: int = 3, window_type="hann", - window_vec: np.ndarray | None = None, + window_vec: NDArray[np.float64] | None = None, clip_values: bool = False, -) -> np.ndarray: +) -> NDArray[np.float64]: """Smoothes a vector using interpolation to a logarithmic scale. Usually done for smoothing of frequency data. This implementation is taken from the pyfar package, see references. Parameters ---------- - vector : `np.ndarray` + vector : NDArray[np.float64] Vector to be smoothed. It is assumed that the first axis is to be smoothed. num_fractions : int, optional @@ -496,7 +505,7 @@ def _fractional_octave_smoothing( Type of window to be used. See `scipy.signal.windows.get_window` for valid types. If the window is `'gaussian'`, the parameter passed will be interpreted as alpha and not sigma. Default: `'hann'`. - window_vec : `np.ndarray`, optional + window_vec : NDArray[np.float64], optional Window vector to be used as a window. `window_type` should be set to `None` if this direct window is going to be used. Default: `None`. clip_values : bool, optional @@ -504,7 +513,7 @@ def _fractional_octave_smoothing( Returns ------- - vec_final : `np.ndarray` + vec_final : NDArray[np.float64] Vector after smoothing. References @@ -528,8 +537,9 @@ def _fractional_octave_smoothing( ) # Linear and logarithmic frequency vector N = len(vector) - l1 = np.arange(N) + l1 = np.arange(N, dtype=np.float64) k_log = (N) ** (l1 / (N - 1)) + l1 += 1.0 beta = np.log2(k_log[1]) # Window length always odd, so that delay can be easily compensated @@ -562,20 +572,25 @@ def _fractional_octave_smoothing( window /= window.sum() # Interpolate to logarithmic scale - vec_int = interp1d( - l1 + 1, vector, kind="cubic", copy=False, assume_sorted=True, axis=0 - ) - vec_log = vec_int(k_log) + vec_log = PchipInterpolator(l1, vector, axis=0)(k_log) + # Smoothe by convolving with window (output is centered) - smoothed = scipy_convolve( - vec_log, window[..., None], mode="same", method="auto" - ) - # Interpolate back to linear scale - smoothed = interp1d( - k_log, smoothed, kind="cubic", copy=False, assume_sorted=True, axis=0 + n_window_half = n_window // 2 + smoothed = oaconvolve( + np.pad( + vec_log, + ((n_window_half, n_window_half - (1 - n_window % 2)), (0, 0)), + mode="edge", + ), + window[..., None], + mode="valid", + axes=0, ) - vec_final = smoothed(l1 + 1) + # Interpolate back to linear scale + vec_final = interp1d( + k_log, smoothed, kind="linear", copy=False, assume_sorted=True, axis=0 + )(l1) if one_dim: vec_final = vec_final.squeeze() @@ -586,13 +601,13 @@ def _fractional_octave_smoothing( def _frequency_weightning( - f: np.ndarray, weightning_mode: str = "a", db_output: bool = True -) -> np.ndarray: + f: NDArray[np.float64], weightning_mode: str = "a", db_output: bool = True +) -> NDArray[np.float64]: """Returns the weights for frequency-weightning. Parameters ---------- - f : `np.ndarray` + f : NDArray[np.float64] Frequency vector. weightning_mode : str, optional Type of weightning. Choose from `'a'` or `'c'`. Default: `'a'`. @@ -601,7 +616,7 @@ def _frequency_weightning( Returns ------- - weights : `np.ndarray` + weights : NDArray[np.float64] Weightning values. References @@ -635,15 +650,17 @@ def _frequency_weightning( def _polyphase_decomposition( - in_sig: np.ndarray, number_polyphase_components: int, flip: bool = False -) -> tuple[np.ndarray, int]: + in_sig: NDArray[np.float64], + number_polyphase_components: int, + flip: bool = False, +) -> tuple[NDArray[np.float64], int]: """Converts input signal array with shape (time samples, channels) into its polyphase representation with shape (time samples, polyphase components, channels). Parameters ---------- - in_sig : `np.ndarray` + in_sig : NDArray[np.float64] Input signal array to be rearranged in polyphase representation. It should have the shape (time samples, channels). number_polyphase_components : int @@ -655,7 +672,7 @@ def _polyphase_decomposition( Returns ------- - poly : `np.ndarray` + poly : NDArray[np.float64] Rearranged vector with polyphase representation. New shape is (time samples, polyphase components, channels). padding : int @@ -686,7 +703,9 @@ def _polyphase_decomposition( return poly, padding -def _polyphase_reconstruction(poly: np.ndarray) -> np.ndarray: +def _polyphase_reconstruction( + poly: NDArray[np.float64], +) -> NDArray[np.float64]: """Returns the reconstructed input signal array from its polyphase representation, possibly with a different length if padded was needed for reconstruction. Polyphase representation shape is assumed to be @@ -694,13 +713,13 @@ def _polyphase_reconstruction(poly: np.ndarray) -> np.ndarray: Parameters ---------- - poly : `np.ndarray` + poly : NDArray[np.float64] Array with 3 dimensions (time samples, polyphase components, channels) as polyphase respresentation of signal. Returns ------- - in_sig : `np.ndarray` + in_sig : NDArray[np.float64] Rearranged vector with shape (time samples, channels). """ @@ -718,7 +737,7 @@ def _polyphase_reconstruction(poly: np.ndarray) -> np.ndarray: return in_sig -def _hz2mel(f: np.ndarray) -> np.ndarray: +def _hz2mel(f: NDArray[np.float64]) -> NDArray[np.float64]: """Convert frequency in Hz into mel. Parameters @@ -739,7 +758,7 @@ def _hz2mel(f: np.ndarray) -> np.ndarray: return 2595 * np.log10(1 + f / 700) -def _mel2hz(mel: np.ndarray) -> np.ndarray: +def _mel2hz(mel: NDArray[np.float64]) -> NDArray[np.float64]: """Convert frequency in mel into Hz. Parameters @@ -762,7 +781,7 @@ def _mel2hz(mel: np.ndarray) -> np.ndarray: def _get_fractional_octave_bandwidth( f_c: float, fraction: int = 1 -) -> np.ndarray: +) -> NDArray[np.float64]: """Returns an array with lower and upper bounds for a given center frequency with (1/fraction)-octave width. @@ -776,7 +795,7 @@ def _get_fractional_octave_bandwidth( Returns ------- - f_bounds : `np.ndarray` + f_bounds : NDArray[np.float64] Array of length 2 with lower and upper bounds. """ @@ -787,19 +806,21 @@ def _get_fractional_octave_bandwidth( ) -def _toeplitz(h: np.ndarray, length_of_input: int) -> np.ndarray: +def _toeplitz( + h: NDArray[np.float64], length_of_input: int +) -> NDArray[np.float64]: """Creates a toeplitz matrix from a system response given an input length. Parameters ---------- - h : `np.ndarray` + h : NDArray[np.float64] System's impulse response. length_of_input : int Input length needed for the shape of the toeplitz matrix. Returns ------- - `np.ndarray` + NDArray[np.float64] Toeplitz matrix with shape (len(h)+length_of_input-1, length_of_input). Convolution is done by using dot product from the right:: @@ -876,19 +897,19 @@ def _get_next_power_2(number, mode: str = "closest") -> int: return int(2**p) -def _euclidean_distance_matrix(x: np.ndarray, y: np.ndarray): +def _euclidean_distance_matrix(x: NDArray[np.float64], y: NDArray[np.float64]): """Compute the euclidean distance matrix between two vectors efficiently. Parameters ---------- - x : `np.ndarray` + x : NDArray[np.float64] First vector or matrix with shape (Point x, Dimensions). - y : `np.ndarray` + y : NDArray[np.float64] Second vector or matrix with shape (Point y, Dimensions). Returns ------- - dist : `np.ndarray` + dist : NDArray[np.float64] Euclidean distance matrix with shape (Point x, Point y). """ @@ -940,32 +961,34 @@ def _get_smoothing_factor_ema( return 1 - np.exp(factor / relaxation_time_s / sampling_rate_hz) -def _wrap_phase(phase_vector: np.ndarray) -> np.ndarray: +def _wrap_phase(phase_vector: NDArray[np.float64]) -> NDArray[np.float64]: """Wraps phase between [-np.pi, np.pi[ after it has been unwrapped. This works for 1D and 2D arrays, more dimensions have not been tested. Parameters ---------- - phase_vector : `np.ndarray` + phase_vector : NDArray[np.float64] Phase vector for which to wrap the phase. Returns ------- - `np.ndarray` + NDArray[np.float64] Wrapped phase vector. """ return (phase_vector + np.pi) % (2 * np.pi) - np.pi -def _get_exact_gain_1khz(f: np.ndarray, sp_db: np.ndarray) -> float: +def _get_exact_gain_1khz( + f: NDArray[np.float64], sp_db: NDArray[np.float64] +) -> float: """Uses linear interpolation to get the exact gain value at 1 kHz. Parameters ---------- - f : `np.ndarray` + f : NDArray[np.float64] Frequency vector. - sp : `np.ndarray` + sp : NDArray[np.float64] Spectrum. It can be in dB or not. Returns @@ -979,7 +1002,7 @@ def _get_exact_gain_1khz(f: np.ndarray, sp_db: np.ndarray) -> float: + "given frequency vector" ) # Get nearest value just before - ind = _find_nearest(1e3, f) + ind = _find_nearest(1e3, f).squeeze() if f[ind] > 1e3: ind -= 1 return (sp_db[ind + 1] - sp_db[ind]) / (f[ind + 1] - f[ind]) * ( @@ -1007,7 +1030,7 @@ def gaussian_window( Returns ------- - w : `np.ndarray` + w : NDArray[np.float64] Gaussian window. References @@ -1053,7 +1076,7 @@ def _get_chirp_rate(range_hz: list, length_seconds: float) -> float: return np.log2(range_hz_array[1] / range_hz_array[0]) / length_seconds -def _correct_for_real_phase_spectrum(phase_spectrum: np.ndarray): +def _correct_for_real_phase_spectrum(phase_spectrum: NDArray[np.float64]): """This function takes in a wrapped phase spectrum and corrects it to be for a real signal (assuming the last frequency bin corresponds to nyquist, i.e., time data had an even length). This effectively adds a @@ -1062,13 +1085,13 @@ def _correct_for_real_phase_spectrum(phase_spectrum: np.ndarray): Parameters ---------- - phase_spectrum : np.ndarray + phase_spectrum : NDArray[np.float64] Wrapped phase to be corrected. It is assumed that its last element corresponds to the nyquist frequency. Returns ------- - np.ndarray + NDArray[np.float64] Phase spectrum that can correspond to a real signal. """ @@ -1084,32 +1107,38 @@ def _correct_for_real_phase_spectrum(phase_spectrum: np.ndarray): def _scale_spectrum( - spectrum: np.ndarray, + spectrum: NDArray[np.float64] | NDArray[np.complex128], mode: str | None, time_length_samples: int, sampling_rate_hz: int, - window: np.ndarray | None = None, -) -> np.ndarray: - """Scale the spectrum directly from the (unscaled) FFT. It is assumed that - the time data was not windowed. + window: NDArray[np.float64] | None = None, +) -> NDArray[np.float64]: + """Scale the spectrum directly from the unscaled ("backward" normalization) + (R)FFT. If a window was applied, it is necessary to compute the right + scaling factor. Parameters ---------- - spectrum : `np.ndarray` + spectrum : NDArray[np.float64] | NDArray[np.complex128] Spectrum to scale. It is assumed that the frequency bins are along the first dimension. mode : str, None Type of scaling to use. `"power spectral density"`, `"power spectrum"`, `"amplitude spectral density"`, `"amplitude spectrum"`. Pass `None` - to avoid any scaling and return the same spectrum. + to avoid any scaling and return the same spectrum. Using a power + representation will returned the squared spectrum. time_length_samples : int Original length of the time data. sampling_rate_hz : int Sampling rate. + window : NDArray[np.float64], None, optional + Applied window when obtaining the spectrum. It is necessary to compute + the correct scaling factor. In case of None, "boxcar" window is + assumed. Default: None. Returns ------- - `np.ndarray` + NDArray[np.float64] | NDArray[np.complex128] Scaled spectrum Notes @@ -1162,7 +1191,7 @@ def _scale_spectrum( def _get_fractional_impulse_peak_index( - time_data: np.ndarray, polynomial_points: int = 1 + time_data: NDArray[np.float64], polynomial_points: int = 1 ): """ Obtain the index for the peak in subsample precision using the root @@ -1170,7 +1199,7 @@ def _get_fractional_impulse_peak_index( Parameters ---------- - time_data : `np.ndarray` + time_data : NDArray[np.float64] Time vector with shape (time samples, channels). polynomial_points : int, optional Number of points to take for the polynomial interpolation and root @@ -1178,7 +1207,7 @@ def _get_fractional_impulse_peak_index( Returns ------- - latency_samples : `np.ndarray` + latency_samples : NDArray[np.float64] Latency of impulses (in samples). It has shape (channels). """ @@ -1251,9 +1280,9 @@ def _get_fractional_impulse_peak_index( def _remove_ir_latency_from_phase( - freqs: np.ndarray, - phase: np.ndarray, - time_data: np.ndarray, + freqs: NDArray[np.float64], + phase: NDArray[np.float64], + time_data: NDArray[np.float64], sampling_rate_hz: int, padding_factor: int, ): @@ -1262,11 +1291,11 @@ def _remove_ir_latency_from_phase( Parameters ---------- - freqs : `np.ndarray` + freqs : NDArray[np.float64] Frequency vector. - phase : `np.ndarray` + phase : NDArray[np.float64] Phase vector. - time_data : `np.ndarray` + time_data : NDArray[np.float64] Corresponding time signal. sampling_rate_hz : int Sample rate. @@ -1275,7 +1304,7 @@ def _remove_ir_latency_from_phase( Returns ------- - new_phase : `np.ndarray` + new_phase : NDArray[np.float64] New phase response without impulse delay. """ @@ -1285,14 +1314,14 @@ def _remove_ir_latency_from_phase( def _min_phase_ir_from_real_cepstrum( - time_data: np.ndarray, padding_factor: int + time_data: NDArray[np.float64], padding_factor: int ): """Returns minimum-phase version of a time series using the real cepstrum method. Parameters ---------- - time_data : `np.ndarray` + time_data : NDArray[np.float64] Time series to compute the minimum phase version from. It is assumed to have shape (time samples, channels). padding_factor : int, optional @@ -1302,7 +1331,7 @@ def _min_phase_ir_from_real_cepstrum( Returns ------- - min_phase_time_data : `np.ndarray` + min_phase_time_data : NDArray[np.float64] New time series. """ @@ -1317,14 +1346,14 @@ def _min_phase_ir_from_real_cepstrum( def _get_minimum_phase_spectrum_from_real_cepstrum( - time_data: np.ndarray, padding_factor: int + time_data: NDArray[np.float64], padding_factor: int ): """Returns minimum-phase version of a time series using the real cepstrum method. Parameters ---------- - time_data : `np.ndarray` + time_data : NDArray[np.float64] Time series to compute the minimum phase version from. It is assumed to have shape (time samples, channels). padding_factor : int, optional @@ -1334,7 +1363,7 @@ def _get_minimum_phase_spectrum_from_real_cepstrum( Returns ------- - `np.ndarray` + NDArray[np.float64] New spectrum with minimum phase. """ @@ -1359,7 +1388,9 @@ def _get_minimum_phase_spectrum_from_real_cepstrum( def _fractional_latency( - td1: np.ndarray, td2: np.ndarray | None = None, polynomial_points: int = 1 + td1: NDArray[np.float64], + td2: NDArray[np.float64] | None = None, + polynomial_points: int = 1, ): """This function computes the sub-sample latency between two signals using Zero-Crossing of the analytic (hilbert transformed) correlation function. @@ -1371,7 +1402,7 @@ def _fractional_latency( ---------- td1 : `np.ndaray` Delayed version of the signal. - td2 : `np.ndarray`, optional + td2 : NDArray[np.float64], optional Original version of the signal. If `None` is passed, the latencies are computed between the first channel of td1 and every other. Default: `None`. @@ -1383,7 +1414,7 @@ def _fractional_latency( Returns ------- - lags : `np.ndarray` + lags : NDArray[np.float64] Fractional delays. It has shape (channel). In case td2 was `None`, its length is `channels - 1`. @@ -1406,3 +1437,278 @@ def _fractional_latency( xcor[:, i] = correlate(td2[:, i], td1[:, i]) inds = _get_fractional_impulse_peak_index(xcor, polynomial_points) return td1.shape[0] - inds - 1 + + +def _interpolate_fr( + f_interp: NDArray[np.float64], + fr_interp: NDArray[np.float64], + f_target: NDArray[np.float64], + mode: str | None = None, + interpolation_scheme: str = "linear", +) -> NDArray[np.float64]: + """Interpolate one frequency response to a new frequency vector. + + Parameters + ---------- + f_interp : NDArray[np.float64] + Frequency vector of the frequency response that should be interpolated. + fr_interp : NDArray[np.float64] + Frequency response to be interpolated. + f_target : NDArray[np.float64] + Target frequency vector. + mode : str, optional + Convert to amplitude or power representation from dB during + interpolation (or the other way around) using the modes `"db2power"` + (input in dB, interpolation in power spectrum, output in dB), + `"db2amplitude"`, `"amplitude2db"`, `"power2db"`. Pass `None` to avoid + any conversion. Default: `None`. + interpolation_scheme : str, optional + Type of interpolation to use. See `scipy.interpolation.interp1d` for + details. Choose from `"quadratic"` or `"cubic"` splines, or `"linear"`. + Default: `"linear"`. + + Returns + ------- + NDArray[np.float64] + New interpolated frequency response corresponding to `f_target` vector. + + Notes + ----- + - The input is always assumed to be already sorted. + - In case `f_target` has values outside the boundaries of `f_interp`, + the first and last values of `fr_interp` are used for extrapolation. This + also applies if interpolation is done in dB. If done in amplitude or + power units, the fill value outside the boundaries is 0. + - The interpolation is always done along the first (outer) axis or the + vector. + - Theoretical thoughts on interpolating an amplitude or power + frequency response: + - Using complex and dB values during interpolation are not very precise + when comparing the results in terms of the amplitude or power + spectrum. + - Interpolation can be done with amplitude or power representation with + similar precision. + - Changing the frequency resolution in a linear scale means zero- + padding or trimming the underlying time series. For an amplitude + representation , i.e. spectrum or spectral density, the values must + be scaled using the factor `old_length/new_length`. This ensures that + the RMS values (amplitude spectrum) are still correct, and that + integrating the new power spectral density still renders the total + signal's energy truthfully, i.e. parseval's theorem would still hold. + For the power representation, it also applies with the same squared + factor. + - A direct FFT-result which is not in physical units needs rescaling + depending on the normalization scheme used during the FFT -> IFFT (in + the complex/amplitude representation): + - Forward: scaling factor `old_length/new_length`. + - Backward: no rescaling. + - Orthogonal: scaling factor `(old_length/new_length)**0.5` + - Interpolating the (amplitude or power) spectrum to a logarithmic- + spaced frequency vector can be done without rescaling (the underlying + transformation in the time domain would be warping). Doing so for the + (amplitude or power) spectral density only retains its validity if + the new spectrum is weighted exponentially with increasing frequency + since each bin contains the energy of a larger “frequency band” + (this changes the physical units of the spectral density). Doing so + ensures that integrating the power spectral density over frequency + still retains the energy of the signal (parseval). + - Assuming a different time window in each frequency resolution would + require knowing the specific windows in order to rescale correctly. + Assuming the same time window while zero-padding in the time domain + would mean that no rescaling has to be applied. + + """ + + fill_value = (fr_interp[0], fr_interp[-1]) + + # Conversion if necessary + if mode is not None: + mode = mode.lower() + factor = 20 if "amplitude" in mode else 10 + if mode[:3] == "db2": + fr_interp = 10 ** (fr_interp / factor) + fill_value = (0.0, 0.0) + elif mode[-3:] == "2db": + fr_interp = factor * np.log10( + np.clip( + np.abs(fr_interp), + a_min=np.finfo(np.float64).smallest_normal, + a_max=None, + ) + ) + fill_value = (fr_interp[0], fr_interp[-1]) + else: + raise ValueError(f"Unsupported interpolation mode: {mode}") + + interpolated = interp1d( + f_interp, + fr_interp, + kind=interpolation_scheme, + bounds_error=False, + assume_sorted=True, + fill_value=fill_value, + axis=0, + )(f_target) + + # Back conversion if activated + if mode is not None: + if mode[:3] == "db2": + interpolated = factor * np.log10( + np.clip( + np.abs(interpolated), + a_min=np.finfo(np.float64).smallest_normal, + a_max=None, + ) + ) + elif mode[-3:] == "2db": + interpolated = 10 ** (interpolated / factor) + + return interpolated + + +def _time_smoothing( + x: NDArray[np.float64], + sampling_rate_hz: int, + ascending_time_s: float, + descending_time_s: float | None = None, +) -> NDArray[np.float64]: + """Smoothing for a time series with independent ascending and descending + times using an exponential moving average. It works on 1D and 2D arrays. + The smoothing is always applied along the longest axis. + + If no descending time is provided, `ascending_time_s` is used for both + increasing and decreasing values. + + Parameters + ---------- + x : NDArray[np.float64] + Vector to apply smoothing to. + sampling_rate_hz : int + Sampling rate of the time series `x`. + ascending_time_s : float + Corresponds to the needed time for achieving a 95% accuracy of the + step response when the samples are increasing in value. Pass 0. in + order to avoid any smoothing for rising values. + descending_time_s : float, None, optional + As `ascending_time_s` but for descending values. If None, + `ascending_time_s` is applied. Default: None. + + Returns + ------- + NDArray[np.float64] + Smoothed time series. + + """ + onedim = x.ndim == 1 + x = np.atleast_2d(x) + if x.shape[0] < x.shape[1]: + reverse_axis = True + x = x.T + else: + reverse_axis = False + + assert x.ndim < 3, "This function is only available for 2D arrays" + assert ascending_time_s >= 0.0, "Attack time must be at least 0" + ascending_factor = ( + _get_smoothing_factor_ema(ascending_time_s, sampling_rate_hz) + if ascending_time_s > 0.0 + else 1.0 + ) + + if descending_time_s is None: + b, a = [ascending_factor], [1, -(1 - ascending_factor)] + zi = lfilter_zi(b, a) + y = lfilter( + b, + a, + x, + axis=0, + zi=np.asarray([zi * x[0, ch] for ch in range(x.shape[1])]).T, + )[0] + if reverse_axis: + y = y.T + if onedim: + return y.squeeze() + return y + + assert descending_time_s >= 0.0, "Release time must at least 0" + assert not ( + ascending_time_s == 0.0 and descending_time_s == ascending_time_s + ), "These times will not apply any smoothing" + + descending_factor = ( + _get_smoothing_factor_ema(descending_time_s, sampling_rate_hz) + if descending_time_s > 0.0 + else 1.0 + ) + + y = np.zeros_like(x) + y[0, :] = x[0, :] + + for n in np.arange(1, x.shape[0]): + for ch in range(x.shape[1]): + smoothing_factor = ( + ascending_factor + if x[n, ch] > y[n - 1, ch] + else descending_factor + ) + y[n, ch] = ( + smoothing_factor * x[n, ch] + + (1.0 - smoothing_factor) * y[n - 1, ch] + ) + + if reverse_axis: + y = y.T + if onedim: + return y.squeeze() + return y + + +def _get_correlation_of_latencies( + time_data: NDArray[np.float64], + other_time_data: NDArray[np.float64], + latencies: NDArray[np.int_], +) -> NDArray[np.float64]: + """Compute the pearson correlation coefficient of each channel between + `time_data` and `other_time_data` in order to obtain an estimation on the + quality of the latency computation. + + Parameters + ---------- + time_data : NDArray[np.float64] + Original time data. This is the "undelayed" version if the latency + is positive. It must have either one channel or a matching number + of channels with `other_time_data`. + other_time_data : NDArray[np.float64] + "Delayed" time data, when the latency is positive. + latencies : NDArray[np.int_] + Computed latencies for each channel. + + Returns + ------- + NDArray[np.float64] + Correlation coefficient for each channel. + + """ + one_channel = time_data.shape[1] == 1 + + correlations = np.zeros(len(latencies)) + + for ch in range(len(latencies)): + if latencies[ch] > 0: + undelayed = time_data[:, 0] if one_channel else time_data[:, ch] + delayed = other_time_data[:, ch] + else: + undelayed = other_time_data[:, ch] + delayed = time_data[:, 0] if one_channel else time_data[:, ch] + + # Remove delay samples + delayed = delayed[abs(latencies[ch]) :] + + # Get effective length + length_to_check = min(len(delayed), len(undelayed)) + + delayed = delayed[:length_to_check] + undelayed = undelayed[:length_to_check] + correlations[ch] = pearsonr(delayed, undelayed)[0] + return correlations diff --git a/dsptoolbox/_standard.py b/dsptoolbox/_standard.py index 46b855b..2638868 100644 --- a/dsptoolbox/_standard.py +++ b/dsptoolbox/_standard.py @@ -11,10 +11,13 @@ _wrap_phase, ) from warnings import warn +from numpy.typing import NDArray def _latency( - in1: np.ndarray, in2: np.ndarray | None = None, polynomial_points: int = 0 + in1: NDArray[np.float64], + in2: NDArray[np.float64] | None = None, + polynomial_points: int = 0, ): """Computes the latency between two functions using the correlation method. The variable polynomial_points is only a dummy to share the same function @@ -35,8 +38,8 @@ def _latency( def _welch( - x: np.ndarray, - y: np.ndarray | None, + x: NDArray[np.float64], + y: NDArray[np.float64] | None, fs_hz: int, window_type: str = "hann", window_length_samples: int = 1024, @@ -44,14 +47,14 @@ def _welch( detrend: bool = True, average: str = "mean", scaling: str | None = "power spectral density", -) -> np.ndarray: +) -> NDArray[np.float64]: """Cross spectral density computation with Welch's method. Parameters ---------- - x : `np.ndarray` + x : NDArray[np.float64] First signal with shape (time samples, channel). - y : `np.ndarray` or `None` + y : NDArray[np.float64] or `None` Second signal with shape (time samples, channel). If `None`, the auto- spectrum of `x` will be computed. fs_hz : int @@ -77,7 +80,7 @@ def _welch( Returns ------- - csd : `np.ndarray` + csd : NDArray[np.float64] Complex cross spectral density vector if x and y are different. Alternatively, the (real) autocorrelation power spectral density when x and y are the same. If density or spectrum depends on scaling. @@ -97,11 +100,11 @@ def _welch( """ autospectrum = y is None - if type(x) is not np.ndarray: + if type(x) is not NDArray[np.float64]: x = np.asarray(x).squeeze() if not autospectrum: - if type(y) is not np.ndarray: + if type(y) is not NDArray[np.float64]: y = np.asarray(y).squeeze() assert x.shape == y.shape, "Shapes of data do not match" # NOTE: Computing the spectrum in a vectorized manner for all channels @@ -229,12 +232,12 @@ def _welch( return csd -def _group_delay_direct(phase: np.ndarray, delta_f: float = 1): +def _group_delay_direct(phase: NDArray[np.float64], delta_f: float = 1): """Computes group delay by differentiation of the unwrapped phase. Parameters ---------- - phase : `np.ndarray` + phase : NDArray[np.float64] Complex spectrum or phase for the direct method delta_f : float, optional Frequency step for the phase. If it equals 1, group delay is computed @@ -242,7 +245,7 @@ def _group_delay_direct(phase: np.ndarray, delta_f: float = 1): Returns ------- - gd : `np.ndarray` + gd : NDArray[np.float64] Group delay vector either in s or in samples if no frequency step is given. @@ -257,16 +260,16 @@ def _group_delay_direct(phase: np.ndarray, delta_f: float = 1): def _minimum_phase( - magnitude: np.ndarray, + magnitude: NDArray[np.float64], whole_spectrum: bool = False, unwrapped: bool = True, odd_length: bool = False, -) -> np.ndarray: +) -> NDArray[np.float64]: """Computes minimum phase system from magnitude spectrum. Parameters ---------- - magnitude : `np.ndarray` + magnitude : NDArray[np.float64] Spectrum for which to compute the minimum phase. If real, it is assumed to be already the magnitude. whole_spectrum : bool, optional @@ -281,7 +284,7 @@ def _minimum_phase( Returns ------- - minimum_phase : `np.ndarray` + minimum_phase : NDArray[np.float64] Minimal phase of the system. """ @@ -310,7 +313,7 @@ def _minimum_phase( def _stft( - x: np.ndarray, + x: NDArray[np.float64], fs_hz: int, window_length_samples: int = 2048, window_type: str = "hann", @@ -324,7 +327,7 @@ def _stft( Parameters ---------- - x : `np.ndarray` + x : NDArray[np.float64] First signal fs_hz : int Sampling rate in Hz. @@ -353,11 +356,11 @@ def _stft( Returns ------- - time_s : `np.ndarray` + time_s : NDArray[np.float64] Time vector in seconds for each frame. - freqs_hz : `np.ndarray` + freqs_hz : NDArray[np.float64] Frequency vector. - stft : `np.ndarray` + stft : NDArray[np.float64] STFT matrix with shape (frequency, time, channel). References @@ -445,7 +448,7 @@ def _stft( def _csm( - time_data: np.ndarray, + time_data: NDArray[np.float64], sampling_rate_hz: int, window_length_samples: int = 1024, window_type: str = "hann", @@ -459,7 +462,7 @@ def _csm( Parameters ---------- - time_data : `np.ndarray` + time_data : NDArray[np.float64] Signal sampling_rate_hz : int Sampling rate in Hz. @@ -484,9 +487,9 @@ def _csm( Returns ------- - f : `np.ndarray` + f : NDArray[np.float64] Frequency vector - csm : `np.ndarray` + csm : NDArray[np.float64] Cross spectral matrix with shape (frequency, channels, channels). References @@ -537,7 +540,7 @@ def _csm( def _center_frequencies_fractional_octaves_iec( nominal, num_fractions -) -> tuple[np.ndarray, np.ndarray]: +) -> tuple[NDArray[np.float64], NDArray[np.float64]]: """Returns the exact center frequencies for fractional octave bands according to the IEC 61260:1:2014 standard. octave ratio @@ -642,7 +645,7 @@ def _center_frequencies_fractional_octaves_iec( def _exact_center_frequencies_fractional_octaves( num_fractions, frequency_range -) -> np.ndarray: +) -> NDArray[np.float64]: """Calculate the center frequencies of arbitrary fractional octave bands. Parameters @@ -707,7 +710,7 @@ def _kaiser_window_beta(A): def _kaiser_window_fractional( length: int, side_lobe_suppression_db: float, fractional_delay: float -) -> np.ndarray: +) -> NDArray[np.float64]: """Create a kaiser window with a fractional offset. Parameters @@ -721,7 +724,7 @@ def _kaiser_window_fractional( Returns ------- - `np.ndarray` + NDArray[np.float64] Kaiser window. """ @@ -742,7 +745,7 @@ def _kaiser_window_fractional( def _indices_above_threshold_dbfs( - time_vec: np.ndarray, + time_vec: NDArray[np.float64], threshold_dbfs: float, attack_smoothing_coeff: int, release_smoothing_coeff: int, @@ -753,7 +756,7 @@ def _indices_above_threshold_dbfs( Parameters ---------- - time_vec : `np.ndarray` + time_vec : NDArray[np.float64] Time series for which to find indices above power threshold. Can only take one channel. threshold_dbfs : float @@ -768,7 +771,7 @@ def _indices_above_threshold_dbfs( Returns ------- - indices_above : `np.ndarray` + indices_above : NDArray[np.float64] Array of type boolean with length of time_vec indicating indices above threshold with `True` and below with `False`. @@ -801,12 +804,14 @@ def _indices_above_threshold_dbfs( return indices_above -def _detrend(time_data: np.ndarray, polynomial_order: int) -> np.ndarray: +def _detrend( + time_data: NDArray[np.float64], polynomial_order: int +) -> NDArray[np.float64]: """Compute and return detrended signal. Parameters ---------- - time_data : np.ndarray + time_data : NDArray[np.float64] Time data of the signal with shape (time samples, channels). polynomial_order : int Polynomial order of the fitted polynomial that will be removed @@ -814,7 +819,7 @@ def _detrend(time_data: np.ndarray, polynomial_order: int) -> np.ndarray: Returns ------- - new_time_data : np.ndarray + new_time_data : NDArray[np.float64] Detrended time data with shape (time samples, channels). """ @@ -825,18 +830,19 @@ def _detrend(time_data: np.ndarray, polynomial_order: int) -> np.ndarray: return time_data -def _rms(x: np.ndarray) -> float | np.ndarray: +def _rms(x: NDArray[np.float64]) -> float | NDArray[np.float64]: """Root mean squared value of a discrete time series. Parameters ---------- - x : `np.ndarray` + x : NDArray[np.float64] Time series. Returns ------- - rms : float or `np.ndarray` - Root mean squared of a signal. Float or np.ndarray depending on input. + rms : float or NDArray[np.float64] + Root mean squared of a signal. Float or NDArray[np.float64] depending + on input. """ single_dim = False @@ -856,16 +862,16 @@ def _rms(x: np.ndarray) -> float | np.ndarray: def _get_framed_signal( - td: np.ndarray, + td: NDArray[np.float64], window_length_samples: int, step_size: int, keep_last_frames: bool = True, -) -> np.ndarray: +) -> NDArray[np.float64]: """This method computes a framed version of a signal and returns it. Parameters ---------- - td : `np.ndarray` + td : NDArray[np.float64] Signal with shape (time samples, channels). window_length_samples : int Window length in samples. @@ -877,7 +883,7 @@ def _get_framed_signal( Returns ------- - td_framed : `np.ndarray` + td_framed : NDArray[np.float64] Framed signal with shape (time samples, frames, channels). """ @@ -911,21 +917,21 @@ def _get_framed_signal( def _reconstruct_framed_signal( - td_framed: np.ndarray, + td_framed: NDArray[np.float64], step_size: int, - window: str | np.ndarray | None = None, + window: str | NDArray[np.float64] | None = None, original_signal_length: int | None = None, safety_threshold: float = 1e-4, -) -> np.ndarray: +) -> NDArray[np.float64]: """Gets and returns a framed signal into its vector representation. Parameters ---------- - td_framed : `np.ndarray` + td_framed : NDArray[np.float64] Framed signal with shape (time samples, frame, channel). step_size : int Step size in samples between frames (also known as hop length). - window : str, `np.ndarray`, optional + window : str, NDArray[np.float64], optional Window (if applies). Pass `None` to avoid using a window during reconstruction. Default: `None`. original_signal_length : int, optional @@ -940,7 +946,7 @@ def _reconstruct_framed_signal( Returns ------- - td : `np.ndarray` + td : NDArray[np.float64] Reconstructed signal. """ @@ -950,7 +956,7 @@ def _reconstruct_framed_signal( if window is not None: if type(window) is str: window = windows.get_window(window, td_framed.shape[0]) - elif type(window) is np.ndarray: + elif type(window) is NDArray[np.float64]: assert window.ndim == 1, "Window must be a 1D-array" assert ( window.shape[0] == td_framed.shape[0] @@ -983,7 +989,7 @@ def _reconstruct_framed_signal( def _get_window_envelope( - window: np.ndarray, + window: NDArray[np.float64], total_length_samples: int, step_size_samples: int, number_frames: int, @@ -1008,7 +1014,7 @@ def _fractional_delay_filter( delay_samples: float, filter_order: int, side_lobe_suppression_db: float, -) -> tuple[int, np.ndarray]: +) -> tuple[int, NDArray[np.float64]]: """This function delivers fractional delay filters according to specifications. Besides, additional integer delay, that might be necessary to compute the output, is returned as well. @@ -1033,7 +1039,7 @@ def _fractional_delay_filter( ------- integer_delay : int Additional integer delay necessary to achieve total desired delay. - h : `np.ndarray` + h : NDArray[np.float64] Filter's impulse response for fractional delay. References diff --git a/dsptoolbox/audio_io/_audio_io.py b/dsptoolbox/audio_io/_audio_io.py index 874a904..6426444 100644 --- a/dsptoolbox/audio_io/_audio_io.py +++ b/dsptoolbox/audio_io/_audio_io.py @@ -23,7 +23,8 @@ def standard_callback(signal: Signal): Function to be used as callback for the output stream. The signature must be valid for sounddevice's callback:: - call(outdata: np.ndarray, frames: int, time, status) -> None + call(outdata: NDArray[np.float64], frames: int, time, status)\ + -> None """ # Normalize @@ -36,7 +37,7 @@ def call(outdata: ndarray, frames: int, time, status) -> None: Parameters ---------- - outdata : `np.ndarray` + outdata : NDArray[np.float64] Samples as numpy array with shape (samples, channels). frames : int Block size in samples. diff --git a/dsptoolbox/audio_io/audio_io.py b/dsptoolbox/audio_io/audio_io.py index 02b4f69..7a07285 100644 --- a/dsptoolbox/audio_io/audio_io.py +++ b/dsptoolbox/audio_io/audio_io.py @@ -429,7 +429,7 @@ def play_through_stream( audio_callback(signal: Signal) -> callable - callback(outdata: np.ndarray, frames: int, + callback(outdata: NDArray[np.float64], frames: int, time: CData, status: CallbackFlags) -> None See `sounddevice`'s examples of callbacks for more general @@ -513,7 +513,7 @@ def output_stream( callback : callable Function that defines the audio callback:: - callback(outdata: np.ndarray, frames: int, + callback(outdata: NDArray[np.float64], frames: int, time: CData, status: CallbackFlags) -> None finished_callback : callable diff --git a/dsptoolbox/beamforming/_beamforming.py b/dsptoolbox/beamforming/_beamforming.py index 106dced..1b56884 100644 --- a/dsptoolbox/beamforming/_beamforming.py +++ b/dsptoolbox/beamforming/_beamforming.py @@ -3,9 +3,10 @@ """ import numpy as np -from .._general_helpers import _euclidean_distance_matrix import matplotlib.pyplot as plt from seaborn import set_style +from numpy.typing import NDArray +from .._general_helpers import _euclidean_distance_matrix set_style("whitegrid") @@ -56,7 +57,7 @@ def number_of_points(self): return self.coordinates.shape[0] @property - def coordinates(self) -> np.ndarray: + def coordinates(self) -> NDArray[np.float64]: return self._coordinates.copy() @coordinates.setter @@ -87,23 +88,25 @@ def extent(self): return extent # ======== distances ====================================================== - def get_distances_to_point(self, point: np.ndarray) -> np.ndarray: + def get_distances_to_point( + self, point: NDArray[np.float64] + ) -> NDArray[np.float64]: """Compute distances (euclidean) from given point to all points of the object efficiently. Parameters ---------- - point : `np.ndarray` + point : NDArray[np.float64] Point or points to which to compute the distances from all other points. Its shape should be (point, coordinate). Returns ------- - distances : `np.ndarray` + distances : NDArray[np.float64] Distances with shape (points, new_points). """ - if type(point) is not np.ndarray: + if type(point) is not NDArray[np.float64]: point = np.asarray(point) if point.ndim == 1: point = point[None, ...] @@ -164,7 +167,7 @@ def plot_points(self, projection: str | None = None): fig.tight_layout() return fig, ax - def find_nearest_point(self, point) -> tuple[int, np.ndarray]: + def find_nearest_point(self, point) -> tuple[int, NDArray[np.float64]]: """This method returns the coordinates and index of the nearest point to a given point using euclidean distance. @@ -177,7 +180,7 @@ def find_nearest_point(self, point) -> tuple[int, np.ndarray]: ------- index : int Index of the nearest point. - coord : `np.ndarray` + coord : NDArray[np.float64] Position vector with shape (x, y, z) of the nearest point. """ @@ -195,26 +198,26 @@ def find_nearest_point(self, point) -> tuple[int, np.ndarray]: def _clean_sc_deconvolve( - map: np.ndarray, - csm: np.ndarray, - h: np.ndarray, - h_H: np.ndarray, + map: NDArray[np.float64], + csm: NDArray[np.float64], + h: NDArray[np.float64], + h_H: NDArray[np.float64], maximum_iterations: int, remove_diagonal_csm: bool, safety_factor: float, -) -> np.ndarray: +) -> NDArray[np.float64]: """Computes and returns the degraded csm. Parameters ---------- - map : `np.ndarray` + map : NDArray[np.float64] Initial beamforming map to be deconvolved for a single frequency with shape (point). - csm : `np.ndarray` + csm : NDArray[np.float64] Cross-spectral matrix for a single frequency with shape (mic, mic). - h : `np.ndarray` + h : NDArray[np.float64] Steering vector for a single frequency with shape (mic, grid point). - h_H : `np.ndarray` + h_H : NDArray[np.float64] Steering vector (hermitian transposed) for a single frequency with shape (grid point, mic). maximum_iterations : int @@ -228,7 +231,7 @@ def _clean_sc_deconvolve( Returns ------- - `np.ndarray` + NDArray[np.float64] Deconvolved beamforming map. References diff --git a/dsptoolbox/beamforming/beamforming.py b/dsptoolbox/beamforming/beamforming.py index d73ffd1..caed06d 100644 --- a/dsptoolbox/beamforming/beamforming.py +++ b/dsptoolbox/beamforming/beamforming.py @@ -8,6 +8,7 @@ from scipy.integrate import simpson from matplotlib.figure import Figure from matplotlib.axes import Axes +from numpy.typing import NDArray from ..classes import Signal from .. import fractional_delay, merge_signals, pad_trim @@ -67,19 +68,21 @@ def __init__(self, positions: dict): """ super().__init__(positions) - def reconstruct_map_shape(self, map: np.ndarray) -> np.ndarray: + def reconstruct_map_shape( + self, map: NDArray[np.float64] + ) -> NDArray[np.float64]: """Placeholder for a user-defined map reconstruction. Here, it returns same given map. Use inheritance from the `Grid` class to overwrite this with an own implementation. Parameters ---------- - map : `np.ndarray` + map : NDArray[np.float64] Map to be reshaped. Returns ------- - map : `np.ndarray` + map : NDArray[np.float64] Reshaped map. Here with same passed shape as before. """ @@ -163,17 +166,19 @@ def __init__(self, line1, line2, dimensions, value3): } super().__init__(positions) - def reconstruct_map_shape(self, map_vector: np.ndarray) -> np.ndarray: + def reconstruct_map_shape( + self, map_vector: NDArray[np.float64] + ) -> NDArray[np.float64]: """Reshapes the map to be a matrix that fits the grid. Parameters ---------- - map_vector : `np.ndarray` + map_vector : NDArray[np.float64] Map (as a vector) to be reshaped. Returns ------- - map : `np.ndarray` + map : NDArray[np.float64] Reshaped map. """ @@ -186,13 +191,13 @@ def reconstruct_map_shape(self, map_vector: np.ndarray) -> np.ndarray: return map_vector.reshape(self.original_lengths) def plot_map( - self, map: np.ndarray, range_db: float = 20 + self, map: NDArray[np.float64], range_db: float = 20 ) -> tuple[Figure, Axes]: """Plot a map done with this type of grid. Parameters ---------- - map : `np.ndarray` + map : NDArray[np.float64] Beamformer map. range_db : float, optional Range in dB to plot. @@ -289,17 +294,19 @@ def __init__(self, line_x, line_y, line_z): } super().__init__(positions) - def reconstruct_map_shape(self, map_vector: np.ndarray) -> np.ndarray: + def reconstruct_map_shape( + self, map_vector: NDArray[np.float64] + ) -> NDArray[np.float64]: """Reshapes the map to be a matrix that fits the grid. Parameters ---------- - map_vector : `np.ndarray` + map_vector : NDArray[np.float64] Map (as a vector) to be reshaped. Returns ------- - map : `np.ndarray` + map : NDArray[np.float64] Reshaped map. """ @@ -313,7 +320,7 @@ def reconstruct_map_shape(self, map_vector: np.ndarray) -> np.ndarray: def plot_map( self, - map: np.ndarray, + map: NDArray[np.float64], third_dimension: str, value_third_dimension: float, range_db: float = 20, @@ -322,7 +329,7 @@ def plot_map( Parameters ---------- - map : `np.ndarray` + map : NDArray[np.float64] Beamformer map. third_dimension : str Choose the dimension that is normal to plane. Choose from `'x'`, @@ -544,12 +551,12 @@ def __compute_array_center(self): Parameters ---------- - coord : `np.ndarray` + coord : NDArray[np.float64] Coordinates of array with shape (points, xyz). Returns ------- - `np.ndarray` + NDArray[np.float64] Array with coordinates for mic closest to center with shape (x, y, z). ind : int @@ -712,8 +719,8 @@ def __init__( object. """ - assert ( - type(multi_channel_signal) is Signal + assert isinstance( + multi_channel_signal, Signal ), "Multi-channel signal must be of type Signal" assert ( type(mic_array) is MicArray @@ -859,7 +866,7 @@ def get_beamformer_map( center_frequency_hz: float, octave_fraction: int = 3, remove_csm_diagonal: bool = True, - ) -> np.ndarray: + ) -> NDArray[np.float64]: """Run delay-and-sum beamforming in the given frequency range. Parameters @@ -875,7 +882,7 @@ def get_beamformer_map( Returns ------- - map : `np.ndarray` + map : NDArray[np.float64] Beamforming map """ @@ -955,7 +962,7 @@ def get_beamformer_map( maximum_iterations: int | None = None, safety_factor: float = 0.5, remove_csm_diagonal: bool = False, - ) -> np.ndarray: + ) -> NDArray[np.float64]: """Returns a deconvolved beaforming map. Parameters @@ -980,7 +987,7 @@ def get_beamformer_map( Returns ------- - map : `np.ndarray` + map : NDArray[np.float64] Beamformer map. References @@ -1083,7 +1090,7 @@ def get_beamformer_map( center_frequency_hz: float, octave_fraction: int = 3, number_eigenvalues: int | None = None, - ) -> np.ndarray: + ) -> NDArray[np.float64]: """Returns a beaforming map created with orthogonal beamforming. Parameters @@ -1100,7 +1107,7 @@ def get_beamformer_map( Returns ------- - map : np.ndarray + map : NDArray[np.float64] Beamformer map. References @@ -1202,7 +1209,7 @@ def get_beamformer_map( center_frequency_hz: float, octave_fraction: int = 3, gamma: float = 10, - ) -> np.ndarray: + ) -> NDArray[np.float64]: """Returns a beaforming map created with functional beamforming. Parameters @@ -1217,7 +1224,7 @@ def get_beamformer_map( Returns ------- - map : np.ndarray + map : NDArray[np.float64] Beamformer map. References @@ -1303,7 +1310,7 @@ def get_beamformer_map( center_frequency_hz: float, octave_fraction: int = 3, gamma: float = 10, - ) -> np.ndarray: + ) -> NDArray[np.float64]: """Returns a beaforming map created with MVDR beamforming. Parameters @@ -1316,7 +1323,7 @@ def get_beamformer_map( Returns ------- - map : np.ndarray + map : NDArray[np.float64] Beamformer map. References @@ -1586,8 +1593,8 @@ def mix_sources_on_array( # ========== Steering vector formulations ===================================== def classic_steering( - wave_number: np.ndarray, grid: Grid, mic: MicArray -) -> np.ndarray: + wave_number: NDArray[np.float64], grid: Grid, mic: MicArray +) -> NDArray[np.float64]: """Classic formulation for steering vector (formulation 1 in reference paper). @@ -1602,7 +1609,7 @@ def classic_steering( Returns ------- - steering_vector : `np.ndarray` + steering_vector : NDArray[np.float64] Complex steering vector with shape (frequency, nmics, ngrid). References @@ -1636,8 +1643,8 @@ def classic_steering( def inverse_steering( - wave_number: np.ndarray, grid: Grid, mic: MicArray -) -> np.ndarray: + wave_number: NDArray[np.float64], grid: Grid, mic: MicArray +) -> NDArray[np.float64]: """Inverse formulation for steering vector (formulation 2 in reference paper). @@ -1652,7 +1659,7 @@ def inverse_steering( Returns ------- - steering_vector : `np.ndarray` + steering_vector : NDArray[np.float64] Complex steering vector with shape (frequency, nmics, ngrid). References @@ -1687,8 +1694,8 @@ def inverse_steering( def true_power_steering( - wave_number: np.ndarray, grid: Grid, mic: MicArray -) -> np.ndarray: + wave_number: NDArray[np.float64], grid: Grid, mic: MicArray +) -> NDArray[np.complex128]: """Formulation for true power steering vector (formulation 3 in reference paper). @@ -1703,7 +1710,7 @@ def true_power_steering( Returns ------- - steering_vector : `np.ndarray` + steering_vector : NDArray[np.complex128] Complex steering vector with shape (frequency, nmics, ngrid). References @@ -1740,8 +1747,8 @@ def true_power_steering( def true_location_steering( - wave_number: np.ndarray, grid: Grid, mic: MicArray -) -> np.ndarray: + wave_number: NDArray[np.float64], grid: Grid, mic: MicArray +) -> NDArray[np.float64]: """Formulation for true location steering vector (formulation 4 in reference paper). @@ -1756,7 +1763,7 @@ def true_location_steering( Returns ------- - steering_vector : `np.ndarray` + steering_vector : NDArray[np.float64] Complex steering vector with shape (frequency, ngrid, nmics). References diff --git a/dsptoolbox/classes/__init__.py b/dsptoolbox/classes/__init__.py index 60739ac..e9a3746 100644 --- a/dsptoolbox/classes/__init__.py +++ b/dsptoolbox/classes/__init__.py @@ -5,20 +5,24 @@ - `Signal` (core class for all computations, it is constructed from time data and a sampling rate) +- `ImpulseResponse` (class containing a signal that characterizes a system's + response) - `MultiBandSignal` (signal with multiple bands and multirate capabilities) - `Filter` (filter class with filtering methods) - `FilterBank` (class containing a group of `Filters` and their metadata) """ -from .filter_class import Filter +from .filter import Filter from .filterbank import FilterBank -from .signal_class import Signal +from .signal import Signal +from .impulse_response import ImpulseResponse from .multibandsignal import MultiBandSignal __all__ = [ "Filter", "FilterBank", "Signal", + "ImpulseResponse", "MultiBandSignal", ] diff --git a/dsptoolbox/classes/filter_class.py b/dsptoolbox/classes/filter.py similarity index 75% rename from dsptoolbox/classes/filter_class.py rename to dsptoolbox/classes/filter.py index 020d7a5..2a5f07d 100644 --- a/dsptoolbox/classes/filter_class.py +++ b/dsptoolbox/classes/filter.py @@ -10,9 +10,11 @@ from matplotlib.figure import Figure from matplotlib.axes import Axes import scipy.signal as sig +from numpy.typing import NDArray, ArrayLike -from .signal_class import Signal -from ._filter import ( +from .signal import Signal +from .impulse_response import ImpulseResponse +from .filter_helpers import ( _biquad_coefficients, _impulse, _group_delay_filter, @@ -22,7 +24,7 @@ _filter_and_downsample, _filter_and_upsample, ) -from ._plots import _zp_plot +from .plots import _zp_plot from ..plots import general_plot from .._general_helpers import _check_format_in_path, _pad_trim @@ -50,13 +52,13 @@ def __init__( `scipy.signal.firwin` and `_biquad_coefficients`. See down below for the parameters needed for creating the filters. Alternatively, you can pass directly the filter coefficients while setting - `filter_type = 'other'`. + `filter_type = "other"`. Parameters ---------- filter_type : str, optional - String defining the filter type. Options are `'iir'`, `'fir'`, - `'biquad'` or `'other'`. Default: creates a dummy biquad bell + String defining the filter type. Options are `"iir"`, `"fir"`, + `"biquad"` or `"other"`. Default: creates a dummy biquad bell filter with no gain. filter_configuration : dict, optional Dictionary containing configuration for the filter. @@ -72,31 +74,31 @@ def __init__( filter_id (optional). - order (int): Filter order - - freqs (float, array-like): array with len 2 when 'bandpass' - or 'bandstop'. - - type_of_pass (str): 'bandpass', 'lowpass', 'highpass', - 'bandstop'. - - filter_design_method (str): Default: 'butter'. Supported methods - are: 'butter', 'bessel', 'ellip', 'cheby1', 'cheby2'. - - bandpass_ripple (float): maximum bandpass ripple in dB for - 'ellip' and 'cheby1'. - - stopband_ripple (float): maximum stopband ripple in dB for - 'ellip' and 'cheby2'. + - freqs (float, array-like): array with len 2 when "bandpass" + or "bandstop". + - type_of_pass (str): "bandpass", "lowpass", "highpass", + "bandstop". + - filter_design_method (str): Default: "butter". Supported methods + are: "butter", "bessel", "ellip", "cheby1", "cheby2". + - passband_ripple (float): maximum passband ripple in dB for + "ellip" and "cheby1". + - stopband_attenuation (float): minimum stopband attenuation in dB + for "ellip" and "cheby2". For `fir`: Keys: order, freqs, type_of_pass, filter_design_method (optional), - width (optional, necessary for 'kaiser'), filter_id (optional). + width (optional, necessary for "kaiser"), filter_id (optional). - order (int): Filter order, i.e., number of taps - 1. - - freqs (float, array-like): array with len 2 when 'bandpass' - or 'bandstop'. - - type_of_pass (str): 'bandpass', 'lowpass', 'highpass', - 'bandstop'. + - freqs (float, array-like): array with len 2 when "bandpass" + or "bandstop". + - type_of_pass (str): "bandpass", "lowpass", "highpass", + "bandstop". - filter_design_method (str): Window to be used. Default: - 'hamming'. Supported types are: 'boxcar', 'triang', - 'blackman', 'hamming', 'hann', 'bartlett', 'flattop', - 'parzen', 'bohman', 'blackmanharris', 'nuttall', 'barthann', - 'cosine', 'exponential', 'tukey', 'taylor'. + "hamming". Supported types are: "boxcar", "triang", + "blackman", "hamming", "hann", "bartlett", "flattop", + "parzen", "bohman", "blackmanharris", "nuttall", "barthann", + "cosine", "exponential", "tukey", "taylor". - width (float): estimated width of transition region in Hz for kaiser window. Default: `None`. @@ -105,7 +107,8 @@ def __init__( - eq_type (int or str): 0 = Peaking, 1 = Lowpass, 2 = Highpass, 3 = Bandpass_skirt, 4 = Bandpass_peak, 5 = Notch, 6 = Allpass, - 7 = Lowshelf, 8 = Highshelf. + 7 = Lowshelf, 8 = Highshelf, 9 = Lowpass_first_order, + 10 = Highpass_first_order. - freqs: float or array-like with length 2 (depending on eq_type). - gain (float): in dB. - q (float): Q-factor. @@ -136,6 +139,194 @@ def __init__( } self.set_filter_parameters(filter_type.lower(), filter_configuration) + @staticmethod + def iir_design( + order: int, + frequency_hz: float | ArrayLike, + type_of_pass: str, + filter_design_method: str, + passband_ripple_db: float | None = None, + stopband_attenuation_db: float | None = None, + sampling_rate_hz: int | None = None, + ): + """Return an IIR filter using `scipy.signal.iirfilter`. IIR filters are + always implemented as SOS by default. + + Parameters + ---------- + order : int + Filter order. + frequency_hz : float | ArrayLike + Frequency or frequencies of the filter in Hz. + type_of_pass : str, {"lowpass", "highpass", "bandpass", "bandstop"} + Type of filter. + filter_design_method : str, {"butter", "bessel", "ellip", "cheby1",\ + "cheby2"} + Design method for the IIR filter. + passband_ripple_db : float, None, optional + Passband ripple in dB for "cheby1" and "ellip". Default: None. + stopband_attenuation_db : float, None, optional + Passband ripple in dB for "cheby2" and "ellip". Default: None. + sampling_rate_hz : int + Sampling rate in Hz. + + Returns + ------- + Filter + + """ + return Filter( + "iir", + { + "order": order, + "freqs": frequency_hz, + "type_of_pass": type_of_pass, + "filter_design_method": filter_design_method, + "passband_ripple": passband_ripple_db, + "stopband_attenuation": stopband_attenuation_db, + }, + sampling_rate_hz, + ) + + @staticmethod + def biquad( + eq_type: str, + frequency_hz: float | ArrayLike, + gain_db: float, + q: float, + sampling_rate_hz: int, + ): + """Return a biquad filter according to [1]. + + Parameters + ---------- + eq_type : str, {"peaking", "lowpass", "highpass", "bandpass_skirt",\ + "bandpass_peak", "notch", "allpass", "lowshelf", "highshelf", \ + "lowpass_first_order", "highpass_first_order", "inverter"} + EQ type. + frequency_hz : float + Frequency of the biquad in Hz. + gain_db : float + Gain of biquad in dB. + q : float + Quality factor. + sampling_rate_hz : int + Sampling rate in Hz. + + Returns + ------- + Filter + + Reference + --------- + - [1]: https://webaudio.github.io/Audio-EQ-Cookbook/audio-eq- + cookbook.html. + + """ + return Filter( + "biquad", + { + "eq_type": eq_type, + "freqs": frequency_hz, + "gain": gain_db, + "q": q, + }, + sampling_rate_hz, + ) + + @staticmethod + def fir_design( + order: int, + frequency_hz: float | ArrayLike, + type_of_pass: str, + filter_design_method: str, + width_hz: float | None = None, + sampling_rate_hz: int | None = None, + ): + """Design an FIR filter using `scipy.signal.firwin`. + + Parameters + ---------- + order : int + Filter order. It corresponds to the number of taps - 1. + frequency_hz : float | ArrayLike + Frequency or frequencies of the filter in Hz. + type_of_pass : str, {"lowpass", "highpass", "bandpass", "bandstop"} + Type of filter. + filter_design_method : str, {"boxcar", "triang",\ + "blackman", "hamming", "hann", "bartlett", "flattop",\ + "parzen", "bohman", "blackmanharris", "nuttall", "barthann",\ + "cosine", "exponential", "tukey", "taylor"} + Design method for the FIR filter. + width_hz : float, None, optional + estimated width of transition region in Hz for kaiser window. + Default: `None`. + sampling_rate_hz : int + Sampling rate in Hz. + + Returns + ------- + Filter + + """ + return Filter( + "fir", + { + "order": order, + "freqs": frequency_hz, + "type_of_pass": type_of_pass, + "filter_design_method": filter_design_method, + "width": width_hz, + }, + sampling_rate_hz, + ) + + @staticmethod + def from_ba( + b: ArrayLike, + a: ArrayLike, + sampling_rate_hz: int, + ): + """Create a filter from some b (numerator) and a (denominator) + coefficients. + + Parameters + ---------- + b : ArrayLike + Numerator coefficients. + a : ArrayLike + Denominator coefficients. + sampling_rate_hz : int + Sampling rate in Hz. + + Returns + ------- + Filter + + """ + return Filter("other", {"ba": [b, a]}, sampling_rate_hz) + + @staticmethod + def from_sos( + sos: NDArray[np.float64], + sampling_rate_hz: int, + ): + """Create a filter from second-order sections. + + Parameters + ---------- + sos : NDArray[np.float64] + Second-order sections. + sampling_rate_hz : int + Sampling rate in Hz. + + Returns + ------- + Filter + + """ + return Filter("other", {"sos": sos}, sampling_rate_hz) + def initialize_zi(self, number_of_channels: int = 1): """Initializes zi for steady-state filtering. The number of parallel zi's can be defined externally. @@ -387,10 +578,10 @@ def set_filter_parameters( if filter_type == "iir": if "filter_design_method" not in filter_configuration: filter_configuration["filter_design_method"] = "butter" - if "bandpass_ripple" not in filter_configuration: - filter_configuration["bandpass_ripple"] = None - if "stopband_ripple" not in filter_configuration: - filter_configuration["stopband_ripple"] = None + if "passband_ripple" not in filter_configuration: + filter_configuration["passband_ripple"] = None + if "stopband_attenuation" not in filter_configuration: + filter_configuration["stopband_attenuation"] = None self.sos = sig.iirfilter( N=filter_configuration["order"], Wn=filter_configuration["freqs"], @@ -398,8 +589,8 @@ def set_filter_parameters( analog=False, fs=self.sampling_rate_hz, ftype=filter_configuration["filter_design_method"], - rp=filter_configuration["bandpass_ripple"], - rs=filter_configuration["stopband_ripple"], + rp=filter_configuration["passband_ripple"], + rs=filter_configuration["stopband_attenuation"], output="sos", ) self.filter_type = filter_type @@ -515,7 +706,7 @@ def get_filter_metadata(self): def _get_metadata_string(self): """Helper for creating a string containing all filter info.""" - txt = f"""Filter – ID: {self.info['filter_id']}\n""" + txt = f"""Filter – ID: {self.info["filter_id"]}\n""" temp = "" for n in range(len(txt)): temp += "-" @@ -523,13 +714,13 @@ def _get_metadata_string(self): for k in self.info.keys(): if k == "ba": continue - txt += f"""{str(k).replace('_', ' '). + txt += f"""{str(k).replace("_", " "). capitalize()}: {self.info[k]}\n""" return txt def get_ir( self, length_samples: int = 512, zero_phase: bool = False - ) -> Signal: + ) -> ImpulseResponse: """Gets an impulse response of the filter with given length. Parameters @@ -539,7 +730,7 @@ def get_ir( Returns ------- - ir_filt : `Signal` + ir_filt : `ImpulseResponse` Impulse response of the filter. """ @@ -553,27 +744,72 @@ def get_ir( ) length_samples = len(b) b = _pad_trim(b, length_samples) - return Signal( - None, b, self.sampling_rate_hz, "ir", constrain_amplitude=False + return ImpulseResponse( + None, b, self.sampling_rate_hz, constrain_amplitude=False ) # IIR or zero phase IR ir_filt = _impulse(length_samples) - ir_filt = Signal( + ir_filt = ImpulseResponse( None, ir_filt, self.sampling_rate_hz, - "ir", constrain_amplitude=False, ) return self.filter_signal(ir_filt, zero_phase=zero_phase) + def get_transfer_function( + self, frequency_vector_hz: NDArray[np.float64] + ) -> NDArray[np.complex128]: + """Obtain the complex transfer function of the filter analytically + evaluated for a given frequency vector. + + Parameters + ---------- + frequency_vector_hz : NDArray[np.float64] + Frequency vector for which to compute the transfer function + + Returns + ------- + NDArray[np.complex128] + Complex transfer function + + Notes + ----- + - This method uses scipy's freqz to compute the transfer function. In + the case of FIR filters, it might be significantly faster and more + precise to use a direct FFT approach. + + """ + assert ( + frequency_vector_hz.ndim == 1 + ), "Frequency vector can only have one dimension" + assert ( + frequency_vector_hz.max() <= self.sampling_rate_hz / 2 + ), "Queried frequency vector has values larger than nyquist" + if self.filter_type in ("iir", "biquad"): + if hasattr(self, "sos"): + return sig.sosfreqz( + self.sos, frequency_vector_hz, fs=self.sampling_rate_hz + )[1] + return sig.freqz( + self.ba[0], + self.ba[1], + frequency_vector_hz, + fs=self.sampling_rate_hz, + )[1] + + # FIR + return sig.freqz( + self.ba[0], [1], frequency_vector_hz, self.sampling_rate_hz + )[1] + def get_coefficients( self, mode: str = "sos" ) -> ( - list[np.ndarray] - | np.ndarray - | tuple[np.ndarray, np.ndarray, np.ndarray] + list[NDArray[np.float64]] + | NDArray[np.float64] + | tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]] | None ): """Returns the filter coefficients. @@ -581,16 +817,16 @@ def get_coefficients( Parameters ---------- mode : str, optional - Type of filter coefficients to be returned. Choose from `'sos'`, - `'ba'` or `'zpk'`. Default: `'sos'`. + Type of filter coefficients to be returned. Choose from `"sos"`, + `"ba"` or `"zpk"`. Default: `"sos"`. Returns ------- coefficients : array-like Array with filter coefficients with shape depending on mode: - - `'ba'`: list(b, a) with b and a of type `np.ndarray`. - - `'sos'`: `np.ndarray` with shape (n_sections, 6). - - `'zpk'`: tuple(z, p, k) with z, p, k of type `np.ndarray` + - `"ba"`: list(b, a) with b and a of type NDArray[np.float64]. + - `"sos"`: NDArray[np.float64] with shape (n_sections, 6). + - `"zpk"`: tuple(z, p, k) with z, p, k of type NDArray[np.float64] - Return `None` if user decides that ba->sos is too costly. The threshold is for filters with order > 500. @@ -667,8 +903,8 @@ def plot_magnitude( Range for which to plot the magnitude response. Default: [20, 20000]. normalize : str, optional - Mode for normalization, supported are `'1k'` for normalization - with value at frequency 1 kHz or `'max'` for normalization with + Mode for normalization, supported are `"1k"` for normalization + with value at frequency 1 kHz or `"max"` for normalization with maximal value. Use `None` for no normalization. Default: `None`. show_info_box : bool, optional Shows an information box on the plot. Default: `True`. @@ -887,7 +1123,7 @@ def save_filter(self, path: str = "filter"): path : str, optional Path for the filter to be saved. Use only folder1/folder2/name (it can be passed with .pkl at the end or without it). - Default: `'filter'` (local folder, object named filter). + Default: `"filter"` (local folder, object named filter). """ path = _check_format_in_path(path, "pkl") diff --git a/dsptoolbox/classes/_filter.py b/dsptoolbox/classes/filter_helpers.py similarity index 91% rename from dsptoolbox/classes/_filter.py rename to dsptoolbox/classes/filter_helpers.py index d36de64..f6cfe49 100644 --- a/dsptoolbox/classes/_filter.py +++ b/dsptoolbox/classes/filter_helpers.py @@ -6,7 +6,9 @@ from warnings import warn from enum import Enum import scipy.signal as sig -from .signal_class import Signal +from numpy.typing import NDArray + +from .signal import Signal from .multibandsignal import MultiBandSignal from .._general_helpers import _polyphase_decomposition @@ -25,12 +27,16 @@ def _get_biquad_type(number: int | None = None, name: str | None = None): "allpass", "lowshelf", "highshelf", + "lowpass_first_order", + "highpass_first_order", + "inverter", ) assert name in valid_names, ( f"{name} is not a valid name. Please " + """select from the ('peaking', 'lowpass', 'highpass', 'bandpass_skirt', 'bandpass_peak', 'notch', 'allpass', 'lowshelf', - 'highshelf')""" + 'highshelf', 'lowpass_first_order', 'highpass_first_order', + 'inverter')""" ) class biquad(Enum): @@ -43,6 +49,9 @@ class biquad(Enum): allpass = 6 lowshelf = 7 highshelf = 8 + lowpass_first_order = 9 + highpass_first_order = 10 + inverter = 11 if number is None: assert ( @@ -59,8 +68,8 @@ class biquad(Enum): def _biquad_coefficients( eq_type: int | str = 0, fs_hz: int = 48000, - frequency_hz: float | list | tuple | np.ndarray = 1000, - gain_db: float = 1, + frequency_hz: float | list | tuple | NDArray[np.float64] = 1000, + gain_db: float = 0, q: float = 1, ): """Creates the filter coefficients for biquad filters. @@ -84,7 +93,7 @@ def _biquad_coefficients( + "not supported. A mean of passed frequencies was used for the " + "design but this might not give the intended result!" ) - A = np.sqrt(10 ** (gain_db / 20.0)) + A = 10 ** (gain_db / 40) if eq_type in (0, 7, 8) else 10 ** (gain_db / 20) Omega = 2.0 * np.pi * (frequency_hz / fs_hz) sn = np.sin(Omega) cs = np.cos(Omega) @@ -99,44 +108,44 @@ def _biquad_coefficients( a[1] = -2 * cs a[2] = 1 - alpha / A elif eq_type == 1: # Lowpass - b[0] = (1 - cs) / 2 - b[1] = 1 - cs + b[0] = (1 - cs) / 2 * A + b[1] = (1 - cs) * A b[2] = b[0] a[0] = 1 + alpha a[1] = -2 * cs a[2] = 1 - alpha elif eq_type == 2: # Highpass - b[0] = (1 + cs) / 2.0 - b[1] = -1 * (1 + cs) + b[0] = (1 + cs) / 2.0 * A + b[1] = -1 * (1 + cs) * A b[2] = b[0] a[0] = 1 + alpha a[1] = -2 * cs a[2] = 1 - alpha elif eq_type == 3: # Bandpass skirt - b[0] = sn / 2 + b[0] = sn / 2 * A b[1] = 0 b[2] = -b[0] a[0] = 1 + alpha a[1] = -2 * cs a[2] = 1 - alpha elif eq_type == 4: # Bandpass peak - b[0] = alpha + b[0] = alpha * A b[1] = 0 b[2] = -b[0] a[0] = 1 + alpha a[1] = -2 * cs a[2] = 1 - alpha elif eq_type == 5: # Notch - b[0] = 1 - b[1] = -2 * cs + b[0] = 1 * A + b[1] = -2 * cs * A b[2] = b[0] a[0] = 1 + alpha a[1] = -2 * cs a[2] = 1 - alpha elif eq_type == 6: # Allpass - b[0] = 1 - alpha - b[1] = -2 * cs - b[2] = 1 + alpha + b[0] = (1 - alpha) * A + b[1] = -2 * cs * A + b[2] = (1 + alpha) * A a[0] = 1 + alpha a[1] = -2 * cs a[2] = 1 - alpha @@ -154,6 +163,29 @@ def _biquad_coefficients( a[0] = (A + 1) - (A - 1) * cs + 2 * np.sqrt(A) * alpha a[1] = 2 * ((A - 1) - (A + 1) * cs) a[2] = (A + 1) - (A - 1) * cs - 2 * np.sqrt(A) * alpha + elif eq_type == 9: # Lowpass first order + K = 1.0 / np.tan(Omega / 2.0) + b[0] = A + b[1] = A + b[2] = 0.0 + a[0] = 1.0 + K + a[1] = 1.0 - K + a[2] = 0.0 + elif eq_type == 10: # Highpass first order + K = 1.0 / np.tan(Omega / 2.0) + b[0] = K * A + b[1] = -K * A + b[2] = 0.0 + a[0] = 1.0 + K + a[1] = 1.0 - K + a[2] = 0.0 + elif eq_type == 11: # Inverter + b[0] = A + b[1] = 0.0 + b[2] = 0.0 + a[0] = 1.0 + a[1] = 0.0 + a[2] = 0.0 else: raise Exception("eq_type not supported") return b, a @@ -171,7 +203,7 @@ def _impulse(length_samples: int = 512, delay_samples: int = 0): Returns ------- - imp : `np.ndarray` + imp : NDArray[np.float64] Impulse. """ @@ -196,9 +228,9 @@ def _group_delay_filter(ba, length_samples: int = 512, fs_hz: int = 48000): Returns ------- - f : `np.ndarray` + f : NDArray[np.float64] Frequency vector. - gd : `np.ndarray` + gd : NDArray[np.float64] Group delay in seconds. """ @@ -321,7 +353,7 @@ def _filter_on_signal_ba( Signal to be filtered. ba : list List with ba coefficients of filter. Form ba=[b, a] where b and a - are of type `np.ndarray`. + are of type NDArray[np.float64]. channels : array-like, optional Channel or array of channels to be filtered. When `None`, all channels are filtered. Default: `None`. @@ -477,10 +509,10 @@ def _filterbank_on_signal( def _lfilter_fir( - b: np.ndarray, - a: np.ndarray, - x: np.ndarray, - zi: np.ndarray | None = None, + b: NDArray[np.float64], + a: NDArray[np.float64], + x: NDArray[np.float64], + zi: NDArray[np.float64] | None = None, axis: int = 0, ): """Variant to the `scipy.signal.lfilter` that uses `scipy.signal.convolve` @@ -529,11 +561,11 @@ def _lfilter_fir( def _filter_and_downsample( - time_data: np.ndarray, + time_data: NDArray[np.float64], down_factor: int, ba_coefficients: list, polyphase: bool, -) -> np.ndarray: +) -> NDArray[np.float64]: """Filters and downsamples time data. If polyphase is `True`, it is assumed that the filter is FIR and only b-coefficients are used. In that case, an efficient downsampling is done, otherwise standard filtering @@ -541,7 +573,7 @@ def _filter_and_downsample( Parameters ---------- - time_data : `np.ndarray` + time_data : NDArray[np.float64] Time data to be filtered and resampled. Shape should be (time samples, channels). down_factor : int @@ -554,7 +586,7 @@ def _filter_and_downsample( Returns ------- - new_time_data : `np.ndarray` + new_time_data : NDArray[np.float64] New time data with downsampling. """ @@ -598,7 +630,7 @@ def _filter_and_downsample( def _filter_and_upsample( - time_data: np.ndarray, + time_data: NDArray[np.float64], up_factor: int, ba_coefficients: list, polyphase: bool, @@ -614,7 +646,7 @@ def _filter_and_upsample( Parameters ---------- - time_data : `np.ndarray` + time_data : NDArray[np.float64] Time data to be filtered and resampled. Shape should be (time samples, channels). up_factor : int @@ -627,7 +659,7 @@ def _filter_and_upsample( Returns ------- - new_time_data : `np.ndarray` + new_time_data : NDArray[np.float64] New time data with downsampling. """ diff --git a/dsptoolbox/classes/filterbank.py b/dsptoolbox/classes/filterbank.py index 60561f0..1ce4592 100644 --- a/dsptoolbox/classes/filterbank.py +++ b/dsptoolbox/classes/filterbank.py @@ -4,11 +4,12 @@ from warnings import warn from matplotlib.figure import Figure from matplotlib.axes import Axes +from numpy.typing import NDArray -from .signal_class import Signal +from .signal import Signal from .multibandsignal import MultiBandSignal -from .filter_class import Filter -from ._filter import _filterbank_on_signal +from .filter import Filter +from .filter_helpers import _filterbank_on_signal from ..generators import dirac from ..plots import general_plot from .._general_helpers import _get_normalized_spectrum, _check_format_in_path @@ -86,7 +87,7 @@ def initialize_zi(self, number_of_channels: int = 1): f.initialize_zi(number_of_channels) @property - def sampling_rate_hz(self) -> int | np.ndarray: + def sampling_rate_hz(self) -> int | NDArray[np.int_]: return self.__sampling_rate_hz @sampling_rate_hz.setter @@ -444,6 +445,52 @@ def get_ir( ) return ir + def get_transfer_function( + self, frequency_vector_hz: NDArray[np.float64], mode: str = "parallel" + ) -> NDArray[np.complex128]: + """Compute the complex transfer function of the filter bank for + specified frequencies. The output is based on the filter bank filtering + mode. + + Parameters + ---------- + frequency_vector_hz : NDArray[np.float64] + Frequency vector to evaluate frequencies at. + mode : str, optional + Way of applying the filter bank. If `"parallel"`, the resulting + transfer function will have shape (frequency, filter). In the cases + of `"sequential"` and `"summed"`, it will have shape (frequency). + + Returns + ------- + NDArray[np.complex128] + Complex transfer function of the filter bank. + + """ + mode = mode.lower() + assert mode in ( + "parallel", + "sequential", + "summed", + ), f"{mode} is not a valid mode. Use parallel, sequential or summed" + match mode: + case "parallel": + h = np.zeros( + (len(frequency_vector_hz), self.number_of_filters), + dtype=np.complex128, + ) + for ind, f in enumerate(self.filters): + h[:, ind] = f.get_transfer_function(frequency_vector_hz) + case "sequential": + h = np.ones(len(frequency_vector_hz), dtype=np.complex128) + for ind, f in enumerate(self.filters): + h *= f.get_transfer_function(frequency_vector_hz) + case "summed": + h = np.ones(len(frequency_vector_hz), dtype=np.complex128) + for ind, f in enumerate(self.filters): + h += f.get_transfer_function(frequency_vector_hz) + return h + # ======== Prints and plots =============================================== def show_info(self): """Show information about the filter bank.""" diff --git a/dsptoolbox/classes/impulse_response.py b/dsptoolbox/classes/impulse_response.py new file mode 100644 index 0000000..099cff8 --- /dev/null +++ b/dsptoolbox/classes/impulse_response.py @@ -0,0 +1,377 @@ +import numpy as np +from numpy.typing import NDArray +from matplotlib.figure import Figure +from matplotlib.axes import Axes + +from .signal import Signal +from ..plots import general_subplots_line + + +class ImpulseResponse(Signal): + def __init__( + self, + path: str | None = None, + time_data: NDArray[np.float64] | None = None, + sampling_rate_hz: int | None = None, + constrain_amplitude: bool = True, + ): + """Instantiate impulse response. + + Parameters + ---------- + path : str, optional + A path to audio files. Reading is done with the soundfile library. + Wave and Flac audio files are accepted. Default: `None`. + time_data : array-like, NDArray[np.float64], optional + Time data of the signal. It is saved as a matrix with the form + (time samples, channel number). Default: `None`. + sampling_rate_hz : int, optional + Sampling rate of the signal in Hz. Default: `None`. + constrain_amplitude : bool, optional + When `True`, audio is normalized to 0 dBFS peak level in case that + there are amplitude values greater than 1. Otherwise, there is no + normalization and the audio data is not constrained to [-1, 1]. + A warning is always shown when audio gets normalized and the used + normalization factor is saved as `amplitude_scale_factor`. + Default: `True`. + + Returns + ------- + ImpulseResponse + + """ + super().__init__( + path, + time_data, + sampling_rate_hz, + constrain_amplitude=constrain_amplitude, + ) + self.set_spectrum_parameters(method="standard") + + @staticmethod + def from_signal(signal: Signal): + """Create an impulse response from a signal. + + Parameters + ---------- + signal : `Signal` + + Returns + ------- + ImpulseResponse + + """ + ir = ImpulseResponse( + None, + signal.time_data, + signal.sampling_rate_hz, + signal.constrain_amplitude, + ) + ir.amplitude_scale_factor = signal.amplitude_scale_factor + ir.time_data_imaginary = signal.time_data_imaginary + return ir + + @staticmethod + def from_file(path: str): + """Create an impulse response from a path to a wav or flac audio file. + + Parameters + ---------- + path : str + Path to file. + + Returns + ------- + ImpulseResponse + + """ + s = Signal.from_file(path) + return ImpulseResponse.from_signal(s) + + @staticmethod + def from_time_data( + time_data: NDArray[np.float64], + sampling_rate_hz: int, + constrain_amplitude: bool = True, + ): + """Create an impulse response from an array of PCM samples. + + Parameters + ---------- + time_data : array-like, NDArray[np.float64], optional + Time data of the signal. It is saved as a matrix with the form + (time samples, channel number). Default: `None`. + sampling_rate_hz : int, optional + Sampling rate of the signal in Hz. Default: `None`. + constrain_amplitude : bool, optional + When `True`, audio is normalized to 0 dBFS peak level in case that + there are amplitude values greater than 1. Otherwise, there is no + normalization and the audio data is not constrained to [-1, 1]. + A warning is always shown when audio gets normalized and the used + normalization factor is saved as `amplitude_scale_factor`. + Default: `True`. + + Returns + ------- + ImpulseResponse + + """ + s = Signal.from_time_data( + time_data, sampling_rate_hz, constrain_amplitude + ) + return ImpulseResponse.from_signal(s) + + def add_channel( + self, + path: str | None = None, + new_time_data: NDArray[np.float64] | None = None, + sampling_rate_hz: int | None = None, + padding_trimming: bool = True, + ): + """Adds new channels to this signal object. + + Parameters + ---------- + path : str, optional + Path to the file containing new channel information. + new_time_data : NDArray[np.float64], optional + np.array with new channel data. + sampling_rate_hz : int, optional + Sampling rate for the new data + padding_trimming : bool, optional + Activates padding or trimming at the end of signal in case the + new data does not match previous data. Default: `True`. + + """ + super().add_channel( + path, new_time_data, sampling_rate_hz, padding_trimming + ) + if hasattr(self, "window"): + current_shape = self.time_data.shape + self.window = np.concatenate( + [ + self.window, + np.ones( + ( + current_shape[0], + current_shape[1] - self.window.shape[1], + ) + ), + ], + axis=1, + ) + + def remove_channel(self, channel_number: int = -1): + """Removes a channel. + + Parameters + ---------- + channel_number : int, optional + Channel number to be removed. Default: -1 (last). + + """ + super().remove_channel(channel_number) + if hasattr(self, "window"): + self.window = np.delete(self.window, channel_number, axis=1) + + def swap_channels(self, new_order): + """Rearranges the channels in the new given order. + + Parameters + ---------- + new_order : array-like + New rearrangement of channels. + + """ + super().swap_channels(new_order) + if hasattr(self, "window"): + self.window = self.window[:, np.asarray(new_order)] + + def get_channels(self, channels): + """Returns a signal object with the selected channels. Beware that + first channel index is 0! + + Parameters + ---------- + channels : array-like or int + Channels to be returned as a new Signal object. + + Returns + ------- + new_sig : `Signal` + New signal object with selected channels. + + """ + super().swap_channels(channels) + if hasattr(self, "window"): + self.window = self.window[:, np.asarray(channels)] + + def set_window(self, window: NDArray[np.float64]): + """Sets the window used for the IR. + + Parameters + ---------- + window : NDArray[np.float64] + Window used for the IR. + + """ + assert ( + window.shape == self.time_data.shape + ), f"{window.shape} does not match shape {self.time_data.shape}" + self.window = window + + def set_coherence(self, coherence: NDArray[np.float64]): + """Sets the coherence measurements of the transfer function. + It only works for `signal_type = ('ir', 'h1', 'h2', 'h3', 'rir')`. + + Parameters + ---------- + coherence : NDArray[np.float64] + Coherence matrix. + + """ + assert coherence.shape[0] == ( + self.time_data.shape[0] // 2 + 1 + ), "Length of signals and given coherence do not match" + assert coherence.shape[1] == self.number_of_channels, ( + "Number of channels between given coherence and signal " + + "does not match" + ) + self.coherence = coherence + + def get_coherence(self) -> tuple[NDArray[np.float64], NDArray[np.float64]]: + """Returns the coherence matrix. + + Returns + ------- + f : NDArray[np.float64] + Frequency vector. + coherence : NDArray[np.float64] + Coherence matrix. + + """ + assert hasattr( + self, "coherence" + ), "There is no coherence data saved in the Signal object" + f, _ = self.get_spectrum() + return f, self.coherence + + def plot_spl( + self, + normalize_at_peak: bool = False, + range_db: float | None = 100.0, + window_length_s: float = 0.0, + ) -> tuple[Figure, list[Axes]]: + """Plots the momentary sound pressure level (dB or dBFS) of each + channel. If the signal is calibrated and not normalized at peak, the + values correspond to dB, otherwise they are dBFS. + + Parameters + ---------- + normalize_at_peak : bool, optional + When `True`, each channel gets normalize by its peak value. + Default: `False`. + range_db : float, optional + This is the range in dB used for plotting. Each plot will be in the + range [peak + 1 - range_db, peak + 1]. Pass `None` to avoid setting + any range. Default: 100. + window_length_s : float, optional + When different than 0, a moving average along the time axis is done + with the given length. Default: 0. + + Returns + ------- + fig : `matplotlib.figure.Figure` + Figure. + ax : list of `matplotlib.axes.Axes` + Axes. + + Notes + ----- + - All values are clipped to be at least -800 dBFS. + - If it is an analytic signal and normalization is applied, the peak + value of the real part is used as the normalization factor. + - If the time window is not 0, effects at the edges of the signal might + be present due to zero-padding. + + """ + fig, ax = super().plot_spl( + normalize_at_peak, range_db, window_length_s + ) + + peak_values = 10 * np.log10(np.max(self.time_data**2.0, axis=0)) + + add_to_peak = 1 # Add 1 dB for better plotting + max_values = ( + peak_values + add_to_peak + if not normalize_at_peak + else np.ones(self.number_of_channels) + ) + + for n in range(self.number_of_channels): + if hasattr(self, "window"): + ax[n].plot( + self.time_vector_s, + 20 + * np.log10( + np.clip( + np.abs(self.window[:, n] / 1.1), + a_min=1e-40, + a_max=None, + ) + ) + + max_values[n], + alpha=0.75, + ) + return fig, ax + + def plot_time(self) -> tuple[Figure, list[Axes]]: + """Plots time signals. + + Returns + ------- + fig : `matplotlib.figure.Figure` + Figure. + ax : list of `matplotlib.axes.Axes` + Axes. + + """ + fig, ax = super().plot_time() + if hasattr(self, "window"): + mx = np.max(np.abs(self.time_data), axis=0) * 1.1 + + for n in range(self.number_of_channels): + ax[n].plot( + self.time_vector_s, + self.window[:, n] * mx[n] / 1.1, + alpha=0.75, + ) + return fig, ax + + def plot_coherence(self) -> tuple[Figure, list[Axes]]: + """Plots coherence measurements if there are any. + + Returns + ------- + fig : `matplotlib.figure.Figure` + Figure. + ax : list of `matplotlib.axes.Axes` + Axes. + + """ + f, coh = self.get_coherence() + fig, ax = general_subplots_line( + x=f, + matrix=coh, + column=True, + sharey=True, + log=True, + ylabels=[ + rf"$\gamma^2$ Coherence {n}" + for n in range(self.number_of_channels) + ], + xlabels="Frequency / Hz", + range_y=[-0.1, 1.1], + returns=True, + ) + return fig, ax diff --git a/dsptoolbox/classes/_lattice_ladder_filter.py b/dsptoolbox/classes/lattice_ladder_filter.py similarity index 88% rename from dsptoolbox/classes/_lattice_ladder_filter.py rename to dsptoolbox/classes/lattice_ladder_filter.py index 8fc552c..b9f8453 100644 --- a/dsptoolbox/classes/_lattice_ladder_filter.py +++ b/dsptoolbox/classes/lattice_ladder_filter.py @@ -2,9 +2,10 @@ This file contains alternative filter implementations. """ -from .signal_class import Signal +from .signal import Signal from warnings import warn import numpy as np +from numpy.typing import NDArray class LatticeLadderFilter: @@ -24,8 +25,8 @@ class LatticeLadderFilter: def __init__( self, - k_coefficients: np.ndarray, - c_coefficients: np.ndarray | None = None, + k_coefficients: NDArray[np.float64], + c_coefficients: NDArray[np.float64] | None = None, sampling_rate_hz: int | None = None, ): """Constructs a lattice or lattice/ladder filter. If `k_coefficients` @@ -38,10 +39,10 @@ def __init__( Parameters ---------- - k_coefficients : `np.ndarray` + k_coefficients : NDArray[np.float64] Reflection coefficients. It can be a 1d array or a 2d-array for second-order sections with shape (section, coefficients). - c_coefficients : `np.ndarray`, optional + c_coefficients : NDArray[np.float64], optional Feedforward coefficients. It can be a 1d-array or a 2d-array for second-order sections. Default: `None`. sampling_rate_hz : int @@ -95,7 +96,7 @@ def __init__( self.iir_filter = False self.k = k_coefficients self.c = c_coefficients - self.state: np.ndarray | None = None + self.state: NDArray[np.float64] | None = None self.sampling_rate_hz = sampling_rate_hz def initialize_zi(self, n_channels: int): @@ -118,7 +119,7 @@ def filter_signal( ---------- signal : `Signal` Signal to filter. - channels : `np.ndarray`, int, optional + channels : NDArray[np.float64], int, optional Channels to filter. If `None`, all channels of the signal are filtered. activate_zi : bool, optional @@ -177,11 +178,11 @@ def filter_signal( def _lattice_ladder_filtering_sos( - k: np.ndarray, - c: np.ndarray, - td: np.ndarray, - state: np.ndarray | None = None, -) -> tuple[np.ndarray, np.ndarray | None]: + k: NDArray[np.float64], + c: NDArray[np.float64], + td: NDArray[np.float64], + state: NDArray[np.float64] | None = None, +) -> tuple[NDArray[np.float64], NDArray[np.float64] | None]: """Filtering using a lattice/ladder structure of second-order sections. See `_lattice_ladder_filtering` for the parameter explanation. @@ -223,8 +224,10 @@ def _lattice_ladder_filtering_sos( def _lattice_filtering_fir( - k: np.ndarray, td: np.ndarray, state: np.ndarray | None = None -) -> tuple[np.ndarray, np.ndarray | None]: + k: NDArray[np.float64], + td: NDArray[np.float64], + state: NDArray[np.float64] | None = None, +) -> tuple[NDArray[np.float64], NDArray[np.float64] | None]: """Filtering using a lattice structure.""" passed_state = True if state is None: @@ -251,23 +254,23 @@ def _lattice_filtering_fir( def _lattice_ladder_filtering_iir( - k: np.ndarray, - c: np.ndarray, - td: np.ndarray, - state: np.ndarray | None = None, -) -> tuple[np.ndarray, np.ndarray | None]: + k: NDArray[np.float64], + c: NDArray[np.float64], + td: NDArray[np.float64], + state: NDArray[np.float64] | None = None, +) -> tuple[NDArray[np.float64], NDArray[np.float64] | None]: """Filtering using a lattice ladder structure (general IIR filter). The implementation follows [1]. Parameters ---------- - k : `np.ndarray` + k : NDArray[np.float64] Reflection coefficients. - c : `np.ndarray` + c : NDArray[np.float64] Feedforward coefficients. - td : `np.ndarray` + td : NDArray[np.float64] Time data assumed to have shape (time samples, channel). - state : `np.ndarray`, optional + state : NDArray[np.float64], optional Initial state for each channel as a 2D-matrix with shape (filter order, channel). State of the filter in the beginning. The last state corresponds to the last reflection coefficient (furthest to the @@ -275,9 +278,9 @@ def _lattice_ladder_filtering_iir( Returns ------- - new_td : `np.ndarray` + new_td : NDArray[np.float64] Filtered time data. - state : `np.ndarray` + state : NDArray[np.float64] Filter's state after filtering. It can be `None` if `None` was originally passed for `state`. @@ -312,24 +315,24 @@ def _lattice_ladder_filtering_iir( def _get_lattice_ladder_coefficients_iir( - b: np.ndarray, a: np.ndarray -) -> tuple[np.ndarray, np.ndarray]: + b: NDArray[np.float64], a: NDArray[np.float64] +) -> tuple[NDArray[np.float64], NDArray[np.float64]]: """Compute reflection coefficients `k` and ladder coefficients `c` from feedforward `b` and feedbackward `a` coefficients according to the equations presented in [1]. Parameters ---------- - b : `np.ndarray` + b : NDArray[np.float64] Feedforward coefficients of a filter. - a : `np.ndarray` + a : NDArray[np.float64] Feedbackward coefficients. Returns ------- - k : `np.ndarray` + k : NDArray[np.float64] Reflection coefficients with the length of the order . - c : `np.ndarray` + c : NDArray[np.float64] Ladder coefficients. References @@ -361,20 +364,20 @@ def _get_lattice_ladder_coefficients_iir( def _get_lattice_ladder_coefficients_iir_sos( - sos: np.ndarray, -) -> tuple[np.ndarray, np.ndarray]: + sos: NDArray[np.float64], +) -> tuple[NDArray[np.float64], NDArray[np.float64]]: """Compute the lattice/ladder coefficients for second-order IIR sections. Parameters ---------- - sos : `np.ndarray` + sos : NDArray[np.float64] Second-order sections with shape (..., 6) as used by `scipy.signal`. Returns ------- - k_sos : `np.ndarray` + k_sos : NDArray[np.float64] Reflection coefficients for second-order sections. - c_sos : `np.ndarray` + c_sos : NDArray[np.float64] Ladder coefficients for second-order sections. """ @@ -396,18 +399,20 @@ def _get_lattice_ladder_coefficients_iir_sos( return k, c -def _get_lattice_coefficients_fir(b: np.ndarray) -> np.ndarray: +def _get_lattice_coefficients_fir( + b: NDArray[np.float64], +) -> NDArray[np.float64]: """Compute reflection coefficients `k` for an FIR filter according to the equations presented in [1]. Parameters ---------- - b : `np.ndarray` + b : NDArray[np.float64] Feedforward coefficients of a filter. Returns ------- - k : `np.ndarray` + k : NDArray[np.float64] Reflection coefficients. References diff --git a/dsptoolbox/classes/multibandsignal.py b/dsptoolbox/classes/multibandsignal.py index 92d90d1..d177a55 100644 --- a/dsptoolbox/classes/multibandsignal.py +++ b/dsptoolbox/classes/multibandsignal.py @@ -1,9 +1,11 @@ -from numpy import zeros, array, unique, atleast_1d, ndarray, complex128 +from numpy import zeros, array, unique, atleast_1d, complex128 +import numpy as np +from numpy.typing import NDArray from copy import deepcopy from pickle import dump, HIGHEST_PROTOCOL from warnings import warn -from .signal_class import Signal +from .signal import Signal from .._general_helpers import _check_format_in_path @@ -87,11 +89,10 @@ def bands(self, new_bands): if new_bands: # Check length and number of channels self.number_of_channels = new_bands[0].number_of_channels - self.signal_type = new_bands[0].signal_type sr = [] complex_data = new_bands[0].time_data_imaginary is not None for s in new_bands: - assert type(s) is Signal, ( + assert isinstance(s, Signal), ( f"{type(s)} is not a valid " + "band type. Use Signal objects" ) @@ -99,9 +100,6 @@ def bands(self, new_bands): "Signals have different number of channels. This " + "behaviour is not supported" ) - assert ( - s.signal_type == self.signal_type - ), "Signal types do not match" assert (s.time_data_imaginary is not None) == complex_data, ( "Some bands have imaginary time data and others do " + "not. This behavior is not supported." @@ -143,6 +141,10 @@ def same_sampling_rate(self, new_same): def number_of_bands(self) -> int: return len(self.bands) + def __get_type_of_signal_bands(self): + """Return type of saved bands (either Signal or ImpulseResponse).""" + return type(self.bands[0]) + def __len__(self): return len(self.bands) @@ -162,7 +164,6 @@ def _generate_metadata(self): self.info["number_of_bands"] = self.number_of_bands if self.bands: self.info["same_sampling_rate"] = self.same_sampling_rate - self.info["signal_type"] = self.signal_type if self.same_sampling_rate: if hasattr(self, "sampling_rate_hz"): self.info["sampling_rate_hz"] = self.sampling_rate_hz @@ -292,7 +293,7 @@ def _get_metadata_str(self): # ======== Getters ======================================================== def get_all_bands( self, channel: int = 0 - ) -> Signal | tuple[list[ndarray], list[ndarray]]: + ) -> Signal | tuple[list[NDArray[np.float64]], list[NDArray[np.float64]]]: """Broadcasts and returns the `MultiBandSignal` as a `Signal` object with all bands as channels in the output. This is done only for a single channel of the original signal. @@ -304,7 +305,7 @@ def get_all_bands( Returns ------- - sig : `Signal` or list of `np.ndarray` and list of int + sig : `Signal` or list of NDArray[np.float64] and list of int Multichannel signal with all the bands. If the `MultiBandSignal` does not have the same sampling rate for all signals, a list with the time data vectors and a list containing their sampling @@ -331,13 +332,9 @@ def get_all_bands( self.bands[n].time_data[:, channel] + self.bands[n].time_data_imaginary[:, channel] * 1j ) - sig = Signal( - None, - new_time_data, - self.sampling_rate_hz, - signal_type=self.signal_type, + return self.__get_type_of_signal_bands()( + None, new_time_data, self.sampling_rate_hz ) - return sig else: new_time_data = [] sr = [] @@ -357,7 +354,9 @@ def get_all_bands( def get_all_time_data( self, - ) -> tuple[ndarray, int] | list[tuple[ndarray, int]]: + ) -> ( + tuple[NDArray[np.float64], int] | list[tuple[NDArray[np.float64], int]] + ): """ Get all time data saved in the MultiBandSignal. If it has consistent sampling rate, a single array with shape (time samples, band, channel) @@ -366,13 +365,16 @@ def get_all_time_data( Returns ------- - if `same_sampling_rate` : - time_data : `np.ndarray` + if `self.same_sampling_rate=True` : + + time_data : NDArray[np.float64] Time samples. int Sampling rate in Hz + else : - list[tuple[`np.ndarray`, int]] + + list[tuple[NDArray[np.float64], int]] List with each band where time samples and sampling rate are contained. diff --git a/dsptoolbox/classes/_phaseLinearizer.py b/dsptoolbox/classes/phase_linearizer.py similarity index 88% rename from dsptoolbox/classes/_phaseLinearizer.py rename to dsptoolbox/classes/phase_linearizer.py index 2774532..4066cec 100644 --- a/dsptoolbox/classes/_phaseLinearizer.py +++ b/dsptoolbox/classes/phase_linearizer.py @@ -1,8 +1,9 @@ -from .filter_class import Filter -from .signal_class import Signal +from .filter import Filter +from .impulse_response import ImpulseResponse import numpy as np -from scipy.integrate import cumulative_simpson +from scipy.integrate import cumulative_trapezoid from scipy.interpolate import interp1d +from numpy.typing import NDArray from .._general_helpers import ( _correct_for_real_phase_spectrum, _pad_trim, @@ -19,10 +20,10 @@ class PhaseLinearizer: def __init__( self, - phase_response: np.ndarray, + phase_response: NDArray[np.float64], time_data_length_samples: int, sampling_rate_hz: int, - target_group_delay_samples: np.ndarray | None = None, + target_group_delay_samples: NDArray[np.float64] | None = None, ): """PhaseLinearizer creates an FIR filter that can linearize a phase response. Use the method `set_parameters` to define specific design @@ -30,16 +31,16 @@ def __init__( Parameters ---------- - phase_response : `np.ndarray` + phase_response : NDArray[np.float64] Wrapped phase response that should be linearized. It is expected to contain only the positive frequencies (including dc and eventually nyquist). - time_data_length_samples : `np.ndarray` + time_data_length_samples : NDArray[np.float64] Length of the time signal that gave the phase response. sampling_rate_hz : int Sampling rate corresponding to the passed phase response. It is also used for the designed FIR filter. - target_group_delay_samples : `np.ndarray` or `None`, optional + target_group_delay_samples : NDArray[np.float64] or `None`, optional If passed, this overwrites the phase response and becomes the target for the FIR filter. It must be given in samples for the whole spectrum (only positive frequencies). For producing @@ -56,18 +57,21 @@ def __init__( f"Phase response with length {len(phase_response)} and " + f"length {time_data_length_samples} do not match." ) + assert ( + phase_response.ndim == 1 + ), "Phase response should have only one dimension" self.phase_response = phase_response self.sampling_rate_hz = sampling_rate_hz self.set_parameters() if target_group_delay_samples is not None: self._set_target_group_delay(target_group_delay_samples) - def _set_target_group_delay(self, target_group_delay: np.ndarray): + def _set_target_group_delay(self, target_group_delay: NDArray[np.float64]): """Set target group delay to use instead of phase response. Parameters ---------- - target_group_delay : `np.ndarray` + target_group_delay : NDArray[np.float64] Target group delay (in samples) to use. """ @@ -127,12 +131,10 @@ def get_filter(self) -> Filter: sampling_rate_hz=self.sampling_rate_hz, ) - def get_filter_as_ir(self) -> Signal: - return Signal( - None, self._design(), self.sampling_rate_hz, signal_type="ir" - ) + def get_filter_as_ir(self) -> ImpulseResponse: + return ImpulseResponse(None, self._design(), self.sampling_rate_hz) - def _design(self) -> np.ndarray: + def _design(self) -> NDArray[np.float64]: """Compute filter.""" if not hasattr(self, "target_group_delay"): gd = self._get_group_delay() @@ -189,7 +191,7 @@ def _design(self) -> np.ndarray: gd_time_length_samples = new_gd_time_length_samples # Get new phase using group target group delay - new_phase = -cumulative_simpson(target_gd, initial=0) + new_phase = -cumulative_trapezoid(target_gd, initial=0) # Correct if nyquist is given if gd_time_length_samples % 2 == 0: new_phase = _correct_for_real_phase_spectrum( @@ -207,7 +209,7 @@ def _design(self) -> np.ndarray: ir = _pad_trim(ir, trim_length) return ir - def _get_group_delay(self) -> np.ndarray: + def _get_group_delay(self) -> NDArray[np.float64]: """Return the unscaled group delay.""" return -np.gradient(np.unwrap(self.phase_response)) diff --git a/dsptoolbox/classes/_plots.py b/dsptoolbox/classes/plots.py similarity index 99% rename from dsptoolbox/classes/_plots.py rename to dsptoolbox/classes/plots.py index 6c85c7f..54c5892 100644 --- a/dsptoolbox/classes/_plots.py +++ b/dsptoolbox/classes/plots.py @@ -1,6 +1,7 @@ """ Very specific plots which are harder to create from the general templates """ + import matplotlib.pyplot as plt from matplotlib.ticker import ScalarFormatter import numpy as np diff --git a/dsptoolbox/classes/signal_class.py b/dsptoolbox/classes/signal.py similarity index 86% rename from dsptoolbox/classes/signal_class.py rename to dsptoolbox/classes/signal.py index a3b7ce2..e8b352f 100644 --- a/dsptoolbox/classes/signal_class.py +++ b/dsptoolbox/classes/signal.py @@ -9,10 +9,11 @@ import soundfile as sf from matplotlib.figure import Figure from matplotlib.axes import Axes -from scipy.signal import convolve +from scipy.signal import oaconvolve +from numpy.typing import NDArray from ..plots import general_plot, general_subplots_line, general_matrix_plot -from ._plots import _csm_plot +from .plots import _csm_plot from .._general_helpers import ( _get_normalized_spectrum, _pad_trim, @@ -40,8 +41,6 @@ def __init__( path: str | None = None, time_data=None, sampling_rate_hz: int | None = None, - signal_type: str = "general", - signal_id: str = "", constrain_amplitude: bool = True, ): """Signal class that saves time data, channel and sampling rate @@ -52,18 +51,11 @@ def __init__( path : str, optional A path to audio files. Reading is done with the soundfile library. Wave and Flac audio files are accepted. Default: `None`. - time_data : array-like, `np.ndarray`, optional + time_data : array-like, NDArray[np.float64], optional Time data of the signal. It is saved as a matrix with the form (time samples, channel number). Default: `None`. sampling_rate_hz : int, optional Sampling rate of the signal in Hz. Default: `None`. - signal_type : str, optional - A generic signal type. Some functionalities are only unlocked for - signal types `'ir'`, `'h1'`, `'h2'`, `'h3'`, `'rir'`, `'chirp'`, - `'noise'` or `'dirac'`. Default: `'general'`. - signal_id : str, optional - An even more generic signal id that can be set by the user. - Default: `''`. constrain_amplitude : bool, optional When `True`, audio is normalized to 0 dBFS peak level in case that there are amplitude values greater than 1. Otherwise, there is no @@ -87,12 +79,8 @@ def __init__( plot_csm. General: save_signal, get_stream_samples. - Only for `signal_type in ('rir', 'ir', 'h1', 'h2', 'h3')`: - set_window, set_coherence, plot_group_delay, plot_coherence. """ - self.signal_id = signal_id - self.signal_type = signal_type # Handling amplitude self.constrain_amplitude = constrain_amplitude self.scale_factor = None @@ -123,14 +111,57 @@ def __init__( ), "A sampling rate should be passed!" self.sampling_rate_hz = sampling_rate_hz self.time_data = time_data - if signal_type in ("rir", "ir", "h1", "h2", "h3", "chirp", "dirac"): - self.set_spectrum_parameters(method="standard", scaling=None) - else: - self.set_spectrum_parameters() + self.set_spectrum_parameters() self.set_csm_parameters() self.set_spectrogram_parameters() self._generate_metadata() + @staticmethod + def from_file(path: str): + """Create a signal from a path to a wav or flac audio file. + + Parameters + ---------- + path : str + Path to file. + + Returns + ------- + Signal + + """ + return Signal(path) + + @staticmethod + def from_time_data( + time_data: NDArray[np.float64], + sampling_rate_hz: int, + constrain_amplitude: bool = True, + ): + """Create a signal from an array of PCM samples. + + Parameters + ---------- + time_data : array-like, NDArray[np.float64], optional + Time data of the signal. It is saved as a matrix with the form + (time samples, channel number). Default: `None`. + sampling_rate_hz : int, optional + Sampling rate of the signal in Hz. Default: `None`. + constrain_amplitude : bool, optional + When `True`, audio is normalized to 0 dBFS peak level in case that + there are amplitude values greater than 1. Otherwise, there is no + normalization and the audio data is not constrained to [-1, 1]. + A warning is always shown when audio gets normalized and the used + normalization factor is saved as `amplitude_scale_factor`. + Default: `True`. + + Returns + ------- + Signal + + """ + return Signal(None, time_data, sampling_rate_hz, constrain_amplitude) + def __update_state(self): """Internal update of object state. If for instance time data gets added, new spectrum, csm or stft has to be computed. @@ -155,8 +186,6 @@ def _generate_metadata(self): self.info["signal_length_seconds"] = ( self.time_data.shape[0] / self.sampling_rate_hz ) - self.info["signal_type"] = self.signal_type - self.info["signal_id"] = self.signal_id def _generate_time_vector(self): """Internal method to generate a time vector on demand.""" @@ -167,13 +196,13 @@ def _generate_time_vector(self): # ======== Properties and setters ========================================= @property - def time_data(self) -> np.ndarray: + def time_data(self) -> NDArray[np.float64]: return self.__time_data.copy() @time_data.setter def time_data(self, new_time_data): # Shape of Time Data array - if not type(new_time_data) is np.ndarray: + if not type(new_time_data) is NDArray[np.float64]: new_time_data = np.asarray(new_time_data) if new_time_data.ndim > 2: new_time_data = new_time_data.squeeze() @@ -231,24 +260,6 @@ def sampling_rate_hz(self, new_sampling_rate_hz): ), "Sampling rate can only be an integer" self.__sampling_rate_hz = new_sampling_rate_hz - @property - def signal_type(self) -> str: - return self.__signal_type - - @signal_type.setter - def signal_type(self, new_signal_type): - assert type(new_signal_type) is str, "Signal type must be a string" - self.__signal_type = new_signal_type.lower() - - @property - def signal_id(self) -> str: - return self.__signal_id - - @signal_id.setter - def signal_id(self, new_signal_id: str): - assert type(new_signal_id) is str, "Signal ID must be a string" - self.__signal_id = new_signal_id.lower() - @property def number_of_channels(self) -> int: return self.__number_of_channels @@ -263,21 +274,19 @@ def number_of_channels(self, new_number): self.__number_of_channels = new_number @property - def time_vector_s(self) -> np.ndarray: + def time_vector_s(self) -> NDArray[np.float64]: if self.__time_vector_update: self._generate_time_vector() return self.__time_vector_s @property - def time_data_imaginary(self) -> np.ndarray | None: + def time_data_imaginary(self) -> NDArray[np.float64] | None: if self.__time_data_imaginary is None: - # warn('Imaginary part of time data was called, but there is ' + - # 'None. None is returned.') return None return self.__time_data_imaginary.copy() @time_data_imaginary.setter - def time_data_imaginary(self, new_imag: np.ndarray): + def time_data_imaginary(self, new_imag: NDArray[np.float64]): if new_imag is not None: assert ( new_imag.shape == self.__time_data.shape @@ -371,14 +380,6 @@ def set_spectrum_parameters( "welch", "standard", ), f"{method} is not a valid method. Use welch or standard" - if self.signal_type in ("h1", "h2", "h3", "rir", "ir"): - if method != "standard": - method = "standard" - warn( - f"For a signal of type {self.signal_type} " - + "the spectrum has to be the standard one and not welch." - + " This has been automatically changed." - ) _new_spectrum_parameters = dict( method=method, smoothe=smoothe, @@ -401,50 +402,6 @@ def set_spectrum_parameters( self._spectrum_parameters = _new_spectrum_parameters self.__spectrum_state_update = True - def set_window(self, window: np.ndarray): - """Sets the window used for the IR. It only works for - `signal_type in ('ir', 'h1', 'h2', 'h3', 'rir')`. - - Parameters - ---------- - window : `np.ndarray` - Window used for the IR. - - """ - valid_signal_types = ("ir", "h1", "h2", "h3", "rir") - assert self.signal_type in valid_signal_types, ( - f"{self.signal_type} is not valid. Please set it to ir or " - + "h1, h2, h3, rir" - ) - assert ( - window.shape == self.time_data.shape - ), f"{window.shape} does not match shape {self.time_data.shape}" - self.window = window - - def set_coherence(self, coherence: np.ndarray): - """Sets the coherence measurements of the transfer function. - It only works for `signal_type = ('ir', 'h1', 'h2', 'h3', 'rir')`. - - Parameters - ---------- - coherence : `np.ndarray` - Coherence matrix. - - """ - valid_signal_types = ("ir", "h1", "h2", "h3", "rir") - assert self.signal_type in valid_signal_types, ( - f"{self.signal_type} is not valid. Please set it to ir or " - + "h1, h2, h3, rir" - ) - assert coherence.shape[0] == ( - self.time_data.shape[0] // 2 + 1 - ), "Length of signals and given coherence do not match" - assert coherence.shape[1] == self.number_of_channels, ( - "Number of channels between given coherence and signal " - + "does not match" - ) - self.coherence = coherence - def set_csm_parameters( self, window_length_samples: int = 1024, @@ -574,7 +531,7 @@ def set_spectrogram_parameters( def add_channel( self, path: str | None = None, - new_time_data: np.ndarray | None = None, + new_time_data: NDArray[np.float64] | None = None, sampling_rate_hz: int | None = None, padding_trimming: bool = True, ): @@ -584,7 +541,7 @@ def add_channel( ---------- path : str, optional Path to the file containing new channel information. - new_time_data : `np.ndarray`, optional + new_time_data : NDArray[np.float64], optional np.array with new channel data. sampling_rate_hz : int, optional Sampling rate for the new data @@ -607,7 +564,7 @@ def add_channel( f"{sampling_rate_hz} does not match {self.sampling_rate_hz} " + "as the sampling rate" ) - if not type(new_time_data) is np.ndarray: + if not type(new_time_data) is NDArray[np.float64]: new_time_data = np.array(new_time_data) if new_time_data.ndim > 2: new_time_data = new_time_data.squeeze() @@ -644,10 +601,6 @@ def add_channel( self.time_data = np.concatenate( [self.time_data, new_time_data], axis=1 ) - if hasattr(self, "window"): - self.window = np.concatenate( - [self.window, np.ones(new_time_data.shape)], axis=1 - ) self.__update_state() def remove_channel(self, channel_number: int = -1): @@ -724,14 +677,12 @@ def get_channels(self, channels): ) new_sig = self.copy() new_sig.time_data = self.time_data[:, channels] - if hasattr(new_sig, "window"): - new_sig.window = new_sig.window[:, channels] return new_sig # ======== Getters ======================================================== def get_spectrum( self, force_computation=False - ) -> tuple[np.ndarray, np.ndarray]: + ) -> tuple[NDArray[np.float64], NDArray[np.complex128]]: """Returns spectrum. Parameters @@ -741,9 +692,9 @@ def get_spectrum( Returns ------- - spectrum_freqs : `np.ndarray` + spectrum_freqs : NDArray[np.float64] Frequency vector. - spectrum : `np.ndarray` + spectrum : NDArray[np.complex128] Spectrum matrix for each channel. """ @@ -820,15 +771,15 @@ def get_spectrum( def get_csm( self, force_computation=False - ) -> tuple[np.ndarray, np.ndarray]: + ) -> tuple[NDArray[np.float64], NDArray[np.float64]]: """Get Cross spectral matrix for all channels with the shape (frequencies, channels, channels). Returns ------- - f_csm : `np.ndarray` + f_csm : NDArray[np.float64] Frequency vector. - csm : `np.ndarray` + csm : NDArray[np.float64] Cross spectral matrix with shape (frequency, channels, channels). """ @@ -851,7 +802,9 @@ def get_csm( def get_spectrogram( self, force_computation: bool = False - ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + ) -> tuple[ + NDArray[np.float64], NDArray[np.float64], NDArray[np.complex128] + ]: """Returns a matrix containing the STFT of a specific channel. Parameters @@ -861,11 +814,11 @@ def get_spectrogram( Returns ------- - t_s : `np.ndarray` + t_s : NDArray[np.float64] Time vector. - f_hz : `np.ndarray` + f_hz : NDArray[np.float64] Frequency vector. - spectrogram : `np.ndarray` + spectrogram : NDArray[np.complex128] Complex spectrogram with shape (frequency, time, channel). Notes @@ -899,23 +852,6 @@ def get_spectrogram( ) return t_s, f_hz, spectrogram - def get_coherence(self) -> tuple[np.ndarray, np.ndarray]: - """Returns the coherence matrix. - - Returns - ------- - f : `np.ndarray` - Frequency vector. - coherence : `np.ndarray` - Coherence matrix. - - """ - assert hasattr( - self, "coherence" - ), "There is no coherence data saved in the Signal object" - f, _ = self.get_spectrum() - return f, self.coherence - # ======== Plots ========================================================== def plot_magnitude( self, @@ -1044,12 +980,6 @@ def plot_time(self) -> tuple[Figure, list[Axes]]: for n in range(self.number_of_channels): mx = np.max(np.abs(self.time_data[:, n])) * 1.1 - if hasattr(self, "window"): - ax[n].plot( - self.time_vector_s, - self.window[:, n] * mx / 1.1, - alpha=0.75, - ) if plot_complex: ax[n].plot( self.time_vector_s, @@ -1106,19 +1036,14 @@ def plot_spl( (int(window_length_s * self.sampling_rate_hz + 0.5), 1) ) window /= len(window) - td_squared = convolve( - td_squared, window, mode="same", method="auto" - ) + td_squared = oaconvolve(td_squared, window, mode="same", axes=0) complex_data = self.time_data_imaginary is not None if complex_data: td_squared_imaginary = self.time_data_imaginary**2.0 if window_length_s > 0: - td_squared_imaginary = convolve( - td_squared_imaginary, - window, - mode="same", - method="auto", + td_squared_imaginary = oaconvolve( + td_squared_imaginary, window, mode="same", axes=0 ) complex_etc = 10 * np.log10( np.clip( @@ -1166,20 +1091,6 @@ def plot_spl( ) for n in range(self.number_of_channels): - if hasattr(self, "window"): - ax[n].plot( - self.time_vector_s, - 20 - * np.log10( - np.clip( - np.abs(self.window[:, n] / 1.1), - a_min=1e-40, - a_max=None, - ) - ) - + max_values[n], - alpha=0.75, - ) if complex_data: ax[n].plot(self.time_vector_s, complex_etc[:, n], alpha=0.75) if range_db is not None: @@ -1195,8 +1106,6 @@ def plot_group_delay( smoothing: int = 0, ) -> tuple[Figure, Axes]: """Plots group delay of each channel. - Only works if `signal_type in ('ir', 'h1', 'h2', 'h3', 'rir', 'chirp', - 'noise', 'dirac')`. Parameters ---------- @@ -1219,21 +1128,6 @@ def plot_group_delay( Axes. """ - valid_signal_types = ( - "rir", - "ir", - "h1", - "h2", - "h3", - "chirp", - "noise", - "dirac", - ) - assert self.signal_type in valid_signal_types, ( - f"{self.signal_type} is not valid. Please set it to ir or " - + "h1, h2, h3, rir" - ) - # Handle spectrum parameters prior_spectrum_parameters = self._spectrum_parameters self.set_spectrum_parameters("standard", scaling=None, smoothe=0) @@ -1311,6 +1205,7 @@ def plot_spectrogram( factor = 10 else: factor = 20 + zlabel = "dBFS" else: factor = 20 zlabel = "dBFS" @@ -1319,7 +1214,7 @@ def plot_spectrogram( if self.calibrated_signal: stft_db -= 20 * np.log10(2e-5) - zlabel = "dB" + zlabel = "dB(SPL)" stft_db = np.nan_to_num(stft_db, nan=np.min(stft_db)) fig, ax = general_matrix_plot( @@ -1337,36 +1232,6 @@ def plot_spectrogram( ) return fig, ax - def plot_coherence(self) -> tuple[Figure, list[Axes]]: - """Plots coherence measurements if there are any. - - Returns - ------- - fig : `matplotlib.figure.Figure` - Figure. - ax : list of `matplotlib.axes.Axes` - Axes. - - """ - if not hasattr(self, "coherence"): - raise AttributeError("There is no coherence data saved") - f, coh = self.get_coherence() - fig, ax = general_subplots_line( - x=f, - matrix=coh, - column=True, - sharey=True, - log=True, - ylabels=[ - rf"$\gamma^2$ Coherence {n}" - for n in range(self.number_of_channels) - ], - xlabels="Frequency / Hz", - range_y=[-0.1, 1.1], - returns=True, - ) - return fig, ax - def plot_phase( self, range_hz=[20, 20e3], @@ -1389,8 +1254,8 @@ def plot_phase( 1/smoothing-octave band. This only applies smoothing to the plot data. Default: 0. remove_ir_latency : bool, optional - If the signal is of type `"rir"` or `"ir"`, the delay of the - impulse can be removed. Default: `False`. + If the signal is an impulse response, the delay of the impulse can + be removed. Default: `False`. Returns ------- @@ -1416,10 +1281,6 @@ def plot_phase( self._spectrum_parameters["smoothe"] = prior_smoothing if remove_ir_latency: - assert self.signal_type in ( - "rir", - "ir", - ), f"{self.signal_type} is not valid, use rir or ir" ph = _remove_ir_latency_from_phase( f, ph, self.time_data, self.sampling_rate_hz, 8 ) @@ -1530,14 +1391,12 @@ def copy(self): def _get_metadata_string(self) -> str: """Helper for creating a string containing all signal info.""" - txt = f"""Signal – ID: {self.info['signal_id']}\n""" + txt = "" temp = "" for n in range(len(txt)): temp += "-" txt += temp + "\n" for k in self.info.keys(): - if k == "signal_id": - continue txt += f"""{str(k).replace('_', ' '). capitalize()}: {self.info[k]}\n""" return txt @@ -1585,7 +1444,7 @@ def stream_samples(self, blocksize_samples: int, signal_mode: bool = True): Returns ------- - sig : `np.ndarray` or `Signal` + sig : NDArray[np.float64] or `Signal` Numpy array with samples used for reproduction with shape (time_samples, channels) or `Signal` object. stop_flag : bool @@ -1604,13 +1463,7 @@ def stream_samples(self, blocksize_samples: int, signal_mode: bool = True): self.streaming_position + blocksize_samples ) if signal_mode: - sig = Signal( - None, - sig, - self.sampling_rate_hz, - self.signal_type, - self.signal_id, - ) + sig = Signal(None, sig, self.sampling_rate_hz) # In an audio stream, welch's method for acquiring a spectrum # is not very logical... sig.set_spectrum_parameters(method="standard", scaling=None) diff --git a/dsptoolbox/classes/_svfilter.py b/dsptoolbox/classes/sv_filter.py similarity index 95% rename from dsptoolbox/classes/_svfilter.py rename to dsptoolbox/classes/sv_filter.py index 75dedba..8a4d69e 100644 --- a/dsptoolbox/classes/_svfilter.py +++ b/dsptoolbox/classes/sv_filter.py @@ -6,7 +6,8 @@ import numpy as np from matplotlib.figure import Figure from matplotlib.axes import Axes -from .signal_class import Signal +from numpy.typing import NDArray +from .signal import Signal from .multibandsignal import MultiBandSignal from ..generators import dirac @@ -101,7 +102,9 @@ def _process_sample( return yl, yh, yb, yl - self.resonance * yb + yh - def _process_vector(self, input: np.ndarray) -> np.ndarray: + def _process_vector( + self, input: NDArray[np.float64] + ) -> NDArray[np.float64]: """Process a whole multichannel array. The outputs are a 3d-array with shape (time sample, band, channel). There are 4 bands: lowpass, highpass, bandpass and allpass. They are returned in this order. @@ -142,11 +145,8 @@ def filter_signal(self, signal: Signal) -> MultiBandSignal: td = self._process_vector(signal.time_data) return MultiBandSignal( [ - Signal( - None, - td[:, i, :], - sampling_rate_hz=self.sampling_rate_hz, - signal_type=signal.signal_type, + type(signal)( + None, td[:, i, :], sampling_rate_hz=self.sampling_rate_hz ) for i in range(4) ] @@ -168,7 +168,6 @@ def get_ir(self, length_samples: int = 1024) -> MultiBandSignal: """ d = dirac(length_samples, sampling_rate_hz=self.sampling_rate_hz) - d.signal_type = "ir" self._reset_state() return self.filter_signal(d) @@ -197,7 +196,6 @@ def plot_magnitude( """ d = self.get_ir(length_samples).get_all_bands() - d.signal_type = "ir" d.set_spectrum_parameters(method="standard") fig, ax = d.plot_magnitude( range_hz=range_hz, @@ -229,7 +227,6 @@ def plot_group_delay( """ d = self.get_ir(length_samples).get_all_bands() - d.signal_type = "ir" d.set_spectrum_parameters(method="standard") fig, ax = d.plot_group_delay(range_hz=range_hz) ax.legend(["Lowpass", "Highpass", "Bandpass", "Allpass"]) @@ -259,7 +256,6 @@ def plot_phase( """ d = self.get_ir(length_samples).get_all_bands() - d.signal_type = "ir" d.set_spectrum_parameters(method="standard") fig, ax = d.plot_phase(range_hz=range_hz, unwrap=unwrap) ax.legend(["Lowpass", "Highpass", "Bandpass", "Allpass"]) diff --git a/dsptoolbox/distances/_distances.py b/dsptoolbox/distances/_distances.py index ff429bd..4f95ca5 100644 --- a/dsptoolbox/distances/_distances.py +++ b/dsptoolbox/distances/_distances.py @@ -4,22 +4,23 @@ import numpy as np from scipy.integrate import simpson +from numpy.typing import NDArray from .._general_helpers import _compute_number_frames, _pad_trim from .._standard import _rms def _log_spectral_distance( - x: np.ndarray, y: np.ndarray, f: np.ndarray + x: NDArray[np.float64], y: NDArray[np.float64], f: NDArray[np.float64] ) -> float: """Computes log spectral distance between two signals. Parameters ---------- - x : `np.ndarray` + x : NDArray[np.float64] First power spectrum. - y : `np.ndarray` + y : NDArray[np.float64] Second power spectrum. - f : `np.ndarray` + f : NDArray[np.float64] Frequency vector. Returns @@ -35,17 +36,17 @@ def _log_spectral_distance( def _itakura_saito_measure( - x: np.ndarray, y: np.ndarray, f: np.ndarray + x: NDArray[np.float64], y: NDArray[np.float64], f: NDArray[np.float64] ) -> float: """Computes log spectral distance between two signals. Parameters ---------- - x : `np.ndarray` + x : NDArray[np.float64] First power spectrum. - y : `np.ndarray` + y : NDArray[np.float64] Second power spectrum. - f : `np.ndarray` + f : NDArray[np.float64] Frequency vector. Returns @@ -59,33 +60,35 @@ def _itakura_saito_measure( return ism -def _snr(s: np.ndarray, n: np.ndarray) -> float | np.ndarray: +def _snr( + s: NDArray[np.float64], n: NDArray[np.float64] +) -> float | NDArray[np.float64]: """Computes SNR from the passed numpy arrays. Parameters ---------- - s : `np.ndarray` + s : NDArray[np.float64] Signal - n : `np.ndarray` + n : NDArray[np.float64] Noise Returns ------- - snr : float or `np.ndarray` + snr : float or NDArray[np.float64] SNR between signals. It can be an array if signals are multichannel. """ return 20 * np.log10(_rms(s) / _rms(n)) -def _sisdr(s: np.ndarray, shat: np.ndarray) -> float: +def _sisdr(s: NDArray[np.float64], shat: NDArray[np.float64]) -> float: """Scale-invariant signal-to-distortion ratio Parameters ---------- - s : `np.ndarray` + s : NDArray[np.float64] Target signal. - shat : `np.ndarray` + shat : NDArray[np.float64] Modified or approximated signal. Returns @@ -102,11 +105,11 @@ def _sisdr(s: np.ndarray, shat: np.ndarray) -> float: def _fw_snr_seg_per_channel( - x: np.ndarray, - xhat: np.ndarray, - snr_range_db: np.ndarray, + x: NDArray[np.float64], + xhat: NDArray[np.float64], + snr_range_db: NDArray[np.float64], gamma: float, - time_window: np.ndarray, + time_window: NDArray[np.float64], step_samples: int, ) -> float: """This function gets an original signal x and a modified signal xhat @@ -117,15 +120,15 @@ def _fw_snr_seg_per_channel( Parameters ---------- - x : `np.ndarray` + x : NDArray[np.float64] Original signal with shape (time_samples, bands). - xhat : `np.ndarray` + xhat : NDArray[np.float64] Modified signal with shape (time_samples, bands). - snr_range_db : `np.ndarray` with length 2 + snr_range_db : NDArray[np.float64] with length 2 SNR range in dB. gamma : float Gamma exponent for the weighting function. See reference for details. - time_window : `np.ndarray` + time_window : NDArray[np.float64] Time window to be used. step : int Hop length between each time frame. diff --git a/dsptoolbox/distances/distances.py b/dsptoolbox/distances/distances.py index 5456dcf..8ca5cf1 100644 --- a/dsptoolbox/distances/distances.py +++ b/dsptoolbox/distances/distances.py @@ -5,6 +5,7 @@ import numpy as np from scipy.signal import windows +from numpy.typing import NDArray from .. import Signal from ..filterbanks import auditory_filters_gammatone @@ -25,7 +26,7 @@ def log_spectral( f_range_hz=[20, 20000], energy_normalization: bool = True, spectrum_parameters: dict | None = None, -) -> np.ndarray: +) -> NDArray[np.float64]: """Computes log spectral distance between two signals. Parameters @@ -50,7 +51,7 @@ def log_spectral( Returns ------- - distances : `np.ndarray` + distances : NDArray[np.float64] Log spectral distance per channel for the given signals. References @@ -114,7 +115,7 @@ def itakura_saito( f_range_hz=[20, 20000], energy_normalization: bool = True, spectrum_parameters: dict | None = None, -) -> np.ndarray: +) -> NDArray[np.float64]: """Computes itakura-saito measure between two signals. Beware that this measure is not symmetric (x, y) != (y, x). @@ -140,7 +141,7 @@ def itakura_saito( Returns ------- - distances : `np.ndarray` + distances : NDArray[np.float64] Itakura-saito measure for the given signals. References @@ -197,7 +198,7 @@ def itakura_saito( return distances -def snr(signal: Signal, noise: Signal) -> np.ndarray: +def snr(signal: Signal, noise: Signal) -> NDArray[np.float64]: """Classical Signal-to-noise ratio. If noise only has one channel, it is assumed to be the noise for all channels of signal. @@ -210,7 +211,7 @@ def snr(signal: Signal, noise: Signal) -> np.ndarray: Returns ------- - snr_per_channel : `np.ndarray` + snr_per_channel : NDArray[np.float64] SNR value per channel References @@ -228,7 +229,9 @@ def snr(signal: Signal, noise: Signal) -> np.ndarray: return np.atleast_1d(_snr(signal.time_data, noise.time_data)) -def si_sdr(target_signal: Signal, modified_signal: Signal) -> np.ndarray: +def si_sdr( + target_signal: Signal, modified_signal: Signal +) -> NDArray[np.float64]: """Computes scale-invariant signal to distortion ratio from a target and a modified signal. If target signal only has one channel, it is assumed to be the target for all the channels in the modified signal. @@ -244,7 +247,7 @@ def si_sdr(target_signal: Signal, modified_signal: Signal) -> np.ndarray: Returns ------- - sdr : `np.ndarray` + sdr : NDArray[np.float64] SI-SDR per channel. References @@ -285,7 +288,7 @@ def fw_snr_seg( f_range_hz=[20, 10e3], snr_range_db=[-10, 35], gamma: float = 0.2, -) -> np.ndarray: +) -> NDArray[np.float64]: """Frequency-weighted segmental SNR (fwSNRseg) computation between two signals. @@ -319,7 +322,7 @@ def fw_snr_seg( Returns ------- - snr_per_channel : `np.ndarray` + snr_per_channel : NDArray[np.float64] Frequency-weighted, time-segmented SNR per channel. References diff --git a/dsptoolbox/effects/_effects.py b/dsptoolbox/effects/_effects.py index 33c98e4..f3b76cf 100644 --- a/dsptoolbox/effects/_effects.py +++ b/dsptoolbox/effects/_effects.py @@ -5,14 +5,15 @@ from .._general_helpers import _get_smoothing_factor_ema from ..plots import general_plot import numpy as np +from numpy.typing import NDArray # import matplotlib.pyplot as plt # ========= Distortion ======================================================== def _arctan_distortion( - inp: np.ndarray, distortion_level_db: float, offset_db: float -) -> np.ndarray: + inp: NDArray[np.float64], distortion_level_db: float, offset_db: float +) -> NDArray[np.float64]: """Applies arctan distortion.""" offset_linear = 10 ** (offset_db / 20) distortion_level_linear = 10 ** (distortion_level_db / 20) @@ -24,8 +25,8 @@ def _arctan_distortion( def _hard_clip_distortion( - inp: np.ndarray, distortion_level_db: float, offset_db: float -) -> np.ndarray: + inp: NDArray[np.float64], distortion_level_db: float, offset_db: float +) -> NDArray[np.float64]: """Applies hard clipping distortion.""" offset_linear = 10 ** (offset_db / 20) distortion_level_linear = 10 ** (distortion_level_db / 20) @@ -37,8 +38,8 @@ def _hard_clip_distortion( def _soft_clip_distortion( - inp: np.ndarray, distortion_level_db: float, offset_db: float -) -> np.ndarray: + inp: NDArray[np.float64], distortion_level_db: float, offset_db: float +) -> NDArray[np.float64]: """Applies non-linear cubic distortion.""" offset_linear = 10 ** (offset_db / 20) distortion_level_linear = 10 ** (distortion_level_db / 20) @@ -51,15 +52,15 @@ def _soft_clip_distortion( def _clean_signal( - inp: np.ndarray, distortion_level_db: float, offset_db: float -) -> np.ndarray: + inp: NDArray[np.float64], distortion_level_db: float, offset_db: float +) -> NDArray[np.float64]: """Returns the unchanged clean signal.""" return inp # ========= Compressor ======================================================== def _compressor( - x: np.ndarray, + x: NDArray[np.float64], threshold_db: float, ratio: float, knee_factor_db: float, @@ -67,12 +68,12 @@ def _compressor( release_samples: int, mix_compressed: float, downward_compression: bool, -) -> np.ndarray: +) -> NDArray[np.float64]: """Compresses the dynamic range of a signal. Parameters ---------- - x : `np.ndarray` + x : NDArray[np.float64] Signal to compress. threshold_db : float Threshold level. @@ -93,7 +94,7 @@ def _compressor( Returns ------- - x_ : `np.ndarray` + x_ : NDArray[np.float64] Compressed signal. """ @@ -167,7 +168,7 @@ def _get_knee_func( if downward_compression: - def compress_in_db(x: np.ndarray | float): + def compress_in_db(x: NDArray[np.float64] | float): if type(x) is float: if x - T < -W / 2: return x @@ -192,7 +193,7 @@ def compress_in_db(x: np.ndarray | float): else: - def compress_in_db(x: np.ndarray | float): + def compress_in_db(x: NDArray[np.float64] | float): if type(x) is float: if x - T < -W / 2: return T + (x - T) / R @@ -219,14 +220,14 @@ def compress_in_db(x: np.ndarray | float): def _find_attack_hold_release( - x: np.ndarray, + x: NDArray[np.float64], threshold_db: float, attack_samples: int, hold_samples: int, release_samples: int, - side_chain: np.ndarray, + side_chain: NDArray[np.float64], indices_above: bool, -) -> tuple[np.ndarray, np.ndarray, np.ndarray]: +) -> tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]]: """This function finds the indices corresponding to attack, hold and release. It returns boolean arrays. It can only handle 1D-arrays as input! diff --git a/dsptoolbox/effects/effects.py b/dsptoolbox/effects/effects.py index c68e4f6..648d730 100644 --- a/dsptoolbox/effects/effects.py +++ b/dsptoolbox/effects/effects.py @@ -22,6 +22,7 @@ from scipy.signal.windows import get_window import numpy as np +from numpy.typing import NDArray from warnings import warn __all__ = [ @@ -61,7 +62,7 @@ def apply( Modified signal. """ - if type(signal) is Signal: + if isinstance(signal, Signal): return self._apply_this_effect(signal) elif type(signal) is MultiBandSignal: new_mbs = signal.copy() @@ -82,20 +83,20 @@ def _apply_this_effect(self, signal: Signal) -> Signal: return signal def _add_gain_in_db( - self, time_data: np.ndarray, gain_db: float - ) -> np.ndarray: + self, time_data: NDArray[np.float64], gain_db: float + ) -> NDArray[np.float64]: """General gain stage. Parameters ---------- - time_data : `np.ndarray` + time_data : NDArray[np.float64] Time samples of the signal. gain_db : float Gain in dB. Returns ------- - new_time_data : `np.ndarray` + new_time_data : NDArray[np.float64] Time data with new gain. """ @@ -103,11 +104,13 @@ def _add_gain_in_db( return time_data return time_data * 10 ** (gain_db / 20) - def _save_peak_values(self, inp: np.ndarray): + def _save_peak_values(self, inp: NDArray[np.float64]): """Save the peak values of an input.""" self._peak_values = np.max(np.abs(inp), axis=0) - def _restore_peak_values(self, inp: np.ndarray) -> np.ndarray: + def _restore_peak_values( + self, inp: NDArray[np.float64] + ) -> NDArray[np.float64]: """Restore saved peak values of a signal.""" if not hasattr(self, "_peak_values"): return inp @@ -119,11 +122,13 @@ def _restore_peak_values(self, inp: np.ndarray) -> np.ndarray: return inp return inp * (self._peak_values / np.max(np.abs(inp), axis=0)) - def _save_rms_values(self, inp: np.ndarray): + def _save_rms_values(self, inp: NDArray[np.float64]): """Save the RMS values of a signal.""" self._rms_values = _rms(inp) - def _restore_rms_values(self, inp: np.ndarray) -> np.ndarray: + def _restore_rms_values( + self, inp: NDArray[np.float64] + ) -> NDArray[np.float64]: """Restore the RMS values of a signal.""" if not hasattr(self, "_rms_values"): return inp @@ -149,7 +154,7 @@ def __init__( adaptive_mode: bool = True, threshold_rms_dbfs: float = -40, block_length_s: float = 0.1, - spectrum_to_subtract: np.ndarray | bool = False, + spectrum_to_subtract: NDArray[np.float64] | bool = False, ): """Constructor for a spectral subtractor denoising effect. More parameters can be passed using the method `set_advanced_parameters`. @@ -173,7 +178,7 @@ def __init__( blocks of the signal. The real block length in samples is always clipped to the closest power of 2 for efficiency of the FFT. Default: 0.1. - spectrum_to_subtract : np.ndarray or `False`, optional + spectrum_to_subtract : NDArray[np.float64] or `False`, optional If a spectrum is passed, it is used as the one to subtract and all other parameters are ignored. This should be the result of the squared magnitude of the FFT without any scaling in order to avoid @@ -361,7 +366,7 @@ def set_parameters( adaptive_mode: bool | None = None, threshold_rms_dbfs: float | None = None, block_length_s: float | None = None, - spectrum_to_subtract: np.ndarray = False, + spectrum_to_subtract: NDArray[np.float64] = False, ): """Sets the audio effects parameters. Pass `None` to leave the previously selected value for each parameter unchanged. @@ -385,7 +390,7 @@ def set_parameters( blocks of the signal. The real block length in samples is always clipped to the closest power of 2 for efficiency of the FFT. Default: 0.1. - spectrum_to_subtract : np.ndarray, optional + spectrum_to_subtract : NDArray[np.float64], optional If a spectrum is passed, it is used as the one to subtract and all other parameters are ignored. This should be the result of the squared magnitude of the FFT without any scaling in order to avoid @@ -634,9 +639,9 @@ def __init__( def set_advanced_parameters( self, type_of_distortion="arctan", - distortion_levels_db: np.ndarray = 20, - mix_percent: np.ndarray = 100, - offset_db: np.ndarray = -np.inf, + distortion_levels_db: NDArray[np.float64] = 20, + mix_percent: NDArray[np.float64] = 100, + offset_db: NDArray[np.float64] = -np.inf, post_gain_db: float = 0, ): r"""This sets the parameters of the distortion. Multiple @@ -655,20 +660,21 @@ def set_advanced_parameters( (`'arctan'`, `'hard clip'`, `'soft clip'`, `'clean'`) or a callable containing a user-defined distortion. Its signature must be:: - func(time_data: np.ndarray, distortion_level_db: float, - offset_db: float) -> np.ndarray + func(time_data: NDArray[np.float64], + distortion_level_db: float, offset_db: float) \ + -> NDArray[np.float64] The output data is assumed to have shape (time samples, channels) as the input data. If a list is passed, `distortion_levels_db`, `mix_percent` and `offset_db` must have the same length as the list. Default: `'arctan'`. - distortion_levels : `np.ndarray`, optional + distortion_levels : NDArray[np.float64], optional This defines how strong the distortion effect is applied. It can vary according to the non-linear function. Usually, a range between 0 and 50 should be reasonable, though any value is possible. If multiple types of distortion are being used, this should be an array corresponding to each distortion. Default: 20. - mix_percent : `np.ndarray`, optional + mix_percent : NDArray[np.float64], optional This defines how much of each distortion is used in the final mix. If `type_of_distortion` is only one string or callable, mix_percent is its amount in the final mix with the clean signal. @@ -676,7 +682,7 @@ def set_advanced_parameters( 40 leads to 40% distorted, 60% clean. If multiple types of distortion are being used, this should be an array corresponding to each distortion and its sum must be 100. Default: 100. - offset_db : `np.ndarray`, optional + offset_db : NDArray[np.float64], optional This offset corresponds to the offset shown in [1]. It must be a value between -np.inf and 0. The bigger this value, the more even harmonics are caused by the distortion. Pass -np.inf to avoid any @@ -1083,7 +1089,9 @@ class Tremolo(AudioEffect): """ def __init__( - self, depth: float = 0.5, modulator: LFO | np.ndarray | None = None + self, + depth: float = 0.5, + modulator: LFO | NDArray[np.float64] | None = None, ): """Constructor for a tremolo effect. @@ -1092,7 +1100,7 @@ def __init__( depth : float, optional Depth of the amplitude variation. This must be a positive value. Default: 0.5. - modulator : `LFO` or `np.ndarray` + modulator : `LFO` or NDArray[np.float64] This is the modulator signal that modifies the amplitude of the carrier signal. It can either be a LFO or a numpy array. If the length of the numpy array is different to that of the carrier @@ -1106,14 +1114,16 @@ def __init__( modulator = LFO(1, "harmonic") self.__set_parameters(depth, modulator) - def __set_parameters(self, depth: float, modulator: LFO | np.ndarray): + def __set_parameters( + self, depth: float, modulator: LFO | NDArray[np.float64] + ): """Internal method to change parameters.""" if modulator is not None: assert type(modulator) in ( LFO, - np.ndarray, + NDArray[np.float64], ), "Unsupported modulator type. Use LFO or numpy.ndarray" - if type(modulator) is np.ndarray: + if type(modulator) is NDArray[np.float64]: modulator = modulator.squeeze() assert ( modulator.ndim == 1 @@ -1128,7 +1138,7 @@ def __set_parameters(self, depth: float, modulator: LFO | np.ndarray): def set_parameters( self, depth: float | None = None, - modulator: LFO | np.ndarray | None = None, + modulator: LFO | NDArray[np.float64] | None = None, ): """Set the parameters for the tremolo effect. Passing `None` in this function leaves them unchanged. @@ -1138,7 +1148,7 @@ def set_parameters( depth : float, optional Depth of the amplitude variation. This must be a positive value. Default: `None`. - modulator : `LFO` or `np.ndarray`, optional + modulator : `LFO` or NDArray[np.float64], optional This is the modulator signal that modifies the amplitude of the carrier signal. It can either be a LFO or a numpy array. If the length of the numpy array is different to that of the carrier @@ -1170,9 +1180,9 @@ class Chorus(AudioEffect): def __init__( self, - depths_ms: float | np.ndarray = 5, - base_delays_ms: float | np.ndarray = 15, - modulators: LFO | list | tuple | np.ndarray | None = None, + depths_ms: float | NDArray[np.float64] = 5, + base_delays_ms: float | NDArray[np.float64] = 15, + modulators: LFO | list | tuple | NDArray[np.float64] | None = None, mix_percent: float = 100, ): """Constructor for a chorus effect. Multiple voices with modulated @@ -1186,11 +1196,11 @@ def __init__( around the base delay. The bigger, the more dramatic the effect. Each voice can have a different depth. If a single value is passed, it is used for all voices. Default: 5. - base_delays_ms : `np.ndarray`, optional + base_delays_ms : NDArray[np.float64], optional Base delays for each voice. By default, 15 ms are used for all voices but different values can be passed per voice. Default: 15. - modulators : `LFO` or list or tuple or `np.ndarray`, optional + modulators : `LFO` or list or tuple or NDArray[np.float64], optional This is the modulators signal that modifies the delay of the carrier signal. It can either be an LFO, a list or tuple of LFOs or a numpy array with delay values in milliseconds. If the length of @@ -1221,9 +1231,9 @@ def __init__( def __set_parameters( self, - depths_ms: float | np.ndarray, - base_delays_ms: float | np.ndarray, - modulators: LFO | list | tuple | np.ndarray, + depths_ms: float | NDArray[np.float64], + base_delays_ms: float | NDArray[np.float64], + modulators: LFO | list | tuple | NDArray[np.float64], mix_percent: float, ): """Internal method to change parameters.""" @@ -1245,7 +1255,7 @@ def __set_parameters( if modulators is not None: if type(modulators) in (list, tuple): nv_mod = len(modulators) - elif type(modulators) is np.ndarray: + elif type(modulators) is NDArray[np.float64]: modulators = np.atleast_2d(modulators) nv_mod = modulators.shape[1] else: @@ -1274,9 +1284,9 @@ def __set_parameters( LFO, list, tuple, - np.ndarray, + NDArray[np.float64], ), "Unsupported modulators type. Use LFO or numpy.ndarray" - if type(modulators) is np.ndarray: + if type(modulators) is NDArray[np.float64]: modulators = np.atleast_2d(modulators) modulators.shape[1] == self.number_of_voices, ( "The modulators signal must " @@ -1322,9 +1332,9 @@ def __set_parameters( def set_parameters( self, - depths_ms: float | np.ndarray | None = None, - base_delays_ms: float | np.ndarray | None = None, - modulators: LFO | list | tuple | np.ndarray | None = None, + depths_ms: float | NDArray[np.float64] | None = None, + base_delays_ms: float | NDArray[np.float64] | None = None, + modulators: LFO | list | tuple | NDArray[np.float64] | None = None, mix_percent: float | None = None, ): """Sets the advanced parameters for the chorus effect. By passing @@ -1337,7 +1347,7 @@ def set_parameters( depths_ms : float, optional Depth of the delay variation in ms. This must be a positive value. Default: `None`. - modulators : LFO or list or tuple or `np.ndarray`, optional + modulators : LFO or list or tuple or NDArray[np.float64], optional This defines the modulators signal. It can be a single LFO object or a list containing an LFO for each voice. Alternatively, a numpy.ndarray with shape (time samples, voice) can be passed. If @@ -1361,7 +1371,7 @@ def _apply_this_effect(self, signal: Signal) -> Signal: le = len(signal) # Get valid modulation signals - if type(self.modulators) is not np.ndarray: + if type(self.modulators) is not NDArray[np.float64]: modulation = np.zeros((le, self.number_of_voices)) for ind, m in enumerate(self.modulators): modulation[:, ind] = ( diff --git a/dsptoolbox/filterbanks/__init__.py b/dsptoolbox/filterbanks/__init__.py index 8bee361..d4773c1 100644 --- a/dsptoolbox/filterbanks/__init__.py +++ b/dsptoolbox/filterbanks/__init__.py @@ -31,6 +31,8 @@ - `convert_into_lattice_filter()`: Turns a conventional filter into its lattice/ladder representation. - `pinking_filter()`: Get a -3 dB/octave filter. +- `matched_biquad()`: Analog-matched biquad filters. +- `gaussian_kernel()`: IIR first-order approximation of a gaussian window. """ @@ -44,11 +46,13 @@ complementary_fir_filter, convert_into_lattice_filter, pinking_filter, + matched_biquad, + gaussian_kernel, ) -from ..classes._lattice_ladder_filter import LatticeLadderFilter -from ..classes._phaseLinearizer import PhaseLinearizer -from ..classes._svfilter import StateVariableFilter +from ..classes.lattice_ladder_filter import LatticeLadderFilter +from ..classes.phase_linearizer import PhaseLinearizer +from ..classes.sv_filter import StateVariableFilter __all__ = [ "linkwitz_riley_crossovers", @@ -63,4 +67,6 @@ "PhaseLinearizer", "StateVariableFilter", "pinking_filter", + "matched_biquad", + "gaussian_kernel", ] diff --git a/dsptoolbox/filterbanks/_filterbank.py b/dsptoolbox/filterbanks/_filterbank.py index d84d141..90f4590 100644 --- a/dsptoolbox/filterbanks/_filterbank.py +++ b/dsptoolbox/filterbanks/_filterbank.py @@ -7,6 +7,7 @@ from os import sep from pickle import dump, HIGHEST_PROTOCOL from copy import deepcopy +from numpy.typing import NDArray from scipy.signal import ( sosfilt, @@ -16,7 +17,13 @@ bilinear, tf2sos, ) -from ..classes import Signal, MultiBandSignal, FilterBank, Filter +from ..classes import ( + Signal, + MultiBandSignal, + FilterBank, + Filter, + ImpulseResponse, +) from ..generators import dirac from ..plots import general_plot @@ -299,14 +306,9 @@ def filter_signal( b = [] for n in range(self.number_of_bands): - b.append( - Signal( - None, - new_time_data[:, :, n], - s.sampling_rate_hz, - signal_type=s.signal_type, - ) - ) + # Extract if signal is ImpulseResponse or Signal and create + # accordingly + b.append(type(s)(None, new_time_data[:, :, n], s.sampling_rate_hz)) d = dict( readme="MultiBandSignal made using Linkwitz-Riley filter bank", filterbank_freqs=self.freqs, @@ -371,7 +373,7 @@ def get_ir( length_samples: int = 1024, mode: str = "parallel", zero_phase: bool = False, - ) -> Signal | MultiBandSignal: + ) -> ImpulseResponse | MultiBandSignal: """Returns impulse response from the filter bank. For this filter bank only `mode='parallel'` is valid and there is no zero phase filtering. @@ -389,7 +391,7 @@ def get_ir( Returns ------- - ir : `MultiBandSignal` or `Signal` + ir : `ImpulseResponse`, `MultiBandSignal` Impulse response of the filter bank. """ @@ -690,9 +692,9 @@ def __init__( self, filters: list, info: dict, - frequencies: np.ndarray, - coefficients: np.ndarray, - normalizations: np.ndarray, + frequencies: NDArray[np.float64], + coefficients: NDArray[np.float64], + normalizations: NDArray[np.float64], ): """Constructor for the Gamma Tone Filter Bank. It is only available as a constant sampling rate filter bank. @@ -703,11 +705,11 @@ def __init__( List with gamma tone filters. info : dict Dictionary containing basic information about the filter bank. - frequencies : `np.ndarray` + frequencies : NDArray[np.float64] Frequencies used for the filters. - coefficients : `np.ndarray` + coefficients : NDArray[np.float64] Filter coefficients. - normalizations : `np.ndarray` + normalizations : NDArray[np.float64] Normalizations. """ @@ -1391,7 +1393,7 @@ def _reconstruct_from_crossover_upsample( def _get_2nd_order_linkwitz_riley( f0: float, sampling_rate_hz: int -) -> tuple[np.ndarray, np.ndarray]: +) -> tuple[NDArray[np.float64], NDArray[np.float64]]: """Return filters (SOS representation) for a 2nd-order linkwitz-riley crossover. These are based on sallen-key filters (with Q=0.5). In order to obtain an allpass sum response, one band must be phase-inverted. Here, @@ -1406,9 +1408,9 @@ def _get_2nd_order_linkwitz_riley( Returns ------- - low_sos : `np.ndarray` + low_sos : NDArray[np.float64] SOS for low band. - high_sos : `np.ndarray` + high_sos : NDArray[np.float64] SOS for high band. """ @@ -1428,3 +1430,164 @@ def _get_2nd_order_linkwitz_riley( b, a = bilinear(b_s, a_s, warped) high_sos = tf2sos(b, a) return low_sos, high_sos + + +def _get_matched_peaking_eq(f, g_db, q, q_factor, fs): + """Analog-matched peaking eq coefficients.""" + if q_factor is None: + # Manually extracted approximation for gains between -20 and 20 + # at normalized frequency = 0.02 + q_factor = np.max([np.abs(0.0868 * g_db + 1.264), 0.55]) + assert q_factor > 0, "Q-factor should be greater than 0" + + omega0 = 2 * np.pi * f / fs + g = 10 ** (g_db / 20) + q *= q_factor + + a, A, phi = __get_matched_eq_helpers(omega0, q) + + R1 = g**2 * (A @ phi) + R2 = g**2 * (-A[0] + A[1] + 4 * (phi[0] - phi[1]) * A[2]) + B0 = A[0] + B2 = (R1 - R2 * phi[1] - B0) / (4 * phi[1] ** 2) + B1 = R2 + B0 + 4 * (phi[1] - phi[0]) * B2 + W = 0.5 * (B0**0.5 + B1**0.5) + + # b coefficients + b0 = 0.5 * (W + (W**2 + B2) ** 0.5) + b1 = 0.5 * (B0**0.5 - B1**0.5) + b2 = -B2 / (4 * b0) + return np.array([b0, b1, b2]), a + + +def _get_matched_lowpass_eq(f, g_db, q, fs): + """Analog-matched lowpass eq coefficents.""" + omega0 = 2 * np.pi * f / fs + Q = q + + a, A, phi = __get_matched_eq_helpers(omega0, q) + + R1 = Q**2 * (A @ phi) + B0 = A[0] + B1 = (R1 - B0 * phi[0]) / phi[1] + b0 = 0.5 * (np.sum(a) + B1**0.5) + b1 = np.sum(a) - b0 + b2 = 0 + + b = np.array([b0, b1, b2]) * 10 ** (g_db / 20) + return b, a + + +def _get_matched_highpass_eq(f, g_db, q, fs): + """Analog-matched highpass eq coefficents.""" + omega0 = 2 * np.pi * f / fs + Q = q + a, A, phi = __get_matched_eq_helpers(omega0, q) + + b0 = (A @ phi) ** 0.5 / 4 / phi[1] * Q * 10 ** (g_db / 20) + b1 = -2 * b0 + b2 = b0 + return np.array([b0, b1, b2]), a + + +def _get_matched_bandpass_eq(f, g_db, q, fs): + """Analog-matched bandpass eq coefficents.""" + omega0 = 2 * np.pi * f / fs + + a, A, phi = __get_matched_eq_helpers(omega0, q) + + R1 = A @ phi + R2 = -A[0] + A[1] + 4 * (phi[0] - phi[1]) * A[2] + B2 = (R1 - R2 * phi[1]) / 4 / phi[1] ** 2 + B1 = R2 + 4 * (phi[1] - phi[0]) * B2 + b1 = -0.5 * B1**0.5 + b0 = 0.5 * ((B2 + b1**2) ** 0.5 - b1) + b2 = -b0 - b1 + b = np.array([b0, b1, b2]) * 10 ** (g_db / 20) + return b, a + + +def _get_matched_shelving_eq(f, g_db, fs, lowshelf): + """Analog-matched low/highshelf eq coefficients with fixed + `q=np.sqrt(2)/2`. + + """ + fc = f / (fs / 2) + + G = 10 ** (g_db / 20) + + if lowshelf: + G = 1 / G + + if np.abs(1 - G) < 1e-6: + G = 1 + 1e-6 + + f1 = fc / (0.16 + 1.543 * fc**2) ** 0.5 + f2 = fc / (0.947 + 3.806 * fc**2) ** 0.5 + hny = (fc**4 + G) / (fc**4 + 1 / G) + + phi1 = np.sin(np.pi / 2 * f1) ** 2 + phi2 = np.sin(np.pi / 2 * f2) ** 2 + h1 = (fc**4 + f1**4 * G) / (fc**4 + f1**4 / G) + h2 = (fc**4 + f2**4 * G) / (fc**4 + f2**4 / G) + + d1 = (h1 - 1) * (1 - phi1) + c11 = -phi1 * d1 + c12 = (hny - h1) * phi1**2 + + d2 = (h2 - 1) * (1 - phi2) + c21 = -phi2 * d2 + c22 = (hny - h2) * phi2**2 + + alpha1 = (c22 * d1 - c12 * d2) / (c11 * c22 - c12 * c21) + alpha2 = (d1 - c11 * alpha1) / c12 + + beta1 = alpha1 + beta2 = hny * alpha2 + + A0 = 1 + A1 = alpha2 + A2 = 0.25 * (alpha1 - alpha2) + + B0 = 1 + B1 = beta2 + B2 = 0.25 * (beta1 - beta2) + + V = 0.5 * (A0**0.5 + A1**0.5) + a0 = 0.5 * (V + (V**2 + A2) ** 0.5) + a1 = 1 - V + a2 = -0.25 * A2 / a0 + + W = 0.5 * (B0**0.5 + B1**0.5) + b0 = 0.5 * (W + (W**2 + B2) ** 0.5) + b1 = 1 - W + b2 = -0.25 * B2 / b0 + return np.array([b0, b1, b2]) / (G if lowshelf else 1.0), np.array( + [a0, a1, a2] + ) + + +def __get_matched_eq_helpers(omega0, q): + """Return the some general helpers for matched biquad filters. The + normalized angular frequency and the quality factor (possibly scaled) are + needed. + + Returns + ------- + a, A, phi + + """ + q = 1 / (2 * q) + # a coefficients + if q <= 1: + a1 = -2 * np.exp(-q * omega0) * np.cos((1 - q**2) ** 0.5 * omega0) + else: + a1 = -2 * np.exp(-q * omega0) * np.cosh((q**2 - 1) ** 0.5 * omega0) + a2 = np.exp(-2 * q * omega0) + + # In-between factors + A = np.array([(1 + a1 + a2) ** 2, (1 - a1 + a2) ** 2, -4 * a2]).squeeze() + sin_omega = np.sin(omega0 / 2) ** 2 + phi = np.array([1 - sin_omega, sin_omega, 0]) + phi[2] = 4 * phi[0] * phi[1] + return np.array([1, a1, a2]), A, phi diff --git a/dsptoolbox/filterbanks/filterbanks.py b/dsptoolbox/filterbanks/filterbanks.py index 927a453..ac978c5 100644 --- a/dsptoolbox/filterbanks/filterbanks.py +++ b/dsptoolbox/filterbanks/filterbanks.py @@ -4,21 +4,29 @@ """ import numpy as np -from scipy.signal import windows, bilinear_zpk, freqz_zpk +from scipy.signal import windows, bilinear_zpk, freqz_zpk, tf2sos import warnings from .. import ( Filter, FilterBank, - fractional_octave_frequencies, - erb_frequencies, ) -from ..classes._lattice_ladder_filter import ( +from ..tools import fractional_octave_frequencies, erb_frequencies +from ..classes.lattice_ladder_filter import ( LatticeLadderFilter, _get_lattice_ladder_coefficients_iir, _get_lattice_coefficients_fir, _get_lattice_ladder_coefficients_iir_sos, ) -from ._filterbank import LRFilterBank, GammaToneFilterBank, QMFCrossover +from ._filterbank import ( + LRFilterBank, + GammaToneFilterBank, + QMFCrossover, + _get_matched_peaking_eq, + _get_matched_lowpass_eq, + _get_matched_highpass_eq, + _get_matched_bandpass_eq, + _get_matched_shelving_eq, +) from .._standard import _kaiser_window_fractional @@ -570,3 +578,190 @@ def pinking_filter(frequency_0_db: float, sampling_rate_hz: int) -> Filter: return Filter( "other", {"zpk": [z, p, k]}, sampling_rate_hz=sampling_rate_hz ) + + +def matched_biquad( + eq_type: str, + freq_hz: float, + gain_db: float, + q: float, + sampling_rate_hz: int, + q_factor: float | None = None, +) -> Filter: + """This returns a biquad digital filter (EQ) that is matched to better + fit an analog prototype than the standard biquad implementation as defined + in [1]. This is due to the frequency warping that occurs when the frequency + approaches nyquist. See notes for details. + + Parameters + ---------- + eq_type : str + Type of biquad filter to create. Choose from "peaking", "lowpass", + "highpass", "bandpass", "lowshelf", "highshelf". + freq_hz : float + Characteristic frequency in Hz. + gain_db : float + Characteristic gain in dB. + q : float + Quality factor. The frequency response differs in its bandwidth from + the standard biquad implementation due to frequency warping. This + is specially clear for normalized frequencies higher than 0.2. + Beware that the implemented shelving filters do not support setting + a quality factor. Analyzing the resulting magnitude response carefully + is advised. + sampling_rate_hz : int + Sampling rate for the digital filter. + q_factor : float, None, optional + Factor by which to scale `q` for peaking filters. This is useful for + attempting to obtain similar bandwidths as the standard biquad + implementation in [1]. If None, an approximation formula is used, which + works well in the gain range [-20, 20] dB and normalized frequency + [0, 0.2]. With increasing frequency, the warping of the standard + implementation produces larger errors, so approximating the bandwidth + there would defeat the purpose of the matching biquad. It always should + be greater than 0. Default: None. + + Returns + ------- + `Filter` + Matched biquad filter. + + Notes + ----- + Frequency warping generates significant deformations of the frequency + response of a digital filter near nyquist when compared to the analog + prototype. These filters alleviate for this at the expense of a more + involved computation. Using matched biquads is only useful when + designing filters or filter banks that have normalized frequencies above + 0.15 or 0.2, e.g., above 7.2 kHz for 48 kHz sampling rate. + + The approach used here comes from [2], though there are others. See + references. + + For shelving filters, [5] is implemented. This implementation does not + support selecting a quality factor, i.e., q is fixed to `sqrt(2)/2`. + + References + ---------- + - [1]: R. Bristow-Johnson, Cookbook formulae for audio EQ biquad filter + coefficients. + - [2]: M. Vicanek. Matched Second Order Digital Filters. 2016. + - [3]: S. J. Orfanidis, Digital Parametric Equalizer Design With Prescribed + Nyquist-Frequency Gain. 1997. + - [4]: M. Massberg, Digital Low-Pass Filter Design with Analog-Matched + Magnitude Response. 2011. + - [5]: M. Vicanek. Matched Two-Pole Digital Shelving Filters. 2024. + + """ + eq_type = eq_type.lower() + assert eq_type in ( + "peaking", + "lowpass", + "highpass", + "lowshelf", + "highshelf", + "bandpass", + ), f"{eq_type} is not valid as eq type" + assert ( + freq_hz > 0 and freq_hz < sampling_rate_hz / 2 + ), f"{freq_hz} is not a valid frequency" + assert q > 0, "Quality factor must be greater than zero" + + match eq_type: + case "peaking": + ba = _get_matched_peaking_eq( + freq_hz, gain_db, q, q_factor, sampling_rate_hz + ) + case "lowpass": + ba = _get_matched_lowpass_eq(freq_hz, gain_db, q, sampling_rate_hz) + case "highpass": + ba = _get_matched_highpass_eq( + freq_hz, gain_db, q, sampling_rate_hz + ) + case "bandpass": + ba = _get_matched_bandpass_eq( + freq_hz, gain_db, q, sampling_rate_hz + ) + case "lowshelf": + ba = _get_matched_shelving_eq( + freq_hz, gain_db, sampling_rate_hz, True + ) + case "highshelf": + ba = _get_matched_shelving_eq( + freq_hz, gain_db, sampling_rate_hz, False + ) + + return Filter( + "other", + {"ba": ba}, + sampling_rate_hz, + ) + + +def gaussian_kernel( + kernel_length_seconds: float, + kernel_boundary_value: float = 1e-2, + approximation_order: int = 12, + sampling_rate_hz: int = None, +): + """Approximate a gaussian FIR window with a first-order IIR approximation + kernel according to [1]. The resulting filter must be applied using + zero-phase filtering. + + Parameters + ---------- + kernel_length_seconds : float + Kernel length in seconds used to define the width of the gaussian + bell in relation to time. It corresponds to the time between `t=0` and + `t=t0` where `y(t0)=kernel_boundary_value`. + kernel_boundary_value : float, optional + Value that the gaussian window should reach after + `kernel_length_seconds`. Default: 1e-2. + approximation_order : int, optional + Order of the approximation. This corresponds to the number of times + that the filter will be applied (when using zero-phase filtering). The + higher this number, the better the approximation. This should be + an even number. Values around 10 will be sufficient in most cases. + Default: 12. + sampling_rate_hz : int + Sampling rate in Hz for the filter. + + Returns + ------- + Filter + IIR filter with the approximation kernel. It should always be applied + using zero-phase filtering! + + References + ---------- + - [1]: Alvarez, Mazorra, "Signal and Image Restoration using Shock Filters + and Anisotropic Diffusion," SIAM J. on Numerical Analysis, vol. 31, no. + 2, pp. 590-605, 1994. http://www.jstor.org/stable/2158018 + + """ + assert approximation_order % 2 == 0, "Approximation order must be even" + assert sampling_rate_hz is not None, "Sampling rate should not be None" + + K = approximation_order // 2 + + # Obtain sigma from kernel width definition in regards to time + kernel_length_samples = kernel_length_seconds * sampling_rate_hz + sigma = ( + kernel_length_samples + / (2.0 * np.log(1 / kernel_boundary_value)) ** 0.5 + ) + + # Before eq. (6) + lambdaa = sigma**2.0 / (2.0 * K) + + # Below eq. (9) + mu = (1.0 + 2.0 * lambdaa - (1.0 + 4.0 * lambdaa) ** 0.5) / (2.0 * lambdaa) + + # Eq. (7) + b = np.array([1.0]) * (mu / lambdaa) ** 0.5 + a = np.array([1.0, -mu]) + + sos = tf2sos(b, a) + sos = np.repeat(sos, K, axis=0) + + return Filter("other", {"sos": sos}, sampling_rate_hz) diff --git a/dsptoolbox/generators/generators.py b/dsptoolbox/generators/generators.py index c7f76c3..ee43818 100644 --- a/dsptoolbox/generators/generators.py +++ b/dsptoolbox/generators/generators.py @@ -5,14 +5,15 @@ """ import numpy as np -from ..classes.signal_class import Signal +from ..classes.signal import Signal +from ..classes.impulse_response import ImpulseResponse from .._general_helpers import ( _normalize, _fade, _pad_trim, _frequency_weightning, ) -from ..classes._filter import _impulse +from ..classes.filter_helpers import _impulse def noise( @@ -126,10 +127,7 @@ def noise( ) time_data[:l_samples, :] = vec - id = type_of_noise.lower() + " noise" - noise_sig = Signal( - None, time_data, sampling_rate_hz, signal_type="noise", signal_id=id - ) + noise_sig = Signal(None, time_data, sampling_rate_hz) return noise_sig @@ -245,13 +243,7 @@ def chirp( if number_of_channels != 1: chirp_n = np.repeat(chirp_n, repeats=number_of_channels, axis=1) # Signal - chirp_sig = Signal( - None, - chirp_n, - sampling_rate_hz, - signal_type="chirp", - signal_id=type_of_chirp, - ) + chirp_sig = Signal(None, chirp_n, sampling_rate_hz) return chirp_sig @@ -260,9 +252,9 @@ def dirac( delay_samples: int = 0, number_of_channels: int = 1, sampling_rate_hz: int | None = None, -) -> Signal: - """Generates a dirac impulse Signal with the specified length and - sampling rate. +) -> ImpulseResponse: + """Generates a dirac impulse (ImpulseResponse) with the specified length + and sampling rate. Parameters ---------- @@ -277,7 +269,7 @@ def dirac( Returns ------- - imp : `Signal` + imp : `ImpulseResponse` Signal with dirac impulse. """ @@ -298,7 +290,7 @@ def dirac( td[:, n] = _impulse( length_samples=length_samples, delay_samples=delay_samples ) - imp = Signal(None, td, sampling_rate_hz, signal_type="dirac") + imp = ImpulseResponse(None, td, sampling_rate_hz) return imp @@ -388,7 +380,7 @@ def harmonic( td = _pad_trim(td, l_samples + p_samples) # Signal - harmonic_sig = Signal(None, td, sampling_rate_hz, signal_type="general") + harmonic_sig = Signal(None, td, sampling_rate_hz) return harmonic_sig @@ -542,5 +534,5 @@ def oscillator( td = _pad_trim(td, l_samples + p_samples) # Signal - harmonic_sig = Signal(None, td, sampling_rate_hz, signal_type="general") + harmonic_sig = Signal(None, td, sampling_rate_hz) return harmonic_sig diff --git a/dsptoolbox/plots/plots.py b/dsptoolbox/plots/plots.py index 1d69676..f5a241e 100644 --- a/dsptoolbox/plots/plots.py +++ b/dsptoolbox/plots/plots.py @@ -42,7 +42,7 @@ def general_plot( ---------- x : array-like Vector for x axis. Pass `None` to ignore. - matrix : `np.ndarray` + matrix : NDArray[np.float64] Matrix with data to plot. range_x : array-like, optional Range to show for x axis. Default: None. @@ -138,7 +138,7 @@ def general_subplots_line( ---------- x : array-like Vector for x axis. - matrix : `np.ndarray` + matrix : NDArray[np.float64] Matrix with data to plot. column : bool, optional When `True`, the subplots are organized in one column. Default: `True`. @@ -236,7 +236,7 @@ def general_matrix_plot( Parameters ---------- - matrix : `np.ndarray` + matrix : NDArray[np.float64] Matrix with data to plot. range_x : array-like, optional Range to show for x axis. Default: `None`. diff --git a/dsptoolbox/room_acoustics/_room_acoustics.py b/dsptoolbox/room_acoustics/_room_acoustics.py index 7aebd61..23d3a7b 100644 --- a/dsptoolbox/room_acoustics/_room_acoustics.py +++ b/dsptoolbox/room_acoustics/_room_acoustics.py @@ -3,6 +3,7 @@ """ import numpy as np +from numpy.typing import NDArray from scipy.stats import pearsonr from warnings import warn from ..plots import general_plot @@ -10,7 +11,7 @@ def _reverb( - h, + h: NDArray[np.float64], fs_hz, mode, ir_start: int | None = None, @@ -21,7 +22,7 @@ def _reverb( Parameters ---------- - h : `np.ndarray` + h : NDArray[np.float64] Time series. fs_hz : int Sampling rate in Hz. @@ -87,12 +88,14 @@ def _reverb( return factor / np.abs(p[0]), corr -def _find_ir_start(ir, threshold_dbfs: float = -20) -> int: +def _find_ir_start( + ir: NDArray[np.float64], threshold_dbfs: float = -20 +) -> int: """Find start of an IR using a threshold. Done for 1D-arrays. Parameters ---------- - ir : `np.ndarray` + ir : NDArray[np.float64] IR as a 1D-array. threshold_dbfs : float, optional Threshold that should be surpassed at the start of the IR in dBFS. @@ -116,14 +119,14 @@ def _find_ir_start(ir, threshold_dbfs: float = -20) -> int: def _complex_mode_identification( - spectra: np.ndarray, maximum_singular_value: bool = True -) -> np.ndarray: + spectra: NDArray[np.complex128], maximum_singular_value: bool = True +) -> NDArray[np.float64]: """Complex transfer matrix and CMIF from: http://papers.vibetech.com/Paper17-CMIF.pdf Parameters ---------- - spectra : `np.ndarray` + spectra : NDArray[np.complex128] Matrix containing spectra of the necessary IR. maximum_singular_value : bool, optional When `True`, the maximum singular value at each frequency line is @@ -131,7 +134,7 @@ def _complex_mode_identification( Returns ------- - cmif : `np.ndarray` + cmif : NDArray[np.float64] Complex mode identificator function. References @@ -159,20 +162,22 @@ def _complex_mode_identification( return cmif -def _generate_rir(room_dim, alpha, s_pos, r_pos, rt, mo, sr) -> np.ndarray: +def _generate_rir( + room_dim, alpha, s_pos, r_pos, rt, mo, sr +) -> NDArray[np.float64]: """Generate RIR using image source model according to Brinkmann, et al. Parameters ---------- - room_dim : `np.ndarray` + room_dim : NDArray[np.float64] Room dimensions in meters. - alpha : float or `np.ndarray` + alpha : float or NDArray[np.float64] Mean absorption coefficient of the room or array with the absorption coefficient for each wall (length 6. Ordered as north, south, east, west, floor, ceiling). - s_pos : `np.ndarray` + s_pos : NDArray[np.float64] Source position. - r_pos : `np.ndarray` + r_pos : NDArray[np.float64] Receiver position. rt : float Desired reverberation time to achieve in RIR. @@ -183,7 +188,7 @@ def _generate_rir(room_dim, alpha, s_pos, r_pos, rt, mo, sr) -> np.ndarray: Returns ------- - rir : `np.ndarray` + rir : NDArray[np.float64] Time vector of the RIR. References @@ -363,21 +368,21 @@ def area(self, new_area): self.__area = new_area def modal_density( - self, f_hz: float | np.ndarray, c: float = 343 - ) -> float | np.ndarray: + self, f_hz: float | NDArray[np.float64], c: float = 343 + ) -> float | NDArray[np.float64]: """Compute and return the modal density for a given cut-off frequency and speed of sound. Parameters ---------- - f_hz : float or `np.ndarray` + f_hz : float or NDArray[np.float64] Frequency or array of frequencies. c : float, optional Speed of sound in m/s. Default: 343. Returns ------- - float or `np.ndarray` + float or NDArray[np.float64] Modal density. """ @@ -515,7 +520,9 @@ def get_mixing_time( self.mixing_time_s = mixing_time_s return self.mixing_time_s - def get_room_modes(self, max_order: int = 6, c: float = 343) -> np.ndarray: + def get_room_modes( + self, max_order: int = 6, c: float = 343.0 + ) -> NDArray[np.float64]: """Computes and returns room modes for a shoebox room assuming hard reflecting walls. @@ -531,7 +538,7 @@ def get_room_modes(self, max_order: int = 6, c: float = 343) -> np.ndarray: Returns ------- - modes : np.ndarray + modes : NDArray[np.float64] Array containing the frequencies of the room modes as well as their characteristics (orders in each room dimension. This is necessary to know if it is an axial, a tangential or oblique mode). @@ -580,7 +587,7 @@ def get_analytical_transfer_function( receiver_pos : array-like Receiver position in meters. It must be inside the room, otherwise an assertion error is raised. - freqs : `np.ndarray` + freqs : NDArray[np.float64] Frequency vector for which to compute the transfer function. max_mode_order : int, optional Maximum mode order to be regarded. It should be high enough to @@ -594,9 +601,9 @@ def get_analytical_transfer_function( Returns ------- - p : `np.ndarray` + p : NDArray[np.float64] Complex transfer function, non-normalized. - modes : `np.ndarray` + modes : NDArray[np.float64] Modes for which the transfer function was computed. It has shape (mode, frequency and order xyz) and it is sorted by frequency. @@ -859,13 +866,13 @@ def add_detailed_absorption(self, detailed_absorption: dict): def _add_reverberant_tail_noise( - rir: np.ndarray, mixing_time_s: int, t60: float, sr: int -) -> np.ndarray: + rir: NDArray[np.float64], mixing_time_s: int, t60: float, sr: int +) -> NDArray[np.float64]: """Adds a reverberant tail as noise to an IR. Parameters ---------- - rir : `np.ndarray` + rir : NDArray[np.float64] Impulse response as 1D-array. mixing_time_s : int Mixing time in samples. @@ -876,7 +883,7 @@ def _add_reverberant_tail_noise( Returns ------- - rir_late : `np.ndarray` + rir_late : NDArray[np.float64] RIR with added decaying noise as late reverberant tail. """ @@ -905,12 +912,14 @@ def _add_reverberant_tail_noise( return rir -def _d50_from_rir(td: np.ndarray, fs: int, automatic_trimming: bool) -> float: +def _d50_from_rir( + td: NDArray[np.float64], fs: int, automatic_trimming: bool +) -> float: """Compute definition D50 from a given RIR (1D-Array). Parameters ---------- - td : `np.ndarray` + td : NDArray[np.float64] IR. fs : int Sampling rate in Hz. @@ -940,12 +949,14 @@ def _d50_from_rir(td: np.ndarray, fs: int, automatic_trimming: bool) -> float: return np.sum(td[:window]) / np.sum(td[:stop]) -def _c80_from_rir(td: np.ndarray, fs: int, automatic_trimming: bool) -> float: +def _c80_from_rir( + td: NDArray[np.float64], fs: int, automatic_trimming: bool +) -> float: """Compute clarity C80 from a given RIR (1D-Array). Parameters ---------- - td : `np.ndarray` + td : NDArray[np.float64] IR. fs : int Sampling rate in Hz. @@ -977,12 +988,14 @@ def _c80_from_rir(td: np.ndarray, fs: int, automatic_trimming: bool) -> float: return 10 * np.log10(np.sum(td[:window]) / np.sum(td[window:stop])) -def _ts_from_rir(td: np.ndarray, fs: int, automatic_trimming: bool) -> float: +def _ts_from_rir( + td: NDArray[np.float64], fs: int, automatic_trimming: bool +) -> float: """Compute center time from a given RIR (1D-Array). Parameters ---------- - td : `np.ndarray` + td : NDArray[np.float64] IR. fs : int Sampling rate in Hz. @@ -1016,7 +1029,7 @@ def _ts_from_rir(td: np.ndarray, fs: int, automatic_trimming: bool) -> float: def _obtain_optimal_reverb_time( - time_vector: np.ndarray, edc: np.ndarray + time_vector: NDArray[np.float64], edc: NDArray[np.float64] ) -> tuple[float, float]: """Compute the optimal reverberation time by analyzing the best linear fit (with the smallest least-squares error) from T10 until T60. If EDT @@ -1025,9 +1038,9 @@ def _obtain_optimal_reverb_time( Parameters ---------- - time_vector : `np.ndarray` + time_vector : NDArray[np.float64] Time vector corresponding to the edc. - edc : `np.ndarray` + edc : NDArray[np.float64] Energy decay curve in dB and normalized so that 0 dB corresponds to the impulse. @@ -1061,7 +1074,7 @@ def _obtain_optimal_reverb_time( else: start = -5.0 - steps: np.ndarray = np.arange(start - 20, start - 60, -1) + steps: NDArray[np.float64] = np.arange(start - 20, start - 60, -1) end, r = _get_best_linear_fit_for_edc(time_vector, edc, start, steps) if r > -0.95: warn( @@ -1076,10 +1089,10 @@ def _obtain_optimal_reverb_time( def _get_best_linear_fit_for_edc( - time_vector: np.ndarray, - edc: np.ndarray, + time_vector: NDArray[np.float64], + edc: NDArray[np.float64], start_value: float, - steps: np.ndarray, + steps: NDArray[np.float64], ): """Obtain the best end value for a linear regression of the EDC based on the lowest pearson correlation coefficient, i.e., with the maximum of @@ -1087,13 +1100,13 @@ def _get_best_linear_fit_for_edc( Parameters ---------- - time_vector : `np.ndarray` + time_vector : NDArray[np.float64] Time vector. - edc : `np.ndarray` + edc : NDArray[np.float64] Energy decay curve. start_value : float Start value of the EDC in dB for the regression. - steps : `np.ndarray` + steps : NDArray[np.float64] Array of all ending values of the EDC in dB to take into account. Returns @@ -1117,20 +1130,20 @@ def _get_best_linear_fit_for_edc( def _get_polynomial_coeffs_from_edc( - time_vector: np.ndarray, - edc: np.ndarray, + time_vector: NDArray[np.float64], + edc: NDArray[np.float64], start_value: float, end_value: float, -) -> tuple[np.ndarray, float]: +) -> tuple[NDArray[np.float64], float]: """Return the polynomial coefficients from the energy decay curve for given starting and ending values. This can be used for all reverberation time computations. Parameters ---------- - time_vector : `np.ndarray` + time_vector : NDArray[np.float64] Time vector in seconds corresponding to the energy decay curve. - edc : `np.ndarray` + edc : NDArray[np.float64] Energy decay curve in dB normalized to 0 dB at the point of the impulse. start_value : float @@ -1140,7 +1153,7 @@ def _get_polynomial_coeffs_from_edc( Returns ------- - coeff : `np.ndarray` + coeff : NDArray[np.float64] Polynomial coefficients for x^1 and x^0, respectively. r_coefficient : float Pearson's correlation coefficient r. It takes values between [-1, 1] @@ -1160,12 +1173,14 @@ def _get_polynomial_coeffs_from_edc( def _compute_energy_decay_curve( - time_data: np.ndarray, + time_data: NDArray[np.float64], impulse_index: int, trim_automatically: bool, fs_hz: int, -) -> np.ndarray: +) -> NDArray[np.float64]: """Get the energy decay curve from an energy time curve.""" + # start_index might be the last index below -20 dB relative to peak value. + # If so, the normalization of the edc should be done with the beginning if trim_automatically: start_index, stopping_index, impulse_index = _trim_ir( time_data, @@ -1179,9 +1194,8 @@ def _compute_energy_decay_curve( signal_power = time_data[start_index:stopping_index] ** 2 edc = np.sum(signal_power) - np.cumsum(signal_power) epsilon = 1e-50 - edc = 10 * np.log10( - np.clip(edc / edc[impulse_index], a_min=epsilon, a_max=None) - ) + edc = 10 * np.log10(np.clip(edc, a_min=epsilon, a_max=None)) + edc -= edc[impulse_index] return edc diff --git a/dsptoolbox/room_acoustics/room_acoustics.py b/dsptoolbox/room_acoustics/room_acoustics.py index a16ed1c..81dd4d4 100644 --- a/dsptoolbox/room_acoustics/room_acoustics.py +++ b/dsptoolbox/room_acoustics/room_acoustics.py @@ -4,8 +4,9 @@ import numpy as np from scipy.signal import find_peaks, convolve +from numpy.typing import NDArray -from ..classes import Signal, MultiBandSignal, Filter +from ..classes import Signal, MultiBandSignal, Filter, ImpulseResponse from ..filterbanks import fractional_octave_bands, linkwitz_riley_crossovers from ._room_acoustics import ( _reverb, @@ -23,22 +24,21 @@ def reverb_time( - signal: Signal | MultiBandSignal, + signal: ImpulseResponse | MultiBandSignal, mode: str = "T20", - ir_start: int | np.ndarray | None = None, + ir_start: int | NDArray[np.int_] | None = None, automatic_trimming: bool = True, -) -> tuple[np.ndarray, np.ndarray]: +) -> tuple[NDArray[np.float64], NDArray[np.float64]]: """Computes reverberation time. Topt, T20, T30, T60 and EDT. Parameters ---------- - signal : `Signal` or `MultiBandSignal` - Signal for which to compute reverberation times. It must be type - `'ir'` or `'rir'`. + signal : `ImpulseResponse` or `MultiBandSignal` + IR for which to compute reverberation times. mode : str, optional Reverberation time mode. Options are `'Topt'`, `'T20'`, `'T30'`, `'T60'` or `'EDT'`. Default: `'Topt'`. - ir_start : int or array-like, optional + ir_start : int or array-like, NDArray[np.int_], optional If it is an integer, it is assumed as the start of the IR for all channels (and all bands). For more specific cases, pass a 1d-array containing the start indices for each channel or a 2d-array with @@ -51,10 +51,10 @@ def reverb_time( Returns ------- - reverberation_times : `np.ndarray` + reverberation_times : NDArray[np.float64] Reverberation times for each channel. Shape is (band, channel) if `MultiBandSignal` object is passed. - correlation_coefficient : `np.ndarray` + correlation_coefficient : NDArray[np.float64] Pearson correlation coefficient to determine the accuracy of the reverberation time estimation. It has shape (channels) or (band, channels) if `MultiBandSignal` object is passed. See notes @@ -76,12 +76,8 @@ def reverb_time( by 6. """ - if type(signal) is Signal: + if type(signal) is ImpulseResponse: ir_start = _check_ir_start_reverb(signal, ir_start) - assert signal.signal_type in ("ir", "rir"), ( - f"{signal.signal_type} is not a valid signal type for " - + "reverb_time. It should be ir or rir" - ) mode = mode.upper() valid_modes = ("TOPT", "T20", "T30", "T60", "EDT") assert mode in valid_modes, ( @@ -119,24 +115,25 @@ def reverb_time( ) else: raise TypeError( - "Passed signal should be of type Signal or MultiBandSignal" + f"Passed signal has type {type(signal)}. It should be of type" + + " ImpulseResponse or MultiBandSignal" ) return reverberation_times, correlation_coefficients def find_modes( - signal: Signal, + signal: ImpulseResponse, f_range_hz=[50, 200], dist_hz: float = 5, prominence_db: float | None = None, antiresonances: bool = False, -) -> np.ndarray: +) -> NDArray[np.float64]: """Finds the room modes of a set of RIR using the peaks of the complex mode indicator function (CMIF). Parameters ---------- - signal : `Signal` + signal : `ImpulseResponse` Signal containing the RIR'S from which to find the modes. f_range_hz : array-like, optional Vector setting range for mode search. Default: [50, 200]. @@ -151,7 +148,7 @@ def find_modes( Returns ------- - f_modes : `np.ndarray` + f_modes : NDArray[np.float64] Vector containing frequencies where modes have been localized. References @@ -166,12 +163,11 @@ def find_modes( assert len(f_range_hz) == 2, ( "Range of frequencies must have a " + "minimum and a maximum value" ) - - assert signal.signal_type in ("rir", "ir"), ( - f"{signal.signal_type} is not a valid signal type. It should " - + "be either rir or ir" - ) + assert ( + type(signal) is ImpulseResponse + ), "This is only valid for an impulse response" signal.set_spectrum_parameters("standard") + # Pad signal to have a resolution of around 1 Hz length = signal.sampling_rate_hz signal = pad_trim(signal, length) @@ -195,7 +191,7 @@ def find_modes( id_cmif, _ = find_peaks( 10 * np.log10(cmif), distance=dist_samp, - width=dist_samp, + # width=dist_samp, # Is width here a good idea? prominence=prominence_db, ) f_modes = f[id_cmif] @@ -205,7 +201,7 @@ def find_modes( def convolve_rir_on_signal( signal: Signal, - rir: Signal, + rir: ImpulseResponse, keep_peak_level: bool = True, keep_length: bool = True, ) -> Signal: @@ -218,8 +214,8 @@ def convolve_rir_on_signal( ---------- signal : Signal Signal to which the RIR is applied. All channels are affected. - rir : Signal - Single-channel Signal object containing the RIR. + rir : ImpulseResponse + Single-channel impulse response containing the RIR. keep_peak_level : bool, optional When `True`, output signal is normalized to the peak level of the original signal. Default: `True`. @@ -233,10 +229,9 @@ def convolve_rir_on_signal( Convolved signal with RIR. """ - assert rir.signal_type in ( - "rir", - "ir", - ), f"{rir.signal_type} is not a valid signal type. Set it to rir or ir." + assert isinstance( + rir, ImpulseResponse + ), "This is only valid for an impulse response" assert ( signal.time_data.shape[0] > rir.time_data.shape[0] ), "The RIR is longer than the signal to convolve it with." @@ -268,25 +263,26 @@ def convolve_rir_on_signal( new_sig = signal.copy() new_sig.time_data = new_time_data - new_sig.signal_id += " (convolved with RIR)" return new_sig -def find_ir_start(signal: Signal, threshold_dbfs: float = -20) -> np.ndarray: +def find_ir_start( + signal: ImpulseResponse, threshold_dbfs: float = -20 +) -> NDArray[np.int_]: """This function finds the start of an IR defined as the first sample before a certain threshold is surpassed. For room impulse responses, -20 dB relative to peak level is recommended according to [1]. Parameters ---------- - signal : `Signal` - IR signal. + signal : `ImpulseResponse` + IR. threshold_dbfs : float, optional Threshold that should be passed (in dBFS). Default: -20. Returns ------- - start_index : `np.ndarray` + start_index : NDArray[np.int_] Index of IR start for each channel. References @@ -299,7 +295,7 @@ def find_ir_start(signal: Signal, threshold_dbfs: float = -20) -> np.ndarray: start_index = np.empty(signal.number_of_channels, dtype=int) for n in range(signal.number_of_channels): start_index[n] = _find_ir_start(signal.time_data[:, n], threshold_dbfs) - return start_index.astype(int) + return start_index.astype(np.int_) def generate_synthetic_rir( @@ -312,7 +308,7 @@ def generate_synthetic_rir( apply_bandpass: bool = False, use_detailed_absorption: bool = False, max_order: int | None = None, -) -> Signal: +) -> ImpulseResponse: """This function returns a synthetized RIR in a shoebox-room using the image source model. The implementation is based on Brinkmann, et al. See References for limitations and advantages of this method. @@ -350,7 +346,7 @@ def generate_synthetic_rir( Returns ------- - rir : `Signal` + rir : `ImpulseResponse` Newly generated RIR. References @@ -430,7 +426,7 @@ def generate_synthetic_rir( rir_band = _pad_trim(rir_band, total_length_samples) # Prune possible nan values np.nan_to_num(rir_band, copy=False, nan=0) - rir0 = Signal(None, rir_band, sampling_rate_hz) + rir0 = ImpulseResponse(None, rir_band, sampling_rate_hz) rir_multi = fb.filter_signal(rir0, zero_phase=True) rir += rir_multi.bands[ind].time_data[:, 0] @@ -444,13 +440,7 @@ def generate_synthetic_rir( rir, room.mixing_time_s, room.t60_s, sr=sampling_rate_hz ) - rir_output = Signal( - None, - rir, - sampling_rate_hz, - signal_type="rir", - signal_id="Synthetized RIR using the image source method", - ) + rir_output = ImpulseResponse(None, rir, sampling_rate_hz) # Bandpass signal in order to have a realistic audio signal representation if apply_bandpass: @@ -470,7 +460,7 @@ def generate_synthetic_rir( def descriptors( - rir: Signal | MultiBandSignal, + rir: ImpulseResponse | MultiBandSignal, mode: str = "d50", automatic_trimming_rir: bool = True, ): @@ -478,7 +468,7 @@ def descriptors( Parameters ---------- - rir : `Signal` or `MultiBandSignal` + rir : `ImpulseResponse` or `MultiBandSignal` Room impulse response. If it is a multi-channel signal, the descriptor given back has the shape (channel). If it is a `MultiBandSignal`, the descriptor has shape (band, channel). @@ -500,7 +490,7 @@ def descriptors( Returns ------- - output_descriptor : `np.ndarray` + output_descriptor : NDArray[np.float64] Array containing the output descriptor. If RIR is a `Signal`, it has shape (channel). If RIR is a `MultiBandSignal`, the array has shape (band, channel). @@ -518,7 +508,7 @@ def descriptors( "br", "ts", ), "Given mode is not in the available descriptors" - if type(rir) is Signal: + if isinstance(rir, ImpulseResponse): if mode == "d50": func = _d50_from_rir elif mode == "c80": @@ -549,17 +539,17 @@ def descriptors( return desc -def _bass_ratio(rir: Signal) -> np.ndarray: +def _bass_ratio(rir: ImpulseResponse) -> NDArray[np.float64]: """Core computation of bass ratio. Parameters ---------- - rir : `Signal` + rir : `ImpulseResponse` RIR. Returns ------- - br : `np.ndarray` + br : NDArray[np.float64] Bass ratio per channel. """ @@ -575,8 +565,9 @@ def _bass_ratio(rir: Signal) -> np.ndarray: def _check_ir_start_reverb( - sig: Signal | MultiBandSignal, ir_start: int | np.ndarray | list | tuple -) -> np.ndarray | list | None: + sig: ImpulseResponse | MultiBandSignal, + ir_start: int | NDArray[np.int_] | list | tuple | None, +) -> NDArray[np.float64] | list | None: """This method checks `ir_start` and parses it into the necessary form if relevant. For a `Signal`, it is a vector with the same number of elements as channels of `sig`. For `MultiBandSignal`, it is a 2d-array @@ -588,16 +579,18 @@ def _check_ir_start_reverb( """ if ir_start is not None: - if type(ir_start) in (list, tuple, np.ndarray): - ir_start = np.atleast_1d(ir_start).astype(int) + if type(ir_start) in (list, tuple, NDArray[np.float64]): + ir_start = np.atleast_1d(ir_start).astype(np.int_) assert ( np.issubdtype(type(ir_start), np.integer) or type(ir_start) is np.ndarray ), "Unsupported type for ir_start" - if type(sig) is Signal: + if isinstance(sig, ImpulseResponse): if np.issubdtype(type(ir_start), np.integer): - ir_start = np.ones(sig.number_of_channels, dtype=int) * ir_start + ir_start = ( + np.ones(sig.number_of_channels, dtype=np.int_) * ir_start + ) elif ir_start is None: return [None] * sig.number_of_channels assert ( @@ -608,7 +601,7 @@ def _check_ir_start_reverb( ir_start = ( np.ones( (sig.number_of_bands, sig.number_of_channels), - dtype=int, + dtype=np.int_, ) * ir_start ) @@ -624,5 +617,5 @@ def _check_ir_start_reverb( sig.number_of_channels, ), "Shape of ir_start is not valid for the passed signal" if ir_start.dtype not in (int, np.intp): - ir_start = ir_start.astype(int) + ir_start = ir_start.astype(np.int_) return ir_start diff --git a/dsptoolbox/standard_functions.py b/dsptoolbox/standard_functions.py index 6a8115c..ff23fb5 100644 --- a/dsptoolbox/standard_functions.py +++ b/dsptoolbox/standard_functions.py @@ -7,10 +7,10 @@ """ import numpy as np +from numpy.typing import NDArray import pickle from scipy.signal import resample_poly, convolve, hilbert -# from scipy.special import iv as bessel_first_mod from fractions import Fraction from warnings import warn @@ -22,8 +22,6 @@ ) from ._standard import ( _latency, - _center_frequencies_fractional_octaves_iec, - _exact_center_frequencies_fractional_octaves, _indices_above_threshold_dbfs, _detrend, _rms, @@ -36,6 +34,7 @@ _check_format_in_path, _get_smoothing_factor_ema, _fractional_latency, + _get_correlation_of_latencies, ) @@ -43,7 +42,7 @@ def latency( in1: Signal | MultiBandSignal, in2: Signal | MultiBandSignal | None = None, polynomial_points: int = 0, -) -> np.ndarray[int | float]: +) -> tuple[NDArray[np.float64] | NDArray[np.int_], NDArray[np.float64]]: """Computes latency between two signals using the correlation method. If there is no second signal, the latency between the first and the other channels is computed. `in1` is to be understood as a delayed version @@ -58,6 +57,10 @@ def latency( returned for the respective channel. To avoid fractional latency, use `polynomial_points = 0`. + The quality of the estimation is assessed by computing the pearson + correlation coefficient between the two time series after compensating the + delay. See notes for details. + Parameters ---------- in1 : `Signal` or `MultiBandSignal` @@ -74,10 +77,18 @@ def latency( Returns ------- - lags : `np.ndarray` - Delays. For `Signal`, the output shape is (channel). + lags : NDArray[np.float64] + Delays in samples. For `Signal`, the output shape is (channel). In case in2 is `None`, the length is `channels - 1`. In the case of `MultiBandSignal`, output shape is (band, channel). + correlations : NDArray[np.float64] + Correlation for computed delays with the same shape as lags. + + Notes + ----- + - The correlation coefficients have values between [-1, 1]. The closer the + absolute value is to 1, the better the latency estimation. This is always + computed using the integer latency for performance. References ---------- @@ -113,9 +124,15 @@ def latency( in1.number_of_channels > 1 ), "Signal must have at least 2 channels to compare" td2 = None - return latency_func( + latencies = latency_func( in1.time_data, td2, polynomial_points=polynomial_points ) + return latencies, _get_correlation_of_latencies( + td2 if td2 is not None else in1.time_data[:, 0][..., None], + in1.time_data if td2 is not None else in1.time_data[:, 1:], + np.round(latencies, 0).astype(np.int_), + ) + elif isinstance(in1, MultiBandSignal): if in2 is not None: assert isinstance( @@ -132,8 +149,11 @@ def latency( lags = np.zeros( (in1.number_of_bands, in1.number_of_channels), dtype=data_type ) + correlations = np.zeros( + (in1.number_of_bands, in1.number_of_channels), dtype=np.float64 + ) for band in range(in1.number_of_bands): - lags[band, :] = latency( + lags[band, :], correlations[band, :] = latency( in1.bands[band], in2.bands[band], polynomial_points=polynomial_points, @@ -143,8 +163,12 @@ def latency( (in1.number_of_bands, in1.number_of_channels - 1), dtype=data_type, ) + correlations = np.zeros( + (in1.number_of_bands, in1.number_of_channels - 1), + dtype=np.float64, + ) for band in range(in1.number_of_bands): - lags[band, :] = latency( + lags[band, :], correlations[band, :] = latency( in1.bands[band], None, polynomial_points=polynomial_points ) return lags @@ -351,83 +375,6 @@ def resample(sig: Signal, desired_sampling_rate_hz: int) -> Signal: return new_sig -def fractional_octave_frequencies( - num_fractions=1, frequency_range=(20, 20e3), return_cutoff=False -) -> ( - tuple[np.ndarray, np.ndarray, tuple[np.ndarray, np.ndarray]] - | tuple[np.ndarray, np.ndarray] -): - """Return the octave center frequencies according to the IEC 61260:1:2014 - standard. This implementation has been taken from the pyfar package. See - references. - - For numbers of fractions other than `1` and `3`, only the - exact center frequencies are returned, since nominal frequencies are not - specified by corresponding standards. - - Parameters - ---------- - num_fractions : int, optional - The number of bands an octave is divided into. Eg., ``1`` refers to - octave bands and ``3`` to third octave bands. The default is ``1``. - frequency_range : array, tuple - The lower and upper frequency limits, the default is - ``frequency_range=(20, 20e3)``. - - Returns - ------- - nominal : array, float - The nominal center frequencies in Hz specified in the standard. - Nominal frequencies are only returned for octave bands and third octave - bands. Otherwise, an empty array is returned. - exact : array, float - The exact center frequencies in Hz, resulting in a uniform distribution - of frequency bands over the frequency range. - cutoff_freq : tuple, array, float - The lower and upper critical frequencies in Hz of the bandpass filters - for each band as a tuple corresponding to `(f_lower, f_upper)`. - - References - ---------- - - The pyfar package: https://github.com/pyfar/pyfar - - """ - nominal = np.array([]) - - f_lims = np.asarray(frequency_range) - if f_lims.size != 2: - raise ValueError( - "You need to specify a lower and upper limit frequency." - ) - if f_lims[0] > f_lims[1]: - raise ValueError( - "The second frequency needs to be higher than the first." - ) - - if num_fractions in [1, 3]: - nominal, exact = _center_frequencies_fractional_octaves_iec( - nominal, num_fractions - ) - - mask = (nominal >= f_lims[0]) & (nominal <= f_lims[1]) - nominal = nominal[mask] - exact = exact[mask] - - else: - exact = _exact_center_frequencies_fractional_octaves( - num_fractions, f_lims - ) - - if return_cutoff: - octave_ratio = 10 ** (3 / 10) - freqs_upper = exact * octave_ratio ** (1 / 2 / num_fractions) - freqs_lower = exact * octave_ratio ** (-1 / 2 / num_fractions) - f_crit = (freqs_lower, freqs_upper) - return nominal, exact, f_crit - else: - return nominal, exact - - def normalize( sig: Signal | MultiBandSignal, peak_dbfs: float = -6, @@ -561,94 +508,9 @@ def fade( return new_sig -def erb_frequencies( - freq_range_hz=[20, 20000], - resolution: float = 1, - reference_frequency_hz: float = 1000, -) -> np.ndarray: - """Get frequencies that are linearly spaced on the ERB frequency scale. - This implementation was taken and adapted from the pyfar package. See - references. - - Parameters - ---------- - freq_range : array-like, optional - The upper and lower frequency limits in Hz between which the frequency - vector is computed. Default: [20, 20e3]. - resolution : float, optional - The frequency resolution in ERB units. 1 returns frequencies that are - spaced by 1 ERB unit, a value of 0.5 would return frequencies that are - spaced by 0.5 ERB units. Default: 1. - reference_frequency : float, optional - The reference frequency in Hz relative to which the frequency vector - is constructed. Default: 1000. - - Returns - ------- - frequencies : `np.ndarray` - The frequencies in Hz that are linearly distributed on the ERB scale - with a spacing given by `resolution` ERB units. - - References - ---------- - - The pyfar package: https://github.com/pyfar/pyfar - - B. C. J. Moore, An introduction to the psychology of hearing, - (Leiden, Boston, Brill, 2013), 6th ed. - - V. Hohmann, “Frequency analysis and synthesis using a gammatone - filterbank,” Acta Acust. united Ac. 88, 433-442 (2002). - - P. L. Søndergaard, and P. Majdak, “The auditory modeling toolbox,” - in The technology of binaural listening, edited by J. Blauert - (Heidelberg et al., Springer, 2013) pp. 33-56. - - """ - - # check input - if ( - not isinstance(freq_range_hz, (list, tuple, np.ndarray)) - or len(freq_range_hz) != 2 - ): - raise ValueError("freq_range must be an array like of length 2") - if freq_range_hz[0] > freq_range_hz[1]: - freq_range_hz = [freq_range_hz[1], freq_range_hz[0]] - if resolution <= 0: - raise ValueError("Resolution must be larger than zero") - - # convert the frequency range and reference to ERB scale - # (Hohmann 2002, Eq. 16) - erb_range = ( - 9.2645 - * np.sign(freq_range_hz) - * np.log(1 + np.abs(freq_range_hz) * 0.00437) - ) - erb_ref = ( - 9.2645 - * np.sign(reference_frequency_hz) - * np.log(1 + np.abs(reference_frequency_hz) * 0.00437) - ) - - # get the referenced range - erb_ref_range = np.array([erb_ref - erb_range[0], erb_range[1] - erb_ref]) - - # construct the frequencies on the ERB scale - n_points = np.floor(erb_ref_range / resolution).astype(int) - erb_points = ( - np.arange(-n_points[0], n_points[1] + 1) * resolution + erb_ref - ) - - # convert to frequencies in Hz - frequencies = ( - 1 - / 0.00437 - * np.sign(erb_points) - * (np.exp(np.abs(erb_points) / 9.2645) - 1) - ) - - return frequencies - - def true_peak_level( signal: Signal | MultiBandSignal, -) -> tuple[np.ndarray, np.ndarray]: +) -> tuple[NDArray[np.float64], NDArray[np.float64]]: """Computes true-peak level of a signal using the standardized method by the Rec. ITU-R BS.1770-4. See references. @@ -659,10 +521,10 @@ def true_peak_level( Returns ------- - true_peak_levels : `np.ndarray` + true_peak_levels : NDArray[np.float64] True-peak levels (in dBTP) as an array with shape (channels) or (band, channels) in case that the input signal is `MultiBandSignal`. - peak_levels : `np.ndarray` + peak_levels : NDArray[np.float64] Peak levels (in dBFS) as an array with shape (channels) or (band, channels) in case that the input signal is `MultiBandSignal`. @@ -995,7 +857,9 @@ def detrend( raise TypeError("Pass either a Signal or a MultiBandSignal") -def rms(sig: Signal | MultiBandSignal, in_dbfs: bool = True) -> np.ndarray: +def rms( + sig: Signal | MultiBandSignal, in_dbfs: bool = True +) -> NDArray[np.float64]: """Returns Root Mean Squared (RMS) value for each channel. Parameters @@ -1008,7 +872,7 @@ def rms(sig: Signal | MultiBandSignal, in_dbfs: bool = True) -> np.ndarray: Returns ------- - rms_values : `np.ndarray` + rms_values : NDArray[np.float64] Array with RMS values. If a `Signal` is passed, it has shape (channel). If a `MultiBandSignal` is passed, its shape is (bands, channel). @@ -1178,7 +1042,6 @@ def calibrate_signal( if isinstance(signal, Signal): calibrated_signal = signal.copy() - calibrated_signal.signal_id += " – Calibrated (time data in Pa)" calibrated_signal.constrain_amplitude = False calibrated_signal.time_data *= calibration_factors calibrated_signal.calibrated_signal = True @@ -1187,7 +1050,6 @@ def calibrate_signal( for b in calibrated_signal: b.constrain_amplitude = False b.time_data *= calibration_factors - b.signal_id += " – Calibrated (time data in Pa)" b.calibrated_signal = True else: raise TypeError( @@ -1221,7 +1083,7 @@ def envelope( Returns ------- - `np.ndarray` + NDArray[np.float64] Signal envelope. It has the shape (time sample, channel) or (time sample, band, channel) in case of `MultiBandSignal`. diff --git a/dsptoolbox/tools.py b/dsptoolbox/tools.py new file mode 100644 index 0000000..a11306d --- /dev/null +++ b/dsptoolbox/tools.py @@ -0,0 +1,419 @@ +""" +This module contains general math and dsp utilities. These functions are solely +based on arrays and primitive data types. +""" + +import numpy as np +from numpy.typing import NDArray +from typing import Any +from scipy.interpolate import interp1d + +from ._general_helpers import ( + _fractional_octave_smoothing as fractional_octave_smoothing, + _wrap_phase as wrap_phase, + _get_smoothing_factor_ema as get_smoothing_factor_ema, + _interpolate_fr as interpolate_fr, + _time_smoothing as time_smoothing, + _scale_spectrum as scale_spectrum, +) + +from ._standard import ( + _center_frequencies_fractional_octaves_iec, + _exact_center_frequencies_fractional_octaves, +) + + +def log_frequency_vector( + frequency_range_hz: list[float], n_bins_per_octave: int +) -> NDArray[np.float64]: + """Obtain a logarithmically spaced frequency vector with a specified number + of frequency bins per octave. + + Parameters + ---------- + frequency_range_hz : list[float] + Frequency with length 2 for defining the frequency range. The lowest + frequency should be above 0. + n_bins_per_octave : int + Number of frequency bins in each octave. + + Returns + ------- + NDArray[np.float64] + Log-spaced frequency vector + + """ + assert frequency_range_hz[0] > 0, "The first frequency bin should not be 0" + + n_octave = np.log2(frequency_range_hz[1] / frequency_range_hz[0]) + return frequency_range_hz[0] * 2 ** ( + np.arange(0, n_octave, 1 / n_bins_per_octave) + ) + + +def to_db( + x: NDArray[np.float64], + amplitude_input: bool, + dynamic_range_db: float | None = None, + min_value: float | None = float(np.finfo(np.float64).smallest_normal), +) -> NDArray[np.float64]: + """Convert to dB from amplitude or power representation. Clipping small + values can be activated in order to avoid -inf dB outcomes. + + Parameters + ---------- + x : NDArray[np.float64] + Array to convert to dB. + amplitude_input : bool + Set to True if the values in x are in their linear form. False means + they have been already squared, i.e., in their power form. + dynamic_range_db : float, None, optional + If specified, a dynamic range in dB for the vector is applied by + finding its largest value and clipping to `max - dynamic_range_db`. + This will always overwrite `min_value` if specified. Pass None to + ignore. Default: None. + min_value : float, None, optional + Minimum value to clip `x` before converting into dB in order to avoid + `np.nan` or `-np.inf` in the output. Pass None to ignore. Default: + `np.finfo(np.float64).smallest_normal`. + + Returns + ------- + NDArray[np.float64] + New array or float in dB. + + """ + factor = 20.0 if amplitude_input else 10.0 + + if min_value is None and dynamic_range_db is None: + return factor * np.log10(np.abs(x)) + + x_abs = np.abs(x) + + if dynamic_range_db is not None: + min_value = np.max(x_abs) * 10.0 ** (-abs(dynamic_range_db) / factor) + + return factor * np.log10(np.clip(x_abs, a_min=min_value, a_max=None)) + + +def from_db(x: float | NDArray[np.float64], amplitude_output: bool): + """Get the values in their amplitude or power form from dB. + + Parameters + ---------- + x : float, NDArray[np.float64] + Values in dB. + amplitude_output : bool + When True, the values are returned in their linear form. Otherwise, + the squared (power) form is returned. + + Returns + ------- + float NDArray[np.float64] + Converted values + + """ + factor = 20.0 if amplitude_output else 10.0 + return 10 ** (x / factor) + + +def get_exact_value_at_frequency( + freqs_hz: NDArray[np.float64], y: NDArray[Any], f: float = 1e3 +): + """Return the exact value at 1 kHz extracted by using linear interpolation. + + Parameters + ---------- + freqs_hz : NDArray[np.float64] + Frequency vector in Hz. It is assumed to be in ascending order. + y : NDArray[np.float64] + Values to use for the interpolation. + f : float, optional + Frequency to query. Default: 1000. + + Returns + ------- + float + Queried value. + + """ + assert ( + freqs_hz[0] <= f and freqs_hz[-1] >= f + ), "Frequency vector does not contain 1 kHz" + assert freqs_hz.ndim == 1, "Frequency vector can only have one dimension" + assert len(freqs_hz) == len(y), "Lengths do not match" + + # Single value in vector or last value matches + if freqs_hz[-1] == f: + return y[-1] + + ind = np.searchsorted(freqs_hz, f) + if freqs_hz[ind] > f: + ind -= 1 + return (f - freqs_hz[ind]) * (y[ind + 1] - y[ind]) / ( + freqs_hz[ind + 1] - freqs_hz[ind] + ) + y[ind] + + +def log_mean(x: NDArray[np.float64], axis: int = 0): + """Get the mean value while using a logarithmic x-axis. It is assumed that + `x` is initially linearly-spaced. + + Parameters + ---------- + x : NDArray[np.float64] + Vector for which to obtain the mean. + axis : int, optional + Axis along which to compute the mean. + + Returns + ------- + float or NDArray[np.float64] + Logarithmic mean along the selected axis. + + """ + # Linear and logarithmic frequency vector + N = x.shape[axis] + l1 = np.arange(N) + k_log = (N) ** (l1 / (N - 1)) + # Interpolate to logarithmic scale + vec_log = interp1d( + l1 + 1, x, kind="linear", copy=False, assume_sorted=True, axis=axis + )(k_log) + return np.mean(vec_log, axis=axis) + + +def frequency_crossover( + crossover_region_hz: list[float], + logarithmic: bool = True, +): + """Return a callable that can be used to extract values from a crossover + to use on frequency data. This uses a hann window function to generate the + crossover. It is a "fade-in", i.e., the values are 0 before the low + frequency and rise up to 1 at the high frequency of the crossover. + + Parameters + ---------- + crossover_region_hz : list with length 2 + Frequency range for which to create the crossover. + logarithmic : bool, optional + When True, the crossover is defined logarithmically on the frequency + axis. Default: True. + + Returns + ------- + callable + Callable that produces values from the crossover function. The input + should always be in Hz. It can take float or NDArray[np.float64] and + returns the same type. + + """ + f = ( + log_frequency_vector(crossover_region_hz, 250) + if logarithmic + else np.linspace( + crossover_region_hz[0], + crossover_region_hz[1], + int(crossover_region_hz[1] - crossover_region_hz[0]), + ) + ) + length = len(f) + w = np.hanning(length * 2)[:length] + i = interp1d( + f, + w, + kind="cubic", + copy=False, + bounds_error=False, + fill_value=(0.0, 1.0), + assume_sorted=True, + ) + + def func(x: float | NDArray[np.float64]) -> float | NDArray[np.float64]: + return i(x) + + return func + + +def fractional_octave_frequencies( + num_fractions=1, frequency_range=(20, 20e3), return_cutoff=False +) -> ( + tuple[ + NDArray[np.float64], + NDArray[np.float64], + tuple[NDArray[np.float64], NDArray[np.float64]], + ] + | tuple[NDArray[np.float64], NDArray[np.float64]] +): + """Return the octave center frequencies according to the IEC 61260:1:2014 + standard. This implementation has been taken from the pyfar package. See + references. + + For numbers of fractions other than `1` and `3`, only the + exact center frequencies are returned, since nominal frequencies are not + specified by corresponding standards. + + Parameters + ---------- + num_fractions : int, optional + The number of bands an octave is divided into. Eg., ``1`` refers to + octave bands and ``3`` to third octave bands. The default is ``1``. + frequency_range : array, tuple + The lower and upper frequency limits, the default is + ``frequency_range=(20, 20e3)``. + + Returns + ------- + nominal : array, float + The nominal center frequencies in Hz specified in the standard. + Nominal frequencies are only returned for octave bands and third octave + bands. Otherwise, an empty array is returned. + exact : array, float + The exact center frequencies in Hz, resulting in a uniform distribution + of frequency bands over the frequency range. + cutoff_freq : tuple, array, float + The lower and upper critical frequencies in Hz of the bandpass filters + for each band as a tuple corresponding to `(f_lower, f_upper)`. + + References + ---------- + - The pyfar package: https://github.com/pyfar/pyfar + + """ + nominal = np.array([]) + + f_lims = np.asarray(frequency_range) + if f_lims.size != 2: + raise ValueError( + "You need to specify a lower and upper limit frequency." + ) + if f_lims[0] > f_lims[1]: + raise ValueError( + "The second frequency needs to be higher than the first." + ) + + if num_fractions in [1, 3]: + nominal, exact = _center_frequencies_fractional_octaves_iec( + nominal, num_fractions + ) + + mask = (nominal >= f_lims[0]) & (nominal <= f_lims[1]) + nominal = nominal[mask] + exact = exact[mask] + + else: + exact = _exact_center_frequencies_fractional_octaves( + num_fractions, f_lims + ) + + if return_cutoff: + octave_ratio = 10 ** (3 / 10) + freqs_upper = exact * octave_ratio ** (1 / 2 / num_fractions) + freqs_lower = exact * octave_ratio ** (-1 / 2 / num_fractions) + f_crit = (freqs_lower, freqs_upper) + return nominal, exact, f_crit + else: + return nominal, exact + + +def erb_frequencies( + freq_range_hz=[20, 20000], + resolution: float = 1, + reference_frequency_hz: float = 1000, +) -> NDArray[np.float64]: + """Get frequencies that are linearly spaced on the ERB frequency scale. + This implementation was taken and adapted from the pyfar package. See + references. + + Parameters + ---------- + freq_range : array-like, optional + The upper and lower frequency limits in Hz between which the frequency + vector is computed. Default: [20, 20e3]. + resolution : float, optional + The frequency resolution in ERB units. 1 returns frequencies that are + spaced by 1 ERB unit, a value of 0.5 would return frequencies that are + spaced by 0.5 ERB units. Default: 1. + reference_frequency : float, optional + The reference frequency in Hz relative to which the frequency vector + is constructed. Default: 1000. + + Returns + ------- + frequencies : NDArray[np.float64] + The frequencies in Hz that are linearly distributed on the ERB scale + with a spacing given by `resolution` ERB units. + + References + ---------- + - The pyfar package: https://github.com/pyfar/pyfar + - B. C. J. Moore, An introduction to the psychology of hearing, + (Leiden, Boston, Brill, 2013), 6th ed. + - V. Hohmann, “Frequency analysis and synthesis using a gammatone + filterbank,” Acta Acust. united Ac. 88, 433-442 (2002). + - P. L. Søndergaard, and P. Majdak, “The auditory modeling toolbox,” + in The technology of binaural listening, edited by J. Blauert + (Heidelberg et al., Springer, 2013) pp. 33-56. + + """ + + # check input + if ( + not isinstance(freq_range_hz, (list, tuple, np.ndarray)) + or len(freq_range_hz) != 2 + ): + raise ValueError("freq_range must be an array like of length 2") + if freq_range_hz[0] > freq_range_hz[1]: + freq_range_hz = [freq_range_hz[1], freq_range_hz[0]] + if resolution <= 0: + raise ValueError("Resolution must be larger than zero") + + # convert the frequency range and reference to ERB scale + # (Hohmann 2002, Eq. 16) + erb_range = ( + 9.2645 + * np.sign(freq_range_hz) + * np.log(1 + np.abs(freq_range_hz) * 0.00437) + ) + erb_ref = ( + 9.2645 + * np.sign(reference_frequency_hz) + * np.log(1 + np.abs(reference_frequency_hz) * 0.00437) + ) + + # get the referenced range + erb_ref_range = np.array([erb_ref - erb_range[0], erb_range[1] - erb_ref]) + + # construct the frequencies on the ERB scale + n_points = np.floor(erb_ref_range / resolution).astype(int) + erb_points = ( + np.arange(-n_points[0], n_points[1] + 1) * resolution + erb_ref + ) + + # convert to frequencies in Hz + frequencies = ( + 1 + / 0.00437 + * np.sign(erb_points) + * (np.exp(np.abs(erb_points) / 9.2645) - 1) + ) + + return frequencies + + +__all__ = [ + "fractional_octave_smoothing", + "wrap_phase", + "get_smoothing_factor_ema", + "interpolate_fr", + "time_smoothing", + "log_frequency_vector", + "to_db", + "from_db", + "get_exact_value_at_frequency", + "log_mean", + "frequency_crossover", + "erb_frequencies", + "fractional_octave_frequencies", + "scale_spectrum", +] diff --git a/dsptoolbox/transfer_functions/_transfer_functions.py b/dsptoolbox/transfer_functions/_transfer_functions.py index b700b2b..a54b53c 100644 --- a/dsptoolbox/transfer_functions/_transfer_functions.py +++ b/dsptoolbox/transfer_functions/_transfer_functions.py @@ -7,6 +7,7 @@ from scipy.fft import next_fast_len from scipy.stats import pearsonr from warnings import warn +from numpy.typing import NDArray from .._general_helpers import ( _find_nearest, _calculate_window, @@ -17,13 +18,13 @@ def _spectral_deconvolve( - num_fft: np.ndarray, - denum_fft: np.ndarray, + num_fft: NDArray[np.complex128], + denum_fft: NDArray[np.complex128], freqs_hz, time_signal_length: int, mode="regularized", start_stop_hz=None, -) -> np.ndarray: +) -> NDArray[np.complex128]: assert num_fft.shape == denum_fft.shape, "Shapes do not match" assert len(freqs_hz) == len(num_fft), "Frequency vector does not match" @@ -64,7 +65,7 @@ def _window_this_ir_tukey( offset_samples: int = 0, left_to_right_flank_ratio: float = 1.0, adaptive_window: bool = True, -) -> tuple[np.ndarray, np.ndarray, np.ndarray]: +) -> tuple[NDArray[np.float64], NDArray[np.float64], int]: """This function finds the index of the impulse and trims or windows it accordingly. Window used and the start sample are returned. @@ -163,16 +164,16 @@ def _window_this_ir_tukey( def _window_this_ir( vec, total_length: int, window_type: str = "hann", window_parameter=None -) -> tuple[np.ndarray, np.ndarray, np.ndarray]: +) -> tuple[NDArray[np.float64], NDArray[np.float64], int]: """This function windows an impulse response by placing the peak exactly in the middle of the window. It trims or pads at the end if needed. The windowed IR, window and the start sample are passed. Returns ------- - td : `np.ndarray` + td : NDArray[np.float64] Windowed vector. - w : `np.ndarray` + w : NDArray[np.float64] Generated window. ind_low_td : int Sample position of the start. @@ -231,19 +232,19 @@ def _window_this_ir( return td, w, ind_low_td -def _warp_time_series(td: np.ndarray, warping_factor: float): +def _warp_time_series(td: NDArray[np.float64], warping_factor: float): """Warp or unwarp a time series. Parameters ---------- - td : `np.ndarray` + td : NDArray[np.float64] Time series with shape (time samples, channels). warping_factor : float The warping factor to use. Returns ------- - warped_td : `np.ndarray` + warped_td : NDArray[np.float64] Time series in the (un)warped domain. """ @@ -277,7 +278,7 @@ def _get_harmonic_times( chirp_length_s: float, n_harmonics: int, time_offset_seconds: float = 0.0, -) -> np.ndarray: +) -> NDArray[np.float64]: """Get the time at which each harmonic IR occur relative to the fundamental IR in a measurement with an exponential chirp. This is computed according to [1]. If the fundamental happens at time `t=0`, all harmonics will be at @@ -296,7 +297,7 @@ def _get_harmonic_times( Returns ------- - np.ndarray + NDArray[np.float64] Array with the times for each harmonic in ascending order. The values are given in seconds. @@ -310,7 +311,7 @@ def _get_harmonic_times( def _trim_ir( - time_data: np.ndarray, + time_data: NDArray[np.float64], fs_hz: int, offset_start_s: float, ) -> tuple[int, int, int]: diff --git a/dsptoolbox/transfer_functions/transfer_functions.py b/dsptoolbox/transfer_functions/transfer_functions.py index fa3a0ef..0135aaa 100644 --- a/dsptoolbox/transfer_functions/transfer_functions.py +++ b/dsptoolbox/transfer_functions/transfer_functions.py @@ -3,6 +3,7 @@ """ import numpy as np +from numpy.typing import NDArray from scipy.signal import minimum_phase as min_phase_scipy from scipy.fft import rfft as rfft_scipy, next_fast_len as next_fast_length_fft from scipy.interpolate import interp1d @@ -15,8 +16,8 @@ _get_harmonic_times, _trim_ir, ) -from ..classes import Signal, Filter -from ..classes._filter import _group_delay_filter +from ..classes import Signal, Filter, ImpulseResponse +from ..classes.filter_helpers import _group_delay_filter from .._general_helpers import ( _remove_ir_latency_from_phase, _min_phase_ir_from_real_cepstrum, @@ -51,7 +52,7 @@ def spectral_deconvolve( threshold_db=-30, padding: bool = False, keep_original_length: bool = False, -) -> Signal: +) -> ImpulseResponse: """Deconvolution by spectral division of two signals. If the denominator signal only has one channel, the deconvolution is done using that channel for all channels of the numerator. @@ -159,9 +160,7 @@ def spectral_deconvolve( start_stop_hz=start_stop_hz, mode=mode, ) - new_sig = Signal( - None, new_time_data, num.sampling_rate_hz, signal_type="ir" - ) + new_sig = ImpulseResponse(None, new_time_data, num.sampling_rate_hz) if padding: if keep_original_length: new_sig.time_data = _pad_trim(new_sig.time_data, original_length) @@ -169,7 +168,7 @@ def spectral_deconvolve( def window_ir( - signal: Signal, + signal: ImpulseResponse, total_length_samples: int, adaptive: bool = True, constant_percentage: float = 0.75, @@ -177,7 +176,7 @@ def window_ir( at_start: bool = True, offset_samples: int = 0, left_to_right_flank_length_ratio: float = 1.0, -) -> tuple[Signal, np.ndarray]: +) -> tuple[ImpulseResponse, NDArray[np.float64]]: """Windows an IR with trimming and selection of constant valued length. This is equivalent to a tukey window whose flanks can be selected to be any type. The peak of the impulse response is aligned to correspond to @@ -185,7 +184,7 @@ def window_ir( Parameters ---------- - signal : `Signal` + signal : `ImpulseResponse` Signal to window total_length_samples : int Total window length in samples. @@ -218,9 +217,9 @@ def window_ir( Returns ------- - new_sig : `Signal` + new_sig : `ImpulseResponse` Windowed signal. The used window is also saved under `new_sig.window`. - start_positions_samples : `np.ndarray` + start_positions_samples : NDArray[np.float64] This array contains the position index of the start of the IR in each channel of the original IR (relative to the possibly padded windowed IR). @@ -239,10 +238,9 @@ def window_ir( parts of the window are set to 0 in order to make them visible. """ - assert signal.signal_type in ( - "rir", - "ir", - ), f"{signal.signal_type} is not a valid signal type. Use rir or ir." + assert ( + type(signal) is ImpulseResponse + ), "This is only valid for an impulse response" assert ( constant_percentage < 1 and constant_percentage >= 0 ), "Constant percentage can not be larger than 1 or smaller than 0" @@ -282,17 +280,17 @@ def window_ir( def window_centered_ir( - signal: Signal, + signal: ImpulseResponse, total_length_samples: int, window_type: str | tuple = "hann", -) -> tuple[Signal, np.ndarray]: +) -> tuple[ImpulseResponse, NDArray[np.float64]]: """This function windows an IR placing its peak in the middle. It trims it to the total length of the window or pads it to the desired length (padding in the end, window has `total_length`). Parameters ---------- - signal: `Signal` + signal: `ImpulseResponse` Signal to window total_length_samples: int Total window length in samples. @@ -305,9 +303,9 @@ def window_centered_ir( Returns ------- - new_sig : `Signal` + new_sig : `ImpulseResponse` Windowed signal. The used window is also saved under `new_sig.window`. - start_positions_samples : `np.ndarray` + start_positions_samples : NDArray[np.float64] This array contains the position index of the start of the IR in each channel of the original IR. @@ -318,10 +316,9 @@ def window_centered_ir( given length. """ - assert signal.signal_type in ( - "rir", - "ir", - ), f"{signal.signal_type} is not a valid signal type. Use rir or ir." + assert ( + type(signal) is ImpulseResponse + ), "This is only valid for an impulse response" new_time_data = np.zeros((total_length_samples, signal.number_of_channels)) start_positions_samples = np.zeros(signal.number_of_channels, dtype=int) @@ -348,7 +345,7 @@ def compute_transfer_function( mode="h2", window_length_samples: int = 1024, spectrum_parameters: dict | None = None, -) -> tuple[Signal, np.ndarray, np.ndarray]: +) -> tuple[ImpulseResponse, NDArray[np.complex128], NDArray[np.float64]]: r"""Gets transfer function H1, H2 or H3 (for stochastic signals). H1: for noise in the output signal. `Gxy/Gxx`. H2: for noise in the input signal. `Gyy/Gyx`. @@ -375,13 +372,13 @@ def compute_transfer_function( Returns ------- - tf_sig : `Signal` - Transfer functions as `Signal` object. Coherences are also computed - and saved in the `Signal` object. - tf : `np.ndarray` - Complex transfer function as type `np.ndarray` with shape (frequency, - channel). - coherence : `np.ndarray` + tf_sig : `ImpulseResponse` + Transfer functions as `ImpulseResponse` object. Coherences are also + computed and saved in the `ImpulseResponse` object. + tf : NDArray[np.complex128] + Complex transfer function as type NDArray[np.complex128] with shape + (frequency, channel). + coherence : NDArray[np.float64] Coherence of the measurement with shape (frequency, channel). Notes @@ -471,30 +468,29 @@ def compute_transfer_function( elif mode == "h3".casefold(): tf[:, n] = G_xy / np.abs(G_xy) * (G_yy / G_xx) ** 0.5 coherence[:, n] = np.abs(G_xy) ** 2 / G_xx / G_yy - tf_sig = Signal( + tf_sig = ImpulseResponse( None, np.fft.irfft(tf, axis=0, n=window_length_samples), output.sampling_rate_hz, - signal_type=mode.lower(), ) tf_sig.set_coherence(coherence) return tf_sig, tf, coherence def average_irs( - signal: Signal, mode: str = "time", normalize_energy: bool = True -) -> Signal: + signal: ImpulseResponse, mode: str = "time", normalize_energy: bool = True +) -> ImpulseResponse: """Averages all channels of a given IR. It can either use a time domain average while time-aligning all channels to the one with the longest latency, or average directly their magnitude and phase responses. Parameters ---------- - signal : `Signal` + signal : `ImpulseResponse` Signal with channels to be averaged over. mode : str, optional It can be either `"time"` or `"spectral"`. When `"time"` is selected, - the IRs are time-aligned to the channel with the biggest latency + the IRs are time-aligned to the channel with the largest latency and then averaged in the time domain. `"spectral"` averages directly the magnitude and phase of each IR. Default: `"time"`. normalize_energy : bool, optional @@ -505,14 +501,13 @@ def average_irs( Returns ------- - avg_sig : `Signal` - Averaged signal. + avg_sig : `ImpulseResponse` + Averaged impulse response. """ - assert signal.signal_type in ("rir", "ir"), ( - "Averaging is valid for signal types rir or ir and not " - + f"{signal.signal_type}" - ) + assert ( + type(signal) is ImpulseResponse + ), "This is only valid for an impulse response" mode = mode.lower() assert mode in ( "time", @@ -566,17 +561,16 @@ def average_irs( def min_phase_from_mag( - spectrum: np.ndarray, + spectrum: NDArray[np.float64], sampling_rate_hz: int, original_length_time_data: int | None = None, - signal_type: str = "ir", -): +) -> ImpulseResponse: """Returns a minimum-phase signal from a magnitude spectrum using the discrete hilbert transform. Parameters ---------- - spectrum : `np.ndarray` + spectrum : NDArray[np.float64] Spectrum (no scaling) with only positive frequencies. sampling_rate_hz : int Signal's sampling rate in Hz. @@ -585,12 +579,10 @@ def min_phase_from_mag( necessary for reconstruction of the time data since the first half of the spectrum (only positive frequencies) is ambiguous. Pass `None` to assume an even length. Default: `None`. - signal_type : str, optional - Type of signal to be returned. Default: `'ir'`. Returns ------- - sig_min_phase : `Signal` + sig_min_phase : `ImpulseResponse` Signal with same magnitude spectrum but minimum phase. References @@ -619,23 +611,19 @@ def min_phase_from_mag( time_data = np.fft.irfft( spectrum * np.exp(1j * phase), axis=0, n=original_length_time_data ) - sig_min_phase = Signal( - None, - time_data=time_data, - sampling_rate_hz=sampling_rate_hz, - signal_type=signal_type, + sig_min_phase = ImpulseResponse( + None, time_data=time_data, sampling_rate_hz=sampling_rate_hz ) return sig_min_phase def lin_phase_from_mag( - spectrum: np.ndarray, + spectrum: NDArray[np.float64], sampling_rate_hz: int, original_length_time_data: int | None = None, group_delay_ms: str | float = "minimum", check_causality: bool = True, - signal_type: str = "ir", -) -> Signal: +) -> ImpulseResponse: """Returns a linear phase signal from a magnitude spectrum. It is possible to return the smallest causal group delay by checking the minimum phase version of the signal and choosing a constant group delay that is never @@ -647,7 +635,7 @@ def lin_phase_from_mag( Parameters ---------- - spectrum : `np.ndarray` + spectrum : NDArray[np.float64] Spectrum with only positive frequencies and 0. sampling_rate_hz : int Signal's sampling rate in Hz. @@ -664,8 +652,6 @@ def lin_phase_from_mag( check_causality : bool, optional When `True`, it is assessed for each channel that the given group delay is not lower than the minimum group delay. Default: `True`. - signal_type : str, optional - Type of signal to be returned. Default: `'ir'`. Returns ------- @@ -733,18 +719,17 @@ def lin_phase_from_mag( phase = _correct_for_real_phase_spectrum(_wrap_phase(phase)) lin_spectrum[:, n] = spectrum[:, n] * np.exp(1j * phase) time_data = np.fft.irfft(lin_spectrum, axis=0, n=original_length_time_data) - sig_lin_phase = Signal( - None, - time_data=time_data, - sampling_rate_hz=sampling_rate_hz, - signal_type=signal_type, + sig_lin_phase = ImpulseResponse( + None, time_data=time_data, sampling_rate_hz=sampling_rate_hz ) return sig_lin_phase def min_phase_ir( - sig: Signal, method: str = "real cepstrum", padding_factor: int = 8 -) -> Signal: + sig: ImpulseResponse, + method: str = "real cepstrum", + padding_factor: int = 8, +) -> ImpulseResponse: """Returns same IR with minimum phase. Three methods are available for computing the minimum phase version of the IR: `'real cepstrum'` (using filtering the real-cepstral domain) and `'equiripple'` (for @@ -755,7 +740,7 @@ def min_phase_ir( Parameters ---------- - sig : `Signal` + sig : `ImpulseResponse` IR for which to compute minimum phase IR. method : str, optional For general cases, `'real cepstrum'`. If the IR is symmetric (like a @@ -768,14 +753,13 @@ def min_phase_ir( Returns ------- - min_phase_sig : `Signal` + min_phase_sig : `ImpulseResponse` Minimum-phase IR as time signal. """ - assert sig.signal_type in ( - "rir", - "ir", - ), "Signal type must be either rir or ir" + assert ( + type(sig) is ImpulseResponse + ), "This is only valid for an impulse response" method = method.lower() assert method in ("real cepstrum", "equiripple"), ( f"{method} is not valid. Use either real cepstrum or " + "equiripple" @@ -811,7 +795,7 @@ def group_delay( method="matlab", smoothing: int = 0, remove_ir_latency: bool = False, -) -> tuple[np.ndarray, np.ndarray]: +) -> tuple[NDArray[np.float64], NDArray[np.float64]]: """Computes and returns group delay. Parameters @@ -833,9 +817,9 @@ def group_delay( Returns ------- - freqs : `np.ndarray` + freqs : NDArray[np.float64] Frequency vector in Hz. - group_delays : `np.ndarray` + group_delays : NDArray[np.float64] Matrix containing group delays in seconds with shape (gd, channel). """ @@ -861,13 +845,9 @@ def group_delay( signal._spectrum_parameters = spec_parameters if remove_ir_latency: - assert signal.signal_type in ( - "rir", - "ir", - ), ( - f"{signal.signal_type} is not a valid signal type. Use ir " - + "or rir" - ) + assert ( + type(signal) is ImpulseResponse + ), "This is only valid for an impulse response" sp = _remove_ir_latency_from_phase( f, np.angle(sp), signal.time_data, signal.sampling_rate_hz, 1 ) @@ -890,10 +870,10 @@ def group_delay( def minimum_phase( - signal: Signal, + signal: ImpulseResponse, method: str = "real cepstrum", padding_factor: int = 8, -) -> tuple[np.ndarray, np.ndarray]: +) -> tuple[NDArray[np.float64], NDArray[np.float64]]: """Gives back a matrix containing the minimum phase signal for each channel. Two methods are available for computing the minimum phase of a system: `'real cepstrum'` (windowing in the cepstral domain) or @@ -913,19 +893,15 @@ def minimum_phase( Returns ------- - f : `np.ndarray` + f : NDArray[np.float64] Frequency vector. - min_phases : `np.ndarray` + min_phases : NDArray[np.float64] Minimum phases as matrix with shape (phase, channel). """ - assert signal.signal_type in ( - "rir", - "ir", - "h1", - "h2", - "h3", - ), "Signal type must be rir or ir" + assert ( + type(signal) is ImpulseResponse + ), "This is only valid for an impulse response" method = method.lower() assert method in ( "real cepstrum", @@ -962,15 +938,15 @@ def minimum_phase( def minimum_group_delay( - signal: Signal, + signal: ImpulseResponse, smoothing: int = 0, padding_factor: int = 8, -) -> tuple[np.ndarray, np.ndarray]: +) -> tuple[NDArray[np.float64], NDArray[np.float64]]: """Computes minimum group delay of given IR using the real cepstrum method. Parameters ---------- - signal : `Signal` + signal : `ImpulseResponse` IR for which to compute minimal group delay. smoothing : int, optional Octave fraction by which to apply smoothing. `0` avoids any smoothing @@ -982,9 +958,9 @@ def minimum_group_delay( Returns ------- - f : `np.ndarray` + f : NDArray[np.float64] Frequency vector. - min_gd : `np.ndarray` + min_gd : NDArray[np.float64] Minimum group delays in seconds as matrix with shape (gd, channel). References @@ -992,7 +968,9 @@ def minimum_group_delay( - https://www.roomeqwizard.com/help/help_en-GB/html/minimumphase.html """ - assert signal.signal_type in ("rir", "ir"), "Only valid for rir or ir" + assert ( + type(signal) is ImpulseResponse + ), "This is only valid for an impulse response" f, min_phases = minimum_phase(signal, padding_factor=padding_factor) min_gd = np.zeros_like(min_phases) for n in range(signal.number_of_channels): @@ -1003,15 +981,15 @@ def minimum_group_delay( def excess_group_delay( - signal: Signal, + signal: ImpulseResponse, smoothing: int = 0, remove_ir_latency: bool = False, -) -> tuple[np.ndarray, np.ndarray]: +) -> tuple[NDArray[np.float64], NDArray[np.float64]]: """Computes excess group delay of an IR. Parameters ---------- - signal : `Signal` + signal : `ImpulseResponse` IR for which to compute minimal group delay. smoothing : int, optional Octave fraction by which to apply smoothing. `0` avoids any smoothing @@ -1022,9 +1000,9 @@ def excess_group_delay( Returns ------- - f : `np.ndarray` + f : NDArray[np.float64] Frequency vector. - ex_gd : `np.ndarray` + ex_gd : NDArray[np.float64] Excess group delays in seconds with shape (excess_gd, channel). References @@ -1032,7 +1010,9 @@ def excess_group_delay( - https://www.roomeqwizard.com/help/help_en-GB/html/minimumphase.html """ - assert signal.signal_type in ("rir", "ir"), "Only valid for rir or ir" + assert ( + type(signal) is ImpulseResponse + ), "This is only valid for an impulse response" f, min_gd = minimum_group_delay( signal, smoothing=0, padding_factor=8 if remove_ir_latency else 1 ) @@ -1051,12 +1031,12 @@ def excess_group_delay( def combine_ir_with_dirac( - ir: Signal, + ir: ImpulseResponse, crossover_frequency: float, take_lower_band: bool, order: int = 8, normalization: str | None = None, -) -> Signal: +) -> ImpulseResponse: """Combine an IR with a perfect impulse at a given crossover frequency using a linkwitz-riley crossover. Forward-Backward filtering is done so that no phase distortion occurs. They can optionally be energy matched @@ -1095,7 +1075,9 @@ def combine_ir_with_dirac( added dirac impulse has time to grow smoothly. """ - assert ir.signal_type in ("rir", "ir"), "Only valid for rir or ir" + assert ( + type(ir) is ImpulseResponse + ), "This is only valid for an impulse response" if normalization is not None: normalization = normalization.lower() assert normalization in ( @@ -1161,10 +1143,10 @@ def combine_ir_with_dirac( def ir_to_filter( - signal: Signal, channel: int = 0, phase_mode: str = "direct" + signal: ImpulseResponse, channel: int = 0, phase_mode: str = "direct" ) -> Filter: - """This function takes in a signal with type `'ir'` or `'rir'` and turns - the selected channel into an FIR filter. With `phase_mode` it is possible + """This function takes in an impulse response and turns the selected + channel into an FIR filter. With `phase_mode` it is possible to use minimum phase or minimum linear phase. Parameters @@ -1184,10 +1166,9 @@ def ir_to_filter( FIR filter object. """ - assert signal.signal_type in ("ir", "rir", "h1", "h2", "h3"), ( - f"{signal.signal_type} is not valid. Use one of " - + """('ir', 'rir', 'h1', 'h2', 'h3')""" - ) + assert ( + type(signal) is ImpulseResponse + ), "This is only valid for an impulse response" assert ( channel < signal.number_of_channels ), f"Signal does not have a channel {channel}" @@ -1216,7 +1197,7 @@ def ir_to_filter( return filt -def filter_to_ir(fir: Filter) -> Signal: +def filter_to_ir(fir: Filter) -> ImpulseResponse: """Takes in an FIR filter and converts it into an IR by taking its b coefficients. @@ -1235,34 +1216,29 @@ def filter_to_ir(fir: Filter) -> Signal: fir.filter_type == "fir" ), "This is only valid is only available for FIR filters" b, _ = fir.get_coefficients(mode="ba") - new_sig = Signal( - None, - b, - sampling_rate_hz=fir.sampling_rate_hz, - signal_type="ir", - signal_id="IR from FIR filter", - ) + new_sig = ImpulseResponse(None, b, sampling_rate_hz=fir.sampling_rate_hz) return new_sig def window_frequency_dependent( - ir: Signal, + ir: ImpulseResponse, cycles: int, channel: int | None = None, frequency_range_hz: list | None = None, scaling: str | None = None, + end_window_value: float = 0.5, ): """A spectrum with frequency-dependent windowing defined by cycles is returned. To this end, a variable gaussian window is applied. A width of 5 cycles means that there are 5 periods of each frequency - before the window values hit 0.5, i.e., -6 dB. + before the window values hit `end_window_value`. This is computed only for real-valued signals (positive frequencies). Parameters ---------- - ir : `Signal` + ir : `ImpulseResponse` Impulse response from which to extract the spectrum. cycles : int Number of cycles to include for each frequency bin. It defines @@ -1278,13 +1254,16 @@ def window_frequency_dependent( `"amplitude spectral density"`, `"fft"` or `None`. The first two take the window into account. `"fft"` scales the forward FFT by `1/N**0.5` and `None` leaves the spectrum completely unscaled. Default: `None`. + end_window_value : float, optional + This is the value that the gaussian window should have at its width. + Default: 0.5. Returns ------- - f : `np.ndarray` + f : NDArray[np.float64] Frequency vector. - spec : `np.ndarray` - Spectrum with shape (frequency, channel). + spec : NDArray[np.complex128] + Complex spectrum with shape (frequency, channel). Notes ----- @@ -1302,7 +1281,9 @@ def window_frequency_dependent( correct way to obtain the spectrum. """ - assert ir.signal_type in ("rir", "ir"), "Only valid for rir or ir" + assert ( + type(ir) is ImpulseResponse + ), "This is only valid for an impulse response" scaling = scaling if scaling is None else scaling.lower() assert scaling in ( "amplitude spectrum", @@ -1336,18 +1317,22 @@ def window_frequency_dependent( # Samples for each frequency according to number of cycles if f[0] == 0: - f[0] = f[1] + if len(f) > 1: + f[0] = f[1] cycles_per_freq_samples = np.round(fs / f * cycles).astype(int) - if f[0] == f[1]: - f[0] = 0 + if len(f) > 1: + if f[0] == f[1]: + f[0] = 0 spec = np.zeros((len(f), td.shape[1]), dtype=np.complex128) + # Alpha such that window is exactly end_window_value after the number of + # required samples for each frequency half = (td.shape[0] - 1) / 2 - alpha_factor = np.log(4) ** 0.5 * half - ind_max = np.argmax(np.abs(td), axis=0) + alpha_factor = np.log(1 / (end_window_value) ** 2) ** 0.5 * half # Construct window vectors + ind_max = np.argmax(np.abs(td), axis=0) n = np.zeros_like(td) for ch in range(td.shape[1]): n[:, ch] = np.arange(-ind_max[ch], td.shape[0] - ind_max[ch]) @@ -1355,12 +1340,12 @@ def window_frequency_dependent( # Scaling function if scaling == "amplitude spectrum": - def scaling_func(window: np.ndarray): + def scaling_func(window: NDArray[np.float64]): return 2**0.5 / np.sum(window, axis=0, keepdims=True) elif scaling == "amplitude spectral density": - def scaling_func(window: np.ndarray): + def scaling_func(window: NDArray[np.float64]): return ( 2 / np.sum(window**2, axis=0, keepdims=True) @@ -1370,17 +1355,15 @@ def scaling_func(window: np.ndarray): elif scaling == "fft": scaling_value = fast_length**-0.5 - def scaling_func(window: np.ndarray): + def scaling_func(window: NDArray[np.float64]): return scaling_value else: - def scaling_func(window: np.ndarray): + def scaling_func(window: NDArray[np.float64]): return 1 - # Precompute window factors: - # Alpha such that window is exactly 0.5 after the number of - # required samples for each frequency + # Precompute some window factors n = -0.5 * (n / half) ** 2 alpha = (alpha_factor / cycles_per_freq_samples) ** 2 for ind, ind_f in enumerate(inds_f): @@ -1390,7 +1373,7 @@ def scaling_func(window: np.ndarray): def warp_ir( - ir: Signal, + ir: ImpulseResponse, warping_factor: float, shift_ir: bool = True, total_length: int | None = None, @@ -1402,7 +1385,7 @@ def warp_ir( Parameters ---------- - ir : `Signal` + ir : `ImpulseResponse` Impulse response to (un)warp. warping_factor : float Warping factor. It has to be in the range ]-1; 1[. @@ -1442,7 +1425,9 @@ def warp_ir( NY, USA, 2001, pp. 35-38, doi: 10.1109/ASPAA.2001.969536. """ - assert ir.signal_type in ("rir", "ir"), "Signal has to be an IR or a RIR" + assert ( + type(ir) is ImpulseResponse + ), "This is only valid for an impulse response" assert np.abs(warping_factor) < 1, "Warping factor has to be in ]-1; 1[" td = ir.time_data @@ -1463,39 +1448,41 @@ def warp_ir( return f_unwarped, warped_ir -def find_ir_latency(ir: Signal) -> np.ndarray: +def find_ir_latency(ir: ImpulseResponse) -> NDArray[np.float64]: """Find the subsample maximum of each channel of the IR using the its minimum phase equivalent. Parameters ---------- - ir : `Signal` + ir : `ImpulseResponse` Impulse response to find the maximum. Returns ------- - latency_samples : `np.ndarray` + latency_samples : NDArray[np.float64] Array with the position of each channel's maximum in samples. """ - assert ir.signal_type in ("rir", "ir"), "Only valid for rir or ir" + assert ( + type(ir) is ImpulseResponse + ), "This is only valid for an impulse response" min_ir = min_phase_ir(ir) - return latency(ir, min_ir, 1) + return latency(ir, min_ir, 1)[0] def harmonics_from_chirp_ir( - ir: Signal, + ir: ImpulseResponse, chirp_range_hz: list, chirp_length_s: float, n_harmonics: int = 5, offset_percentage: float = 0.05, -) -> list[Signal]: +) -> list[ImpulseResponse]: """Get the individual harmonics (distortion) IRs of an IR computed with an exponential chirp. Parameters ---------- - ir : `Signal` + ir : `ImpulseResponse` Impulse response obtained through deconvolution with an exponential chirp. chirp_range_hz : list of length 2 @@ -1513,7 +1500,7 @@ def harmonics_from_chirp_ir( Returns ------- - harmonics : list[Signal] + harmonics : list[ImpulseResponse] List containing the IRs of each harmonic in ascending order. The fundamental is not in the list. @@ -1524,10 +1511,9 @@ def harmonics_from_chirp_ir( not be checked in this function. """ - assert ir.signal_type in ( - "ir", - "rir", - ), "Signal type has to be either ir or rir" + assert ( + type(ir) is ImpulseResponse + ), "This is only valid for an impulse response" assert ( offset_percentage < 1 and offset_percentage >= 0 ), "Offset must be smaller than one" @@ -1573,22 +1559,21 @@ def harmonics_from_chirp_ir( def harmonic_distortion_analysis( - ir: Signal | list[Signal], + ir: ImpulseResponse | list[ImpulseResponse], chirp_range_hz: list | None = None, chirp_length_s: float | None = None, n_harmonics: int | None = 8, smoothing: int = 12, generate_plot: bool = True, ) -> dict: - """ - Analyze non-linear distortion coming from an IR measured with an + """Analyze non-linear distortion coming from an IR measured with an exponential chirp. The range of the chirp and its length must be known. The distortion spectra of each harmonic, as well as THD+N and THD, are returned. Optionally, a plot can be generated. Parameters ---------- - ir : `Signal` or list[`Signal`] + ir : `ImpulseResponse` or list[`ImpulseResponse`] Impulse response. It should only have one channel. Alternatively, a list containing the fundamental IR and all harmonics can be passed, in which case `chirp_range_hz`, `chirp_length_s` and `n_harmonics` @@ -1636,7 +1621,7 @@ def harmonic_distortion_analysis( """ if type(ir) is list: for each_ir in ir: - assert type(each_ir) is Signal, "Unsupported type" + assert isinstance(each_ir, ImpulseResponse), "Unsupported type" assert ( each_ir.number_of_channels == 1 ), "Only single-channel IRs are supported" @@ -1650,7 +1635,7 @@ def harmonic_distortion_analysis( chirp_range_hz = [0, ir2.sampling_rate_hz // 2] passed_harmonics = True - elif type(ir) is Signal: + elif isinstance(ir, ImpulseResponse): assert ( chirp_length_s is not None and chirp_range_hz is not None @@ -1777,20 +1762,19 @@ def harmonic_distortion_analysis( def trim_ir( - ir: Signal, + ir: ImpulseResponse, channel: int = 0, start_offset_s: float = 20e-3, -) -> tuple[Signal, int, int]: - """ - Trim an IR in the beginning and end. This method acts only on one channel - and returns it trimmed. For defining the ending, a smooth envelope of the - energy time curve (ETC) is used, as well as the assumption that the energy - should decay monotonically after the impulse arrives. See notes for +) -> tuple[ImpulseResponse, int, int]: + """Trim an IR in the beginning and end. This method acts only on one + channel and returns it trimmed. For defining the ending, a smooth envelope + of the energy time curve (ETC) is used, as well as the assumption that the + energy should decay monotonically after the impulse arrives. See notes for details. Parameters ---------- - ir : `Signal` + ir : `ImpulseResponse` Impulse response to trim. channel : int, optional Channel to take from `rir`. Default: 0. @@ -1802,7 +1786,7 @@ def trim_ir( Returns ------- - trimmed_ir : `Signal` + trimmed_ir : `ImpulseResponse` IR with the new length. start : int Start index of the trimmed IR in the original vector. diff --git a/dsptoolbox/transforms/_transforms.py b/dsptoolbox/transforms/_transforms.py index 83c33cc..2d03efc 100644 --- a/dsptoolbox/transforms/_transforms.py +++ b/dsptoolbox/transforms/_transforms.py @@ -3,6 +3,7 @@ """ import numpy as np +from numpy.typing import NDArray from scipy.signal import get_window @@ -17,7 +18,7 @@ def _pitch2frequency(tuning_a_hz: float = 440): Returns ------- - freqs : `np.ndarray` + freqs : NDArray[np.float64] Frequencies for each pitch. It always has length 128. """ @@ -60,19 +61,19 @@ def get_center_frequency(self): domain = x[-1] - x[0] return ind / domain - def get_scale_lengths(self, frequencies: np.ndarray, fs: int): + def get_scale_lengths(self, frequencies: NDArray[np.float64], fs: int): """Returns the lengths of the queried frequencies. Parameters ---------- - frequencies : `np.ndarray` + frequencies : NDArray[np.float64] Frequencies for which to scale the wavelet. fs : int Sampling rate in Hz. Returns ------- - `np.ndarray` + NDArray[np.float64] Lengths of wavelets in samples. """ @@ -143,11 +144,13 @@ def __init__( self.step = step self.interpolation = interpolation - def _get_x(self) -> np.ndarray: + def _get_x(self) -> NDArray[np.float64]: """Returns x vector for the mother wavelet.""" return np.arange(self.bounds[0], self.bounds[1] + self.step, self.step) - def get_base_wavelet(self) -> tuple[np.ndarray, np.ndarray]: + def get_base_wavelet( + self, + ) -> tuple[NDArray[np.float64], NDArray[np.float64]]: """Return complex morlet wavelet.""" x = self._get_x() return x, 1 / np.sqrt(np.pi * self.b) * np.exp( @@ -159,22 +162,22 @@ def get_center_frequency(self) -> float: return 1 / self.scale def get_wavelet( - self, f: float | np.ndarray, fs: int - ) -> np.ndarray | list[np.ndarray]: + self, f: float | NDArray[np.float64], fs: int + ) -> NDArray[np.float64] | list[NDArray[np.float64]]: """Return wavelet scaled for a specific frequency and sampling rate. The wavelet values can also be linearly interpolated for a higher accuracy at the expense of computation time. Parameters ---------- - f : float or `np.ndarray` + f : float or NDArray[np.float64] Queried frequency or array of frequencies. fs : int Sampling rate in Hz. Returns ------- - wave : `np.ndarray` or list of `np.ndarray` + wave : NDArray[np.float64] or list of NDArray[np.float64] Wavelet function. It is either a 1d-array for a single frequency or a list of arrays for multiple frequencies. @@ -200,7 +203,9 @@ def get_wavelet( wave.append(wavef) return wave - def _get_interpolated_wave(self, base: np.ndarray, inds: np.ndarray): + def _get_interpolated_wave( + self, base: NDArray[np.float64], inds: NDArray[np.float64] + ): """Return the wavelet function for a selection of index using linear interpolation. @@ -220,16 +225,20 @@ def _get_interpolated_wave(self, base: np.ndarray, inds: np.ndarray): def _squeeze_scalogram( - scalogram: np.ndarray, freqs: np.ndarray, fs: int, delta_w: float = 0.05 -) -> np.ndarray: + scalogram: NDArray[np.float64], + freqs: NDArray[np.float64], + fs: int, + delta_w: float = 0.05, + apply_frequency_normalization: bool = False, +) -> NDArray[np.float64]: """Synchrosqueeze a scalogram. Parameters ---------- - scalogram : `np.ndarray` + scalogram : NDArray[np.float64] Complex scalogram from the CWT with shape (frequency, time sample, channel). - freqs : `np.ndarray` + freqs : NDArray[np.float64] Frequency vector. fs : int Sampling rate in Hz. @@ -237,16 +246,22 @@ def _squeeze_scalogram( Maximum relative difference in frequency allowed in the phase transform for taking summing the result of the scalogram. If it's too small, it might lead to significant energy leaks. Default: 0.05. + apply_frequency_normalization : bool, optional + When `True`, each scale is scaled by taking into account the + normalization as shown in Eq. (2.4) of [1]. `False` does not apply + any normalization. Default: `False`. Returns ------- - sync : `np.ndarray` + sync : NDArray[np.float64] Synchrosqueezed scalogram. References ---------- - https://dsp.stackexchange.com/questions/71398/synchrosqueezing-wavelet -transform-explanation + - [1]: Ingrid Daubechies, Jianfeng Lu, Hau-Tieng Wu. Synchrosqueezed + wavelet transforms: An empirical mode decomposition-like tool. 2011. """ scalpow = np.abs(scalogram) ** 2 @@ -262,8 +277,9 @@ def _squeeze_scalogram( ph *= fs # Scale to represent physical frequencies # Normalization factor - normalizations = 1 / (freqs / fs) # Scales - normalizations **= -3 / 2 + if apply_frequency_normalization: + normalizations = 1 / (freqs / fs) # Scales + normalizations **= -3 / 2 # Thresholds delta_f = delta_w * freqs @@ -276,12 +292,16 @@ def _squeeze_scalogram( ind = np.argmin(diff) if diff[ind] > delta_f[f]: continue - sync[ind, t, ch] += scalogram[f, t, ch] * normalizations[f] + if apply_frequency_normalization: + sync[ind, t, ch] += scalogram[f, t, ch] * normalizations[f] + continue + + sync[ind, t, ch] += scalogram[f, t, ch] return sync def _get_length_longest_wavelet( - wave: Wavelet | MorletWavelet, f: np.ndarray, fs: int + wave: Wavelet | MorletWavelet, f: NDArray[np.float64], fs: int ): """Get longest wavelet for a frequency vector. This is useful information for zero-padding to avoid boundary effects. @@ -290,7 +310,7 @@ def _get_length_longest_wavelet( ---------- wave : `Wavelet` or `MorletWavelet` Wavelet object. - f : `np.ndarray` + f : NDArray[np.float64] Frequency vector. fs : int Sampling rate in Hz. diff --git a/dsptoolbox/transforms/transforms.py b/dsptoolbox/transforms/transforms.py index 1278161..7f00e4d 100644 --- a/dsptoolbox/transforms/transforms.py +++ b/dsptoolbox/transforms/transforms.py @@ -2,7 +2,8 @@ Here are methods considered as somewhat special or less common. """ -from ..classes.signal_class import Signal +from ..classes.signal import Signal +from ..classes.impulse_response import ImpulseResponse from ..classes.multibandsignal import MultiBandSignal from ..plots import general_matrix_plot from .._standard import _reconstruct_framed_signal @@ -16,6 +17,7 @@ ) import numpy as np +from numpy.typing import NDArray from scipy.signal.windows import get_window from scipy.fft import dct from scipy.signal import oaconvolve, resample_poly @@ -32,7 +34,9 @@ pass -def cepstrum(signal: Signal, mode="power") -> np.ndarray: +def cepstrum( + signal: Signal, mode="power" +) -> NDArray[np.float64] | NDArray[np.complex128]: """Returns the cepstrum of a given signal in the Quefrency domain. Parameters @@ -45,7 +49,7 @@ def cepstrum(signal: Signal, mode="power") -> np.ndarray: Returns ------- - ceps : `np.ndarray` + ceps : NDArray[np.float64] or NDArray[np.complex128] Cepstrum. References @@ -81,8 +85,14 @@ def log_mel_spectrogram( generate_plot: bool = True, stft_parameters: dict | None = None, ) -> ( - tuple[np.ndarray, np.ndarray, np.ndarray] - | tuple[np.ndarray, np.ndarray, np.ndarray, plt.Figure, plt.Axes] + tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]] + | tuple[ + NDArray[np.float64], + NDArray[np.float64], + NDArray[np.float64], + plt.Figure, + plt.Axes, + ] ): """Returns the log mel spectrogram of the specific signal and channel. @@ -108,20 +118,20 @@ def log_mel_spectrogram( Returns ------- - time_s : `np.ndarray` + time_s : NDArray[np.float64] Time vector. - f_mel : `np.ndarray` + f_mel : NDArray[np.float64] Frequency vector in Mel. - log_mel_sp : `np.ndarray` + log_mel_sp : NDArray[np.float64] Log mel spectrogram with shape (frequency, time frame, channel). When `generate_plot=True`: - time_s : `np.ndarray` + time_s : NDArray[np.float64] Time vector. - f_mel : `np.ndarray` + f_mel : NDArray[np.float64] Frequency vector in Mel. - log_mel_sp : `np.ndarray` + log_mel_sp : NDArray[np.float64] Log mel spectrogram with shape (frequency, time frame, channel). fig : `matplotlib.figure.Figure` Figure. @@ -151,8 +161,11 @@ def log_mel_spectrogram( def mel_filterbank( - f_hz: np.ndarray, range_hz=None, n_bands: int = 40, normalize: bool = True -) -> tuple[np.ndarray, np.ndarray]: + f_hz: NDArray[np.float64], + range_hz=None, + n_bands: int = 40, + normalize: bool = True, +) -> tuple[NDArray[np.float64], NDArray[np.float64]]: """Creates equidistant mel triangle filters in a given range. The returned matrix can be used to convert Hz into Mel in a spectrogram. @@ -162,7 +175,7 @@ def mel_filterbank( Parameters ---------- - f_hz : `np.ndarray` + f_hz : NDArray[np.float64] Frequency vector. range_hz : array-like with length 2, optional Range (in Hz) in which to create the filters. If `None`, the whole @@ -175,9 +188,9 @@ def mel_filterbank( Returns ------- - mel_filters : `np.ndarray` + mel_filters : NDArray[np.float64] Mel filters matrix with shape (bands, frequency). - mel_center_freqs : `np.ndarray` + mel_center_freqs : NDArray[np.float64] Vector containing mel center frequencies. """ @@ -288,12 +301,18 @@ def plot_waterfall( def mfcc( signal: Signal, channel: int = 0, - mel_filters: np.ndarray | None = None, + mel_filters: NDArray[np.float64] | None = None, generate_plot: bool = True, stft_parameters: dict | None = None, ) -> ( - tuple[np.ndarray, np.ndarray, np.ndarray] - | tuple[np.ndarray, np.ndarray, np.ndarray, plt.Figure, plt.Axes] + tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]] + | tuple[ + NDArray[np.float64], + NDArray[np.float64], + NDArray[np.float64], + plt.Figure, + plt.Axes, + ] ): """Mel-frequency cepstral coefficients for a windowed signal are computed and returned using the discrete cosine transform of type 2 (see @@ -307,7 +326,7 @@ def mfcc( channel : int, optional Channel of the signal for which to plot the MFCC when `generate_plot=True`. Default: 0. - mel_filters : `np.ndarray`, optional + mel_filters : NDArray[np.float64], optional Hz-to-Mel transformation matrix with shape (mel band, frequency Hz). It can be created using `mel_filterbank`. If `None` is passed, the filters are automatically computed regarding the whole @@ -324,23 +343,23 @@ def mfcc( Returns ------- - time_s : `np.ndarray` + time_s : NDArray[np.float64] Time vector. - f_mel : `np.ndarray` + f_mel : NDArray[np.float64] Frequency vector in mel. If `mel_filters` is passed, this is only a list with entries [0, n_mel_filters]. - mfcc : `np.ndarray` + mfcc : NDArray[np.float64] Mel-frequency cepstral coefficients with shape (cepstral coefficients, time frame, channel). When `generate_plot=True`: - time_s : `np.ndarray` + time_s : NDArray[np.float64] Time vector. - f_mel : `np.ndarray` + f_mel : NDArray[np.float64] Frequency vector in mel. If `mel_filters` is passed, this is only a list with entries [0, n_mel_filters]. - mfcc : `np.ndarray` + mfcc : NDArray[np.float64] Mel-frequency cepstral coefficients with shape (cepstral coefficients, time frame, channel). fig : `matplotlib.figure.Figure` @@ -390,7 +409,7 @@ def mfcc( def istft( - stft: np.ndarray, + stft: NDArray[np.complex128], original_signal: Signal | None = None, parameters: dict | None = None, sampling_rate_hz: int | None = None, @@ -411,7 +430,7 @@ def istft( Parameters ---------- - stft : `np.ndarray` + stft : NDArray[np.complex128] Complex STFT with shape (frequency, time frame, channel). It is assumed that only positive frequencies (including 0) are present. original_signal : `Signal`, optional @@ -538,8 +557,14 @@ def chroma_stft( compression: float = 0.5, plot_channel: int = -1, ) -> ( - tuple[np.ndarray, np.ndarray, np.ndarray] - | tuple[np.ndarray, np.ndarray, np.ndarray, plt.Figure, plt.Axes] + tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]] + | tuple[ + NDArray[np.float64], + NDArray[np.float64], + NDArray[np.float64], + plt.Figure, + plt.Axes, + ] ): """This computes the Chroma Features and Pitch STFT. See [1] for details. @@ -558,12 +583,12 @@ def chroma_stft( Returns ------- - t : `np.ndarray` + t : NDArray[np.float64] Time vector corresponding to each time frame. - chroma_stft : `np.ndarray` + chroma_stft : NDArray[np.float64] Chroma Features with shape (note, time frame, channel). First index is C, second C#, etc. (Until B). - pitch_stft : `np.ndarray` + pitch_stft : NDArray[np.float64] Pitch log-STFT with shape (pitch, time frame, channel). First index is note 0 (MIDI), i.e., C0. When `plot_channel != -1`: @@ -628,32 +653,38 @@ def chroma_stft( def cwt( signal: Signal, - frequencies: np.ndarray, + frequencies: NDArray[np.float64], wavelet: Wavelet | MorletWavelet, - channel: np.ndarray | None = None, + channel: NDArray[np.float64] | None = None, synchrosqueezed: bool = False, -) -> np.ndarray: + apply_synchrosqueezed_normalization: bool = False, +) -> NDArray[np.complex128]: """Returns a scalogram by means of the continuous wavelet transform. Parameters ---------- signal : `Signal` Signal for which to compute the cwt. - frequencies : `np.ndarray` + frequencies : NDArray[np.float64] Frequencies to query with the wavelet. wavelet : `Wavelet` or `MorletWavelet` Type of wavelet to use. It must be a class inherited from the `Wavelet` class. - channel : `np.ndarray`, optional + channel : NDArray[np.float64], optional Channel for which to compute the cwt. If `None`, all channels are computed. Default: `None`. synchrosqueezed : bool, optional When `True`, the scalogram is synchrosqueezed using the phase transform. Default: `False`. + apply_synchrosqueezed_normalization : bool, optional + When `True`, each scale is scaled by taking into account the + normalization as shown in Eq. (2.4) of [1]. `False` does not apply + any normalization. This is only done for synchrosqueezed scalograms. + Default: `False`. Returns ------- - scalogram : `np.ndarray` + scalogram : NDArray[np.complex128] Complex scalogram scalogram with shape (frequency, time sample, channel). @@ -661,6 +692,14 @@ def cwt( ----- - Zero-padding in the beginning is done for reducing boundary effects. + References + ---------- + - [1]: Ingrid Daubechies, Jianfeng Lu, Hau-Tieng Wu. Synchrosqueezed + wavelet transforms: An empirical mode decomposition-like tool. 2011. + - General information about synchrosqueezing: + https://dsp.stackexchange.com/questions/71398/synchrosqueezing-wavelet + -transform-explanation + """ if channel is None: channel = np.arange(signal.number_of_channels) @@ -673,6 +712,7 @@ def cwt( for ind_f, f in enumerate(frequencies): wv = np.array(wavelet.get_wavelet(f, signal.sampling_rate_hz)) + wv /= np.abs(wv).sum() scalogram[ind_f, ...] = oaconvolve( td, wv[..., None], axes=0, mode="same" @@ -680,13 +720,18 @@ def cwt( if synchrosqueezed: scalogram = _squeeze_scalogram( - scalogram, frequencies, signal.sampling_rate_hz + scalogram, + frequencies, + signal.sampling_rate_hz, + apply_frequency_normalization=apply_synchrosqueezed_normalization, ) return scalogram -def hilbert(signal: Signal | MultiBandSignal) -> Signal | MultiBandSignal: +def hilbert( + signal: Signal | ImpulseResponse | MultiBandSignal, +) -> Signal | ImpulseResponse | MultiBandSignal: """Compute the analytic signal using the hilbert transform of the real signal. @@ -712,7 +757,7 @@ def hilbert(signal: Signal | MultiBandSignal) -> Signal | MultiBandSignal: complex_ts = Signal.time_data + Signal.time_data_imaginary*1j """ - if type(signal) is Signal: + if isinstance(signal, Signal): td = signal.time_data sp = np.fft.fft(td, axis=0) @@ -738,14 +783,14 @@ def hilbert(signal: Signal | MultiBandSignal) -> Signal | MultiBandSignal: def vqt( signal: Signal, - channel: np.ndarray | None = None, + channel: NDArray[np.int_] | None = None, q: float = 1, gamma: float = 50, octaves: list = [1, 5], bins_per_octave: int = 24, a4_tuning: int = 440, window: str | tuple = "hann", -) -> tuple[np.ndarray, np.ndarray]: +) -> tuple[NDArray[np.float64], NDArray[np.complex128]]: """Variable-Q Transform. This is a special case of the continuous wavelet transform with complex morlet wavelets for the time-frequency analysis. Constant-Q Transform can be obtained by setting `gamma = 0`. @@ -754,7 +799,7 @@ def vqt( ---------- signal : `Signal` Signal for which to compute the cqt coefficients. - channel : `np.ndarray` or int, optional + channel : NDArray[np.float64] or int, optional Channel(s) for which to compute the cqt coefficients. If `None`, all channels are computed. Default: `None`. q : float, optional @@ -781,9 +826,9 @@ def vqt( Returns ------- - f : `np.ndarray` + f : NDArray[np.float64] Frequency vector. - vqt : `np.ndarray` + vqt : NDArray[np.complex128] VQT coefficients with shape (frequency, time samples, channel). References diff --git a/examples/distances_module.ipynb b/examples/distances_module.ipynb index 50f3935..9bf985d 100644 --- a/examples/distances_module.ipynb +++ b/examples/distances_module.ipynb @@ -41,7 +41,7 @@ "s1 = dsp.Signal(join('data', 'speech.flac'))\n", "\n", "# Get a \"distorted\" signal – here convolved with a RIR\n", - "rir = dsp.Signal(join('data', 'rir.wav'), signal_type='rir')\n", + "rir = dsp.ImpulseResponse(join('data', 'rir.wav'))\n", "s2 = dsp.Signal(join('data', 'speech.flac'))\n", "s2 = dsp.room_acoustics.convolve_rir_on_signal(s2, rir)" ] diff --git a/examples/general.ipynb b/examples/general.ipynb index 5a28516..b32c580 100644 --- a/examples/general.ipynb +++ b/examples/general.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -49,17 +49,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# ========== Importing ========================================================\n", "# Give path to data directly, wav and flac are supported\n", "speech = dsp.Signal(\n", - " path=join('data', 'speech.flac'), time_data=None, sampling_rate_hz=None,\n", - " # Optional parameters:\n", - " signal_type='general', # Type of signal\n", - " signal_id='here is some random info or id about the signal')\n", + " path=join('data', 'speech.flac'), time_data=None, sampling_rate_hz=None\n", + ")\n", "# If a path is given, time_data and sampling rate should be set to None\n", "\n", "# If you already have time data as vector, it can be passed to Signal \n", @@ -75,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -90,9 +88,7 @@ "source": [ "Note:\n", "- The `time_data` attribute is a numpy vector with shape (time_samples, channels). Even when the passed data is trasposed, the constructor assumes that the longest dimension contains the time samples and inverts the array.\n", - "- lists and tuples can also be passed, but every element should have the same length since it is a requirement to convert them into numpy arrays.\n", - "- `signal_type` is a marker (string) for the signal. Default types are `'ir'` (impulse response), `'h1'` (transfer function of type $H_1$), `'h2'`, `'h3'` or `'rir'` (room impulse response). Some functionalities like plotting group delay are only valid for these types. See documentation for details.\n", - "- `signal_id` is a placeholder for the user to save metadata about the object." + "- lists and tuples can also be passed, but every element should have the same length since it is a requirement to convert them into numpy arrays." ] }, { @@ -105,20 +101,18 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Signal – ID: \n", - "--------------\n", + "\n", "Sampling rate hz: 48000\n", "Number of channels: 1\n", "Signal length samples: 189056\n", "Signal length seconds: 3.9386666666666668\n", - "Signal type: general\n", "\n" ] } @@ -157,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -182,13 +176,12 @@ "# ========== Time vector ======================================================\n", "time_s = speech.time_vector_s\n", "\n", - "# Only available for signal_type in ('h1', 'h2', 'h3')\n", + "# Only available for impulse response\n", "# coherence_matrix = (())\n", "# speech.set_coherence(coherence_matrix)\n", "# frequency_hz, coherence_matrix = speech.get_coherence()\n", - "# NOTE: See later documentation regarding transfer functions\n", + "# NOTE: See documentation regarding transfer functions\n", "\n", - "# Only available for signal_type in ('ir', 'rir')\n", "# window = np.ones(100)\n", "# speech.set_window(window=window)" ] @@ -203,12 +196,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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", 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", 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"text/plain": [ "
" ] @@ -251,6 +254,9 @@ "# ========== Time signal ======================================================\n", "fig, ax = speech.plot_time()\n", "\n", + "# ========== Time signal dBSPL ================================================\n", + "fig, ax = speech.plot_spl(window_length_s=10e-3)\n", + "\n", "# ========== Magnitude spectrum ===============================================\n", "speech.plot_magnitude(\n", " range_hz=[20, 20e3],\n", diff --git a/examples/room_acoustics_module.ipynb b/examples/room_acoustics_module.ipynb index e25c331..14557a0 100644 --- a/examples/room_acoustics_module.ipynb +++ b/examples/room_acoustics_module.ipynb @@ -36,7 +36,7 @@ "outputs": [], "source": [ "speech = dsp.Signal(join('data', 'speech.flac'))\n", - "rir = dsp.Signal(join('data', 'rir.wav'), signal_type='rir')\n", + "rir = dsp.ImpulseResponse(join('data', 'rir.wav'))\n", "\n", "speech_room = dsp.room_acoustics.convolve_rir_on_signal(\n", " speech, rir, keep_peak_level=True, keep_length=False)\n", diff --git a/examples/transforms_module.ipynb b/examples/transforms_module.ipynb index 5529f13..62e9088 100644 --- a/examples/transforms_module.ipynb +++ b/examples/transforms_module.ipynb @@ -58,7 +58,7 @@ } ], "source": [ - "rir = dsp.Signal(join('data', 'rir.wav'), signal_type='rir')\n", + "rir = dsp.ImpulseResponse(join('data', 'rir.wav'))\n", "rir.set_spectrogram_parameters(window_length_samples=256, overlap_percent=0,\n", " window_type='boxcar')\n", "dsp.transforms.plot_waterfall(rir, dynamic_range_db=30)" diff --git a/tests/test_classes.py b/tests/test_classes.py index 4a8522b..e77cdb3 100644 --- a/tests/test_classes.py +++ b/tests/test_classes.py @@ -156,24 +156,6 @@ def test_setting_properties(self): with pytest.raises(AssertionError): s.sampling_rate_hz = 44100.5 - # Signal type - typ = "test signal" - s.signal_type = typ - assert s.signal_type == typ - - # Setting a wrong signal type - with pytest.raises(AssertionError): - s.signal_type = 15 - - # Signal ID - typ = "test signal" - s.signal_id = typ - assert s.signal_id == typ - - # Setting a wrong signal id - with pytest.raises(AssertionError): - s.signal_id = True - # Number of channels is generated right assert s.number_of_channels == self.channels @@ -182,7 +164,9 @@ def test_setting_properties(self): s.number_of_channels = 10 def test_plot_generation(self): - s = dsp.Signal(time_data=self.time_vec, sampling_rate_hz=self.fs) + s = dsp.ImpulseResponse( + time_data=self.time_vec, sampling_rate_hz=self.fs + ) # Test that all plots are generated without problems s.plot_magnitude() s.plot_magnitude(show_info_box=True) @@ -194,7 +178,6 @@ def test_plot_generation(self): s.plot_spl(True) # Plot phase and group delay - s.signal_type = "rir" s.set_spectrum_parameters(method="standard") s.plot_phase() s.plot_group_delay() @@ -202,10 +185,6 @@ def test_plot_generation(self): # Try to plot coherence with pytest.raises(AttributeError): s.plot_coherence() - # Try to plot group delay without having the correct signal type - with pytest.raises(AssertionError): - s.signal_type = "wrong type for group delay" - s.plot_group_delay() # Try to plot phase having welch's method for magnitude with pytest.raises(AssertionError): s.set_spectrum_parameters(method="welch", window_length_samples=32) @@ -213,7 +192,6 @@ def test_plot_generation(self): # Plot signal with window and imaginary time data d = dsp.generators.dirac(1024, 512, sampling_rate_hz=self.fs) - d.signal_type = "ir" d, _ = dsp.transfer_functions.window_centered_ir(d, len(d)) d = dsp.transforms.hilbert(d) d.plot_time() @@ -463,6 +441,42 @@ def test_other_functionalities(self): with pytest.raises(AssertionError): f.initialize_zi(0) + def test_get_transfer_function(self): + # Functionality + f = dsp.Filter( + "other", + filter_configuration=dict(sos=self.iir), + sampling_rate_hz=self.fs, + ) + freqs = np.linspace(1, 4e3, 200) + f.get_transfer_function(freqs) + + b = sig.firwin( + 1500, + (self.fs // 2 // 2), + pass_zero="lowpass", + fs=self.fs, + window="flattop", + ) + f = dsp.Filter( + "other", + filter_configuration=dict(ba=[b, 1]), + sampling_rate_hz=self.fs, + ) + f.get_transfer_function(freqs) + + f = dsp.Filter( + "biquad", + filter_configuration={ + "eq_type": "peaking", + "freqs": 200, + "gain": 3, + "q": 0.7, + }, + sampling_rate_hz=self.fs, + ) + f.get_transfer_function(freqs) + def test_filter_and_resampling_IIR(self): f = dsp.Filter( "other", @@ -848,6 +862,28 @@ def test_iterator(self): for n in fb: assert dsp.Filter == type(n) + def test_transfer_function(self): + # Create + fb = dsp.FilterBank() + config = dict( + order=5, + freqs=[1500, 2000], + type_of_pass="bandpass", + filter_design_method="bessel", + ) + fb.add_filter(dsp.Filter("iir", config, sampling_rate_hz=self.fs)) + config = dict(order=150, freqs=[1500, 2000], type_of_pass="bandpass") + fb.add_filter(dsp.Filter("fir", config, self.fs)) + + freqs = np.linspace(1, 2e3, 400) + fb.get_transfer_function(freqs, mode="parallel") + fb.get_transfer_function(freqs, mode="sequential") + fb.get_transfer_function(freqs, mode="summed") + + with pytest.raises(AssertionError): + freqs = np.linspace(1, self.fs, 40) + fb.get_transfer_function(freqs, mode="parallel") + class TestMultiBandSignal: fs = 44100 @@ -1028,3 +1064,57 @@ def test_iterator(self): ) for n in mbs: assert dsp.Signal == type(n) + + +class TestImpulseResponse: + fs_hz = 10_000 + seconds = 2 + d = dsp.generators.dirac(seconds * fs_hz, sampling_rate_hz=fs_hz) + + path_rir = join("examples", "data", "rir.wav") + + def get_ir(self): + return dsp.ImpulseResponse.from_file(self.path_rir) + + def test_constructors(self): + rir = self.get_ir() + dsp.ImpulseResponse.from_time_data(rir.time_data, rir.sampling_rate_hz) + dsp.ImpulseResponse.from_signal(dsp.Signal.from_file(self.path_rir)) + + def test_channel_handling_with_window(self): + rir = self.get_ir() + rir = dsp.transfer_functions.window_centered_ir(rir, len(rir))[0] + + # Add channel + window_previous = rir.window[:, 0] + rir.add_channel(self.path_rir) + assert rir.window.shape == rir.time_data.shape + np.testing.assert_array_equal(rir.window[:, 0], window_previous) + np.testing.assert_array_equal(rir.window[:, 1], 1.0) + + # Remove channel + rir.remove_channel(1) + assert rir.window.shape == rir.time_data.shape + np.testing.assert_array_equal(rir.window[:, 0], window_previous) + + # Swap channels + rir.add_channel(self.path_rir) + rir.add_channel(self.path_rir) + rir.swap_channels([2, 1, 0]) + assert rir.window.shape == rir.time_data.shape + np.testing.assert_array_equal(rir.window[:, -1], window_previous) + + def test_plotting_with_window(self): + rir = self.get_ir() + rir = dsp.transfer_functions.window_centered_ir(rir, len(rir))[0] + rir.plot_time() + rir.plot_spl() + + # Expect no coherence saved + with pytest.raises(AssertionError): + rir.plot_coherence() + + rir.add_channel(self.path_rir) + rir.plot_time() + rir.plot_spl() + # dsp.plots.show() diff --git a/tests/test_filterbanks.py b/tests/test_filterbanks.py index 9a219f6..d48bed8 100644 --- a/tests/test_filterbanks.py +++ b/tests/test_filterbanks.py @@ -206,6 +206,65 @@ def test_pinking_filter(self): ) n2 = dsp.merge_signals(n2, n) + def test_matched_biquads(self): + # Only functionality and plausibility + # Parameters + fs_hz = 48000 + freq = 10e3 + gain_db = -20 + q = 2**0.5 / 2 + + for eq_type in [ + "peaking", + "lowpass", + "highpass", + "lowshelf", + "highshelf", + "bandpass", + ]: + dsp.filterbanks.matched_biquad(eq_type, freq, gain_db, q, fs_hz) + + # For comparison with usual biquads + # f = dsp.filterbanks.matched_biquad( + # eq_type, freq, gain_db, q, fs_hz + # ) + # f2 = dsp.Filter( + # "biquad", + # { + # "eq_type": eq_type + # + ("_peak" if eq_type == "bandpass" else ""), + # "freqs": freq, + # "gain": gain_db, + # "q": q, + # }, + # fs_hz, + # ) + # fb = dsp.FilterBank([f, f2]) + # fig, ax = fb.plot_magnitude(length_samples=2**13) + # fig.suptitle(eq_type.capitalize()) + # ax.legend(["Matched", "Standard"]) + # dsp.plots.show() + + def test_gaussian_kernel(self): + # Only functionality + fs_hz = 44100 + n = dsp.generators.noise(sampling_rate_hz=fs_hz) + + # Get kernel and apply filtering + f = dsp.filterbanks.gaussian_kernel(0.02, sampling_rate_hz=fs_hz) + n1 = f.filter_signal(n, zero_phase=True) + + # Compare to normal gaussian window + length = int(0.02 * fs_hz + 0.5) + sigma = length / (2.0 * np.log(1 / 1e-2)) ** 0.5 + w = sig.windows.gaussian(length, sigma, True) + w /= w.sum() + f = dsp.Filter("other", {"ba": [w, [1]]}, fs_hz) + n1 = dsp.merge_signals(n1, f.filter_signal(n, zero_phase=False)) + + # n1.plot_time() + # dsp.plots.show() + class TestLatticeLadderFilter: b = np.array([1, 3, 3, 1]) @@ -215,7 +274,7 @@ def test_lattice_filter_coefficients(self): # Example values taken from Oppenheim, A. V., Schafer, R. W.,, # Buck, J. R. (1999). Discrete-Time Signal Processing. # Prentice-hall Englewood Cliffs. - from dsptoolbox.classes._lattice_ladder_filter import ( + from dsptoolbox.classes.lattice_ladder_filter import ( _get_lattice_ladder_coefficients_iir, ) @@ -231,7 +290,7 @@ def test_lattice_filter_filtering(self): n = dsp.generators.noise(sampling_rate_hz=200) expected = sig.lfilter(self.b / 10, self.a, n.time_data.squeeze()) - from dsptoolbox.classes._lattice_ladder_filter import ( + from dsptoolbox.classes.lattice_ladder_filter import ( _get_lattice_ladder_coefficients_iir, ) diff --git a/tests/test_room_acoustics.py b/tests/test_room_acoustics.py index 563da7c..8fdf222 100644 --- a/tests/test_room_acoustics.py +++ b/tests/test_room_acoustics.py @@ -5,7 +5,7 @@ class TestRoomAcousticsModule: - rir = dsp.Signal(join("examples", "data", "rir.wav"), signal_type="rir") + rir = dsp.ImpulseResponse(join("examples", "data", "rir.wav")) def test_reverb_time(self): # Only functionality diff --git a/tests/test_standard.py b/tests/test_standard.py index 195be51..2af7d1e 100644 --- a/tests/test_standard.py +++ b/tests/test_standard.py @@ -19,16 +19,18 @@ def test_latency(self): # Try latency s = dsp.Signal(None, td_del, self.fs) - vector = dsp.latency(self.audio_multi, s) + vector, corr = dsp.latency(self.audio_multi, s) + assert np.allclose(corr, 1.0) assert np.all(vector == -delay_samples) # Try latency the other way around - vector = dsp.latency(s, self.audio_multi) + vector, corr = dsp.latency(s, self.audio_multi) + assert np.allclose(corr, 1.0) assert np.all(vector == delay_samples) # Raise assertion when number of channels does not match with pytest.raises(AssertionError): - vector = dsp.latency(s.get_channels(0), self.audio_multi) + vector, corr = dsp.latency(s.get_channels(0), self.audio_multi) # Single channel td = s.time_data[:, :2] @@ -37,7 +39,8 @@ def test_latency(self): self.audio_multi.time_data[:, 0] ) s = dsp.Signal(None, td, self.fs) - value = dsp.latency(s) + value, corr = dsp.latency(s) + assert np.allclose(corr, 1.0) assert np.all(-value == delay_samples) # ===== Fractional delays @@ -46,18 +49,17 @@ def test_latency(self): "white", length_seconds=1, sampling_rate_hz=10_000 ) noi_del = dsp.fractional_delay(noi, delay) - assert ( - np.abs( - dsp.latency(noi_del, noi, 2)[0] - delay * noi.sampling_rate_hz - ) - < 0.9 - ) + lat, corr = dsp.latency(noi_del, noi, 2) + assert np.allclose(corr, 1.0, atol=1e-2) + assert np.abs(lat[0] - delay * noi.sampling_rate_hz) < 0.9 noi = dsp.merge_signals(noi_del, noi) - latencies = dsp.latency(noi, polynomial_points=1) + latencies, corr = dsp.latency(noi, polynomial_points=1) assert len(latencies) == noi.number_of_channels - 1 + assert np.allclose(corr, 1.0, atol=1e-2) assert np.abs(latencies[0] + delay * noi.sampling_rate_hz) < 0.5 - latencies = dsp.latency(noi, polynomial_points=5) + latencies, corr = dsp.latency(noi, polynomial_points=5) + assert np.allclose(corr, 1.0, atol=1e-2) assert np.abs(latencies[0] + delay * noi.sampling_rate_hz) < 0.5 def test_pad_trim(self): @@ -154,10 +156,6 @@ def test_resample(self): # necessary to check... dsp.resample(self.audio_multi, desired_sampling_rate_hz=22050) - def test_fractional_octave_frequencies(self): - # Only functionality and not result is checked here - dsp.fractional_octave_frequencies() - def test_normalize(self): td = self.audio_multi.time_data n = dsp.normalize(self.audio_multi, peak_dbfs=-20) @@ -194,10 +192,6 @@ def test_fade(self): td[-fade_le:] *= np.linspace(1, 0, fade_le)[..., None] assert np.all(np.isclose(f_end.time_data, td)) - def test_erb_frequencies(self): - # Only functionality tested here - dsp.erb_frequencies() - def test_true_peak_level(self): # Only functionality is tested here dsp.true_peak_level(self.audio_multi) @@ -214,12 +208,12 @@ def test_fractional_delay(self): # All channels s = dsp.fractional_delay(self.audio_multi, delay_s) - lat = dsp.latency(s, self.audio_multi) + lat = dsp.latency(s, self.audio_multi)[0] assert np.all(np.isclose(np.abs(lat), 150)) # Selected channels only s = dsp.fractional_delay(self.audio_multi, delay_s, channels=0) - lat = dsp.latency(s, self.audio_multi) + lat = dsp.latency(s, self.audio_multi)[0] assert np.all(np.isclose(np.abs(lat), [150, 0, 0])) def test_activity_detector(self): diff --git a/tests/test_tools.py b/tests/test_tools.py new file mode 100644 index 0000000..d52c187 --- /dev/null +++ b/tests/test_tools.py @@ -0,0 +1,17 @@ +import dsptoolbox as dsp +import numpy as np + + +class TestTools: + def test_functionality(self): + # Only assess basic functionality, not results + x = np.linspace(100, 150, 30) + dsp.tools.log_frequency_vector([20, 200], 50) + dsp.tools.frequency_crossover([100, 200], True)(x) + dsp.tools.log_mean(x) + dsp.tools.to_db(x, True, None, None) + dsp.tools.from_db(x, True) + dsp.tools.time_smoothing(x, 200, 0.1, None) + dsp.tools.time_smoothing(x, 200, 0.1, 0.2) + dsp.tools.fractional_octave_frequencies() + dsp.tools.erb_frequencies() diff --git a/tests/test_transfer_functions.py b/tests/test_transfer_functions.py index 29086fe..9104f01 100644 --- a/tests/test_transfer_functions.py +++ b/tests/test_transfer_functions.py @@ -242,7 +242,6 @@ def test_window_centered_ir(self): # ============= Impulse in the middle, no changing lengths, even d = dsp.generators.dirac(1024, 512, sampling_rate_hz=self.fs) - d.signal_type = "rir" d2, _ = dsp.transfer_functions.window_centered_ir(d, len(d)) assert ( np.argmax(d.time_data[:, 0]) == np.argmax(d2.window[:, 0]) @@ -252,7 +251,6 @@ def test_window_centered_ir(self): # ============= Impulse in the middle, no changing lengths, odd d = dsp.generators.dirac(1025, 513, sampling_rate_hz=self.fs) - d.signal_type = "rir" d2, _ = dsp.transfer_functions.window_centered_ir(d, len(d)) assert ( np.argmax(d.time_data[:, 0]) == np.argmax(d2.window[:, 0]) @@ -262,7 +260,7 @@ def test_window_centered_ir(self): def test_ir_to_filter(self): s = self.audio_multi.time_data[:200, 0] - s = dsp.Signal(None, s, self.fs, signal_type="rir") + s = dsp.ImpulseResponse(None, s, self.fs) f = dsp.transfer_functions.ir_to_filter(s, channel=0) b, _ = f.get_coefficients(mode="ba") assert np.all(b == s.time_data[:, 0]) @@ -305,6 +303,8 @@ def test_compute_transfer_function(self): ) # Check that coherence is saved h.get_coherence() + h.plot_coherence() + # dsp.plots.show() def test_average_irs(self): # Only functionality is tested @@ -439,11 +439,11 @@ def test_excess_group_delay(self): def test_min_phase_ir(self): # Only functionality, computation is done using scipy's minimum phase - s = dsp.Signal(join("examples", "data", "rir.wav"), signal_type="rir") + s = dsp.ImpulseResponse(join("examples", "data", "rir.wav")) s = dsp.transfer_functions.min_phase_ir(s) def test_combine_ir(self): - s = dsp.Signal(join("examples", "data", "rir.wav"), signal_type="rir") + s = dsp.ImpulseResponse(join("examples", "data", "rir.wav")) dsp.transfer_functions.combine_ir_with_dirac( s, 1000, True, normalization=None ) @@ -456,7 +456,6 @@ def test_combine_ir(self): def test_find_ir_latency(self): ir = dsp.generators.dirac(self.fs, sampling_rate_hz=self.fs) - ir.signal_type = "ir" delay_seconds = 0.00133 # Some value to have a fractional delay delay_samples = self.fs * delay_seconds ir = dsp.fractional_delay(ir, delay_seconds) @@ -464,11 +463,11 @@ def test_find_ir_latency(self): assert np.isclose(delay_samples, output, atol=0.4) - ir = dsp.Signal(join("examples", "data", "rir.wav"), signal_type="rir") + ir = dsp.ImpulseResponse(join("examples", "data", "rir.wav")) assert dsp.transfer_functions.find_ir_latency(ir) > 0 def test_window_frequency_dependent(self): - s = dsp.Signal(join("examples", "data", "rir.wav"), signal_type="rir") + s = dsp.ImpulseResponse(join("examples", "data", "rir.wav")) f, sp = dsp.transfer_functions.window_frequency_dependent( s, 10, 0, [100, 1000] ) @@ -479,13 +478,13 @@ def test_window_frequency_dependent(self): def test_warp_ir(self): # Only functionality - s = dsp.Signal(join("examples", "data", "rir.wav"), signal_type="rir") + s = dsp.ImpulseResponse(join("examples", "data", "rir.wav")) dsp.transfer_functions.warp_ir(s, -0.6, True, 2**8) dsp.transfer_functions.warp_ir(s, 0.6, False, 2**8) def test_harmonics_from_chirp_ir(self): # Only functionality - ir = dsp.Signal(join("examples", "data", "rir.wav"), signal_type="rir") + ir = dsp.ImpulseResponse(join("examples", "data", "rir.wav")) dsp.transfer_functions.harmonics_from_chirp_ir( ir, chirp_range_hz=[20, 20e3], @@ -495,7 +494,7 @@ def test_harmonics_from_chirp_ir(self): def test_harmonic_distortion_analysis(self): # Only functionality - ir = dsp.Signal(join("examples", "data", "rir.wav"), signal_type="rir") + ir = dsp.ImpulseResponse(join("examples", "data", "rir.wav")) dsp.transfer_functions.harmonic_distortion_analysis( ir, chirp_range_hz=[20, 20e3], @@ -519,7 +518,7 @@ def test_harmonic_distortion_analysis(self): def test_trim_rir(self): # Only functionality - ir = dsp.Signal(join("examples", "data", "rir.wav"), signal_type="rir") + ir = dsp.ImpulseResponse(join("examples", "data", "rir.wav")) dsp.transfer_functions.trim_ir(ir) # Start offset way longer than rir (should be clipped to 0) assert ( diff --git a/tox.ini b/tox.ini new file mode 100644 index 0000000..0081794 --- /dev/null +++ b/tox.ini @@ -0,0 +1,2 @@ +[flake8] +ignore = E203,W503