From 68eabf6ce5c0c5f3b230a81af5fd181ba0619c33 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Sat, 30 Apr 2022 14:29:22 +0200 Subject: [PATCH 01/66] add pyfar to requirements --- requirements_dev.txt | 1 + setup.py | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/requirements_dev.txt b/requirements_dev.txt index 48ef3f2..c82fc14 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -15,3 +15,4 @@ scipy>=1.5.0 matplotlib autodocsumm insipid-sphinx-theme +pyfar>=0.3.0 diff --git a/setup.py b/setup.py index 9c1b396..353c364 100644 --- a/setup.py +++ b/setup.py @@ -12,6 +12,7 @@ history = history_file.read() requirements = [ + 'pyfar>=0.3.0', 'numpy>=1.14.0', 'scipy>=1.5.0', 'matplotlib'] @@ -39,10 +40,10 @@ 'License :: OSI Approved :: MIT License', 'Natural Language :: English', 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', + 'Programming Language :: Python :: 3.10', ], description="Collection of functions commonly used in room acoustics", install_requires=requirements, From 3e81bb61c2695d5fd0788f4339e96cf017a1cfe8 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Sat, 30 Apr 2022 14:34:01 +0200 Subject: [PATCH 02/66] move schroeder integration to edc.py --- pyrato/__init__.py | 2 +- pyrato/edc.py | 43 +++++++++++++++++++++++++++++++++++++++++ pyrato/roomacoustics.py | 43 ----------------------------------------- 3 files changed, 44 insertions(+), 44 deletions(-) diff --git a/pyrato/__init__.py b/pyrato/__init__.py index 6a20be5..bcaff6d 100644 --- a/pyrato/__init__.py +++ b/pyrato/__init__.py @@ -10,7 +10,6 @@ from .roomacoustics import ( reverberation_time_energy_decay_curve, - schroeder_integration, energy_decay_curve_analytic, air_attenuation_coefficient, ) @@ -23,6 +22,7 @@ center_frequencies_third_octaves ) from .edc import ( + schroeder_integration, preprocess_rir, energy_decay_curve_chu, energy_decay_curve_chu_lundeby, diff --git a/pyrato/edc.py b/pyrato/edc.py index 0777a1b..f76aa62 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -193,6 +193,49 @@ def subtract_noise_from_squared_rir(data, noise_level='auto'): return (data.T - noise_level).T +def schroeder_integration(impulse_response, is_energy=False): + r"""Calculate the Schroeder integral of a room impulse response [#]_. The + result is the energy decay curve for the given room impulse response. + + .. math: + + \langle e^2(t) \rangle = N\cdot \int_{t}^{\infty} h^2(\tau) + \mathrm{d} \tau + + Parameters + ---------- + impulse_response : ndarray, double + Room impulse response as array + is_energy : boolean, optional + Whether the input represents energy data or sound pressure values. + + Returns + ------- + energy_decay_curve : ndarray, double + The energy decay curve + + References + ---------- + .. [#] M. R. Schroeder, “New Method of Measuring Reverberation Time,” + The Journal of the Acoustical Society of America, vol. 37, no. 6, + pp. 1187-1187, 1965. + + """ + if not is_energy: + data = np.abs(impulse_response)**2 + else: + data = impulse_response.copy() + + ndim = data.ndim + data = np.atleast_2d(data) + energy_decay_curve = np.fliplr(np.nancumsum(np.fliplr(data), axis=-1)) + + if ndim < energy_decay_curve.ndim: + energy_decay_curve = np.squeeze(energy_decay_curve) + + return energy_decay_curve + + def energy_decay_curve_truncation( data, sampling_rate, diff --git a/pyrato/roomacoustics.py b/pyrato/roomacoustics.py index e0c3184..4c56143 100644 --- a/pyrato/roomacoustics.py +++ b/pyrato/roomacoustics.py @@ -102,49 +102,6 @@ def reverberation_time_energy_decay_curve( return reverberation_time -def schroeder_integration(impulse_response, is_energy=False): - r"""Calculate the Schroeder integral of a room impulse response [#]_. The - result is the energy decay curve for the given room impulse response. - - .. math: - - \langle e^2(t) \rangle = N\cdot \int_{t}^{\infty} h^2(\tau) - \mathrm{d} \tau - - Parameters - ---------- - impulse_response : ndarray, double - Room impulse response as array - is_energy : boolean, optional - Whether the input represents energy data or sound pressure values. - - Returns - ------- - energy_decay_curve : ndarray, double - The energy decay curve - - References - ---------- - .. [#] M. R. Schroeder, “New Method of Measuring Reverberation Time,” - The Journal of the Acoustical Society of America, vol. 37, no. 6, - pp. 1187–1187, 1965. - - """ - if not is_energy: - data = np.abs(impulse_response)**2 - else: - data = impulse_response.copy() - - ndim = data.ndim - data = np.atleast_2d(data) - energy_decay_curve = np.fliplr(np.nancumsum(np.fliplr(data), axis=-1)) - - if ndim < energy_decay_curve.ndim: - energy_decay_curve = np.squeeze(energy_decay_curve) - - return energy_decay_curve - - def energy_decay_curve_analytic( surfaces, alphas, volume, times, source=None, receiver=None, method='eyring', c=343.4, frequency=None, From 7a7f818bfb730e9793581261998fe8dcc2968404 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Sat, 30 Apr 2022 15:05:49 +0200 Subject: [PATCH 03/66] support for pyfar signals in find_impulse_response_start --- pyrato/dsp.py | 18 +++++++++++------- tests/test_dsp.py | 28 ++++++++++++++++++++++------ 2 files changed, 33 insertions(+), 13 deletions(-) diff --git a/pyrato/dsp.py b/pyrato/dsp.py index 5f28b50..2782ee3 100644 --- a/pyrato/dsp.py +++ b/pyrato/dsp.py @@ -21,14 +21,14 @@ def find_impulse_response_start( Parameters ---------- - impulse_response : ndarray, double + impulse_response : pyfar.Signal The impulse response threshold : double, optional Threshold according to ISO3382 in dB Returns ------- - start_sample : int + start_sample : numpy.ndarray, int Sample at which the impulse response starts Note @@ -41,9 +41,10 @@ def find_impulse_response_start( .. [#] ISO 3382-1:2009-10, Acoustics - Measurement of the reverberation time of rooms with reference to other acoustical parameters. pp. 22 """ - ir_squared = np.abs(impulse_response)**2 + ir_squared = np.abs(impulse_response.time)**2 + + mask_start = np.int(0.9*ir_squared.shape[-1]) - mask_start = int(0.9*ir_squared.shape[-1]) if noise_energy == 'auto': mask = np.arange(mask_start, ir_squared.shape[-1]) noise = np.mean(np.take(ir_squared, mask, axis=-1), axis=-1) @@ -55,8 +56,9 @@ def find_impulse_response_start( if np.any(max_value < 10**(threshold/10) * noise) or \ np.any(max_sample > mask_start): - raise ValueError("The SNR is lower than the defined threshold. Check \ - if this is a valid impulse response with sufficient SNR.") + raise ValueError( + "The SNR is lower than the defined threshold. Check " + "if this is a valid impulse response with sufficient SNR.") start_sample_shape = max_sample.shape n_samples = ir_squared.shape[-1] @@ -66,7 +68,7 @@ def find_impulse_response_start( max_value = np.reshape(max_value, n_channels) start_sample = max_sample.copy() - for idx in range(n_channels): + for idx in range(0, n_channels): # Only look for the start sample if the maximum index is bigger than 0 if start_sample[idx] > 0: ir_before_max = ir_squared[idx, :max_sample[idx]+1] \ @@ -154,6 +156,8 @@ def time_shift(signal, n_samples_shift, circular_shift=True, keepdims=False): assuming that the signal is periodic. Use the option `circular_shift=False` to pad with nan values instead. + TODO: + Notes ----- This function is primarily intended to be used when processing impulse diff --git a/tests/test_dsp.py b/tests/test_dsp.py index 22354dc..ec4b718 100644 --- a/tests/test_dsp.py +++ b/tests/test_dsp.py @@ -5,6 +5,7 @@ import numpy as np import numpy.testing as npt import pytest +import pyfar as pf from numpy import genfromtxt import pyrato.dsp as dsp @@ -16,11 +17,14 @@ def test_start_ir_insufficient_snr(): n_samples = 2**9 ir = np.zeros(n_samples, dtype=float) ir[20] = 1 + ir = pf.Signal(ir, 44100) snr = 15 noise = np.random.randn(n_samples) noise = noise / np.sqrt(np.mean(np.abs(noise**2))) * 10**(-snr/20) + noise = pf.Signal(noise, 44100) + ir_noise = ir + noise with pytest.raises(ValueError): @@ -30,14 +34,15 @@ def test_start_ir_insufficient_snr(): def test_start_ir(): n_samples = 2**10 ir = np.zeros(n_samples) - snr = 60 - noise = np.random.randn(n_samples) * 10**(-snr/20) + noise = pf.Signal(np.random.randn(n_samples) * 10**(-snr/20), 44100) start_sample = 24 ir[start_sample] = 1 + ir = pf.Signal(ir, 44100) + start_sample_est = dsp.find_impulse_response_start(ir) assert start_sample_est == start_sample - 1 @@ -54,6 +59,8 @@ def test_start_ir_thresh(): ir[start_sample] = 1 ir[start_sample-4:start_sample] = 10**(-5/10) + ir = pf.Signal(ir, 44100) + start_sample_est = dsp.find_impulse_response_start(ir, threshold=20) assert start_sample_est == start_sample - 4 - 1 @@ -65,24 +72,30 @@ def test_start_ir_multidim(): snr = 60 - noise = np.random.randn(n_channels, n_samples) * 10**(-snr/20) + noise = pf.Signal( + np.random.randn(n_channels, n_samples) * 10**(-snr/20), 44100) start_sample = [24, 5, 43] ir[[0, 1, 2], start_sample] = 1 + ir = pf.Signal(ir, 44100) + ir_awgn = ir + noise start_sample_est = dsp.find_impulse_response_start(ir_awgn) npt.assert_allclose(start_sample_est, np.array(start_sample) - 1) ir = np.zeros((2, n_channels, n_samples)) - noise = np.random.randn(2, n_channels, n_samples) * 10**(-snr/20) + noise = pf.Signal( + np.random.randn(2, n_channels, n_samples) * 10**(-snr/20), 44100) start_sample_1 = [24, 5, 43] ir[0, [0, 1, 2], start_sample_1] = 1 start_sample_2 = [14, 12, 16] ir[1, [0, 1, 2], start_sample_2] = 1 + ir = pf.Signal(ir, 44100) + start_samples = np.vstack((start_sample_1, start_sample_2)) ir_awgn = ir + noise @@ -208,6 +221,8 @@ def test_start_room_impulse_response(): os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), delimiter=',') + rir = pf.Signal(rir, 44100) + actual = dsp.find_impulse_response_start(rir, threshold=20) expected = 0 @@ -221,6 +236,7 @@ def test_start_room_impulse_response_shfted(monkeypatch): delimiter=',') rir_shifted = np.roll(rir, 128, axis=-1) + rir_shifted = pf.Signal(rir_shifted, 44100) actual = dsp.find_impulse_response_start(rir_shifted, threshold=20) expected = 128 @@ -236,7 +252,7 @@ def test_start_ir_thresh_invalid(): ir[start_sample] = 1 # ir[start_sample-4:start_sample] = 10**(-10/10) ir[0:start_sample] = 10**(-5/10) - + ir = pf.Signal(ir, 44100) start_sample_est = dsp.find_impulse_response_start(ir, threshold=20) assert start_sample_est == 0 @@ -249,7 +265,7 @@ def test_start_ir_thresh_invalid_osci(): ir[start_sample] = 1 ir[start_sample-4:start_sample] = 10**(-30/10) ir[0:start_sample-4] = 10**(-5/10) - + ir = pf.Signal(ir, 44100) start_sample_est = dsp.find_impulse_response_start(ir, threshold=20) assert start_sample_est == 0 From 4c71c25f73d89bb1cd4633b2b405155ad1c71dde Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Sat, 30 Apr 2022 15:19:50 +0200 Subject: [PATCH 04/66] pyfar.Signal support for maximum ir function --- pyrato/dsp.py | 2 +- tests/test_dsp.py | 5 +++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/pyrato/dsp.py b/pyrato/dsp.py index 2782ee3..b7189d0 100644 --- a/pyrato/dsp.py +++ b/pyrato/dsp.py @@ -130,7 +130,7 @@ def find_impulse_response_maximum( in the last 10 percent of the IR. """ - ir_squared = np.abs(impulse_response)**2 + ir_squared = np.abs(impulse_response.time)**2 mask_start = int(0.9*ir_squared.shape[-1]) if noise_energy == 'auto': diff --git a/tests/test_dsp.py b/tests/test_dsp.py index ec4b718..c90f877 100644 --- a/tests/test_dsp.py +++ b/tests/test_dsp.py @@ -276,11 +276,12 @@ def test_max_ir(): snr = 60 - noise = np.random.randn(n_samples) * 10**(-snr/20) + noise = pf.Signal( + np.random.randn(n_samples) * 10**(-snr/20), 44100) start_sample = 24 ir[start_sample] = 1 - + ir = pf.Signal(ir, 44100) start_sample_est = dsp.find_impulse_response_maximum(ir) assert start_sample_est == start_sample From a28367def2869dbe273e012714d3e3ee30937695 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Sat, 30 Apr 2022 15:21:14 +0200 Subject: [PATCH 05/66] restructure tests --- tests/test_dsp.py | 148 +++++++++++++++++++++++----------------------- 1 file changed, 74 insertions(+), 74 deletions(-) diff --git a/tests/test_dsp.py b/tests/test_dsp.py index c90f877..eb6361c 100644 --- a/tests/test_dsp.py +++ b/tests/test_dsp.py @@ -104,6 +104,80 @@ def test_start_ir_multidim(): npt.assert_allclose(start_sample_est, start_samples - 1) +def test_start_room_impulse_response(): + rir = genfromtxt( + os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), + delimiter=',') + + rir = pf.Signal(rir, 44100) + + actual = dsp.find_impulse_response_start(rir, threshold=20) + + expected = 0 + + npt.assert_allclose(actual, expected) + + +def test_start_room_impulse_response_shfted(monkeypatch): + rir = genfromtxt( + os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), + delimiter=',') + + rir_shifted = np.roll(rir, 128, axis=-1) + rir_shifted = pf.Signal(rir_shifted, 44100) + actual = dsp.find_impulse_response_start(rir_shifted, threshold=20) + + expected = 128 + + npt.assert_allclose(actual, expected) + + +def test_start_ir_thresh_invalid(): + n_samples = 2**10 + ir = np.zeros(n_samples) + + start_sample = 24 + ir[start_sample] = 1 + # ir[start_sample-4:start_sample] = 10**(-10/10) + ir[0:start_sample] = 10**(-5/10) + ir = pf.Signal(ir, 44100) + start_sample_est = dsp.find_impulse_response_start(ir, threshold=20) + assert start_sample_est == 0 + + +def test_start_ir_thresh_invalid_osci(): + n_samples = 2**10 + ir = np.zeros(n_samples) + + start_sample = 24 + ir[start_sample] = 1 + ir[start_sample-4:start_sample] = 10**(-30/10) + ir[0:start_sample-4] = 10**(-5/10) + ir = pf.Signal(ir, 44100) + start_sample_est = dsp.find_impulse_response_start(ir, threshold=20) + assert start_sample_est == 0 + + +def test_max_ir(): + n_samples = 2**10 + ir = np.zeros(n_samples) + + snr = 60 + + noise = pf.Signal( + np.random.randn(n_samples) * 10**(-snr/20), 44100) + + start_sample = 24 + ir[start_sample] = 1 + ir = pf.Signal(ir, 44100) + start_sample_est = dsp.find_impulse_response_maximum(ir) + assert start_sample_est == start_sample + + ir_awgn = ir + noise + start_sample_est = dsp.find_impulse_response_maximum(ir_awgn) + assert start_sample_est == start_sample + + def test_time_shift_right(): shift_samples = 10 n_samples = 2**9 @@ -214,77 +288,3 @@ def test_time_shift_multitim_multishift(): ir_shifted = dsp.time_shift(ir, -np.array(shift_samples, dtype=int)) npt.assert_allclose(ir_shifted, ir_truth) - - -def test_start_room_impulse_response(): - rir = genfromtxt( - os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), - delimiter=',') - - rir = pf.Signal(rir, 44100) - - actual = dsp.find_impulse_response_start(rir, threshold=20) - - expected = 0 - - npt.assert_allclose(actual, expected) - - -def test_start_room_impulse_response_shfted(monkeypatch): - rir = genfromtxt( - os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), - delimiter=',') - - rir_shifted = np.roll(rir, 128, axis=-1) - rir_shifted = pf.Signal(rir_shifted, 44100) - actual = dsp.find_impulse_response_start(rir_shifted, threshold=20) - - expected = 128 - - npt.assert_allclose(actual, expected) - - -def test_start_ir_thresh_invalid(): - n_samples = 2**10 - ir = np.zeros(n_samples) - - start_sample = 24 - ir[start_sample] = 1 - # ir[start_sample-4:start_sample] = 10**(-10/10) - ir[0:start_sample] = 10**(-5/10) - ir = pf.Signal(ir, 44100) - start_sample_est = dsp.find_impulse_response_start(ir, threshold=20) - assert start_sample_est == 0 - - -def test_start_ir_thresh_invalid_osci(): - n_samples = 2**10 - ir = np.zeros(n_samples) - - start_sample = 24 - ir[start_sample] = 1 - ir[start_sample-4:start_sample] = 10**(-30/10) - ir[0:start_sample-4] = 10**(-5/10) - ir = pf.Signal(ir, 44100) - start_sample_est = dsp.find_impulse_response_start(ir, threshold=20) - assert start_sample_est == 0 - - -def test_max_ir(): - n_samples = 2**10 - ir = np.zeros(n_samples) - - snr = 60 - - noise = pf.Signal( - np.random.randn(n_samples) * 10**(-snr/20), 44100) - - start_sample = 24 - ir[start_sample] = 1 - ir = pf.Signal(ir, 44100) - start_sample_est = dsp.find_impulse_response_maximum(ir) - assert start_sample_est == start_sample - - ir_awgn = ir + noise - start_sample_est = dsp.find_impulse_response_maximum(ir_awgn) - assert start_sample_est == start_sample From 262a1ba7cdaea05e433159be61b97e8ab6057f0c Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Sun, 1 May 2022 11:35:06 +0200 Subject: [PATCH 06/66] time shift with pyfar support --- pyrato/dsp.py | 170 ++++++++++++++++++++++++++++++++------------------ 1 file changed, 109 insertions(+), 61 deletions(-) diff --git a/pyrato/dsp.py b/pyrato/dsp.py index b7189d0..35c03f0 100644 --- a/pyrato/dsp.py +++ b/pyrato/dsp.py @@ -4,6 +4,7 @@ import warnings +import pyfar as pf import numpy as np import scipy.signal as spsignal @@ -150,85 +151,132 @@ def find_impulse_response_maximum( return np.squeeze(max_sample) -def time_shift(signal, n_samples_shift, circular_shift=True, keepdims=False): - """Shift a signal in the time domain by n samples. - This function will perform a circular shift by default, inherently - assuming that the signal is periodic. Use the option `circular_shift=False` - to pad with nan values instead. +def time_shift(signal, shift, circular_shift=True, unit='samples'): + """Apply a time-shift to a signal. - TODO: - - Notes - ----- - This function is primarily intended to be used when processing impulse - responses. + By default, the shift is performed as a cyclic shift on the time axis, + potentially resulting in non-causal signals for negative shift values. + Use the option ``circular_shift=False`` to pad with nan values instead, + note that in this case the return type will be a ``pyfar.TimeData``. Parameters ---------- - signal : ndarray, float - Signal to be shifted - n_samples_shift : integer - Number of samples by which the signal should be shifted. A negative - number of samples will result in a left-shift, while a positive - number of samples will result in a right shift of the signal. + signal : Signal + The signal to be shifted + shift : int, float + The time-shift value. A positive value will result in right shift on + the time axis (delaying of the signal), whereas a negative value + yields a left shift on the time axis (non-causal shift to a earlier + time). If a single value is given, the same time shift will be applied + to each channel of the signal. Individual time shifts for each channel + can be performed by passing an array matching the signals channel + dimensions ``cshape``. + unit : str, optional + Unit of the shift variable, this can be either ``'samples'`` or ``'s'`` + for seconds. By default ``'samples'`` is used. Note that in the case + of specifying the shift time in seconds, the value is rounded to the + next integer sample value to perform the shift. circular_shift : bool, True Perform a circular or non-circular shift. If a non-circular shift is performed, the data will be padded with nan values at the respective beginning or ending of the data, corresponding to the number of samples - the data is shifted. - keepdims : bool, False - Do not squeeze the data before returning. + the data is shifted. In this case, a ``pyfar.TimeData`` object is + returned. + Returns ------- - shifted_signal : ndarray, float - Shifted input signal + pyfar.Signal, pyfar.TimeData + The time-shifted signal. If a circular shift is performed, the return + value will be a ``pyfar.Signal``, in case of a non-circular shift, its + type will be ``pyfar.TimeData``. + + Notes + ----- + This function is primarily intended to be used when processing room impulse + responses. When ``circular_shift=True``, the function input is passed into + ``pyfar.dsp.time_shift``. + + Examples + -------- + Perform a circular shift a set of ideal impulses stored in three different + channels and plot the resulting signals + + .. plot:: + + >>> import pyfar as pf + >>> import matplotlib.pyplot as plt + >>> # generate and shift the impulses + >>> impulse = pf.signals.impulse( + ... 32, amplitude=(1, 1.5, 1), delay=(14, 15, 16)) + >>> shifted = pf.dsp.time_shift(impulse, [-2, 0, 2]) + >>> # time domain plot + >>> pf.plot.use('light') + >>> _, axs = plt.subplots(2, 1) + >>> pf.plot.time(impulse, ax=axs[0]) + >>> pf.plot.time(shifted, ax=axs[1]) + >>> axs[0].set_title('Original signals') + >>> axs[1].set_title('Shifted signals') + >>> plt.tight_layout() + + Perform a non-circular shift the same impulses stored in three different + channels and plot the resulting signals + + .. plot:: + + >>> import pyfar as pf + >>> import matplotlib.pyplot as plt + >>> # generate and shift the impulses + >>> impulse = pf.signals.impulse( + ... 32, amplitude=(1, 1.5, 1), delay=(14, 15, 16)) + >>> shifted = pf.dsp.time_shift( + ... impulse, [-2, 0, 2], circular_shift=False) + >>> # time domain plot + >>> pf.plot.use('light') + >>> _, axs = plt.subplots(2, 1) + >>> pf.plot.time(impulse, ax=axs[0]) + >>> pf.plot.time(shifted, ax=axs[1]) + >>> axs[0].set_title('Original signals') + >>> axs[1].set_title('Shifted signals') + >>> plt.tight_layout() """ - n_samples_shift = np.asarray(n_samples_shift, dtype=int) - if np.any(signal.shape[-1] < n_samples_shift): - msg = "Shifting by more samples than length of the signal." - if circular_shift: - warnings.warn(msg, UserWarning) - else: - raise ValueError(msg) - - signal = np.atleast_2d(signal) - n_samples = signal.shape[-1] - signal_shape = signal.shape - signal = np.reshape(signal, (-1, n_samples)) - n_channels = np.prod(signal.shape[:-1]) - - if n_samples_shift.size == 1: - n_samples_shift = np.broadcast_to(n_samples_shift, n_channels) - elif n_samples_shift.size == n_channels: - n_samples_shift = np.reshape(n_samples_shift, n_channels) + shift = np.atleast_1d(shift) + if shift.size == 1: + shift = np.ones(signal.cshape) * shift + + if unit == 's': + shift_samples = np.round(shift*signal.sampling_rate).astype(int) + elif unit == 'samples': + shift_samples = shift.astype(int) else: - raise ValueError("The number of shift samples has to match the number \ - of signal channels.") - - shifted_signal = signal.copy() - for channel in range(n_channels): - shifted_signal[channel, :] = \ - np.roll( - shifted_signal[channel, :], - n_samples_shift[channel], + raise ValueError( + f"Unit is: {unit}, but has to be 'samples' or 's'.") + + shifted = pf.dsp.time_shift(signal, shift_samples, unit='samples') + + if circular_shift is False: + # Convert to TimeData, as filling with nans will break Fourier trafos + shifted = pf.TimeData( + shifted.time, + shifted.times, + comment=shifted.comment, + dtype=shifted.dtype) + + shift_samples = shift_samples.flatten() + for ch in range(shifted.cshape[0]): + shifted.time[ch] = np.roll( + shifted.time[ch], + shift_samples[ch], axis=-1) - if not circular_shift: - if n_samples_shift[channel] < 0: - # index is negative, so index will reference from the - # end of the array - shifted_signal[channel, n_samples_shift[channel]:] = np.nan + if shift[ch] < 0: + shifted.time[ch, shift[ch]:] = np.nan else: - # index is positive, so index will reference from the - # start of the array - shifted_signal[channel, :n_samples_shift[channel]] = np.nan + shifted.time[ch, :shift[ch]] = np.nan - shifted_signal = np.reshape(shifted_signal, signal_shape) - if not keepdims: - shifted_signal = np.squeeze(shifted_signal) + shifted = shifted.reshape(signal.cshape) - return shifted_signal + return shifted def center_frequencies_octaves(): From 4dae84e5f2eb6cfc7cb0c985d17826d06b7be40d Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Sun, 1 May 2022 12:51:59 +0200 Subject: [PATCH 07/66] time shift cont. and tests --- pyrato/dsp.py | 9 ++-- tests/test_dsp.py | 116 +++++++++++----------------------------------- 2 files changed, 31 insertions(+), 94 deletions(-) diff --git a/pyrato/dsp.py b/pyrato/dsp.py index 35c03f0..bfa703d 100644 --- a/pyrato/dsp.py +++ b/pyrato/dsp.py @@ -263,16 +263,13 @@ def time_shift(signal, shift, circular_shift=True, unit='samples'): comment=shifted.comment, dtype=shifted.dtype) + shifted = shifted.flatten() shift_samples = shift_samples.flatten() for ch in range(shifted.cshape[0]): - shifted.time[ch] = np.roll( - shifted.time[ch], - shift_samples[ch], - axis=-1) if shift[ch] < 0: - shifted.time[ch, shift[ch]:] = np.nan + shifted.time[ch, shift_samples[ch]:] = np.nan else: - shifted.time[ch, :shift[ch]] = np.nan + shifted.time[ch, :shift_samples[ch]] = np.nan shifted = shifted.reshape(signal.cshape) diff --git a/tests/test_dsp.py b/tests/test_dsp.py index eb6361c..f464a98 100644 --- a/tests/test_dsp.py +++ b/tests/test_dsp.py @@ -7,6 +7,7 @@ import pytest import pyfar as pf from numpy import genfromtxt +from scipy.fftpack import shift import pyrato.dsp as dsp @@ -177,114 +178,53 @@ def test_max_ir(): start_sample_est = dsp.find_impulse_response_maximum(ir_awgn) assert start_sample_est == start_sample +# ------------------ +# Time shift +# ------------------ -def test_time_shift_right(): - shift_samples = 10 + +@pytest.mark.parametrize("shift_samples", [10, -10, 0]) +def test_time_shift_left_right(shift_samples): n_samples = 2**9 - ir = np.zeros(n_samples, dtype=float) - ir[20] = 1 + ir = pf.signals.impulse(n_samples, delay=20) - ir_truth = np.zeros(n_samples, dtype=float) - ir_truth[20+shift_samples] = 1 + ir_truth = pf.signals.impulse(n_samples, 20+shift_samples) ir_shifted = dsp.time_shift(ir, shift_samples) - npt.assert_allclose(ir_shifted, ir_truth) + npt.assert_allclose(ir_shifted.time, ir_truth.time) -def test_time_shift_left(): - shift_samples = 10 +def test_time_shift_return_vals(): n_samples = 2**9 - ir = np.zeros(n_samples, dtype=float) - ir[20] = 1 + ir = pf.signals.impulse(n_samples, delay=20) - ir_truth = np.zeros(n_samples, dtype=float) - ir_truth[20-shift_samples] = 1 - ir_shifted = dsp.time_shift(ir, -shift_samples) + ir_shifted = dsp.time_shift(ir, 1, circular_shift=True) + assert type(ir_shifted) == pf.Signal - npt.assert_allclose(ir_shifted, ir_truth) + ir_shifted = dsp.time_shift(ir, 1, circular_shift=False) + assert type(ir_shifted) == pf.TimeData -def test_time_shift_non_circular_right(): +def test_time_shift_non_circular_left_right(): shift_samples = 10 n_samples = 2**9 - ir = np.zeros(n_samples, dtype=float) - ir[20] = 1 + + ir = pf.signals.impulse(n_samples, delay=20) ir_truth = np.zeros(n_samples, dtype=float) ir_truth[20+shift_samples] = 1 ir_truth[:shift_samples] = np.nan - ir_shifted = dsp.time_shift(ir, shift_samples, circular_shift=False) - - npt.assert_allclose(ir_shifted, ir_truth, equal_nan=True) + ir_truth = pf.TimeData(ir_truth, np.arange(n_samples)/ir.sampling_rate) -def test_time_shift_non_circular_left(): - shift_samples = 10 - n_samples = 2**9 - ir = np.zeros(n_samples, dtype=float) - ir[20] = 1 + ir_shifted = dsp.time_shift(ir, shift_samples, circular_shift=False) + npt.assert_allclose(ir_shifted.time, ir_truth.time, equal_nan=True) + shift_samples = -10 ir_truth = np.zeros(n_samples, dtype=float) - ir_truth[20-shift_samples] = 1 - ir_truth[n_samples-shift_samples:] = np.nan - ir_shifted = dsp.time_shift(ir, -shift_samples, circular_shift=False) - - npt.assert_allclose(ir_shifted, ir_truth, equal_nan=True) - - -def test_time_shift_multitim(): - shift_samples = 10 - n_samples = 2**10 - n_channels = 3 - ir = np.zeros((n_channels, n_samples)) - - start_sample = [24, 5, 43] - ir[[0, 1, 2], start_sample] = 1 - - ir_truth = np.zeros((n_channels, n_samples), dtype=float) - start_sample_truth = [24+shift_samples, 5+shift_samples, 43+shift_samples] - ir_truth[[0, 1, 2], start_sample_truth] = 1 - - ir_shifted = dsp.time_shift(ir, shift_samples) - - npt.assert_allclose(ir_shifted, ir_truth) - - ir_truth = np.zeros((n_channels, n_samples), dtype=float) - start_sample_truth = [24-shift_samples, 5-shift_samples, 43-shift_samples] - ir_truth[[0, 1, 2], start_sample_truth] = 1 - - ir_shifted = dsp.time_shift(ir, -shift_samples) - - npt.assert_allclose(ir_shifted, ir_truth) - - -def test_time_shift_multitim_multishift(): - shift_samples = [10, 2, 4] - n_samples = 40 - n_channels = 3 - ir = np.zeros((n_channels, n_samples), dtype=float) - - start_sample = [24, 5, 13] - ir[[0, 1, 2], start_sample] = 1 - - ir_truth = np.zeros((n_channels, n_samples), dtype=float) - start_sample_truth = [ - 24+shift_samples[0], - 5+shift_samples[1], - 13+shift_samples[2]] - ir_truth[[0, 1, 2], start_sample_truth] = 1 - - ir_shifted = dsp.time_shift(ir, shift_samples) - - npt.assert_allclose(ir_shifted, ir_truth) - - ir_truth = np.zeros((n_channels, n_samples), dtype=float) - start_sample_truth = [ - 24-shift_samples[0], - 5-shift_samples[1], - 13-shift_samples[2]] - ir_truth[[0, 1, 2], start_sample_truth] = 1 - - ir_shifted = dsp.time_shift(ir, -np.array(shift_samples, dtype=int)) + ir_truth[20+shift_samples] = 1 + ir_truth[shift_samples:] = np.nan + ir_truth = pf.TimeData(ir_truth, np.arange(n_samples)/ir.sampling_rate) - npt.assert_allclose(ir_shifted, ir_truth) + ir_shifted = dsp.time_shift(ir, shift_samples, circular_shift=False) + npt.assert_allclose(ir_shifted.time, ir_truth.time, equal_nan=True) From 3c97b8dae2db2a6d6709e37ea593e6c2db440e18 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Sun, 1 May 2022 13:00:03 +0200 Subject: [PATCH 08/66] docstring updates time_shift --- pyrato/dsp.py | 21 ++++++++------------- 1 file changed, 8 insertions(+), 13 deletions(-) diff --git a/pyrato/dsp.py b/pyrato/dsp.py index bfa703d..86a6c31 100644 --- a/pyrato/dsp.py +++ b/pyrato/dsp.py @@ -205,12 +205,11 @@ def time_shift(signal, shift, circular_shift=True, unit='samples'): .. plot:: >>> import pyfar as pf + >>> import pyrato as ra >>> import matplotlib.pyplot as plt - >>> # generate and shift the impulses >>> impulse = pf.signals.impulse( ... 32, amplitude=(1, 1.5, 1), delay=(14, 15, 16)) - >>> shifted = pf.dsp.time_shift(impulse, [-2, 0, 2]) - >>> # time domain plot + >>> shifted = ra.time_shift(impulse, [-2, 0, 2]) >>> pf.plot.use('light') >>> _, axs = plt.subplots(2, 1) >>> pf.plot.time(impulse, ax=axs[0]) @@ -219,25 +218,21 @@ def time_shift(signal, shift, circular_shift=True, unit='samples'): >>> axs[1].set_title('Shifted signals') >>> plt.tight_layout() - Perform a non-circular shift the same impulses stored in three different - channels and plot the resulting signals + Perform a non-circular shift a single impulse and plot the results. .. plot:: >>> import pyfar as pf + >>> import pyrato as ra >>> import matplotlib.pyplot as plt - >>> # generate and shift the impulses - >>> impulse = pf.signals.impulse( - ... 32, amplitude=(1, 1.5, 1), delay=(14, 15, 16)) - >>> shifted = pf.dsp.time_shift( - ... impulse, [-2, 0, 2], circular_shift=False) - >>> # time domain plot + >>> impulse = pf.signals.impulse(32, delay=15) + >>> shifted = ra.time_shift(impulse, -10, circular_shift=False) >>> pf.plot.use('light') >>> _, axs = plt.subplots(2, 1) >>> pf.plot.time(impulse, ax=axs[0]) >>> pf.plot.time(shifted, ax=axs[1]) - >>> axs[0].set_title('Original signals') - >>> axs[1].set_title('Shifted signals') + >>> axs[0].set_title('Original signal') + >>> axs[1].set_title('Shifted signal') >>> plt.tight_layout() """ From e929de9e672c1a060e5e2234052525064969a403 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Sun, 1 May 2022 17:56:46 +0200 Subject: [PATCH 09/66] add sort and example to eigenfrequency calculation function --- pyrato/analytic.py | 48 ++++++++++++++++++++++++++++++++++++------ tests/test_analytic.py | 3 ++- 2 files changed, 43 insertions(+), 8 deletions(-) diff --git a/pyrato/analytic.py b/pyrato/analytic.py index 84b6227..fca37af 100644 --- a/pyrato/analytic.py +++ b/pyrato/analytic.py @@ -6,18 +6,21 @@ def eigenfrequencies_rectangular_room_rigid( - dimensions, max_freq, speed_of_sound): + dimensions, max_freq, speed_of_sound=343.9, sort=True): """Calculate the eigenfrequencies of a rectangular room with rigid walls. Parameters ---------- - dimensions : double, ndarray + dimensions : float, numpy.ndarray The dimensions of the room in the form [L_x, L_y, L_z] - max_freq : double + max_freq : float The maximum frequency to consider for the calculation of the - eigenfrequencies of the room - speed_of_sound : double, optional (343.9) - The speed of sound + eigenfrequencies. + speed_of_sound : double, optional + The speed of sound in meters per second. The default is 343.9 + sort : bool, optional + If ``True``, the return values will be sorted with ascending + frequencies. By default this is ``True``. Returns ------- @@ -30,6 +33,32 @@ def eigenfrequencies_rectangular_room_rigid( ---------- .. [2] H. Kuttruff, Room acoustics, pp. 64-66, 4th Ed. Taylor & Francis, 2009. + + Examples + -------- + + Calculate the eigenfrequencies under 75 Hz of a small room and plot. + + >>> import numpy as np + >>> import pyrato as ra + >>> import matplotlib.pyplot as plt + >>> from pyrato.analytic import eigenfrequencies_rectangular_room_rigid + ... + >>> L = [4, 5, 2.6] + >>> f_n, n = eigenfrequencies_rectangular_room_rigid( + ... L, max_freq=75, speed_of_sound=343.6, sort=True) + ... + >>> ax = plt.axes() + >>> ax.semilogx(f_n, np.arange(f_n.size), linestyle='', marker='o') + >>> labels = [str(nn) for nn in n.T] + >>> ax.set_yticks(np.arange(f_n.size)) + >>> ax.set_yticklabels(labels) + >>> ax.set_xticks([30, 40, 50, 60, 70, 80]) + >>> ax.set_xticklabels(['30', '40', '50', '60', '70', '80']) + >>> ax.set_xlabel('Frequency (Hz)') + >>> ax.set_ylabel('Eigenfrequency index [$n_x, n_y, n_z$]') + >>> plt.tight_layout() + """ c = speed_of_sound L = np.asarray(dimensions) @@ -49,7 +78,7 @@ def eigenfrequencies_rectangular_room_rigid( (2*f_max/c)**2 - (n_x/L_x)**2 - (n_y/L_y)**2 ) * L_z))) + 1 - n = np.zeros((3, n_modes)) + n = np.zeros((3, n_modes), dtype=int) idx = 0 n_x_max = int(np.floor(2*f_max/c * L_x)) + 1 @@ -71,6 +100,11 @@ def eigenfrequencies_rectangular_room_rigid( f_n = c/2*np.sqrt(np.sum((n/L[np.newaxis].T)**2, axis=0)) + if sort is True: + sort_idx = np.argsort(f_n) + f_n = f_n[sort_idx] + n = n[:, sort_idx] + return f_n, n diff --git a/tests/test_analytic.py b/tests/test_analytic.py index eb13d10..670d6d8 100644 --- a/tests/test_analytic.py +++ b/tests/test_analytic.py @@ -10,7 +10,8 @@ def test_analytic_shoebox_eigenfreqs(): analytic.eigenfrequencies_rectangular_room_rigid( L, max_freq=1e3, - speed_of_sound=343.9) + speed_of_sound=343.9, + sort=False) f_n = np.array([ 0, From 162cc20f6b3ae6f2576a4ce41a86244c851bf449 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Sun, 1 May 2022 18:12:00 +0200 Subject: [PATCH 10/66] enhance docstrings for analytic model and use pyfar --- pyrato/analytic.py | 80 +++++++++++++++++++++++++++--------------- tests/test_analytic.py | 5 ++- 2 files changed, 56 insertions(+), 29 deletions(-) diff --git a/pyrato/analytic.py b/pyrato/analytic.py index fca37af..2be7188 100644 --- a/pyrato/analytic.py +++ b/pyrato/analytic.py @@ -1,6 +1,7 @@ # -*- coding: utf-8 -*- from itertools import count +import pyfar as pf import numpy as np from scipy import optimize @@ -39,25 +40,27 @@ def eigenfrequencies_rectangular_room_rigid( Calculate the eigenfrequencies under 75 Hz of a small room and plot. - >>> import numpy as np - >>> import pyrato as ra - >>> import matplotlib.pyplot as plt - >>> from pyrato.analytic import eigenfrequencies_rectangular_room_rigid - ... - >>> L = [4, 5, 2.6] - >>> f_n, n = eigenfrequencies_rectangular_room_rigid( - ... L, max_freq=75, speed_of_sound=343.6, sort=True) - ... - >>> ax = plt.axes() - >>> ax.semilogx(f_n, np.arange(f_n.size), linestyle='', marker='o') - >>> labels = [str(nn) for nn in n.T] - >>> ax.set_yticks(np.arange(f_n.size)) - >>> ax.set_yticklabels(labels) - >>> ax.set_xticks([30, 40, 50, 60, 70, 80]) - >>> ax.set_xticklabels(['30', '40', '50', '60', '70', '80']) - >>> ax.set_xlabel('Frequency (Hz)') - >>> ax.set_ylabel('Eigenfrequency index [$n_x, n_y, n_z$]') - >>> plt.tight_layout() + .. plot:: + + >>> import numpy as np + >>> import pyrato as ra + >>> import matplotlib.pyplot as plt + >>> from pyrato.analytic import eigenfrequencies_rectangular_room_rigid + ... + >>> L = [4, 5, 2.6] + >>> f_n, n = eigenfrequencies_rectangular_room_rigid( + ... L, max_freq=75, speed_of_sound=343.6, sort=True) + ... + >>> ax = plt.axes() + >>> ax.semilogx(f_n, np.arange(f_n.size), linestyle='', marker='o') + >>> labels = [str(nn) for nn in n.T] + >>> ax.set_yticks(np.arange(f_n.size)) + >>> ax.set_yticklabels(labels) + >>> ax.set_xticks([30, 40, 50, 60, 70, 80]) + >>> ax.set_xticklabels(['30', '40', '50', '60', '70', '80']) + >>> ax.set_xlabel('Frequency (Hz)') + >>> ax.set_ylabel('Eigenfrequency index [$n_x, n_y, n_z$]') + >>> plt.tight_layout() """ c = speed_of_sound @@ -108,14 +111,15 @@ def eigenfrequencies_rectangular_room_rigid( return f_n, n -def rectangular_room_rigid_walls(dimensions, - source, - receiver, - reverberation_time, - max_freq, - samplingrate=44100, - speed_of_sound=343.9, - n_samples=2**18): +def rectangular_room_rigid_walls( + dimensions, + source, + receiver, + reverberation_time, + max_freq, + samplingrate=44100, + speed_of_sound=343.9, + n_samples=2**18): r"""Calculate the transfer function of a rectangular room based on the analytic model as given in [2]_ . The model is based on the solution for a room with rigid walls. The damping of the modes is included as @@ -143,7 +147,7 @@ def rectangular_room_rigid_walls(dimensions, Returns ------- - rir : ndarray, double + rir : pyfar.Signal The room impulse response eigenfrequencies: ndarray, double The eigenfrequencies for which the room impulse response was @@ -154,6 +158,24 @@ def rectangular_room_rigid_walls(dimensions, .. [2] H. Kuttruff, Room acoustics, pp. 64-66, 4th Ed. Taylor & Francis, 2009. + Example + ------- + Calculate the sound field in a rectangular room with 1 s reverberation + time for a given source and receiver combination. + + .. plot:: + >>> import numpy as np + >>> import pyfar as pf + >>> from pyrato.analytic import rectangular_room_rigid_walls + ... + >>> L = np.array([8, 5, 3])/10 + >>> source_pos = np.array([5, 3, 1.2])/10 + >>> receiver_pos = np.array([1, 1, 1.2])/10 + >>> rir, _ = rectangular_room_rigid_walls( + >>> L, source_pos, receiver_pos, + >>> reverberation_time=1, max_freq=1e3, n_samples=2**16, + >>> speed_of_sound=343.9) + >>> pf.plot.time_freq(rir) """ delta_n_raw = 3*np.log(10)/reverberation_time @@ -196,6 +218,8 @@ def rectangular_room_rigid_walls(dimensions, transfer_function += (coeff_n/den) rir = np.fft.irfft(transfer_function, n=n_samples) + + rir = pf.Signal(rir, samplingrate) return rir, f_n diff --git a/tests/test_analytic.py b/tests/test_analytic.py index 670d6d8..224e8d8 100644 --- a/tests/test_analytic.py +++ b/tests/test_analytic.py @@ -1,6 +1,7 @@ import numpy as np import numpy.testing as npt import pyrato.analytic as analytic +import pyfar as pf def test_analytic_shoebox_eigenfreqs(): @@ -62,7 +63,9 @@ def test_analytic_shoebox_rir(): delimiter=',', skiprows=1) - npt.assert_allclose(rir, ref) + ref = pf.Signal(ref, 44100) + + npt.assert_allclose(rir.time, ref.time) def test_eigenfreq_impedance_1d_real(): From d5f107f61307e44878c605a588f278ed2c50c9df Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Sun, 1 May 2022 18:48:32 +0200 Subject: [PATCH 11/66] new schroeder integration function --- pyrato/edc.py | 74 ++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 70 insertions(+), 4 deletions(-) diff --git a/pyrato/edc.py b/pyrato/edc.py index f76aa62..7d33d2b 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -11,6 +11,7 @@ from pyrato import dsp import pyrato as ra import warnings +import pyfar as pf def estimate_noise_energy( @@ -193,7 +194,72 @@ def subtract_noise_from_squared_rir(data, noise_level='auto'): return (data.T - noise_level).T -def schroeder_integration(impulse_response, is_energy=False): +def schroeder_integration(room_impulse_response, is_energy=False): + r"""Calculate the Schroeder integral of a room impulse response [#]_. The + result is the energy decay curve for the given room impulse response. + + .. math: + + \langle e^2(t) \rangle = N\cdot \int_{t}^{\infty} h^2(\tau) + \mathrm{d} \tau + + Parameters + ---------- + room_impulse_response : pyfar.Signal + Room impulse response as array + is_energy : boolean, optional + Whether the input represents energy data or sound pressure values. + + Returns + ------- + energy_decay_curve : pyfar.TimeData + The energy decay curve + + Note + ---- + This function does not apply any compensation of measurement noise and + integrates the full length of the input signal. It should only be used + if no measurement noise or artifacts are present in the data. + + References + ---------- + .. [#] M. R. Schroeder, “New Method of Measuring Reverberation Time,” + The Journal of the Acoustical Society of America, vol. 37, no. 6, + pp. 1187-1187, 1965. + + Example + ------- + Calculate the Schroeder integral of a simulated RIR and plot. + + .. plot:: + + >>> import numpy as np + >>> import pyfar as pf + >>> import pyrato as ra + >>> from pyrato.analytic import rectangular_room_rigid_walls + ... + >>> L = np.array([8, 5, 3])/10 + >>> source_pos = np.array([5, 3, 1.2])/10 + >>> receiver_pos = np.array([1, 1, 1.2])/10 + >>> rir, _ = rectangular_room_rigid_walls( + ... L, source_pos, receiver_pos, + ... reverberation_time=1, max_freq=1e3, n_samples=2**16, + ... speed_of_sound=343.9) + >>> edc = ra.schroeder_integration(rir) + >>> pf.plot.time(rir/np.abs(rir.time).max(), dB=True, label='RIR') + >>> ax = pf.plot.time( + ... edc/edc.time[..., 0], dB=True, log_prefix=10, label='EDC') + >>> ax.set_ylim(-65, 5) + >>> ax.legend() + """ + + edc = _schroeder_integration( + room_impulse_response.time, is_energy=is_energy) + + return pf.TimeData(edc, room_impulse_response.times) + + +def _schroeder_integration(impulse_response, is_energy=False): r"""Calculate the Schroeder integral of a room impulse response [#]_. The result is the energy decay curve for the given room impulse response. @@ -305,7 +371,7 @@ def energy_decay_curve_truncation( for idx_channel in range(0, n_channels): energy_decay_curve[ idx_channel, :int(intersection_time_idx[idx_channel])] = \ - ra.schroeder_integration( + _schroeder_integration( energy_data[ idx_channel, :int(intersection_time_idx[idx_channel])], is_energy=True) @@ -426,7 +492,7 @@ def energy_decay_curve_lundeby( * sampling_rate) energy_decay_curve[idx_channel, :intersection_time_idx] = \ - ra.schroeder_integration( + _schroeder_integration( energy_data[idx_channel, :intersection_time_idx], is_energy=True) energy_decay_curve[idx_channel] += correction @@ -530,7 +596,7 @@ def energy_decay_curve_chu( energy_data, noise_level=noise_level) - energy_decay_curve = ra.schroeder_integration( + energy_decay_curve = _schroeder_integration( subtracted, is_energy=True) From 274d31ab42c1e877f116878ed882eb480fd5347c Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 09:58:29 +0200 Subject: [PATCH 12/66] adapt preprocess function --- pyrato/edc.py | 19 +++--- tests/test_edc_noise_handling.py | 105 ++++++++++++++++++------------- 2 files changed, 71 insertions(+), 53 deletions(-) diff --git a/pyrato/edc.py b/pyrato/edc.py index 7d33d2b..f776b18 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -89,12 +89,11 @@ def preprocess_rir( The original data shape. """ - n_samples = data.shape[-1] - data = np.atleast_2d(data) - n_channels = np.prod(data.shape[:-1]) + times = data.times + n_channels = np.prod(data.cshape) - data_shape = list(data.shape) - data = np.reshape(data, (-1, n_samples)) + data_shape = list(data.cshape) + data = data.reshape((-1,)) if time_shift: rir_start_idx = dsp.find_impulse_response_start(data) @@ -107,14 +106,16 @@ def preprocess_rir( -min_shift * np.ones(n_channels), dtype=int) result = dsp.time_shift( - data, shift_samples, circular_shift=False, keepdims=True) - + data, shift_samples, circular_shift=False) else: result = data + if not is_energy: - energy_data = np.abs(result)**2 + energy_data = np.abs(result.time)**2 else: - energy_data = result.copy() + energy_data = result.time.copy() + + energy_data = pf.TimeData(energy_data, times) return energy_data, n_channels, data_shape diff --git a/tests/test_edc_noise_handling.py b/tests/test_edc_noise_handling.py index bbaae11..9f9e9a6 100644 --- a/tests/test_edc_noise_handling.py +++ b/tests/test_edc_noise_handling.py @@ -12,6 +12,7 @@ from pyrato import edc as enh from pyrato import dsp from numpy import genfromtxt +import pyfar as pf test_data_path = os.path.join(os.path.dirname(__file__), 'test_data') @@ -55,118 +56,134 @@ def test_preprocessing_1D(): rir = genfromtxt( os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), delimiter=',') - expected = genfromtxt( - os.path.join(test_data_path, 'preprocessing_1D.csv'), - delimiter=',')[np.newaxis] + rir = pf.Signal(rir, 1) actual = enh.preprocess_rir( rir, is_energy=False, time_shift=False, channel_independent=False)[0] - npt.assert_allclose(actual, expected) + + expected = np.atleast_2d(genfromtxt( + os.path.join(test_data_path, 'preprocessing_1D.csv'), + delimiter=',')) + npt.assert_allclose(actual.time, np.atleast_2d(expected)) def test_preprocessing_2D(): rir = genfromtxt( os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), delimiter=',') - expected = genfromtxt( - os.path.join(test_data_path, 'preprocessing_2D.csv'), - delimiter=',') + rir = pf.Signal(rir, 1) + actual = enh.preprocess_rir( rir, is_energy=False, time_shift=False, channel_independent=False)[0] - npt.assert_allclose(actual, expected) - -def test_preprocessing_time_shift_1D(monkeypatch): - rir = genfromtxt( - os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), - delimiter=',') expected = genfromtxt( - os.path.join(test_data_path, 'preprocessing_time_shift_1D.csv'), - delimiter=',')[np.newaxis] + os.path.join(test_data_path, 'preprocessing_2D.csv'), + delimiter=',') + npt.assert_allclose(actual.time, expected) + +def test_preprocessing_time_shift_1D(monkeypatch): + # Patch the RIR start finding to always return same number of samples monkeypatch.setattr( dsp, "find_impulse_response_start", mock_shift_samples_1d) + rir = genfromtxt( + os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), + delimiter=',') + rir = pf.Signal(rir, 1) + actual = enh.preprocess_rir( rir, is_energy=False, time_shift=True, channel_independent=False)[0] - npt.assert_allclose(actual, expected) + expected = np.atleast_2d(genfromtxt( + os.path.join(test_data_path, 'preprocessing_time_shift_1D.csv'), + delimiter=',')) + npt.assert_allclose(actual.time, expected) -def test_preprocessing_time_shift_2D(monkeypatch): - rir = genfromtxt( - os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), - delimiter=',') - expected = genfromtxt( - os.path.join(test_data_path, 'preprocessing_time_shift_2D.csv'), - delimiter=',') +def test_preprocessing_time_shift_2D(monkeypatch): + # Patch the RIR start finding to always return same number of samples monkeypatch.setattr( dsp, "find_impulse_response_start", mock_shift_samples_2d) + rir = pf.Signal( + genfromtxt( + os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), + delimiter=','), + 1) + + expected = np.atleast_2d(genfromtxt( + os.path.join(test_data_path, 'preprocessing_time_shift_2D.csv'), + delimiter=',')) + actual = enh.preprocess_rir( rir, is_energy=False, time_shift=True, channel_independent=False)[0] - npt.assert_allclose(actual, expected) + npt.assert_allclose(actual.time, expected) def test_preprocessing_time_shift_channel_independent_1D(monkeypatch): - rir = genfromtxt( - os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), - delimiter=',') - expected = genfromtxt( - os.path.join( - test_data_path, - 'preprocessing_time_shift_channel_independent_1D.csv'), - delimiter=',')[np.newaxis] - + # Patch the RIR start finding to always return same number of samples monkeypatch.setattr( dsp, "find_impulse_response_start", mock_shift_samples_1d) + rir = pf.Signal( + genfromtxt( + os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), + delimiter=','), + 1) + expected = np.atleast_2d(genfromtxt( + os.path.join( + test_data_path, + 'preprocessing_time_shift_channel_independent_1D.csv'), + delimiter=',')) + actual = enh.preprocess_rir( rir, is_energy=False, time_shift=True, channel_independent=True)[0] - npt.assert_allclose(actual, expected) + npt.assert_allclose(actual.time, expected) def test_preprocessing_time_shift_channel_independent_2D(monkeypatch): - rir = genfromtxt( - os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), - delimiter=',') - expected = genfromtxt( - os.path.join( - test_data_path, - 'preprocessing_time_shift_channel_independent_2D.csv'), - delimiter=',') - + # Patch the RIR start finding to always return same number of samples monkeypatch.setattr( dsp, "find_impulse_response_start", mock_shift_samples_2d) + rir = pf.Signal(genfromtxt( + os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), + delimiter=','), 1) + expected = np.atleast_2d(genfromtxt( + os.path.join( + test_data_path, + 'preprocessing_time_shift_channel_independent_2D.csv'), + delimiter=',')) + actual = enh.preprocess_rir( rir, is_energy=False, time_shift=True, channel_independent=True)[0] - npt.assert_allclose(actual, expected) + npt.assert_allclose(actual.time, expected) def test_smoothed_rir_1D(): From fdde93a6fec469be864cdb229d25bd00921bee67 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 12:13:40 +0200 Subject: [PATCH 13/66] internal smoothing function for lundeby algorithm --- pyrato/edc.py | 12 ++++++------ tests/test_edc_noise_handling.py | 4 ++-- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/pyrato/edc.py b/pyrato/edc.py index f776b18..0623407 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -120,7 +120,7 @@ def preprocess_rir( return energy_data, n_channels, data_shape -def smooth_rir( +def _smooth_rir( data, sampling_rate, smooth_block_length=0.075): @@ -364,7 +364,7 @@ def energy_decay_curve_truncation( time_shift=False, channel_independent=False, plot=False)[0] - time_vector = smooth_rir(energy_data, sampling_rate)[2] + time_vector = _smooth_rir(energy_data, sampling_rate)[2] intersection_time_idx = np.rint(intersection_time * sampling_rate) @@ -476,7 +476,7 @@ def energy_decay_curve_lundeby( time_shift=False, channel_independent=False, plot=False) - time_vector = smooth_rir(energy_data, sampling_rate)[2] + time_vector = _smooth_rir(energy_data, sampling_rate)[2] energy_decay_curve = np.zeros([n_channels, n_samples]) @@ -728,7 +728,7 @@ def energy_decay_curve_chu_lundeby( channel_independent=False, plot=False) - time_vector = smooth_rir(energy_data, sampling_rate)[2] + time_vector = _smooth_rir(energy_data, sampling_rate)[2] energy_decay_curve = np.zeros([n_channels, n_samples]) for idx_channel in range(0, n_channels): @@ -863,7 +863,7 @@ def intersection_time_lundeby( freq_dependent_window_time = (800/freq+10) / 1000 # (1) SMOOTH - time_window_data, time_vector_window, time_vector = smooth_rir( + time_window_data, time_vector_window, time_vector = _smooth_rir( energy_data, sampling_rate, freq_dependent_window_time) # (2) ESTIMATE NOISE @@ -937,7 +937,7 @@ def intersection_time_lundeby( # (6) AVERAGE time_window_data_current_channel, \ time_vector_window_current_channel, \ - time_vector_current_channel = smooth_rir( + time_vector_current_channel = _smooth_rir( energy_data[idx_channel], sampling_rate, window_time) time_window_data_current_channel = np.squeeze( time_window_data_current_channel) diff --git a/tests/test_edc_noise_handling.py b/tests/test_edc_noise_handling.py index 9f9e9a6..fc03596 100644 --- a/tests/test_edc_noise_handling.py +++ b/tests/test_edc_noise_handling.py @@ -193,7 +193,7 @@ def test_smoothed_rir_1D(): expected = genfromtxt( os.path.join(test_data_path, 'smoothed_rir_1D.csv'), delimiter=',')[np.newaxis] - actual = enh.smooth_rir( + actual = enh._smooth_rir( rir, sampling_rate=3000, smooth_block_length=0.075)[0] @@ -207,7 +207,7 @@ def test_smoothed_rir_2D(): expected = genfromtxt( os.path.join(test_data_path, 'smoothed_rir_2D.csv'), delimiter=',') - actual = enh.smooth_rir( + actual = enh._smooth_rir( rir, sampling_rate=3000, smooth_block_length=0.075)[0] From 9e6f67f654c829c466eb1bc453d0f5634291821d Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 12:18:31 +0200 Subject: [PATCH 14/66] make noise subtraction function private --- pyrato/edc.py | 6 +++--- tests/test_edc_noise_handling.py | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/pyrato/edc.py b/pyrato/edc.py index 0623407..e450a09 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -172,7 +172,7 @@ def _smooth_rir( return time_window_data, time_vector_window, time_vector -def subtract_noise_from_squared_rir(data, noise_level='auto'): +def _subtract_noise_from_squared_rir(data, noise_level='auto'): """ Subtracts the noise energy level from the squared RIR. Note, that the RIR has to be squared before. @@ -593,7 +593,7 @@ def energy_decay_curve_chu( time_shift=time_shift, channel_independent=channel_independent) - subtracted = subtract_noise_from_squared_rir( + subtracted = _subtract_noise_from_squared_rir( energy_data, noise_level=noise_level) @@ -713,7 +713,7 @@ def energy_decay_curve_chu_lundeby( channel_independent=channel_independent) n_samples = energy_data.shape[-1] - subtraction = subtract_noise_from_squared_rir( + subtraction = _subtract_noise_from_squared_rir( energy_data, noise_level=noise_level) diff --git a/tests/test_edc_noise_handling.py b/tests/test_edc_noise_handling.py index fc03596..0540965 100644 --- a/tests/test_edc_noise_handling.py +++ b/tests/test_edc_noise_handling.py @@ -221,7 +221,7 @@ def test_substracted_1D(): expected = genfromtxt( os.path.join(test_data_path, 'substracted_1D.csv'), delimiter=',') - actual = enh.subtract_noise_from_squared_rir(rir**2) + actual = enh._subtract_noise_from_squared_rir(rir**2) npt.assert_allclose(actual, expected) @@ -232,7 +232,7 @@ def test_substracted_2D(): expected = genfromtxt( os.path.join(test_data_path, 'substracted_2D.csv'), delimiter=',') - actual = enh.subtract_noise_from_squared_rir(rir**2) + actual = enh._subtract_noise_from_squared_rir(rir**2) npt.assert_allclose(actual, expected) From 3f82c3dede0a82d00cc4132f2cc7216731fb9f93 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 14:21:03 +0200 Subject: [PATCH 15/66] move dsp functions into dsp module --- pyrato/__init__.py | 6 +- pyrato/dsp.py | 183 +++++++++++++++++++++++++++++++++++++++++++++ tests/test_dsp.py | 23 ++++++ 3 files changed, 209 insertions(+), 3 deletions(-) diff --git a/pyrato/__init__.py b/pyrato/__init__.py index bcaff6d..9f54d66 100644 --- a/pyrato/__init__.py +++ b/pyrato/__init__.py @@ -19,16 +19,16 @@ filter_fractional_octave_bands, time_shift, center_frequencies_octaves, - center_frequencies_third_octaves + center_frequencies_third_octaves, + preprocess_rir, + estimate_noise_energy, ) from .edc import ( schroeder_integration, - preprocess_rir, energy_decay_curve_chu, energy_decay_curve_chu_lundeby, energy_decay_curve_lundeby, energy_decay_curve_truncation, - estimate_noise_energy, intersection_time_lundeby, ) diff --git a/pyrato/dsp.py b/pyrato/dsp.py index 86a6c31..83def9d 100644 --- a/pyrato/dsp.py +++ b/pyrato/dsp.py @@ -450,3 +450,186 @@ def filter_fractional_octave_bands( signal_out[band, :] = spsignal.sosfilt(sos, signal) return signal_out + + +def estimate_noise_energy( + data, + interval=[0.9, 1.0], + is_energy=False): + """ This function estimates the noise energy level of a given room impulse + response. The noise is assumed to be Gaussian. + + Parameters + ---------- + data: np.array + The room impulse response with dimension [..., n_samples] + interval : tuple, float, [0.9, 1.] + Defines the interval of the RIR to be evaluated for the estimation. + The interval is relative to the length of the RIR [0 = 0%, 1=100%)] + is_energy: Boolean + Defines if the data is already squared. + + Returns + ------- + noise_energy: float + The energy of the background noise. + """ + + energy_data = preprocess_rir( + data, + is_energy=is_energy, + time_shift=False, + channel_independent=False)[0] + + return _estimate_noise_energy(energy_data.time, interval=interval) + + +def _estimate_noise_energy( + energy_data, + interval=[0.9, 1.0]): + """ This function estimates the noise energy level of a given room impulse + response. The noise is assumed to be Gaussian. + + Parameters + ---------- + data: np.array + The room impulse response with dimension [..., n_samples] + interval : tuple, float, [0.9, 1.] + Defines the interval of the RIR to be evaluated for the estimation. + The interval is relative to the length of the RIR [0 = 0%, 1=100%)] + is_energy: Boolean + Defines if the data is already squared. + + Returns + ------- + noise_energy: float + The energy of the background noise. + """ + + if np.any(energy_data) < 0: + raise ValueError("Energy is negative, check your input signal.") + + region_start_idx = int(energy_data.shape[-1]*interval[0]) + region_end_idx = int(energy_data.shape[-1]*interval[1]) + mask = np.arange(region_start_idx, region_end_idx) + noise_energy = np.nanmean(np.take(energy_data, mask, axis=-1), axis=-1) + + return noise_energy + + +def _smooth_rir( + data, + sampling_rate, + smooth_block_length=0.075): + """ Smoothens the RIR by averaging the data in an specified interval. + + Parameters + ---------- + data : ndarray, double + The room impulse response with dimension [..., n_samples] + sampling_rate: integer + Defines the sampling rate of the room impulse response. + smooth_block_length : double + Defines the block-length of the smoothing algorithm in seconds. + + Returns + ------- + time_window_data : ndarray, double + The smoothed RIR. + time_vector_window : ndarray, double + The respective time vector fitting the smoothed data. + time_vector : ndarray, double + The time vector fitting the original data. + + """ + data = np.atleast_2d(data) + n_samples = data.shape[-1] + n_samples_nan = np.count_nonzero(np.isnan(data), axis=-1) + + n_samples_per_block = int(np.round(smooth_block_length * sampling_rate, 0)) + n_blocks = np.asarray( + np.floor((n_samples-n_samples_nan)/n_samples_per_block), + dtype=int) + + n_blocks_min = int(np.min(n_blocks)) + n_samples_actual = int(n_blocks_min*n_samples_per_block) + reshaped_array = np.reshape( + data[..., :n_samples_actual], + (-1, n_blocks_min, n_samples_per_block)) + time_window_data = np.mean(reshaped_array, axis=-1) + + # Use average time instances corresponding to the average energy level + # instead of time for the first sample of the block + time_vector_window = \ + ((0.5+np.arange(0, n_blocks_min)) * n_samples_per_block/sampling_rate) + + # Use the time corresponding to the sampling of the original data + time_vector = (np.arange(0, n_samples))/sampling_rate + + return time_window_data, time_vector_window, time_vector + + +def preprocess_rir( + data, + is_energy=False, + time_shift=False, + channel_independent=False): + """ Preprocess the room impulse response for further processing: + - Square data + - Shift the RIR to the first sample of the array, compensating for the + delay of the time of arrival of the direct sound. The time shift is + performed as a non-cyclic shift, adding numpy.nan values in the end + of the RIR corresponding to the number of samples the data is + shifted by. + - The time shift can be done channel-independent or not. + + Parameters + ---------- + data : ndarray, double + The room impulse response with dimension [..., n_samples] + is_energy : boolean + Defines, if the data is already squared. + time_shift : boolean + Defines, if the silence at beginning of the RIR should be removed. + channel_independent : boolean + Defines, if the time shift is done channel-independent or not. + + Returns + ------- + energy_data : ndarray, double + The preprocessed RIR + n_channels : integer + The number of channels of the RIR + data_shape : list, integer + The original data shape. + + """ + times = data.times + n_channels = np.prod(data.cshape) + + data_shape = list(data.cshape) + data = data.reshape((-1,)) + + if time_shift: + rir_start_idx = find_impulse_response_start(data) + + if channel_independent and not n_channels == 1: + shift_samples = -rir_start_idx + else: + min_shift = np.amin(rir_start_idx) + shift_samples = np.asarray( + -min_shift * np.ones(n_channels), dtype=int) + + result = time_shift( + data, shift_samples, circular_shift=False) + else: + result = data + + if not is_energy: + energy_data = np.abs(result.time)**2 + else: + energy_data = result.time.copy() + + energy_data = pf.TimeData(energy_data, times) + + return energy_data, n_channels, data_shape diff --git a/tests/test_dsp.py b/tests/test_dsp.py index f464a98..964a0eb 100644 --- a/tests/test_dsp.py +++ b/tests/test_dsp.py @@ -10,6 +10,7 @@ from scipy.fftpack import shift import pyrato.dsp as dsp +import pyrato test_data_path = os.path.join(os.path.dirname(__file__), 'test_data') @@ -228,3 +229,25 @@ def test_time_shift_non_circular_left_right(): ir_shifted = dsp.time_shift(ir, shift_samples, circular_shift=False) npt.assert_allclose(ir_shifted.time, ir_truth.time, equal_nan=True) + + +# ---------------- +# Noise power estimation +# ---------------- + +def test_estimate_noise_power(): + n_samples = 2**18 + rms = 10**(-40/20) + noise = pf.signals.noise(n_samples, rms=rms) + actual = dsp.estimate_noise_energy(noise) + + npt.assert_allclose(actual, rms**2, rtol=1e-3, atol=1e-3) + + +def test_estimate_noise_power_private(): + n_samples = 2**18 + rms = 10**(-40/20) + noise = pf.signals.noise(n_samples, rms=rms) + actual = dsp._estimate_noise_energy(noise.time) + + npt.assert_allclose(actual, rms**2, rtol=1e-3, atol=1e-3) From d002906b7dbaf6950a3512d1f3cf2ea9a07b9d85 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 14:37:09 +0200 Subject: [PATCH 16/66] adapt tests to new dsp structure cont. --- pyrato/dsp.py | 8 +- tests/test_dsp.py | 199 +++++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 202 insertions(+), 5 deletions(-) diff --git a/pyrato/dsp.py b/pyrato/dsp.py index 83def9d..554a637 100644 --- a/pyrato/dsp.py +++ b/pyrato/dsp.py @@ -478,7 +478,7 @@ def estimate_noise_energy( energy_data = preprocess_rir( data, is_energy=is_energy, - time_shift=False, + shift=False, channel_independent=False)[0] return _estimate_noise_energy(energy_data.time, interval=interval) @@ -572,7 +572,7 @@ def _smooth_rir( def preprocess_rir( data, is_energy=False, - time_shift=False, + shift=False, channel_independent=False): """ Preprocess the room impulse response for further processing: - Square data @@ -589,7 +589,7 @@ def preprocess_rir( The room impulse response with dimension [..., n_samples] is_energy : boolean Defines, if the data is already squared. - time_shift : boolean + shift : boolean Defines, if the silence at beginning of the RIR should be removed. channel_independent : boolean Defines, if the time shift is done channel-independent or not. @@ -610,7 +610,7 @@ def preprocess_rir( data_shape = list(data.cshape) data = data.reshape((-1,)) - if time_shift: + if shift: rir_start_idx = find_impulse_response_start(data) if channel_independent and not n_channels == 1: diff --git a/tests/test_dsp.py b/tests/test_dsp.py index 964a0eb..fb705d2 100644 --- a/tests/test_dsp.py +++ b/tests/test_dsp.py @@ -11,10 +11,17 @@ import pyrato.dsp as dsp import pyrato - test_data_path = os.path.join(os.path.dirname(__file__), 'test_data') +def mock_shift_samples_1d(*args, **kwargs): + return np.array([76]) + + +def mock_shift_samples_2d(*args, **kwargs): + return np.array([76, 76]) + + def test_start_ir_insufficient_snr(): n_samples = 2**9 ir = np.zeros(n_samples, dtype=float) @@ -251,3 +258,193 @@ def test_estimate_noise_power_private(): actual = dsp._estimate_noise_energy(noise.time) npt.assert_allclose(actual, rms**2, rtol=1e-3, atol=1e-3) + + +def test_noise_energy_1D(): + rir = pf.Signal(genfromtxt( + os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), + delimiter=','), 1) + expected = genfromtxt( + os.path.join(test_data_path, 'noise_energy_1D.csv'), + delimiter=',') + actual = dsp.estimate_noise_energy( + rir, + interval=[0.9, 1.0], + is_energy=False) + npt.assert_allclose(actual, expected) + + +def test_noise_energy_2D(): + rir = pf.Signal(genfromtxt( + os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), + delimiter=','), 1) + expected = genfromtxt( + os.path.join(test_data_path, 'noise_energy_2D.csv'), + delimiter=',') + actual = dsp.estimate_noise_energy( + rir, + interval=[0.9, 1.0], + is_energy=False) + npt.assert_allclose(actual, expected) + + +def test_preprocessing_1D(): + rir = genfromtxt( + os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), + delimiter=',') + rir = pf.Signal(rir, 1) + actual = dsp.preprocess_rir( + rir, + is_energy=False, + shift=False, + channel_independent=False)[0] + + expected = np.atleast_2d(genfromtxt( + os.path.join(test_data_path, 'preprocessing_1D.csv'), + delimiter=',')) + npt.assert_allclose(actual.time, np.atleast_2d(expected)) + + +def test_preprocessing_2D(): + rir = genfromtxt( + os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), + delimiter=',') + rir = pf.Signal(rir, 1) + + actual = dsp.preprocess_rir( + rir, + is_energy=False, + shift=False, + channel_independent=False)[0] + + expected = genfromtxt( + os.path.join(test_data_path, 'preprocessing_2D.csv'), + delimiter=',') + npt.assert_allclose(actual.time, expected) + + +def test_preprocessing_time_shift_1D(monkeypatch): + # Patch the RIR start finding to always return same number of samples + monkeypatch.setattr( + dsp, + "find_impulse_response_start", + mock_shift_samples_1d) + + rir = genfromtxt( + os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), + delimiter=',') + rir = pf.Signal(rir, 1) + + actual = dsp.preprocess_rir( + rir, + is_energy=False, + shift=True, + channel_independent=False)[0] + + expected = np.atleast_2d(genfromtxt( + os.path.join(test_data_path, 'preprocessing_time_shift_1D.csv'), + delimiter=',')) + npt.assert_allclose(actual.time, expected) + + +def test_preprocessing_time_shift_2D(monkeypatch): + # Patch the RIR start finding to always return same number of samples + monkeypatch.setattr( + dsp, + "find_impulse_response_start", + mock_shift_samples_2d) + + rir = pf.Signal( + genfromtxt( + os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), + delimiter=','), + 1) + + expected = np.atleast_2d(genfromtxt( + os.path.join(test_data_path, 'preprocessing_time_shift_2D.csv'), + delimiter=',')) + + actual = dsp.preprocess_rir( + rir, + is_energy=False, + shift=True, + channel_independent=False)[0] + npt.assert_allclose(actual.time, expected) + + +def test_preprocessing_time_shift_channel_independent_1D(monkeypatch): + # Patch the RIR start finding to always return same number of samples + monkeypatch.setattr( + dsp, + "find_impulse_response_start", + mock_shift_samples_1d) + + rir = pf.Signal( + genfromtxt( + os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), + delimiter=','), + 1) + expected = np.atleast_2d(genfromtxt( + os.path.join( + test_data_path, + 'preprocessing_time_shift_channel_independent_1D.csv'), + delimiter=',')) + + actual = dsp.preprocess_rir( + rir, + is_energy=False, + shift=True, + channel_independent=True)[0] + npt.assert_allclose(actual.time, expected) + + +def test_preprocessing_time_shift_channel_independent_2D(monkeypatch): + # Patch the RIR start finding to always return same number of samples + monkeypatch.setattr( + dsp, + "find_impulse_response_start", + mock_shift_samples_2d) + + rir = pf.Signal(genfromtxt( + os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), + delimiter=','), 1) + expected = np.atleast_2d(genfromtxt( + os.path.join( + test_data_path, + 'preprocessing_time_shift_channel_independent_2D.csv'), + delimiter=',')) + + actual = dsp.preprocess_rir( + rir, + is_energy=False, + shift=True, + channel_independent=True)[0] + npt.assert_allclose(actual.time, expected) + + +def test_smoothed_rir_1D(): + rir = genfromtxt( + os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), + delimiter=',') + expected = genfromtxt( + os.path.join(test_data_path, 'smoothed_rir_1D.csv'), + delimiter=',')[np.newaxis] + actual = dsp._smooth_rir( + rir, + sampling_rate=3000, + smooth_block_length=0.075)[0] + npt.assert_allclose(actual, expected) + + +def test_smoothed_rir_2D(): + rir = genfromtxt( + os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), + delimiter=',') + expected = genfromtxt( + os.path.join(test_data_path, 'smoothed_rir_2D.csv'), + delimiter=',') + actual = dsp._smooth_rir( + rir, + sampling_rate=3000, + smooth_block_length=0.075)[0] + npt.assert_allclose(actual, expected) From 7eefe97e22279b903cd75fc632fa10d2c44f9ea2 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 14:59:40 +0200 Subject: [PATCH 17/66] chu's method adapted to pyfar --- pyrato/edc.py | 286 +++++++++---------------------- tests/test_edc_noise_handling.py | 215 ++--------------------- 2 files changed, 88 insertions(+), 413 deletions(-) diff --git a/pyrato/edc.py b/pyrato/edc.py index e450a09..0fee0a2 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -14,167 +14,31 @@ import pyfar as pf -def estimate_noise_energy( - data, - interval=[0.9, 1.0], - is_energy=False): - """ This function estimates the noise energy level of a given room impulse - response. The noise is assumed to be Gaussian. - - Parameters - ---------- - data: np.array - The room impulse response with dimension [..., n_samples] - interval : tuple, float, [0.9, 1.] - Defines the interval of the RIR to be evaluated for the estimation. - The interval is relative to the length of the RIR [0 = 0%, 1=100%)] - is_energy: Boolean - Defines if the data is already squared. - - Returns - ------- - noise_energy: float - The energy of the background noise. - """ - - energy_data = preprocess_rir( - data, - is_energy=is_energy, - time_shift=False, - channel_independent=False)[0] - - if np.any(energy_data) < 0: - raise ValueError("Energy is negative, check your input signal.") - - region_start_idx = int(energy_data.shape[-1]*interval[0]) - region_end_idx = int(energy_data.shape[-1]*interval[1]) - mask = np.arange(region_start_idx, region_end_idx) - noise_energy = np.nanmean(np.take(energy_data, mask, axis=-1), axis=-1) - - return noise_energy - - -def preprocess_rir( - data, - is_energy=False, - time_shift=False, - channel_independent=False): - """ Preprocess the room impulse response for further processing: - - Square data - - Shift the RIR to the first sample of the array, compensating for the - delay of the time of arrival of the direct sound. The time shift is - performed as a non-cyclic shift, adding numpy.nan values in the end - of the RIR corresponding to the number of samples the data is - shifted by. - - The time shift can be done channel-independent or not. - - Parameters - ---------- - data : ndarray, double - The room impulse response with dimension [..., n_samples] - is_energy : boolean - Defines, if the data is already squared. - time_shift : boolean - Defines, if the silence at beginning of the RIR should be removed. - channel_independent : boolean - Defines, if the time shift is done channel-independent or not. - - Returns - ------- - energy_data : ndarray, double - The preprocessed RIR - n_channels : integer - The number of channels of the RIR - data_shape : list, integer - The original data shape. - - """ - times = data.times - n_channels = np.prod(data.cshape) - - data_shape = list(data.cshape) - data = data.reshape((-1,)) - - if time_shift: - rir_start_idx = dsp.find_impulse_response_start(data) - - if channel_independent and not n_channels == 1: - shift_samples = -rir_start_idx - else: - min_shift = np.amin(rir_start_idx) - shift_samples = np.asarray( - -min_shift * np.ones(n_channels), dtype=int) - - result = dsp.time_shift( - data, shift_samples, circular_shift=False) - else: - result = data - - if not is_energy: - energy_data = np.abs(result.time)**2 - else: - energy_data = result.time.copy() - - energy_data = pf.TimeData(energy_data, times) - - return energy_data, n_channels, data_shape - - -def _smooth_rir( - data, - sampling_rate, - smooth_block_length=0.075): - """ Smoothens the RIR by averaging the data in an specified interval. +def subtract_noise_from_squared_rir(data, noise_level='auto'): + """Subtract the noise power from a squared room impulse response. Parameters ---------- - data : ndarray, double - The room impulse response with dimension [..., n_samples] - sampling_rate: integer - Defines the sampling rate of the room impulse response. - smooth_block_length : double - Defines the block-length of the smoothing algorithm in seconds. + data : pyfar.TimeData + The squared room impulse response. + noise_level : str or numpy.ndarray, float, optional + The noise power for each channel. The default is 'auto', which will + try to estimate the noise power from the room impulse response. Returns ------- - time_window_data : ndarray, double - The smoothed RIR. - time_vector_window : ndarray, double - The respective time vector fitting the smoothed data. - time_vector : ndarray, double - The time vector fitting the original data. + pyfar.TimeData + The squared room impulse response after noise power subtraction. """ - data = np.atleast_2d(data) - n_samples = data.shape[-1] - n_samples_nan = np.count_nonzero(np.isnan(data), axis=-1) - - n_samples_per_block = int(np.round(smooth_block_length * sampling_rate, 0)) - n_blocks = np.asarray( - np.floor((n_samples-n_samples_nan)/n_samples_per_block), - dtype=int) - - n_blocks_min = int(np.min(n_blocks)) - n_samples_actual = int(n_blocks_min*n_samples_per_block) - reshaped_array = np.reshape( - data[..., :n_samples_actual], - (-1, n_blocks_min, n_samples_per_block)) - time_window_data = np.mean(reshaped_array, axis=-1) - - # Use average time instances corresponding to the average energy level - # instead of time for the first sample of the block - time_vector_window = \ - ((0.5+np.arange(0, n_blocks_min)) * n_samples_per_block/sampling_rate) - - # Use the time corresponding to the sampling of the original data - time_vector = (np.arange(0, n_samples))/sampling_rate + subtracted = _subtract_noise_from_squared_rir( + data.time, noise_level=noise_level) - return time_window_data, time_vector_window, time_vector + return pf.TimeData(subtracted, data.times, comment=data.comment) def _subtract_noise_from_squared_rir(data, noise_level='auto'): - """ Subtracts the noise energy level from the squared RIR. Note, that the - RIR has to be squared before. + """Subtract the noise power from a squared room impulse response. Parameters ---------- @@ -188,9 +52,8 @@ def _subtract_noise_from_squared_rir(data, noise_level='auto'): """ if noise_level == "auto": - noise_level = estimate_noise_energy( + noise_level = dsp._estimate_noise_energy( data, - is_energy=True, interval=[0.9, 1.0]) return (data.T - noise_level).T @@ -348,10 +211,10 @@ def energy_decay_curve_truncation( energy_decay_curve: ndarray, double Returns the noise handeled edc. """ - energy_data, n_channels, data_shape = preprocess_rir( + energy_data, n_channels, data_shape = dsp.preprocess_rir( data, is_energy=is_energy, - time_shift=time_shift, + shift=time_shift, channel_independent=channel_independent) n_samples = energy_data.shape[-1] @@ -364,7 +227,7 @@ def energy_decay_curve_truncation( time_shift=False, channel_independent=False, plot=False)[0] - time_vector = _smooth_rir(energy_data, sampling_rate)[2] + time_vector = dsp._smooth_rir(energy_data, sampling_rate)[2] intersection_time_idx = np.rint(intersection_time * sampling_rate) @@ -460,10 +323,10 @@ def energy_decay_curve_lundeby( Measurements in Room Acoustics - ACUSTICA Vol. 81 (1995) """ - energy_data, n_channels, data_shape = preprocess_rir( + energy_data, n_channels, data_shape = dsp.preprocess_rir( data, is_energy=is_energy, - time_shift=time_shift, + shift=time_shift, channel_independent=channel_independent) n_samples = energy_data.shape[-1] intersection_time, late_reverberation_time, noise_estimation = \ @@ -476,7 +339,7 @@ def energy_decay_curve_lundeby( time_shift=False, channel_independent=False, plot=False) - time_vector = _smooth_rir(energy_data, sampling_rate)[2] + time_vector = dsp._smooth_rir(energy_data, sampling_rate)[2] energy_decay_curve = np.zeros([n_channels, n_samples]) @@ -534,8 +397,6 @@ def energy_decay_curve_lundeby( def energy_decay_curve_chu( data, - sampling_rate, - freq='broadband', noise_level='auto', is_energy=False, time_shift=True, @@ -551,12 +412,6 @@ def energy_decay_curve_chu( ---------- data : ndarray, double The room impulse response with dimension [..., n_samples] - sampling_rate: integer - The sampling rate of the room impulse response. - freq: integer OR string - The frequency band. If set to 'broadband', - the time window of the Lundeby-algorithm will not be set in dependence - of frequency. noise_level: ndarray, double OR string If not specified, the noise level is calculated based on the last 10 percent of the RIR. Otherwise specify manually for each channel @@ -585,66 +440,81 @@ def energy_decay_curve_chu( Schroeder’s impulse method and decay-curve averaging method”. In: Journal of the Acoustical Society of America 63.5 (1978), pp. 1444–1450. - """ - energy_data, n_channels, data_shape = preprocess_rir( + Examples + -------- + + .. plot:: + + >>> import numpy as np + >>> import pyfar as pf + >>> import pyrato as ra + >>> from pyrato.analytic import rectangular_room_rigid_walls + ... + >>> L = np.array([8, 5, 3])/10 + >>> source_pos = np.array([5, 3, 1.2])/10 + >>> receiver_pos = np.array([1, 1, 1.2])/10 + >>> rir, _ = rectangular_room_rigid_walls( + ... L, source_pos, receiver_pos, + ... reverberation_time=1, max_freq=1e3, n_samples=2**16, + ... speed_of_sound=343.9) + ... + >>> awgn = pf.signals.noise( + ... rir.n_samples, rms=rir.time.max()*10**(-40/20), + ... sampling_rate=rir.sampling_rate) + >>> rir = rir + awgn + >>> edc = ra.energy_decay_curve_chu(rir) + ... + >>> pf.plot.time(rir/np.abs(rir.time).max(), dB=True, label='RIR') + >>> ax = pf.plot.time( + ... edc/edc.time[..., 0], dB=True, log_prefix=10, label='EDC') + >>> ax.set_ylim(-65, 5) + >>> ax.legend() + + """ + cshape = data.cshape + energy_data, n_channels, data_shape = dsp.preprocess_rir( data, is_energy=is_energy, - time_shift=time_shift, + shift=time_shift, channel_independent=channel_independent) - subtracted = _subtract_noise_from_squared_rir( + subtracted = subtract_noise_from_squared_rir( energy_data, noise_level=noise_level) - energy_decay_curve = _schroeder_integration( - subtracted, - is_energy=True) - - energy_decay_curve = np.atleast_2d(energy_decay_curve) + edc = schroeder_integration(subtracted, is_energy=True) if normalize: # Normalize the EDC... if not channel_independent: # ...by the first element of each channel. - energy_decay_curve = (energy_decay_curve.T - / energy_decay_curve[..., 0]).T + edc.time = (edc.time.T / edc.time[..., 0]).T else: # ...by the maximum first element of all channels. - max_start_value = np.amax(energy_decay_curve[..., 0]) - energy_decay_curve /= max_start_value + max_start_value = np.amax(edc.time[..., 0]) + edc.time /= max_start_value - mask = energy_decay_curve <= 2*np.finfo(float).eps + mask = edc.time <= 2*np.finfo(float).eps if np.any(mask): first_zero = np.nanargmax(mask, axis=-1) for channel in range(n_channels): - energy_decay_curve[channel, first_zero[channel]:] = np.nan + edc.time[channel, first_zero[channel]:] = np.nan if plot: - time_vector = (0.5+np.arange(0, energy_data.shape[-1]))/sampling_rate plt.figure(figsize=(15, 3)) + pf.plot.use('light') plt.subplot(131) - plt.plot(time_vector, 10*np.log10(energy_data.T)) - plt.xlabel('Time [s]') - plt.ylabel('Squared IR [dB]') - plt.grid(True) + pf.plot.time(energy_data, dB=True, log_prefix=10) + plt.ylabel('Squared IR in dB') plt.subplot(132) - plt.plot(time_vector, 10*np.log10(subtracted.T)) - plt.xlabel('Time [s]') - plt.ylabel('Noise subtracted IR [dB]') - plt.grid(True) + pf.plot.time(subtracted, dB=True, log_prefix=10) + plt.ylabel('Noise subtracted RIR in dB') plt.subplot(133) - plt.plot(time_vector, 10*np.log10(energy_decay_curve.T)) - plt.xlabel('Time [s]') - plt.ylabel('Noise-handeled EDC [dB]') - plt.grid(True) - plt.tight_layout() + pf.plot.time(edc, dB=True, log_prefix=10) + plt.ylabel('EDC in dB') - # Recover original data shape: - energy_decay_curve = np.reshape(energy_decay_curve, data_shape) - energy_decay_curve = np.squeeze(energy_decay_curve) - - return energy_decay_curve + return edc.reshape(cshape) def energy_decay_curve_chu_lundeby( @@ -706,10 +576,10 @@ def energy_decay_curve_chu_lundeby( room acoustic parameters,” 2015. """ - energy_data, n_channels, data_shape = preprocess_rir( + energy_data, n_channels, data_shape = dsp.preprocess_rir( data, is_energy=is_energy, - time_shift=time_shift, + shift=time_shift, channel_independent=channel_independent) n_samples = energy_data.shape[-1] @@ -728,14 +598,14 @@ def energy_decay_curve_chu_lundeby( channel_independent=False, plot=False) - time_vector = _smooth_rir(energy_data, sampling_rate)[2] + time_vector = dsp._smooth_rir(energy_data, sampling_rate)[2] energy_decay_curve = np.zeros([n_channels, n_samples]) for idx_channel in range(0, n_channels): intersection_time_idx = np.argmin(np.abs( time_vector - intersection_time[idx_channel])) if noise_level == 'auto': - p_square_at_intersection = estimate_noise_energy( + p_square_at_intersection = dsp.estimate_noise_energy( energy_data[idx_channel], is_energy=True) else: p_square_at_intersection = noise_level[idx_channel] @@ -850,10 +720,10 @@ def intersection_time_lundeby( # Dynamic range 10 ... 20 dB use_dyn_range_for_regression = 20 - energy_data, n_channels, data_shape = preprocess_rir( + energy_data, n_channels, data_shape = dsp.preprocess_rir( data, is_energy=is_energy, - time_shift=time_shift, + shift=time_shift, channel_independent=channel_independent) if freq == "broadband": @@ -863,12 +733,12 @@ def intersection_time_lundeby( freq_dependent_window_time = (800/freq+10) / 1000 # (1) SMOOTH - time_window_data, time_vector_window, time_vector = _smooth_rir( + time_window_data, time_vector_window, time_vector = dsp._smooth_rir( energy_data, sampling_rate, freq_dependent_window_time) # (2) ESTIMATE NOISE if initial_noise_power == 'auto': - noise_estimation = estimate_noise_energy( + noise_estimation = dsp.estimate_noise_energy( energy_data, is_energy=True) else: noise_estimation = initial_noise_power.copy() @@ -937,7 +807,7 @@ def intersection_time_lundeby( # (6) AVERAGE time_window_data_current_channel, \ time_vector_window_current_channel, \ - time_vector_current_channel = _smooth_rir( + time_vector_current_channel = dsp._smooth_rir( energy_data[idx_channel], sampling_rate, window_time) time_window_data_current_channel = np.squeeze( time_window_data_current_channel) diff --git a/tests/test_edc_noise_handling.py b/tests/test_edc_noise_handling.py index 0540965..2503200 100644 --- a/tests/test_edc_noise_handling.py +++ b/tests/test_edc_noise_handling.py @@ -15,7 +15,6 @@ import pyfar as pf test_data_path = os.path.join(os.path.dirname(__file__), 'test_data') - def mock_shift_samples_1d(*args, **kwargs): return np.array([76]) @@ -24,196 +23,6 @@ def mock_shift_samples_2d(*args, **kwargs): return np.array([76, 76]) -def test_noise_energy_1D(): - rir = genfromtxt( - os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), - delimiter=',') - expected = genfromtxt( - os.path.join(test_data_path, 'noise_energy_1D.csv'), - delimiter=',') - actual = enh.estimate_noise_energy( - rir, - interval=[0.9, 1.0], - is_energy=False) - npt.assert_allclose(actual, expected) - - -def test_noise_energy_2D(): - rir = genfromtxt( - os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), - delimiter=',') - expected = genfromtxt( - os.path.join(test_data_path, 'noise_energy_2D.csv'), - delimiter=',') - actual = enh.estimate_noise_energy( - rir, - interval=[0.9, 1.0], - is_energy=False) - npt.assert_allclose(actual, expected) - - -def test_preprocessing_1D(): - rir = genfromtxt( - os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), - delimiter=',') - rir = pf.Signal(rir, 1) - actual = enh.preprocess_rir( - rir, - is_energy=False, - time_shift=False, - channel_independent=False)[0] - - expected = np.atleast_2d(genfromtxt( - os.path.join(test_data_path, 'preprocessing_1D.csv'), - delimiter=',')) - npt.assert_allclose(actual.time, np.atleast_2d(expected)) - - -def test_preprocessing_2D(): - rir = genfromtxt( - os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), - delimiter=',') - rir = pf.Signal(rir, 1) - - actual = enh.preprocess_rir( - rir, - is_energy=False, - time_shift=False, - channel_independent=False)[0] - - expected = genfromtxt( - os.path.join(test_data_path, 'preprocessing_2D.csv'), - delimiter=',') - npt.assert_allclose(actual.time, expected) - - -def test_preprocessing_time_shift_1D(monkeypatch): - # Patch the RIR start finding to always return same number of samples - monkeypatch.setattr( - dsp, - "find_impulse_response_start", - mock_shift_samples_1d) - - rir = genfromtxt( - os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), - delimiter=',') - rir = pf.Signal(rir, 1) - - actual = enh.preprocess_rir( - rir, - is_energy=False, - time_shift=True, - channel_independent=False)[0] - - expected = np.atleast_2d(genfromtxt( - os.path.join(test_data_path, 'preprocessing_time_shift_1D.csv'), - delimiter=',')) - npt.assert_allclose(actual.time, expected) - - -def test_preprocessing_time_shift_2D(monkeypatch): - # Patch the RIR start finding to always return same number of samples - monkeypatch.setattr( - dsp, - "find_impulse_response_start", - mock_shift_samples_2d) - - rir = pf.Signal( - genfromtxt( - os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), - delimiter=','), - 1) - - expected = np.atleast_2d(genfromtxt( - os.path.join(test_data_path, 'preprocessing_time_shift_2D.csv'), - delimiter=',')) - - actual = enh.preprocess_rir( - rir, - is_energy=False, - time_shift=True, - channel_independent=False)[0] - npt.assert_allclose(actual.time, expected) - - -def test_preprocessing_time_shift_channel_independent_1D(monkeypatch): - # Patch the RIR start finding to always return same number of samples - monkeypatch.setattr( - dsp, - "find_impulse_response_start", - mock_shift_samples_1d) - - rir = pf.Signal( - genfromtxt( - os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), - delimiter=','), - 1) - expected = np.atleast_2d(genfromtxt( - os.path.join( - test_data_path, - 'preprocessing_time_shift_channel_independent_1D.csv'), - delimiter=',')) - - actual = enh.preprocess_rir( - rir, - is_energy=False, - time_shift=True, - channel_independent=True)[0] - npt.assert_allclose(actual.time, expected) - - -def test_preprocessing_time_shift_channel_independent_2D(monkeypatch): - # Patch the RIR start finding to always return same number of samples - monkeypatch.setattr( - dsp, - "find_impulse_response_start", - mock_shift_samples_2d) - - rir = pf.Signal(genfromtxt( - os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), - delimiter=','), 1) - expected = np.atleast_2d(genfromtxt( - os.path.join( - test_data_path, - 'preprocessing_time_shift_channel_independent_2D.csv'), - delimiter=',')) - - actual = enh.preprocess_rir( - rir, - is_energy=False, - time_shift=True, - channel_independent=True)[0] - npt.assert_allclose(actual.time, expected) - - -def test_smoothed_rir_1D(): - rir = genfromtxt( - os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), - delimiter=',') - expected = genfromtxt( - os.path.join(test_data_path, 'smoothed_rir_1D.csv'), - delimiter=',')[np.newaxis] - actual = enh._smooth_rir( - rir, - sampling_rate=3000, - smooth_block_length=0.075)[0] - npt.assert_allclose(actual, expected) - - -def test_smoothed_rir_2D(): - rir = genfromtxt( - os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), - delimiter=',') - expected = genfromtxt( - os.path.join(test_data_path, 'smoothed_rir_2D.csv'), - delimiter=',') - actual = enh._smooth_rir( - rir, - sampling_rate=3000, - smooth_block_length=0.075)[0] - npt.assert_allclose(actual, expected) - - def test_substracted_1D(): rir = genfromtxt( os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), @@ -385,12 +194,12 @@ def test_edc_lundeby_chu_2D(monkeypatch): def test_edc_chu_1D(monkeypatch): - rir = genfromtxt( + rir = pf.Signal(genfromtxt( os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), - delimiter=',') - expected = genfromtxt( + delimiter=','), 3000) + expected = np.atleast_2d(genfromtxt( os.path.join(test_data_path, 'edc_chu_1D.csv'), - delimiter=',') + delimiter=',')) monkeypatch.setattr( dsp, @@ -399,23 +208,21 @@ def test_edc_chu_1D(monkeypatch): actual = enh.energy_decay_curve_chu( rir, - sampling_rate=3000, - freq='broadband', is_energy=False, time_shift=True, channel_independent=False, normalize=True, plot=False) - npt.assert_allclose(actual, expected) + npt.assert_allclose(actual.time, expected) def test_edc_chu_2D(monkeypatch): - rir = genfromtxt( + rir = pf.Signal(genfromtxt( os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), - delimiter=',') - expected = genfromtxt( + delimiter=','), 3e3) + expected = np.atleast_2d(genfromtxt( os.path.join(test_data_path, 'edc_chu_2D.csv'), - delimiter=',') + delimiter=',')) monkeypatch.setattr( dsp, @@ -424,14 +231,12 @@ def test_edc_chu_2D(monkeypatch): actual = enh.energy_decay_curve_chu( rir, - sampling_rate=3000, - freq='broadband', is_energy=False, time_shift=True, channel_independent=False, normalize=True, plot=False) - npt.assert_allclose(actual, expected) + npt.assert_allclose(actual.time, expected) def test_intersection_time_1D(monkeypatch): From 63bc2fca152f9183eca04d2053097cf0d0590e50 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 15:37:51 +0200 Subject: [PATCH 18/66] adapt lundeby intersection time calculation --- pyrato/edc.py | 50 +++++++++++++++++++++++++++----- tests/test_edc_noise_handling.py | 18 +++++------- 2 files changed, 50 insertions(+), 18 deletions(-) diff --git a/pyrato/edc.py b/pyrato/edc.py index 0fee0a2..dca671f 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -661,24 +661,22 @@ def energy_decay_curve_chu_lundeby( def intersection_time_lundeby( data, - sampling_rate, freq='broadband', initial_noise_power='auto', is_energy=False, time_shift=False, channel_independent=False, plot=False): + """Calculate the intersection time between impulse response and noise. - """ This function uses the algorithm after Lundeby et al. [#]_ to calculate + This function uses the algorithm after Lundeby et al. [#]_ to calculate the intersection time, lundeby reverberation time, and noise level estimation. Parameters ---------- - data : ndarray, double - The room impulse response with dimension [..., n_samples] - sampling_rate: integer - The sampling rate of the room impulse response. + data : pyfar.Signal + The room impulse response freq: integer OR string The frequency band. If set to 'broadband', the time window of the Lundeby-algorithm will not be set in dependence @@ -711,6 +709,41 @@ def intersection_time_lundeby( .. [#] Lundeby, Virgran, Bietz and Vorlaender - Uncertainties of Measurements in Room Acoustics - ACUSTICA Vol. 81 (1995) + Examples + -------- + + Estimate the intersection time :math:`T_i` and plot the RIR and the + estimated noise power. + + .. plot:: + + >>> import numpy as np + >>> import pyfar as pf + >>> import pyrato as ra + >>> from pyrato.analytic import rectangular_room_rigid_walls + ... + >>> L = np.array([8, 5, 3])/10 + >>> source_pos = np.array([5, 3, 1.2])/10 + >>> receiver_pos = np.array([1, 1, 1.2])/10 + >>> rir, _ = rectangular_room_rigid_walls( + ... L, source_pos, receiver_pos, + ... reverberation_time=1, max_freq=1e3, n_samples=2**16, + ... speed_of_sound=343.9) + >>> rir = rir/np.abs(rir.time).max() + ... + >>> awgn = pf.signals.noise( + ... rir.n_samples, rms=rir.time.max()*10**(-40/20), + ... sampling_rate=rir.sampling_rate) + >>> rir = rir + awgn + >>> inter_time, _, noise_power = ra.intersection_time_lundeby(rir) + ... + >>> ax = pf.plot.time(rir, dB=True, label='RIR') + >>> ax.axvline(inter_time, c='k', linestyle='--', label='$T_i$') + >>> ax.axhline( + ... 10*np.log10(noise_power), c='k', linestyle=':', label='Noise') + >>> ax.set_ylim(-65, 5) + >>> ax.legend() + """ # Define constants: # time intervals per 10 dB decay. Lundeby: 3...10 @@ -725,6 +758,8 @@ def intersection_time_lundeby( is_energy=is_energy, shift=time_shift, channel_independent=channel_independent) + sampling_rate = data.sampling_rate + energy_data = energy_data.time if freq == "broadband": # broadband: use 30 ms windows sizes @@ -738,8 +773,7 @@ def intersection_time_lundeby( # (2) ESTIMATE NOISE if initial_noise_power == 'auto': - noise_estimation = dsp.estimate_noise_energy( - energy_data, is_energy=True) + noise_estimation = dsp._estimate_noise_energy(energy_data) else: noise_estimation = initial_noise_power.copy() diff --git a/tests/test_edc_noise_handling.py b/tests/test_edc_noise_handling.py index 2503200..9784649 100644 --- a/tests/test_edc_noise_handling.py +++ b/tests/test_edc_noise_handling.py @@ -240,12 +240,12 @@ def test_edc_chu_2D(monkeypatch): def test_intersection_time_1D(monkeypatch): - rir = genfromtxt( + rir = pf.Signal(genfromtxt( os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), - delimiter=',') - expected = genfromtxt( + delimiter=','), 3000) + expected = np.atleast_2d(genfromtxt( os.path.join(test_data_path, 'intersection_time_1D.csv'), - delimiter=',')[np.newaxis].T + delimiter=',')).T monkeypatch.setattr( dsp, @@ -254,7 +254,6 @@ def test_intersection_time_1D(monkeypatch): actual = enh.intersection_time_lundeby( rir, - sampling_rate=3000, freq='broadband', is_energy=False, time_shift=False, @@ -264,12 +263,12 @@ def test_intersection_time_1D(monkeypatch): def test_intersection_time_2D(monkeypatch): - rir = genfromtxt( + rir = pf.Signal(genfromtxt( os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), - delimiter=',') - expected = genfromtxt( + delimiter=','), 3000) + expected = np.atleast_2d(genfromtxt( os.path.join(test_data_path, 'intersection_time_2D.csv'), - delimiter=',') + delimiter=',')) monkeypatch.setattr( dsp, @@ -278,7 +277,6 @@ def test_intersection_time_2D(monkeypatch): actual = enh.intersection_time_lundeby( rir, - sampling_rate=3000, freq='broadband', is_energy=False, time_shift=False, From 7cccdd6d7ef3768f10a7978d256b18d5a1dd35d7 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 16:08:16 +0200 Subject: [PATCH 19/66] adapt lundeby method to pyfar --- pyrato/edc.py | 80 +++++++++++++++++++++----------- tests/test_edc_noise_handling.py | 22 ++++----- 2 files changed, 63 insertions(+), 39 deletions(-) diff --git a/pyrato/edc.py b/pyrato/edc.py index dca671f..12768e6 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -273,7 +273,6 @@ def energy_decay_curve_truncation( def energy_decay_curve_lundeby( data, - sampling_rate, freq='broadband', noise_level='auto', is_energy=False, @@ -287,10 +286,8 @@ def energy_decay_curve_lundeby( Parameters ---------- - data : ndarray, double - The room impulse response with dimension [..., n_samples] - sampling_rate: integer - The sampling rate of the room impulse response. + data : pyfar.Signal + The room impulse response. freq: integer OR string The frequency band. If set to 'broadband', the time window of the Lundeby-algorithm will not be set in dependence @@ -314,13 +311,46 @@ def energy_decay_curve_lundeby( Returns ------- - energy_decay_curve: ndarray, double + pyfar.TimeData Returns the noise handeled edc. References ---------- .. [#] Lundeby, Virgran, Bietz and Vorlaender - Uncertainties of Measurements in Room Acoustics - ACUSTICA Vol. 81 (1995) + + Examples + -------- + + Plot the RIR and the EDC calculated after Lundeby. + + .. plot:: + + >>> import numpy as np + >>> import pyfar as pf + >>> import pyrato as ra + >>> from pyrato.analytic import rectangular_room_rigid_walls + ... + >>> L = np.array([8, 5, 3])/10 + >>> source_pos = np.array([5, 3, 1.2])/10 + >>> receiver_pos = np.array([1, 1, 1.2])/10 + >>> rir, _ = rectangular_room_rigid_walls( + ... L, source_pos, receiver_pos, + ... reverberation_time=1, max_freq=1e3, n_samples=2**16, + ... speed_of_sound=343.9) + >>> rir = rir/rir.time.max() + ... + >>> awgn = pf.signals.noise( + ... rir.n_samples, rms=rir.time.max()*10**(-50/20), + ... sampling_rate=rir.sampling_rate) + >>> rir = rir + awgn + >>> edc = ra.energy_decay_curve_lundeby(rir) + ... + >>> ax = pf.plot.time(rir, dB=True, label='RIR') + >>> pf.plot.time(edc, dB=True, log_prefix=10, label='EDC') + >>> ax.set_ylim(-65, 5) + >>> ax.legend() + """ energy_data, n_channels, data_shape = dsp.preprocess_rir( @@ -328,18 +358,19 @@ def energy_decay_curve_lundeby( is_energy=is_energy, shift=time_shift, channel_independent=channel_independent) - n_samples = energy_data.shape[-1] + n_samples = energy_data.n_samples + sampling_rate = data.sampling_rate + intersection_time, late_reverberation_time, noise_estimation = \ intersection_time_lundeby( energy_data, - sampling_rate=sampling_rate, freq=freq, initial_noise_power=noise_level, is_energy=True, time_shift=False, channel_independent=False, plot=False) - time_vector = dsp._smooth_rir(energy_data, sampling_rate)[2] + time_vector = data.times energy_decay_curve = np.zeros([n_channels, n_samples]) @@ -357,7 +388,7 @@ def energy_decay_curve_lundeby( energy_decay_curve[idx_channel, :intersection_time_idx] = \ _schroeder_integration( - energy_data[idx_channel, :intersection_time_idx], + energy_data.time[idx_channel, :intersection_time_idx], is_energy=True) energy_decay_curve[idx_channel] += correction @@ -374,25 +405,16 @@ def energy_decay_curve_lundeby( energy_decay_curve /= max_start_value energy_decay_curve[..., intersection_time_idx:] = np.nan + edc = pf.TimeData( + energy_decay_curve, data.times, comment=data.comment) if plot: - plt.figure(figsize=(15, 3)) - plt.subplot(121) - plt.plot(time_vector, 10*np.log10(energy_data.T)) - plt.xlabel('Time [s]') - plt.ylabel('Squared IR [dB]') - plt.subplot(122) - plt.plot(time_vector[0:energy_decay_curve.shape[-1]], 10*np.log10( - energy_decay_curve.T)) - plt.xlabel('Time [s]') - plt.ylabel('Tr. EDC with correction [dB]') - plt.tight_layout() + ax = pf.plot.time(data, dB=True, label='RIR') + pf.plot.time(edc, dB=True, log_prefix=10, label='EDC') + ax.set_ylim(-65, 5) + ax.legend() - # Recover original data shape: - energy_decay_curve = np.reshape(energy_decay_curve, data_shape) - energy_decay_curve = np.squeeze(energy_decay_curve) - - return energy_decay_curve + return edc.reshape(data.cshape) def energy_decay_curve_chu( @@ -758,7 +780,11 @@ def intersection_time_lundeby( is_energy=is_energy, shift=time_shift, channel_independent=channel_independent) - sampling_rate = data.sampling_rate + + if isinstance(data, pf.Signal): + sampling_rate = data.sampling_rate + elif isinstance(data, pf.TimeData) and not isinstance(data, pf.Signal): + sampling_rate = np.round(1/np.diff(data.times).mean(), decimals=4) energy_data = energy_data.time if freq == "broadband": diff --git a/tests/test_edc_noise_handling.py b/tests/test_edc_noise_handling.py index 9784649..19fcf66 100644 --- a/tests/test_edc_noise_handling.py +++ b/tests/test_edc_noise_handling.py @@ -94,12 +94,12 @@ def test_edc_truncation_2D(monkeypatch): def test_edc_lundeby_1D(monkeypatch): - rir = genfromtxt( + rir = pf.Signal(genfromtxt( os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), - delimiter=',') - expected = genfromtxt( + delimiter=','), 3000) + expected = np.atleast_2d(genfromtxt( os.path.join(test_data_path, 'edc_lundeby_1D.csv'), - delimiter=',') + delimiter=',')) monkeypatch.setattr( dsp, @@ -108,23 +108,22 @@ def test_edc_lundeby_1D(monkeypatch): actual = enh.energy_decay_curve_lundeby( rir, - sampling_rate=3000, freq='broadband', is_energy=False, time_shift=True, channel_independent=False, normalize=True, plot=False) - npt.assert_allclose(actual, expected) + npt.assert_allclose(actual.time, expected) def test_edc_lundeby_2D(monkeypatch): - rir = genfromtxt( + rir = pf.Signal(genfromtxt( os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), - delimiter=',') - expected = genfromtxt( + delimiter=','), 3000) + expected = np.atleast_2d(genfromtxt( os.path.join(test_data_path, 'edc_lundeby_2D.csv'), - delimiter=',') + delimiter=',')) monkeypatch.setattr( dsp, @@ -133,14 +132,13 @@ def test_edc_lundeby_2D(monkeypatch): actual = enh.energy_decay_curve_lundeby( rir, - sampling_rate=3000, freq='broadband', is_energy=False, time_shift=True, channel_independent=False, normalize=True, plot=False) - npt.assert_allclose(actual, expected) + npt.assert_allclose(actual.time, expected) def test_edc_lundeby_chu_1D(monkeypatch): From 6d33a4dba1c11c11cdafc503b4532b5432eba9d7 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 16:24:23 +0200 Subject: [PATCH 20/66] pyfar adption: combined chu and lundeby methods --- pyrato/edc.py | 85 +++++++++++++++++++------------- tests/test_edc_noise_handling.py | 22 ++++----- 2 files changed, 61 insertions(+), 46 deletions(-) diff --git a/pyrato/edc.py b/pyrato/edc.py index 12768e6..62a448b 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -541,7 +541,6 @@ def energy_decay_curve_chu( def energy_decay_curve_chu_lundeby( data, - sampling_rate, freq='broadband', noise_level='auto', is_energy=False, @@ -556,10 +555,8 @@ def energy_decay_curve_chu_lundeby( Parameters ---------- - data : ndarray, double - The room impulse response with dimension [..., n_samples] - sampling_rate: integer - The sampling rate of the room impulse response. + data : pyfar.Signal + The room impulse response. freq: integer OR string The frequency band. If set to 'broadband', the time window of the Lundeby-algorithm will not be set in dependence @@ -583,7 +580,7 @@ def energy_decay_curve_chu_lundeby( Returns ------- - energy_decay_curve: ndarray, double + pyfar.TimeData Returns the noise handeled edc. References @@ -596,6 +593,40 @@ def energy_decay_curve_chu_lundeby( pp. 1444–1450. .. [6] M. Guski, “Influences of external error sources on measurements of room acoustic parameters,” 2015. + + Examples + -------- + + Calculate and plot the EDC using a combination of Chu's and Lundeby's + methods. + + .. plot:: + + >>> import numpy as np + >>> import pyfar as pf + >>> import pyrato as ra + >>> from pyrato.analytic import rectangular_room_rigid_walls + ... + >>> L = np.array([8, 5, 3])/10 + >>> source_pos = np.array([5, 3, 1.2])/10 + >>> receiver_pos = np.array([1, 1, 1.2])/10 + >>> rir, _ = rectangular_room_rigid_walls( + ... L, source_pos, receiver_pos, + ... reverberation_time=1, max_freq=1e3, n_samples=2**16, + ... speed_of_sound=343.9) + >>> rir = rir/rir.time.max() + ... + >>> awgn = pf.signals.noise( + ... rir.n_samples, rms=rir.time.max()*10**(-50/20), + ... sampling_rate=rir.sampling_rate) + >>> rir = rir + awgn + >>> edc = ra.energy_decay_curve_chu_lundeby(rir) + ... + >>> ax = pf.plot.time(rir, dB=True, label='RIR') + >>> pf.plot.time(edc, dB=True, log_prefix=10, label='EDC') + >>> ax.set_ylim(-65, 5) + >>> ax.legend() + """ energy_data, n_channels, data_shape = dsp.preprocess_rir( @@ -603,16 +634,15 @@ def energy_decay_curve_chu_lundeby( is_energy=is_energy, shift=time_shift, channel_independent=channel_independent) - n_samples = energy_data.shape[-1] + n_samples = energy_data.n_samples - subtraction = _subtract_noise_from_squared_rir( + subtraction = subtract_noise_from_squared_rir( energy_data, noise_level=noise_level) intersection_time, late_reverberation_time, noise_level = \ intersection_time_lundeby( energy_data, - sampling_rate=sampling_rate, freq=freq, initial_noise_power=noise_level, is_energy=True, @@ -620,7 +650,7 @@ def energy_decay_curve_chu_lundeby( channel_independent=False, plot=False) - time_vector = dsp._smooth_rir(energy_data, sampling_rate)[2] + time_vector = data.times energy_decay_curve = np.zeros([n_channels, n_samples]) for idx_channel in range(0, n_channels): @@ -628,7 +658,7 @@ def energy_decay_curve_chu_lundeby( time_vector - intersection_time[idx_channel])) if noise_level == 'auto': p_square_at_intersection = dsp.estimate_noise_energy( - energy_data[idx_channel], is_energy=True) + energy_data.time[idx_channel], is_energy=True) else: p_square_at_intersection = noise_level[idx_channel] @@ -636,11 +666,11 @@ def energy_decay_curve_chu_lundeby( correction = (p_square_at_intersection * late_reverberation_time[idx_channel] * (1 / (6*np.log(10))) - * sampling_rate) + * data.sampling_rate) energy_decay_curve[idx_channel, :intersection_time_idx] = \ - ra.schroeder_integration( - subtraction[idx_channel, :intersection_time_idx], + _schroeder_integration( + subtraction.time[idx_channel, :intersection_time_idx], is_energy=True) energy_decay_curve[idx_channel] += correction @@ -656,29 +686,16 @@ def energy_decay_curve_chu_lundeby( energy_decay_curve /= max_start_value energy_decay_curve[..., intersection_time_idx:] = np.nan + edc = pf.TimeData( + energy_decay_curve, data.times, comment=data.comment) if plot: - plt.figure(figsize=(15, 3)) - plt.subplot(131) - plt.plot(time_vector, 10*np.log10(energy_data.T)) - plt.xlabel('Time [s]') - plt.ylabel('Squared IR [dB]') - plt.subplot(132) - plt.plot(time_vector, 10*np.log10(subtraction.T)) - plt.xlabel('Time [s]') - plt.ylabel('Noise subtracted IR [dB]') - plt.subplot(133) - plt.plot(time_vector[0:energy_decay_curve.shape[-1]], 10*np.log10( - energy_decay_curve.T)) - plt.xlabel('Time [s]') - plt.ylabel('Tr. EDC with corr. & subt. [dB]') - plt.tight_layout() - - # Recover original data shape: - energy_decay_curve = np.reshape(energy_decay_curve, data_shape) - energy_decay_curve = np.squeeze(energy_decay_curve) + ax = pf.plot.time(data, dB=True, label='RIR') + pf.plot.time(edc, dB=True, log_prefix=10, label='EDC') + ax.set_ylim(-65, 5) + ax.legend() - return energy_decay_curve + return edc.reshape(data.cshape) def intersection_time_lundeby( diff --git a/tests/test_edc_noise_handling.py b/tests/test_edc_noise_handling.py index 19fcf66..d6bc999 100644 --- a/tests/test_edc_noise_handling.py +++ b/tests/test_edc_noise_handling.py @@ -142,12 +142,12 @@ def test_edc_lundeby_2D(monkeypatch): def test_edc_lundeby_chu_1D(monkeypatch): - rir = genfromtxt( + rir = pf.Signal(genfromtxt( os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), - delimiter=',') - expected = genfromtxt( + delimiter=','), 3000) + expected = np.atleast_2d(genfromtxt( os.path.join(test_data_path, 'edc_lundeby_chu_1D.csv'), - delimiter=',') + delimiter=',')) monkeypatch.setattr( dsp, @@ -156,23 +156,22 @@ def test_edc_lundeby_chu_1D(monkeypatch): actual = enh.energy_decay_curve_chu_lundeby( rir, - sampling_rate=3000, freq='broadband', is_energy=False, time_shift=True, channel_independent=False, normalize=True, plot=False) - npt.assert_allclose(actual, expected) + npt.assert_allclose(actual.time, expected) def test_edc_lundeby_chu_2D(monkeypatch): - rir = genfromtxt( + rir = pf.Signal(genfromtxt( os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), - delimiter=',') - expected = genfromtxt( + delimiter=','), 3000) + expected = np.atleast_2d(genfromtxt( os.path.join(test_data_path, 'edc_lundeby_chu_2D.csv'), - delimiter=',') + delimiter=',')) monkeypatch.setattr( dsp, @@ -181,14 +180,13 @@ def test_edc_lundeby_chu_2D(monkeypatch): actual = enh.energy_decay_curve_chu_lundeby( rir, - sampling_rate=3000, freq='broadband', is_energy=False, time_shift=True, channel_independent=False, normalize=True, plot=False) - npt.assert_allclose(actual, expected) + npt.assert_allclose(actual.time, expected) def test_edc_chu_1D(monkeypatch): From 5796123fc2d5390df3e57d5a4273913cb0cd6320 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 16:35:22 +0200 Subject: [PATCH 21/66] adapt edc truncation to pyfar --- pyrato/edc.py | 73 ++++++++++++++++++++------------ tests/test_edc_noise_handling.py | 22 +++++----- 2 files changed, 57 insertions(+), 38 deletions(-) diff --git a/pyrato/edc.py b/pyrato/edc.py index 62a448b..8bb3dbd 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -168,7 +168,6 @@ def _schroeder_integration(impulse_response, is_energy=False): def energy_decay_curve_truncation( data, - sampling_rate, freq='broadband', noise_level='auto', is_energy=False, @@ -181,10 +180,8 @@ def energy_decay_curve_truncation( Parameters ---------- - data : ndarray, double - The room impulse response with dimension [..., n_samples] - sampling_rate: integer - The sampling rate of the room impulse response. + data : pyfar.Signal + The room impulse response. freq: integer OR string The frequency band. If set to 'broadband', the time window of the Lundeby-algorithm will not be set in dependence @@ -208,35 +205,67 @@ def energy_decay_curve_truncation( Returns ------- - energy_decay_curve: ndarray, double + pyfar.TimeData Returns the noise handeled edc. + + Examples + -------- + + Plot the RIR and the EDC calculated truncating the integration at the + intersection time. + + .. plot:: + + >>> import numpy as np + >>> import pyfar as pf + >>> import pyrato as ra + >>> from pyrato.analytic import rectangular_room_rigid_walls + ... + >>> L = np.array([8, 5, 3])/10 + >>> source_pos = np.array([5, 3, 1.2])/10 + >>> receiver_pos = np.array([1, 1, 1.2])/10 + >>> rir, _ = rectangular_room_rigid_walls( + ... L, source_pos, receiver_pos, + ... reverberation_time=1, max_freq=1e3, n_samples=2**16, + ... speed_of_sound=343.9) + >>> rir = rir/rir.time.max() + ... + >>> awgn = pf.signals.noise( + ... rir.n_samples, rms=rir.time.max()*10**(-50/20), + ... sampling_rate=rir.sampling_rate) + >>> rir = rir + awgn + >>> edc = ra.energy_decay_curve_truncation(rir) + ... + >>> ax = pf.plot.time(rir, dB=True, label='RIR') + >>> pf.plot.time(edc, dB=True, log_prefix=10, label='EDC') + >>> ax.set_ylim(-65, 5) + >>> ax.legend() + """ energy_data, n_channels, data_shape = dsp.preprocess_rir( data, is_energy=is_energy, shift=time_shift, channel_independent=channel_independent) - n_samples = energy_data.shape[-1] + n_samples = data.n_samples intersection_time = intersection_time_lundeby( energy_data, - sampling_rate=sampling_rate, freq=freq, initial_noise_power=noise_level, is_energy=True, time_shift=False, channel_independent=False, plot=False)[0] - time_vector = dsp._smooth_rir(energy_data, sampling_rate)[2] - intersection_time_idx = np.rint(intersection_time * sampling_rate) + intersection_time_idx = np.rint(intersection_time * data.sampling_rate) energy_decay_curve = np.zeros([n_channels, n_samples]) for idx_channel in range(0, n_channels): energy_decay_curve[ idx_channel, :int(intersection_time_idx[idx_channel])] = \ _schroeder_integration( - energy_data[ + energy_data.time[ idx_channel, :int(intersection_time_idx[idx_channel])], is_energy=True) @@ -251,24 +280,16 @@ def energy_decay_curve_truncation( max_start_value = np.amax(energy_decay_curve[..., 0]) energy_decay_curve /= max_start_value - # Recover original data shape: - energy_decay_curve = np.reshape(energy_decay_curve, data_shape) - energy_decay_curve = np.squeeze(energy_decay_curve) + edc = pf.TimeData( + energy_decay_curve, data.times, comment=data.comment) if plot: - plt.figure(figsize=(15, 3)) - plt.subplot(121) - plt.plot(time_vector, 10*np.log10(energy_data.T)) - plt.xlabel('Time [s]') - plt.ylabel('Squared IR [dB]') - plt.subplot(122) - plt.plot(time_vector[0:energy_decay_curve.shape[-1]], 10*np.log10( - energy_decay_curve.T)) - plt.xlabel('Time [s]') - plt.ylabel('EDC: Truncation method [dB]') - plt.tight_layout() + ax = pf.plot.time(data, dB=True, label='RIR') + pf.plot.time(edc, dB=True, log_prefix=10, label='EDC') + ax.set_ylim(-65, 5) + ax.legend() - return energy_decay_curve + return edc.reshape(data.cshape) def energy_decay_curve_lundeby( diff --git a/tests/test_edc_noise_handling.py b/tests/test_edc_noise_handling.py index d6bc999..7e6667f 100644 --- a/tests/test_edc_noise_handling.py +++ b/tests/test_edc_noise_handling.py @@ -46,12 +46,12 @@ def test_substracted_2D(): def test_edc_truncation_1D(monkeypatch): - rir = genfromtxt( + rir = pf.Signal(genfromtxt( os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), - delimiter=',') - expected = genfromtxt( + delimiter=','), 3000) + expected = np.atleast_2d(genfromtxt( os.path.join(test_data_path, 'edc_truncation_1D.csv'), - delimiter=',') + delimiter=',')) monkeypatch.setattr( dsp, @@ -60,22 +60,21 @@ def test_edc_truncation_1D(monkeypatch): actual = enh.energy_decay_curve_truncation( rir, - sampling_rate=3000, freq='broadband', is_energy=False, time_shift=True, channel_independent=False, normalize=True) - npt.assert_allclose(actual, expected) + npt.assert_allclose(actual.time, expected) def test_edc_truncation_2D(monkeypatch): - rir = genfromtxt( + rir = pf.Signal(genfromtxt( os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), - delimiter=',') - expected = genfromtxt( + delimiter=','), 3000) + expected = np.atleast_2d(genfromtxt( os.path.join(test_data_path, 'edc_truncation_2D.csv'), - delimiter=',') + delimiter=',')) monkeypatch.setattr( dsp, @@ -84,13 +83,12 @@ def test_edc_truncation_2D(monkeypatch): actual = enh.energy_decay_curve_truncation( rir, - sampling_rate=3000, freq='broadband', is_energy=False, time_shift=True, channel_independent=False, normalize=True) - npt.assert_allclose(actual, expected) + npt.assert_allclose(actual.time, expected) def test_edc_lundeby_1D(monkeypatch): From 983e9fd34dde1626b687796189d4891030fbc82b Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 17:34:21 +0200 Subject: [PATCH 22/66] adapt reverberation time regression --- pyrato/roomacoustics.py | 86 ++++++++++++++++++++++------------------- tests/test_rt.py | 21 +++++----- 2 files changed, 57 insertions(+), 50 deletions(-) diff --git a/pyrato/roomacoustics.py b/pyrato/roomacoustics.py index 4c56143..03a387c 100644 --- a/pyrato/roomacoustics.py +++ b/pyrato/roomacoustics.py @@ -4,15 +4,11 @@ import re import numpy as np -import matplotlib.pyplot as plt def reverberation_time_energy_decay_curve( energy_decay_curve, - times, - T='T20', - normalize=True, - plot=False): + T='T20'): """Estimate the reverberation time from a given energy decay curve according to the ISO standard 3382 [#]_. @@ -29,9 +25,6 @@ def reverberation_time_energy_decay_curve( the interval [-25, -35] dB. normalize : bool, True Normalize the EDC to the steady state energy level - plot : bool, False - Plot the estimated extrapolation line for visual inspection of the - results. Returns ------- @@ -43,6 +36,35 @@ def reverberation_time_energy_decay_curve( .. [#] ISO 3382, Acoustics - Measurement of the reverberation time of rooms with reference to other acoustical parameters. + Examples + -------- + + Estimate the reverberation time from an energy decay curve. + + >>> import numpy as np + >>> import pyfar as pf + >>> import pyrato as ra + >>> from pyrato.analytic import rectangular_room_rigid_walls + ... + >>> L = np.array([8, 5, 3])/10 + >>> source_pos = np.array([5, 3, 1.2])/10 + >>> receiver_pos = np.array([1, 1, 1.2])/10 + >>> rir, _ = rectangular_room_rigid_walls( + ... L, source_pos, receiver_pos, + ... reverberation_time=1, max_freq=1.5e3, n_samples=2**12, + ... speed_of_sound=343.9, samplingrate=3e3) + >>> rir = rir/rir.time.max() + ... + >>> awgn = pf.signals.noise( + ... rir.n_samples, rms=10**(-50/20), + ... sampling_rate=rir.sampling_rate) + >>> rir = rir + awgn + ... + >>> edc = ra.energy_decay_curve_chu_lundeby(rir) + >>> t_20 = ra.reverberation_time_energy_decay_curve(edc, 'T20') + >>> t_20 + ... array([0.99526253]) + """ intervals = [20, 30, 40, 50, 60] @@ -62,44 +84,28 @@ def reverberation_time_energy_decay_curve( upper = -5 lower = -np.double(re.findall(r'\d+', T)) + upper - if normalize: - energy_decay_curve /= energy_decay_curve[0] - - edc_db = 10*np.log10(np.abs(energy_decay_curve)) - - idx_upper = np.nanargmin(np.abs(upper - edc_db)) - idx_lower = np.nanargmin(np.abs(lower - edc_db)) + edc = energy_decay_curve.time.copy() + edc = edc.reshape((-1, energy_decay_curve.n_samples)) + times = energy_decay_curve.times + edc /= np.atleast_2d(edc[..., 0]).T - A = np.vstack( - [times[idx_upper:idx_lower], np.ones(idx_lower - idx_upper)]).T - gradient, const = np.linalg.lstsq( - A, edc_db[..., idx_upper:idx_lower], rcond=None)[0] + edcs_db = 10*np.log10(np.abs(edc)) - reverberation_time = -60 / gradient + reverberation_times = np.zeros( + np.prod(energy_decay_curve.cshape), dtype=float) - if plot: - plt.figure() - plt.plot( - times, - edc_db, - label='edc') - plt.plot( - times, - times * gradient + const, - label='regression', - linestyle='-.') - ax = plt.gca() - ax.set_ylim((-95, 5)) + for ch, edc_db in enumerate(edcs_db): + idx_upper = np.nanargmin(np.abs(upper - edc_db)) + idx_lower = np.nanargmin(np.abs(lower - edc_db)) - reverberation_time = -60 / gradient + A = np.vstack( + [times[idx_upper:idx_lower], np.ones(idx_lower - idx_upper)]).T + gradient, const = np.linalg.lstsq( + A, edc_db[..., idx_upper:idx_lower], rcond=None)[0] - ax.set_xlim((-0.05, 2*reverberation_time)) - plt.grid(True) - plt.legend() - ax.set_ylabel('EDC [dB]') - ax.set_xlabel('Time [s]') + reverberation_times[ch] = -60 / gradient - return reverberation_time + return reverberation_times def energy_decay_curve_analytic( diff --git a/tests/test_rt.py b/tests/test_rt.py index d4b8ef8..4013709 100644 --- a/tests/test_rt.py +++ b/tests/test_rt.py @@ -8,27 +8,28 @@ import numpy.testing as npt import pyrato as ra +import pyfar as pf +import pytest -def test_rt_from_edc(): +@pytest.mark.parametrize( + 'tx', ['T20', 'T30', 'T40', 'T50', 'T60', 'LDT', 'EDT']) +def test_rt_from_edc(tx): times = np.linspace(0, 1.5, 2**9) m = -60 edc = times * m - edc_exp = 10**(edc/10) - RT = 1. - TX = ['T20', 'T30', 'T40', 'T50', 'T60', 'LDT', 'EDT'] - for T in TX: - RT_est = ra.reverberation_time_energy_decay_curve(edc_exp, times, T=T) - npt.assert_allclose(RT_est, RT) + edc_exp = pf.TimeData(10**(edc/10), times) + RT_est = ra.reverberation_time_energy_decay_curve( + edc_exp, T=tx) + npt.assert_allclose(RT_est, 1.) def test_rt_from_edc_error(): times = np.linspace(0, 1.5, 2**9) m = -60 edc = times * m - edc_exp = 10**(edc/10) + edc_exp = pf.TimeData(10**(edc/10), times) T = 'Bla' with raises(ValueError, match='is not a valid interval.'): - ra.reverberation_time_energy_decay_curve(edc_exp, times, T=T) - # npt.assert_allclose(RT_est, RT) + ra.reverberation_time_energy_decay_curve(edc_exp, T=T) From b619e7e30ed0fa840e70bb00e1341661aace64c9 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 17:38:57 +0200 Subject: [PATCH 23/66] flake8 --- pyrato/edc.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pyrato/edc.py b/pyrato/edc.py index 8bb3dbd..b5c04c2 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -9,7 +9,6 @@ import numpy as np from matplotlib import pyplot as plt from pyrato import dsp -import pyrato as ra import warnings import pyfar as pf From e02faa5a0c71de8a3d3634af1b9a3d50ddac2aa8 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 18:02:28 +0200 Subject: [PATCH 24/66] update examples --- ..._decay_curves_and_reverberation_time.ipynb | 142 +++++++----------- 1 file changed, 53 insertions(+), 89 deletions(-) diff --git a/examples/energy_decay_curves_and_reverberation_time.ipynb b/examples/energy_decay_curves_and_reverberation_time.ipynb index d7efc9e..f74623d 100644 --- a/examples/energy_decay_curves_and_reverberation_time.ipynb +++ b/examples/energy_decay_curves_and_reverberation_time.ipynb @@ -6,6 +6,7 @@ "metadata": {}, "outputs": [], "source": [ + "import pyfar as pf\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "import warnings\n", @@ -92,12 +93,11 @@ " dimensions=room_dimensions, source=src_pos, receiver=rec_pos, reverberation_time=t_60, max_freq=max_freq,\n", " samplingrate=sampling_rate, speed_of_sound=speed_of_sound, n_samples=n_samples)[0]\n", "\n", - "rir_1 /= np.amax(np.abs(rir_1)) # Normalize \n", - "time_vector = np.arange(0, n_samples) / sampling_rate\n", + "rir_1.time /= np.amax(np.abs(rir_1.time)) # Normalize \n", "\n", "# Plot the result:\n", "plt.figure(figsize=(10, 4))\n", - "plot_time(rir_1, sampling_rate, label='RIR')" + "pf.plot.time(rir_1, dB=True)" ] }, { @@ -115,10 +115,11 @@ "outputs": [], "source": [ "psnr = 50\n", - "rir_1_noise = rir_1.copy() + 10**(-(psnr) / 20) * np.random.normal(0,1,rir_1.size)\n", + "awgn = pf.signals.noise(rir_1.n_samples, rms=10**(-(psnr) / 20))\n", + "rir_1_noise = rir_1.copy() + awgn\n", "\n", "plt.figure(figsize=(10, 4))\n", - "plot_time(rir_1_noise, sampling_rate, label='RIR + Noise')" + "pf.plot.time(rir_1_noise, label='RIR + Noise', dB=True)" ] }, { @@ -161,9 +162,9 @@ "outputs": [], "source": [ "plt.figure(figsize=(10, 4))\n", - "plot_time(rir_1_noise, sampling_rate, label='RIR')\n", - "plt.axvline(time_vector[-1]*interval[0], color='red', label='Interval')\n", - "plt.axvline(time_vector[-1]*interval[1], color='red')\n", + "pf.plot.time(rir_1_noise, dB=True, label='RIR')\n", + "plt.axvline(rir_1_noise.signal_length*interval[0], color='red', label='Interval')\n", + "plt.axvline(rir_1_noise.signal_length*interval[1], color='red')\n", "\n", "noise_energy = ra.estimate_noise_energy(\n", " rir_1_noise, interval=interval, is_energy=False)\n", @@ -232,58 +233,20 @@ "metadata": {}, "outputs": [], "source": [ - "rir_noise_shifted = ra.preprocess_rir(rir_1_noise, time_shift=True)[0]\n", - "10*np.log10(np.abs(ra.estimate_noise_energy(rir_noise_shifted, is_energy=True)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(10, 4))\n", - "plot_time(rir_noise_shifted, sampling_rate, log_prefix=10, label='RIR')\n", - "plt.axvline(time_vector[-1]*interval[0], color='red', label='Interval')\n", - "plt.axvline(time_vector[-1]*interval[1], color='red')\n", - "\n", - "\n", - "noise_energy = ra.estimate_noise_energy(\n", - " rir_noise_shifted, interval=interval, is_energy=True)\n", - "\n", - "noise_energy_db = 10*np.log10(noise_energy)\n", - "plt.axhline(noise_energy_db, color='k', linestyle='--', label='Noise Level')\n", - "plt.xlim(0.8*np.max(time_vector), 1.1*np.max(time_vector), )\n", - "\n", - "plt.legend(loc='upper center', ncol=3)\n", + "start_sample = ra.find_impulse_response_start(rir_1_noise)\n", + "rir_noise_shifted = ra.time_shift(rir_1_noise, -start_sample, circular_shift=False)\n", + "edc_full_rir = ra.schroeder_integration(rir_noise_shifted, is_energy=False)\n", "\n", - "output_string = \"The estimated noise level is at {} dB.\".format(noise_energy_db)\n", - "print(output_string)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rir_noise_shifted = ra.preprocess_rir(rir_1_noise, time_shift=True)[0]\n", - "edc_full_rir = ra.schroeder_integration(np.squeeze(rir_noise_shifted), is_energy=True)\n", - "rir_shifted = ra.preprocess_rir(rir_1, time_shift=True)[0]\n", - "edc_full_rir_compare = ra.schroeder_integration(np.squeeze(rir_shifted), is_energy=True)\n", - "edc_full_rir /= edc_full_rir[0]\n", - "edc_full_rir_compare /= edc_full_rir_compare[0]\n", + "start_sample = ra.find_impulse_response_start(rir_1)\n", + "rir_shifted = ra.time_shift(rir_1, -start_sample, circular_shift=False)\n", + "edc_full_rir_compare = ra.schroeder_integration(rir_shifted, is_energy=False)\n", + "edc_full_rir_compare.time /= np.atleast_2d(edc_full_rir_compare.time[..., 0]).T\n", + "edc_full_rir.time /= np.atleast_2d(edc_full_rir.time[..., 0]).T\n", "\n", "plt.figure(figsize=(10, 4))\n", - "plot_time(edc_full_rir_compare, sampling_rate, log_prefix=10, label='Reference')\n", - "plot_time(edc_full_rir, sampling_rate, log_prefix=10, label='With Noise')\n", + "pf.plot.time(edc_full_rir_compare, dB=True, log_prefix=10, label='Reference')\n", + "pf.plot.time(edc_full_rir, dB=True, log_prefix=10, label='With Noise')\n", + "plt.legend()\n", "plt.ylim(-60)" ] }, @@ -293,8 +256,7 @@ "metadata": {}, "outputs": [], "source": [ - "from pyrato import dsp\n", - "np.broadcast_to(-dsp.find_impulse_response_start(rir_1), 1)" + "from pyrato import dsp" ] }, { @@ -316,7 +278,7 @@ "source": [ "intersection_time, late_reveberation_time, noise_level_lundeby = \\\n", " ra.intersection_time_lundeby(\n", - " rir_1_noise, sampling_rate=sampling_rate, freq='broadband', is_energy=False,\n", + " rir_1_noise, freq='broadband', is_energy=False,\n", " time_shift=True, channel_independent=False, plot=True)\n", "\n", "output_string = \"The estimated intersection time is {}s, the late reverberation time is {}s and the estimated noise is {}dB.\".format(intersection_time, late_reveberation_time, 10*np.log10(np.abs(noise_level_lundeby)))\n", @@ -337,15 +299,15 @@ "outputs": [], "source": [ "edc_truncation = ra.energy_decay_curve_truncation(\n", - " rir_1_noise, sampling_rate, freq='broadband', is_energy=False, time_shift=True,\n", + " rir_1_noise, freq='broadband', is_energy=False, time_shift=True,\n", " channel_independent=False, normalize=True, plot=False)\n", "\n", "plt.figure(figsize=(10, 4))\n", - "plot_time(edc_full_rir_compare, sampling_rate, log_prefix=10, label='Reference')\n", - "plot_time(edc_truncation, sampling_rate, log_prefix=10, label='Truncated')\n", - "plt.ylim(-60);\n", - "plt.xlim(0, 3)\n", - "plt.title(\"EDC Truncation\");" + "pf.plot.time(edc_full_rir_compare, dB=True, log_prefix=10, label='Reference')\n", + "pf.plot.time(edc_truncation, dB=True, log_prefix=10, label='Truncated')\n", + "plt.legend()\n", + "plt.ylim(-60)\n", + "plt.xlim(0, 3)" ] }, { @@ -366,7 +328,7 @@ "outputs": [], "source": [ "edc_chu_auto = ra.energy_decay_curve_chu(\n", - " rir_1_noise, sampling_rate, freq='broadband', noise_level='auto', is_energy=False, time_shift=True,\n", + " rir_1_noise, noise_level='auto', is_energy=False, time_shift=True,\n", " channel_independent=False, normalize=True, plot=False)" ] }, @@ -378,7 +340,7 @@ "source": [ "noise_level_man = ra.estimate_noise_energy(rir_1_noise, interval=[0.6, 1.0], is_energy=False)\n", "edc_chu_man = ra.energy_decay_curve_chu(\n", - " rir_1_noise, sampling_rate, freq='broadband', noise_level=noise_level_man, is_energy=False, time_shift=True,\n", + " rir_1_noise, noise_level=noise_level_man, is_energy=False, time_shift=True,\n", " channel_independent=False, normalize=True, plot=False)" ] }, @@ -389,7 +351,7 @@ "outputs": [], "source": [ "edc_chu_noise_lundeby = ra.energy_decay_curve_chu(\n", - " rir_1_noise, sampling_rate, freq='broadband', noise_level=noise_level_lundeby, is_energy=False, time_shift=True,\n", + " rir_1_noise, noise_level=noise_level_lundeby, is_energy=False, time_shift=True,\n", " channel_independent=False, normalize=True, plot=False)" ] }, @@ -400,10 +362,11 @@ "outputs": [], "source": [ "plt.figure(figsize=(10, 4))\n", - "plot_time(edc_full_rir_compare, sampling_rate, log_prefix=10, label='Reference')\n", - "plot_time(edc_chu_auto, sampling_rate, log_prefix=10, label='Subtraction, Last 10% Estimation')\n", - "plot_time(edc_chu_man, sampling_rate, log_prefix=10, label='Subtraction, Manual Noise Estimation')\n", - "plot_time(edc_chu_noise_lundeby, sampling_rate, log_prefix=10, label='Subtraction, Lundeby Noise Estimation')\n", + "pf.plot.time(edc_full_rir_compare, dB=True, log_prefix=10, label='Reference')\n", + "pf.plot.time(edc_chu_auto, dB=True, log_prefix=10, label='Subtraction, Last 10% Estimation')\n", + "pf.plot.time(edc_chu_man, dB=True, log_prefix=10, label='Subtraction, Manual Noise Estimation')\n", + "pf.plot.time(edc_chu_noise_lundeby, dB=True, log_prefix=10, label='Subtraction, Lundeby Noise Estimation')\n", + "plt.legend()\n", "plt.ylim(-60)\n", "plt.xlim(0, 3);" ] @@ -432,12 +395,13 @@ "outputs": [], "source": [ "edc_lundeby = ra.energy_decay_curve_lundeby(\n", - " rir_1_noise, sampling_rate, freq='broadband', is_energy=False, time_shift=True,\n", + " rir_1_noise, is_energy=False, time_shift=True,\n", " channel_independent=False, normalize=True, plot=False)\n", "\n", "plt.figure(figsize=(10, 4))\n", - "plot_time(edc_full_rir_compare, sampling_rate, log_prefix=10, label='Without noise')\n", - "plot_time(edc_lundeby, sampling_rate, log_prefix=10, label='Lundeby')\n", + "pf.plot.time(edc_full_rir_compare, dB=True, log_prefix=10, label='Without noise')\n", + "pf.plot.time(edc_lundeby, dB=True, log_prefix=10, label='Lundeby')\n", + "plt.legend()\n", "plt.ylim(-60)\n", "plt.xlim(0,3)" ] @@ -460,12 +424,13 @@ "outputs": [], "source": [ "edc_chu_lundeby = ra.energy_decay_curve_chu_lundeby(\n", - " rir_1_noise, sampling_rate, freq='broadband', is_energy=False, time_shift=True,\n", + " rir_1_noise, freq='broadband', is_energy=False, time_shift=True,\n", " channel_independent=False, normalize=True, plot=False)\n", "\n", "plt.figure(figsize=(10, 4))\n", - "plot_time(edc_full_rir_compare, sampling_rate, log_prefix=10, label='Reference')\n", - "plot_time(edc_chu_lundeby, sampling_rate, log_prefix=10, label='Chu/Lundeby')\n", + "pf.plot.time(edc_full_rir_compare, dB=True, log_prefix=10, label='Reference')\n", + "pf.plot.time(edc_chu_lundeby, dB=True, log_prefix=10, label='Chu/Lundeby')\n", + "plt.legend()\n", "plt.ylim(-60)\n", "plt.xlim(0,3);" ] @@ -484,14 +449,15 @@ "outputs": [], "source": [ "plt.figure(figsize=(10, 4))\n", - "plot_time(edc_full_rir_compare, sampling_rate, log_prefix=10, label='Reference')\n", - "plot_time(edc_full_rir, sampling_rate, log_prefix=10, label='No Compensation')\n", - "plot_time(edc_truncation, sampling_rate, log_prefix=10, label='Truncation')\n", - "plot_time(edc_chu_noise_lundeby, sampling_rate, log_prefix=10, label='Chu')\n", - "plot_time(edc_lundeby, sampling_rate, log_prefix=10, label='Lundeby')\n", - "plot_time(edc_chu_lundeby, sampling_rate, log_prefix=10, label='Chu/Lundeby')\n", + "pf.plot.time(edc_full_rir_compare, dB=True, log_prefix=10, label='Reference')\n", + "pf.plot.time(edc_full_rir, dB=True, log_prefix=10, label='No Compensation')\n", + "pf.plot.time(edc_truncation, dB=True, log_prefix=10, label='Truncation')\n", + "pf.plot.time(edc_chu_noise_lundeby, dB=True, log_prefix=10, label='Chu')\n", + "pf.plot.time(edc_lundeby, dB=True, log_prefix=10, label='Lundeby')\n", + "pf.plot.time(edc_chu_lundeby, dB=True, log_prefix=10, label='Chu/Lundeby')\n", "plt.xlabel('Time [s]')\n", "plt.ylabel('EDC [dB]')\n", + "plt.legend()\n", "plt.ylim(-60)\n", "plt.xlim(0,3);" ] @@ -507,11 +473,9 @@ "edcs = [edc_full_rir_compare, edc_truncation, edc_chu_auto, edc_lundeby, edc_chu_lundeby]\n", "reverberation_times = np.zeros(len(edcs))\n", "for idx, edc in enumerate(edcs):\n", - " mask = np.isnan(edc)\n", - " times = np.arange(0, edc.shape[-1])/sampling_rate\n", " reverberation_times[idx] = \\\n", " ra.reverberation_time_energy_decay_curve(\n", - " np.squeeze(edc[~mask]), times[~mask], T=Tx)" + " edc, T=Tx)" ] }, { @@ -559,7 +523,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.5" + "version": "3.9.12" } }, "nbformat": 4, From e22c45ce74ed39e2eab47815867e7657ebe5b3aa Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 18:08:52 +0200 Subject: [PATCH 25/66] add libsndfile to readthedocs config --- .readthedocs.yml | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/.readthedocs.yml b/.readthedocs.yml index aa89770..4fc878e 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -5,6 +5,12 @@ # Required version: 2 +# Add libsndfile to the image +build: + image: latest + apt_packages: + - libsndfile1 + # Build documentation in the docs/ directory with Sphinx sphinx: configuration: docs/conf.py From f035a2f5cb0a21ff4bfc613c33a35bfd893a70c2 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 18:11:01 +0200 Subject: [PATCH 26/66] remove filter bank, was moved to pyfar --- pyrato/__init__.py | 6 -- pyrato/dsp.py | 181 --------------------------------------------- 2 files changed, 187 deletions(-) diff --git a/pyrato/__init__.py b/pyrato/__init__.py index 9f54d66..e6e9899 100644 --- a/pyrato/__init__.py +++ b/pyrato/__init__.py @@ -16,10 +16,7 @@ from .dsp import ( find_impulse_response_maximum, find_impulse_response_start, - filter_fractional_octave_bands, time_shift, - center_frequencies_octaves, - center_frequencies_third_octaves, preprocess_rir, estimate_noise_energy, ) @@ -39,10 +36,7 @@ 'air_attenuation_coefficient', 'find_impulse_response_maximum', 'find_impulse_response_start', - 'filter_fractional_octave_bands', 'time_shift', - 'center_frequencies_octaves', - 'center_frequencies_third_octaves', 'preprocess_rir', 'energy_decay_curve_chu', 'energy_decay_curve_chu_lundeby', diff --git a/pyrato/dsp.py b/pyrato/dsp.py index 554a637..b194cfa 100644 --- a/pyrato/dsp.py +++ b/pyrato/dsp.py @@ -271,187 +271,6 @@ def time_shift(signal, shift, circular_shift=True, unit='samples'): return shifted -def center_frequencies_octaves(): - """Return the octave center frequencies according to the IEC 61260:1:2014 - standard. - - Returns - ------- - frequencies : ndarray, float - Octave center frequencies - - """ - nominal = np.array([31.5, 63, 125, 250, 500, 1e3, - 2e3, 4e3, 8e3, 16e3], dtype=np.float) - indices = _frequency_indices(nominal, 1) - exact = exact_center_frequencies_fractional_octaves(indices, 1) - - return nominal, exact - - -def center_frequencies_third_octaves(): - """Return the third octave center frequencies according - to the ICE 61260:1:2014 standard. - - Returns - ------- - frequencies : ndarray, float - third octave center frequencies - - """ - nominal = np.array([ - 25, 31.5, 40, 50, 63, 80, 100, 125, 160, - 200, 250, 315, 400, 500, 630, 800, 1000, - 1250, 1600, 2000, 2500, 3150, 4000, 5000, - 6300, 8000, 10000, 12500, 16000, 20000], dtype=np.float) - - indices = _frequency_indices(nominal, 3) - exact = exact_center_frequencies_fractional_octaves(indices, 3) - - return nominal, exact - - -def exact_center_frequencies_fractional_octaves(indices, num_fractions): - """Returns the exact center frequencies for fractional octave bands - according to the IEC 61260:1:2014 standard. - - octave ratio - .. G = 10^{3/10} - - center frequencies - .. f_m = f_r G^{x/b} - .. f_m = f_e G^{(2x+1)/(2b)} - - where b is the number of octave fractions, f_r is the reference frequency - chosen as 1000Hz and x is the index of the frequency band. - - Parameters - ---------- - indices : array - The indices for which the center frequencies are calculated. - num_fractions : 1, 3 - The number of octave fractions. 1 returns octave center frequencies, - 3 returns third octave center frequencies. - - Returns - ------- - frequencies : ndarray, float - center frequencies of the fractional octave bands - - """ - - reference_freq = 1e3 - octave_ratio = 10**(3/10) - - iseven = np.mod(num_fractions, 2) == 0 - if ~iseven: - exponent = (indices/num_fractions) - else: - exponent = ((2*indices + 1) / num_fractions / 2) - - return reference_freq * octave_ratio**exponent - - -def _frequency_indices(frequencies, num_fractions): - """Return the indices for fractional octave filters. - - Parameters - ---------- - frequencies : array - The nominal frequencies for which the indices for exact center - frequency calculation are to be calculated. - num_fractions : 1, 3 - Number of fractional bands - - Returns - ------- - indices : array - The indices for exact center frequency calculation. - - """ - reference_freq = 1e3 - octave_ratio = 10**(3/10) - - iseven = np.mod(num_fractions, 2) == 0 - if ~iseven: - indices = np.around( - num_fractions * np.log(frequencies/reference_freq) - / np.log(octave_ratio)) - else: - indices = np.around( - 2.0*num_fractions * - np.log(frequencies/reference_freq) / np.log(octave_ratio) - 1)/2 - - return indices - - -def filter_fractional_octave_bands( - signal, samplingrate, num_fractions, - freq_range=(20.0, 20e3), order=6): - """Apply a fractional octave filter to a signal. - Filter bank implementation using second order sections of butterworth - filters for increased numeric accuracy and stability. - - Parameters - ---------- - signal : ndarray - input signal to be filtered - samplingrate : integer - samplingrate of the signal - num_fractions : integer - number of octave fractions - order : integer, optional - order of the butterworth filter - - Returns - ------- - signal_filtered : ndarray - Signal filtered into fractional octave bands. The array has a new axis - with dimension corresponding to the number of frequency bands: - [num_fractions, *signal.shape] - - """ - - if num_fractions not in (1, 3): - raise ValueError("This currently supports only octave and third \ - octave band filters.") - - octave_ratio = 10**(3/10) - - if num_fractions == 1: - nominal, exact = center_frequencies_octaves() - else: - nominal, exact = center_frequencies_third_octaves() - - mask_frequencies = (nominal > freq_range[0]) & (nominal < freq_range[1]) - - nominal = nominal[mask_frequencies] - exact = exact[mask_frequencies] - - signal_out_shape = (exact.size,) + signal.shape - signal_out = np.broadcast_to(signal, signal_out_shape).copy() - - for band in range(exact.size): - freq_upper = exact[band] * octave_ratio**(1/2/num_fractions) - freq_lower = exact[band] * octave_ratio**(-1/2/num_fractions) - - # normalize interval such that the Nyquist frequency is 1 - Wn = np.array([freq_lower, freq_upper]) / samplingrate * 2 - # in case the upper frequency limit is above Nyquist, use a highpass - if Wn[-1] > 1: - warnings.warn('Your upper frequency limit [{}] is above the \ - Nyquist frequency. Using a highpass filter instead of a \ - bandpass'.format(freq_upper)) - Wn = Wn[0] - btype = 'highpass' - else: - btype = 'bandpass' - sos = spsignal.butter(order, Wn, btype=btype, output='sos') - signal_out[band, :] = spsignal.sosfilt(sos, signal) - - return signal_out - - def estimate_noise_energy( data, interval=[0.9, 1.0], From 3c1269846a82a0c1b8f27004d5e5d8862c6a733b Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 18:19:15 +0200 Subject: [PATCH 27/66] cleanup --- pyrato/__init__.py | 2 -- pyrato/dsp.py | 1 - pyrato/roomacoustics.py | 2 -- 3 files changed, 5 deletions(-) diff --git a/pyrato/__init__.py b/pyrato/__init__.py index e6e9899..620b2f5 100644 --- a/pyrato/__init__.py +++ b/pyrato/__init__.py @@ -1,7 +1,5 @@ # -*- coding: utf-8 -*- -"""Top-level package for pyrato.""" - __author__ = \ """Marco Berzborn - Institute for Hearing Technology and Acoustics""" __email__ = 'marco.berzborn@akustik.rwth-aachen.de' diff --git a/pyrato/dsp.py b/pyrato/dsp.py index b194cfa..8d7413c 100644 --- a/pyrato/dsp.py +++ b/pyrato/dsp.py @@ -6,7 +6,6 @@ import pyfar as pf import numpy as np -import scipy.signal as spsignal def find_impulse_response_start( diff --git a/pyrato/roomacoustics.py b/pyrato/roomacoustics.py index 03a387c..2632bb4 100644 --- a/pyrato/roomacoustics.py +++ b/pyrato/roomacoustics.py @@ -1,7 +1,5 @@ # -*- coding: utf-8 -*- -"""Main module.""" - import re import numpy as np From 0aca9161ece3bcd44ea13f3a8b419ecd14f05838 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 18:22:16 +0200 Subject: [PATCH 28/66] move analytic model --- pyrato/analytic/__init__.py | 9 +++++++++ pyrato/{ => analytic}/analytic.py | 0 2 files changed, 9 insertions(+) create mode 100644 pyrato/analytic/__init__.py rename pyrato/{ => analytic}/analytic.py (100%) diff --git a/pyrato/analytic/__init__.py b/pyrato/analytic/__init__.py new file mode 100644 index 0000000..e80c1a8 --- /dev/null +++ b/pyrato/analytic/__init__.py @@ -0,0 +1,9 @@ +from .analytic import ( + rectangular_room_rigid_walls, + eigenfrequencies_rectangular_room_rigid +) + +__all__ = ( + 'rectangular_room_rigid_walls', + 'eigenfrequencies_rectangular_room_rigid' +) diff --git a/pyrato/analytic.py b/pyrato/analytic/analytic.py similarity index 100% rename from pyrato/analytic.py rename to pyrato/analytic/analytic.py From ee9d7a7cef881a19eacb4a93aff0d905486b0c72 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 18:23:48 +0200 Subject: [PATCH 29/66] move impedance based model to new file --- pyrato/analytic/analytic.py | 496 ---------------------------------- pyrato/analytic/impedance.py | 497 +++++++++++++++++++++++++++++++++++ 2 files changed, 497 insertions(+), 496 deletions(-) create mode 100644 pyrato/analytic/impedance.py diff --git a/pyrato/analytic/analytic.py b/pyrato/analytic/analytic.py index 2be7188..315a2e0 100644 --- a/pyrato/analytic/analytic.py +++ b/pyrato/analytic/analytic.py @@ -1,9 +1,6 @@ # -*- coding: utf-8 -*- -from itertools import count - import pyfar as pf import numpy as np -from scipy import optimize def eigenfrequencies_rectangular_room_rigid( @@ -221,496 +218,3 @@ def rectangular_room_rigid_walls( rir = pf.Signal(rir, samplingrate) return rir, f_n - - -def transcendental_equation_eigenfrequencies_impedance(k_n, k, L, zeta): - """The transcendental equation to be solved for the estimation of the - complex eigenfrequencies of the rectangular room with uniform impedances. - This function is intended as the cost function for solving for the roots. - - Parameters - ---------- - k_n : array, double - The real and imaginary part of the complex eigenfrequency - k : double - The real valued wave number - L : double - The room dimension - zeta : array, double - The normalized specific impedance - - Returns - ------- - func : array, double - The real and imaginary part of the transcendental equation - """ - k_n_real = k_n[0] - k_n_imag = k_n[1] - - k_n_complex = k_n_real + 1j*k_n_imag - - left = np.tan(k_n_complex*L) - right = \ - (1j*k*L*np.sum(zeta)) / (k_n_complex*L * (np.prod(zeta) + - (k*L)**2/(k_n_complex*L)**2)) - func = left - right - - return [func.real, func.imag] - - -def transcendental_equation_eigenfrequencies_impedance_newton(k_n, k, L, zeta): - r"""The transcendental equation to be solved for the estimation of the - complex eigenfrequencies of the rectangular room with uniform impedances. - This function is intended as the cost function for solving for the roots. - - .. math:: - - \frac{ i k \left(\zeta_{0} + \zeta_{L}\right)}{k_{n} - \left(\frac{k^{2}}{k_{n}^{2}} + \zeta_{0} \zeta_{L}\right)} + - \tan{\left(L k_{n} \right)} = 0 - - Parameters - ---------- - k_n : complex - The complex eigenvalue - k : double - The real valued wave number - L : double - The room dimension - zeta : array, double - The normalized specific impedance - - Returns - ------- - func : array, double - The complex transcendental equation - """ - left = np.tan(k_n*L) - zeta_prod = zeta[0]*zeta[1] - zeta_sum = zeta[0]+zeta[1] - - right = \ - (1j*k*zeta_sum) / (k_n * (zeta_prod + (k/k_n)**2)) - func = left - right - - return func - - -def gradient_trancendental_equation_eigenfrequencies_impedance( - k_n, k, L, zeta): - r"""The gradient of the transcendental equation for the estimation of the - complex eigenfrequencies of the rectangular room with uniform impedances. - This function is intended as the analytic jacobian function for solving - for the roots of the transcendental equation. - - .. math:: - - L \left(\tan^{2}{\left(L k_{n} \right)} + 1\right) - - \frac{2 i k^{3} \left(\zeta_{0} + \zeta_{L}\right)}{k_{n}^{4} - \left(\frac{k^{2}}{k_{n}^{2}} + \zeta_{0} \zeta_{L}\right)^{2}} + - \frac{ i k \left(\zeta_{0} + \zeta_{L}\right)}{k_{n}^{2} - \left(\frac{k^{2}}{k_{n}^{2}} + \zeta_{0} \zeta_{L}\right)} = 0 - - Parameters - ---------- - k_n : complex - The complex eigenvalue - k : double - The real valued wave number - L : double - The room dimension - zeta : array, double - The normalized specific impedance - - Returns - ------- - func : array, double - The complex transcendental equation - """ - zeta_prod = zeta[0]*zeta[1] - zeta_sum = zeta[0]+zeta[1] - - tan = L * (np.tan(L * k_n)**2 + 1) - denom = (k_n**2 * ((k/k_n)**2 + zeta_prod)) - left = k**3 / denom**2 - right = k / denom - - d_k_n = tan + (-2*left + right)*zeta_sum*1j - return d_k_n - - -def initial_solution_transcendental_equation(k, L, zeta): - """ Initial solution to the transcendental equation for the complex - eigenfrequencies of the rectangular room with uniform impedance at - the boundaries. This will approximate the zeroth order mode. - - Parameters - ---------- - k : array, double - Wave number array - - Returns - ------- - k_0 : array, complex - The complex zero order eigenfrequency - """ - zeta_0 = zeta[0] - zeta_L = zeta[1] - k_0 = 1/L*np.sqrt(-(k*L)**2/zeta_0/zeta_L + 1j*k*L*(1/zeta_0+1/zeta_L)) - - return k_0 - - -def eigenfrequencies_rectangular_room_1d( - L_l, ks, k_max, zeta, gradient=True): - """Estimates the complex eigenvalues in the wavenumber domain for one - dimension by numerically solving for the roots of the transcendental - equation. A initial approximation to the zeroth order mode is applied to - improve the conditioning of the problem. - - Parameters - ---------- - L_l : double - The dimension in m - ks : array, double - The wave numbers for which the eigenvalues are to be solved. - k_max : double - The real part of the largest eigenvalue. This solves as a stopping - criterion independent from the real wave number k. - zeta : array, double - The normalized specific impedance on the boundaries. - gradient : boolean, optional (True) - Use the analytic gradient of the transcendental equation instead - of an numerical approximation in the solver - - Returns - ------- - k_ns : array, complex - The complex eigenvalues for each wavenumber - - Note - ---- - This function assumes that the real part of the largest eigenvalue may be - calculated using the approximation for rigid walls. - - """ - ks = np.atleast_1d(ks) - n_l_max = int(np.ceil(k_max/np.pi*L_l)) - - if gradient: - fprime = gradient_trancendental_equation_eigenfrequencies_impedance - else: - fprime = False - - k_ns_l = np.zeros((n_l_max, len(ks)), dtype=complex) - k_n_init = initial_solution_transcendental_equation(ks[0], L_l, zeta) - for idx_k, k in enumerate(ks): - idx_n = 0 - while k_n_init.real < k_max: - args_costfun = (k, L_l, zeta) - kk_n = optimize.newton( - transcendental_equation_eigenfrequencies_impedance_newton, - k_n_init, - fprime=fprime, - args=args_costfun) - if kk_n.real > k_max: - break - else: - k_ns_l[idx_n, idx_k] = kk_n - k_n_init = (kk_n*L_l + np.pi) / L_l - idx_n += 1 - - k_n_init = k_ns_l[0, idx_k] - - return k_ns_l - - -def normal_eigenfrequencies_rectangular_room_impedance( - L, ks, k_max, zeta): - r"""Caller function for the eigenvalue estimation of all room dimensions. - See the function `eigenfrequencies_rectangular_room_impedance` or - `eigenfrequencies_rectangular_room_1d` for more information. - - Parameters - ---------- - L : array, double - The dimensions in m - ks : array, double - The wave numbers for which the eigenvalues are to be solved. - k_max : double - The real part of the largest eigenvalue. This solves as a stopping - criterion independent from the real wave number k. - zeta : array, double - The normalized specific impedance on the boundaries. - - Returns - ------- - k_ns : list, complex - List of arrays with the complex eigenvalues for each wavenumber and - each room dimension. - """ - k_ns = [] - for dim, L_l, zeta_l in zip(count(), L, zeta): - k_ns_l = eigenfrequencies_rectangular_room_1d( - L_l, ks, k_max, zeta_l) - k_ns.append(k_ns_l) - return k_ns - - -def eigenfrequencies_rectangular_room_impedance( - L, ks, k_max, zeta, only_normal=False): - r"""Estimates the complex eigenvalues in the wavenumber domain for a - rectangular room with arbitrary uniform impedances on the boundary by - numerically solving for the roots of the transcendental equation. - A initial approximation to the zeroth order mode is applied to - improve the conditioning of the problem. The eigenvalues corresponding to - tangential and oblique modes are calculated from the eigenvalues of the - respective axial modes. Resulting eigenvalues with a real part larger than - k_max will be discarded. - - Parameters - ---------- - L : array, double - The dimensions in m - ks : array, double - The wave numbers for which the eigenvalues are to be solved. - k_max : double - The real part of the largest eigenvalue. This solves as a stopping - criterion independent from the real wave number k. - zeta : array, double - The normalized specific impedance on the boundaries. - only_normal : boolean, optional (False) - Only return the eigenvalues corresponding to the axial modes. - The mode indices will still contain the indices for all modes in - the defined frequency range. The complete set of eigenvalues - can be calculated as - :math:`k_n = \sqrt{ k_{n,x}^2+k_{n,y}^2+k_{n,z}^2 }`. - - Returns - ------- - k_ns : array, complex - The complex eigenvalues for each wavenumber - mode_indices : array, integer - The wave number indices of respective eigenvalues. - - Note - ---- - Eigenvalues smaller for a wave number :math:`k < 0.02` will be replaced by - the value for the closest larger wave number to ensure finding the root. - - """ - ks = np.atleast_1d(ks) - mask = ks >= 0.02 - ks_search = ks[mask] - k_ns = normal_eigenfrequencies_rectangular_room_impedance( - L, ks_search, k_max, zeta - ) - for idx in range(0, len(L)): - k_ns[idx] = np.hstack(( - np.tile(k_ns[idx][:, 0], (np.sum(~mask), 1)).T, - k_ns[idx])) - - n_z = np.arange(0, k_ns[2].shape[0]) - n_y = np.arange(0, k_ns[1].shape[0]) - n_x = np.arange(0, k_ns[0].shape[0]) - - combs = np.meshgrid(n_x, n_y, n_z) - perms = np.array(combs).T.reshape(-1, 3) - - kk_ns = np.sqrt( - k_ns[0][perms[:, 0]]**2 + - k_ns[1][perms[:, 1]]**2 + - k_ns[2][perms[:, 2]]**2) - - mask_perms = (kk_ns[:, -1].real < k_max) - - mask_bc = np.broadcast_to( - np.atleast_2d(mask_perms).T, - (len(mask_perms), len(ks))) - - if only_normal: - kk_ns = k_ns - else: - kk_ns = kk_ns[mask_bc].reshape(-1, len(ks)) - - mode_indices = perms[mask_bc[:, 0]] - - return kk_ns, mode_indices - - -def mode_function_impedance(position, eigenvalue, phase): - r""" The modal function for a room with boundary impedances [4]_ . - - .. math:: - - p_{n,i}(x_i) = \cosh(x_i k_{n,i} + \phi_{n,i}) - - Parameters - ---------- - position : ndarray, double, (3,) - The position in Cartesian coordinates - eigenvalue : ndarray, complex, (3, N, n_bins) - The N complex eigenvalues in x,y,z coordinates for each - frequency bin (wavenumber) - phase : ndarray, complex, (3, N, n_bins) - The phase shift introduced by the boundary impedance - - Returns - ------- - p_n : ndarray, complex, (3, N, n_bins) - The modal function - - References - ---------- - .. [4] M. Nolan and J. L. Davy, “Two definitions of the inner product of - modes and their use in calculating non-diffuse reverberant sound - fields,” The Journal of the Acoustical Society of America, vol. - 145, no. 6, pp. 3330–3340, Jun. 2019. - """ - return np.cosh(1j*eigenvalue * position + phase) - - -def pressure_modal_superposition( - ks, omegas, k_ns, mode_indices, r_R, r_S, L, zeta): - r""" Calculate modal composition for a rectangular room with arbitrary - boundary impedances. - - Parameters - ---------- - ks : ndarray, double - The wave number array - omegas : ndarray, double - The angular frequency array :math:`omega` - k_ns : list, complex - List containing the complex eigenvalues for each dimension and - wavenumber - r_R : ndarray, double, (3, n_receivers) - The receiver positions in Cartesian coordinates - r_S : ndarray, double, (3) - The source position in Cartesian coordinates - L : ndarray, double, (3,) - The room dimensions in meters - zeta : ndarray, double, (3, 2) - The normalized impedance :math:`\zeta_i = \frac{Z_i}{\rho_o c}` - for each wall. - - Returns - ------- - spec : ndarray, complex - The complex sum of all mode functions corresponding to the eigenvalues - `k_ns` - - """ - - zeta_0 = zeta[:, 0] - r_R = np.atleast_2d(r_R) - - kk_ns = np.sqrt( - k_ns[0][mode_indices[:, 0]]**2 + - k_ns[1][mode_indices[:, 1]]**2 + - k_ns[2][mode_indices[:, 2]]**2) - - k_ns_xyz = np.array([ - k_ns[0][mode_indices[:, 0]], - k_ns[1][mode_indices[:, 1]], - k_ns[2][mode_indices[:, 2]] - ]) - - phi = np.arctanh(ks/(zeta_0 * k_ns_xyz.T).T) - K_n_sc = \ - np.sinh(1j * k_ns_xyz.T * L) * \ - np.cosh(1j * k_ns_xyz.T * L + 2*phi.T) - - K_n = np.prod((L/2 * (1 + 1/(1j*k_ns_xyz.T * L) * K_n_sc)).T, axis=0) - denom = K_n * (kk_ns**2 - ks**2) - - p_ns_s = np.prod(mode_function_impedance(r_S, k_ns_xyz.T, phi.T).T, axis=0) - - spec = np.zeros((r_R.shape[0], ks.size), dtype=complex) - for idx_R in range(r_R.shape[0]): - p_ns_r = np.prod( - mode_function_impedance( - r_R[idx_R, :], k_ns_xyz.T, phi.T).T, - axis=0) - - nom = 1j*omegas*1.2*p_ns_r*p_ns_s - - spec[idx_R, :] = np.sum(nom / denom, axis=0) - - spec = np.squeeze(spec) - - return spec - - -def rectangular_room_impedance( - L, - r_S, - r_R, - normalized_impedance, - max_freq, - samplingrate=44100, - c=343.9, - n_samples=2**12, - remove_cavity_mode=False): - r""" Calculate the room impulse response and room transfer function for a - rectangular room with arbitrary boundary impedances. - - Parameters - ---------- - L : ndarray, double, (3,) - The room dimensions in meters - r_S : ndarray, double, (3) - The source position in Cartesian coordinates - r_R : ndarray, double, (3, n_receivers) - The receiver positions in Cartesian coordinates - normalized_impedance : ndarray, double, (3, 2) - The normalized impedance :math:`\zeta_i = \frac{Z_i}{\rho_o c}` - for each wall. - max_freq : double - The highest frequency to be considered for the estimation of the - eigenfrequencies. - samplingrate : int, 44100 - The samplingrate - c : float, 343.9 - The speed of sound in m/s - n_samples : int, 2**12 - The number of samples for which the RIR is calculated - remove_cavity_mode : boolean, False - When true, the cavity mode (0, 0, 0) will be removed before summation - of all modes - - Returns - ------- - rir : ndarray, double, (n_receivers, n_samples) - The room impulse response - rtf : ndarray, double, (n_receivers, n_bins) - The room transfer function in the frequency domain - eigenvalues : ndarray, complex - The complex eigenvalues in the form - :math:`k_n = \omega_n / c + i \delta_n` - """ - - zeta = normalized_impedance - freqs = np.fft.rfftfreq(n_samples, 1/samplingrate) - ks = 2*np.pi*freqs/c - - k_max = max_freq*2*np.pi/c - k_ns, mode_indices = eigenfrequencies_rectangular_room_impedance( - L, ks, k_max, zeta, only_normal=True) - - if remove_cavity_mode: - mask = np.prod(np.array([0, 0, 0]) == mode_indices, axis=-1) == 1 - mode_indices = mode_indices[~mask] - - spectrum = pressure_modal_superposition( - ks, freqs*2*np.pi, k_ns, mode_indices, r_R, r_S, L, zeta) - - rir = np.fft.irfft(spectrum) - - k_ns_xyz = np.array([ - k_ns[0][mode_indices[:, 0]], - k_ns[1][mode_indices[:, 1]], - k_ns[2][mode_indices[:, 2]] - ]) - - return rir, spectrum, k_ns_xyz diff --git a/pyrato/analytic/impedance.py b/pyrato/analytic/impedance.py new file mode 100644 index 0000000..c6444db --- /dev/null +++ b/pyrato/analytic/impedance.py @@ -0,0 +1,497 @@ +from itertools import count + +import numpy as np +from scipy import optimize + + +def transcendental_equation_eigenfrequencies_impedance(k_n, k, L, zeta): + """The transcendental equation to be solved for the estimation of the + complex eigenfrequencies of the rectangular room with uniform impedances. + This function is intended as the cost function for solving for the roots. + + Parameters + ---------- + k_n : array, double + The real and imaginary part of the complex eigenfrequency + k : double + The real valued wave number + L : double + The room dimension + zeta : array, double + The normalized specific impedance + + Returns + ------- + func : array, double + The real and imaginary part of the transcendental equation + """ + k_n_real = k_n[0] + k_n_imag = k_n[1] + + k_n_complex = k_n_real + 1j*k_n_imag + + left = np.tan(k_n_complex*L) + right = \ + (1j*k*L*np.sum(zeta)) / (k_n_complex*L * (np.prod(zeta) + + (k*L)**2/(k_n_complex*L)**2)) + func = left - right + + return [func.real, func.imag] + + +def transcendental_equation_eigenfrequencies_impedance_newton(k_n, k, L, zeta): + r"""The transcendental equation to be solved for the estimation of the + complex eigenfrequencies of the rectangular room with uniform impedances. + This function is intended as the cost function for solving for the roots. + + .. math:: + + \frac{ i k \left(\zeta_{0} + \zeta_{L}\right)}{k_{n} + \left(\frac{k^{2}}{k_{n}^{2}} + \zeta_{0} \zeta_{L}\right)} + + \tan{\left(L k_{n} \right)} = 0 + + Parameters + ---------- + k_n : complex + The complex eigenvalue + k : double + The real valued wave number + L : double + The room dimension + zeta : array, double + The normalized specific impedance + + Returns + ------- + func : array, double + The complex transcendental equation + """ + left = np.tan(k_n*L) + zeta_prod = zeta[0]*zeta[1] + zeta_sum = zeta[0]+zeta[1] + + right = \ + (1j*k*zeta_sum) / (k_n * (zeta_prod + (k/k_n)**2)) + func = left - right + + return func + + +def gradient_trancendental_equation_eigenfrequencies_impedance( + k_n, k, L, zeta): + r"""The gradient of the transcendental equation for the estimation of the + complex eigenfrequencies of the rectangular room with uniform impedances. + This function is intended as the analytic jacobian function for solving + for the roots of the transcendental equation. + + .. math:: + + L \left(\tan^{2}{\left(L k_{n} \right)} + 1\right) - + \frac{2 i k^{3} \left(\zeta_{0} + \zeta_{L}\right)}{k_{n}^{4} + \left(\frac{k^{2}}{k_{n}^{2}} + \zeta_{0} \zeta_{L}\right)^{2}} + + \frac{ i k \left(\zeta_{0} + \zeta_{L}\right)}{k_{n}^{2} + \left(\frac{k^{2}}{k_{n}^{2}} + \zeta_{0} \zeta_{L}\right)} = 0 + + Parameters + ---------- + k_n : complex + The complex eigenvalue + k : double + The real valued wave number + L : double + The room dimension + zeta : array, double + The normalized specific impedance + + Returns + ------- + func : array, double + The complex transcendental equation + """ + zeta_prod = zeta[0]*zeta[1] + zeta_sum = zeta[0]+zeta[1] + + tan = L * (np.tan(L * k_n)**2 + 1) + denom = (k_n**2 * ((k/k_n)**2 + zeta_prod)) + left = k**3 / denom**2 + right = k / denom + + d_k_n = tan + (-2*left + right)*zeta_sum*1j + return d_k_n + + +def initial_solution_transcendental_equation(k, L, zeta): + """ Initial solution to the transcendental equation for the complex + eigenfrequencies of the rectangular room with uniform impedance at + the boundaries. This will approximate the zeroth order mode. + + Parameters + ---------- + k : array, double + Wave number array + + Returns + ------- + k_0 : array, complex + The complex zero order eigenfrequency + """ + zeta_0 = zeta[0] + zeta_L = zeta[1] + k_0 = 1/L*np.sqrt(-(k*L)**2/zeta_0/zeta_L + 1j*k*L*(1/zeta_0+1/zeta_L)) + + return k_0 + + +def eigenfrequencies_rectangular_room_1d( + L_l, ks, k_max, zeta, gradient=True): + """Estimates the complex eigenvalues in the wavenumber domain for one + dimension by numerically solving for the roots of the transcendental + equation. A initial approximation to the zeroth order mode is applied to + improve the conditioning of the problem. + + Parameters + ---------- + L_l : double + The dimension in m + ks : array, double + The wave numbers for which the eigenvalues are to be solved. + k_max : double + The real part of the largest eigenvalue. This solves as a stopping + criterion independent from the real wave number k. + zeta : array, double + The normalized specific impedance on the boundaries. + gradient : boolean, optional (True) + Use the analytic gradient of the transcendental equation instead + of an numerical approximation in the solver + + Returns + ------- + k_ns : array, complex + The complex eigenvalues for each wavenumber + + Note + ---- + This function assumes that the real part of the largest eigenvalue may be + calculated using the approximation for rigid walls. + + """ + ks = np.atleast_1d(ks) + n_l_max = int(np.ceil(k_max/np.pi*L_l)) + + if gradient: + fprime = gradient_trancendental_equation_eigenfrequencies_impedance + else: + fprime = False + + k_ns_l = np.zeros((n_l_max, len(ks)), dtype=complex) + k_n_init = initial_solution_transcendental_equation(ks[0], L_l, zeta) + for idx_k, k in enumerate(ks): + idx_n = 0 + while k_n_init.real < k_max: + args_costfun = (k, L_l, zeta) + kk_n = optimize.newton( + transcendental_equation_eigenfrequencies_impedance_newton, + k_n_init, + fprime=fprime, + args=args_costfun) + if kk_n.real > k_max: + break + else: + k_ns_l[idx_n, idx_k] = kk_n + k_n_init = (kk_n*L_l + np.pi) / L_l + idx_n += 1 + + k_n_init = k_ns_l[0, idx_k] + + return k_ns_l + + +def normal_eigenfrequencies_rectangular_room_impedance( + L, ks, k_max, zeta): + r"""Caller function for the eigenvalue estimation of all room dimensions. + See the function `eigenfrequencies_rectangular_room_impedance` or + `eigenfrequencies_rectangular_room_1d` for more information. + + Parameters + ---------- + L : array, double + The dimensions in m + ks : array, double + The wave numbers for which the eigenvalues are to be solved. + k_max : double + The real part of the largest eigenvalue. This solves as a stopping + criterion independent from the real wave number k. + zeta : array, double + The normalized specific impedance on the boundaries. + + Returns + ------- + k_ns : list, complex + List of arrays with the complex eigenvalues for each wavenumber and + each room dimension. + """ + k_ns = [] + for dim, L_l, zeta_l in zip(count(), L, zeta): + k_ns_l = eigenfrequencies_rectangular_room_1d( + L_l, ks, k_max, zeta_l) + k_ns.append(k_ns_l) + return k_ns + + +def eigenfrequencies_rectangular_room_impedance( + L, ks, k_max, zeta, only_normal=False): + r"""Estimates the complex eigenvalues in the wavenumber domain for a + rectangular room with arbitrary uniform impedances on the boundary by + numerically solving for the roots of the transcendental equation. + A initial approximation to the zeroth order mode is applied to + improve the conditioning of the problem. The eigenvalues corresponding to + tangential and oblique modes are calculated from the eigenvalues of the + respective axial modes. Resulting eigenvalues with a real part larger than + k_max will be discarded. + + Parameters + ---------- + L : array, double + The dimensions in m + ks : array, double + The wave numbers for which the eigenvalues are to be solved. + k_max : double + The real part of the largest eigenvalue. This solves as a stopping + criterion independent from the real wave number k. + zeta : array, double + The normalized specific impedance on the boundaries. + only_normal : boolean, optional (False) + Only return the eigenvalues corresponding to the axial modes. + The mode indices will still contain the indices for all modes in + the defined frequency range. The complete set of eigenvalues + can be calculated as + :math:`k_n = \sqrt{ k_{n,x}^2+k_{n,y}^2+k_{n,z}^2 }`. + + Returns + ------- + k_ns : array, complex + The complex eigenvalues for each wavenumber + mode_indices : array, integer + The wave number indices of respective eigenvalues. + + Note + ---- + Eigenvalues smaller for a wave number :math:`k < 0.02` will be replaced by + the value for the closest larger wave number to ensure finding the root. + + """ + ks = np.atleast_1d(ks) + mask = ks >= 0.02 + ks_search = ks[mask] + k_ns = normal_eigenfrequencies_rectangular_room_impedance( + L, ks_search, k_max, zeta + ) + for idx in range(0, len(L)): + k_ns[idx] = np.hstack(( + np.tile(k_ns[idx][:, 0], (np.sum(~mask), 1)).T, + k_ns[idx])) + + n_z = np.arange(0, k_ns[2].shape[0]) + n_y = np.arange(0, k_ns[1].shape[0]) + n_x = np.arange(0, k_ns[0].shape[0]) + + combs = np.meshgrid(n_x, n_y, n_z) + perms = np.array(combs).T.reshape(-1, 3) + + kk_ns = np.sqrt( + k_ns[0][perms[:, 0]]**2 + + k_ns[1][perms[:, 1]]**2 + + k_ns[2][perms[:, 2]]**2) + + mask_perms = (kk_ns[:, -1].real < k_max) + + mask_bc = np.broadcast_to( + np.atleast_2d(mask_perms).T, + (len(mask_perms), len(ks))) + + if only_normal: + kk_ns = k_ns + else: + kk_ns = kk_ns[mask_bc].reshape(-1, len(ks)) + + mode_indices = perms[mask_bc[:, 0]] + + return kk_ns, mode_indices + + +def mode_function_impedance(position, eigenvalue, phase): + r""" The modal function for a room with boundary impedances [4]_ . + + .. math:: + + p_{n,i}(x_i) = \cosh(x_i k_{n,i} + \phi_{n,i}) + + Parameters + ---------- + position : ndarray, double, (3,) + The position in Cartesian coordinates + eigenvalue : ndarray, complex, (3, N, n_bins) + The N complex eigenvalues in x,y,z coordinates for each + frequency bin (wavenumber) + phase : ndarray, complex, (3, N, n_bins) + The phase shift introduced by the boundary impedance + + Returns + ------- + p_n : ndarray, complex, (3, N, n_bins) + The modal function + + References + ---------- + .. [4] M. Nolan and J. L. Davy, “Two definitions of the inner product of + modes and their use in calculating non-diffuse reverberant sound + fields,” The Journal of the Acoustical Society of America, vol. + 145, no. 6, pp. 3330–3340, Jun. 2019. + """ + return np.cosh(1j*eigenvalue * position + phase) + + +def pressure_modal_superposition( + ks, omegas, k_ns, mode_indices, r_R, r_S, L, zeta): + r""" Calculate modal composition for a rectangular room with arbitrary + boundary impedances. + + Parameters + ---------- + ks : ndarray, double + The wave number array + omegas : ndarray, double + The angular frequency array :math:`omega` + k_ns : list, complex + List containing the complex eigenvalues for each dimension and + wavenumber + r_R : ndarray, double, (3, n_receivers) + The receiver positions in Cartesian coordinates + r_S : ndarray, double, (3) + The source position in Cartesian coordinates + L : ndarray, double, (3,) + The room dimensions in meters + zeta : ndarray, double, (3, 2) + The normalized impedance :math:`\zeta_i = \frac{Z_i}{\rho_o c}` + for each wall. + + Returns + ------- + spec : ndarray, complex + The complex sum of all mode functions corresponding to the eigenvalues + `k_ns` + + """ + + zeta_0 = zeta[:, 0] + r_R = np.atleast_2d(r_R) + + kk_ns = np.sqrt( + k_ns[0][mode_indices[:, 0]]**2 + + k_ns[1][mode_indices[:, 1]]**2 + + k_ns[2][mode_indices[:, 2]]**2) + + k_ns_xyz = np.array([ + k_ns[0][mode_indices[:, 0]], + k_ns[1][mode_indices[:, 1]], + k_ns[2][mode_indices[:, 2]] + ]) + + phi = np.arctanh(ks/(zeta_0 * k_ns_xyz.T).T) + K_n_sc = \ + np.sinh(1j * k_ns_xyz.T * L) * \ + np.cosh(1j * k_ns_xyz.T * L + 2*phi.T) + + K_n = np.prod((L/2 * (1 + 1/(1j*k_ns_xyz.T * L) * K_n_sc)).T, axis=0) + denom = K_n * (kk_ns**2 - ks**2) + + p_ns_s = np.prod(mode_function_impedance(r_S, k_ns_xyz.T, phi.T).T, axis=0) + + spec = np.zeros((r_R.shape[0], ks.size), dtype=complex) + for idx_R in range(r_R.shape[0]): + p_ns_r = np.prod( + mode_function_impedance( + r_R[idx_R, :], k_ns_xyz.T, phi.T).T, + axis=0) + + nom = 1j*omegas*1.2*p_ns_r*p_ns_s + + spec[idx_R, :] = np.sum(nom / denom, axis=0) + + spec = np.squeeze(spec) + + return spec + + +def rectangular_room_impedance( + L, + r_S, + r_R, + normalized_impedance, + max_freq, + samplingrate=44100, + c=343.9, + n_samples=2**12, + remove_cavity_mode=False): + r""" Calculate the room impulse response and room transfer function for a + rectangular room with arbitrary boundary impedances. + + Parameters + ---------- + L : ndarray, double, (3,) + The room dimensions in meters + r_S : ndarray, double, (3) + The source position in Cartesian coordinates + r_R : ndarray, double, (3, n_receivers) + The receiver positions in Cartesian coordinates + normalized_impedance : ndarray, double, (3, 2) + The normalized impedance :math:`\zeta_i = \frac{Z_i}{\rho_o c}` + for each wall. + max_freq : double + The highest frequency to be considered for the estimation of the + eigenfrequencies. + samplingrate : int, 44100 + The samplingrate + c : float, 343.9 + The speed of sound in m/s + n_samples : int, 2**12 + The number of samples for which the RIR is calculated + remove_cavity_mode : boolean, False + When true, the cavity mode (0, 0, 0) will be removed before summation + of all modes + + Returns + ------- + rir : ndarray, double, (n_receivers, n_samples) + The room impulse response + rtf : ndarray, double, (n_receivers, n_bins) + The room transfer function in the frequency domain + eigenvalues : ndarray, complex + The complex eigenvalues in the form + :math:`k_n = \omega_n / c + i \delta_n` + """ + + zeta = normalized_impedance + freqs = np.fft.rfftfreq(n_samples, 1/samplingrate) + ks = 2*np.pi*freqs/c + + k_max = max_freq*2*np.pi/c + k_ns, mode_indices = eigenfrequencies_rectangular_room_impedance( + L, ks, k_max, zeta, only_normal=True) + + if remove_cavity_mode: + mask = np.prod(np.array([0, 0, 0]) == mode_indices, axis=-1) == 1 + mode_indices = mode_indices[~mask] + + spectrum = pressure_modal_superposition( + ks, freqs*2*np.pi, k_ns, mode_indices, r_R, r_S, L, zeta) + + rir = np.fft.irfft(spectrum) + + k_ns_xyz = np.array([ + k_ns[0][mode_indices[:, 0]], + k_ns[1][mode_indices[:, 1]], + k_ns[2][mode_indices[:, 2]] + ]) + + return rir, spectrum, k_ns_xyz From 6a57e2565e392017562330d17af7aa80199a183f Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 2 May 2022 18:28:05 +0200 Subject: [PATCH 30/66] separate tests --- tests/test_analytic.py | 398 ------------------------------ tests/test_analytic_impedance.py | 401 +++++++++++++++++++++++++++++++ 2 files changed, 401 insertions(+), 398 deletions(-) create mode 100644 tests/test_analytic_impedance.py diff --git a/tests/test_analytic.py b/tests/test_analytic.py index 224e8d8..521cdd3 100644 --- a/tests/test_analytic.py +++ b/tests/test_analytic.py @@ -66,401 +66,3 @@ def test_analytic_shoebox_rir(): ref = pf.Signal(ref, 44100) npt.assert_allclose(rir.time, ref.time) - - -def test_eigenfreq_impedance_1d_real(): - L = 8/10 - zeta = np.ones(2) * 1e10 - c = 343.9 - - k = [0.1] - k_max = 1e3*2*np.pi/c - - k_ns = analytic.eigenfrequencies_rectangular_room_1d( - L, k, k_max, zeta - ) - - truth = np.array([ - 1e-6, - 2.149375000000000, - 4.298750000000000, - 6.448125000000000, - 8.597500000000000]) * 1e2 - - f_n = np.squeeze(c*k_ns.real/2/np.pi) - npt.assert_allclose(f_n, truth, rtol=1e-3, atol=1e-3) - - -def test_eigenfreq_impedance_1d_real_jac(): - L = 8/10 - zeta = np.ones(2) * 1e10 - c = 343.9 - - k = [0.1] - k_max = 1e3*2*np.pi/c - - k_ns = analytic.eigenfrequencies_rectangular_room_1d( - L, k, k_max, zeta, gradient=True - ) - - truth = np.array([ - 1e-6, - 2.149375000000000, - 4.298750000000000, - 6.448125000000000, - 8.597500000000000]) * 1e2 - - f_n = np.squeeze(c*k_ns.real/2/np.pi) - npt.assert_allclose(f_n, truth, rtol=1e-3, atol=1e-3) - - -def test_analytic_shoebox_eigenfreqs_impedance_multi_k(): - L = np.array([8, 5, 3])/10 - zetas = np.ones((3, 2)) * 1e10 - - c = 343.9 - - k_max = 1e3*2*np.pi/c - k = np.linspace(0, k_max*1.1, 2**10) - - k_ns, _ = analytic.eigenfrequencies_rectangular_room_impedance( - L, k, k_max, zetas - ) - - truth = np.array([ - 4.196434e-05, - 5.731666666666666, - 3.439000000000000, - 6.684214522124330, - 6.877999999999999, - 8.953149545147662, - 2.149375000000000, - 6.121422683363956, - 4.055432639142833, - 7.021291666666667, - 7.206018102296510, - 9.207534939841542, - 4.298750000000000, - 7.164583333333334, - 5.505086063132890, - 7.947199213576930, - 8.110865278285665, - 9.931673491425187, - 6.448125000000000, - 8.627300782597231, - 7.307874999999999, - 9.287466812506130, - 9.427894781743429, - 8.597500000000000, - 9.259790885867778]) * 1e2 - - f_n = np.mean(np.squeeze(c*k_ns.real/2/np.pi), axis=-1) - npt.assert_allclose(np.sort(f_n), np.sort(truth), atol=1e-3, rtol=1e-3) - - -def test_analytic_shoebox_eigenfreqs_impedance(): - L = np.array([8, 5, 3])/10 - zetas = np.ones((3, 2)) * 1e10 - - c = 343.9 - - k = [0.1] - k_max = 1e3*2*np.pi/c - - k_ns, _ = analytic.eigenfrequencies_rectangular_room_impedance( - L, k, k_max, zetas - ) - - truth = np.array([ - 4.440943e-06, - 5.731666666666666, - 3.439000000000000, - 6.684214522124330, - 6.877999999999999, - 8.953149545147662, - 2.149375000000000, - 6.121422683363956, - 4.055432639142833, - 7.021291666666667, - 7.206018102296510, - 9.207534939841542, - 4.298750000000000, - 7.164583333333334, - 5.505086063132890, - 7.947199213576930, - 8.110865278285665, - 9.931673491425187, - 6.448125000000000, - 8.627300782597231, - 7.307874999999999, - 9.287466812506130, - 9.427894781743429, - 8.597500000000000, - 9.259790885867778]) * 1e2 - - f_n = np.squeeze(c*k_ns.real/2/np.pi) - npt.assert_allclose(np.sort(f_n), np.sort(truth), atol=1e-3, rtol=1e-3) - - -def test_analytic_eigenfrequencies_impedance_cplx(): - L = np.array([8, 5, 3])/10 - zetas = np.ones((3, 2)) * 1e10 - - c = 343.9 - - k_max = 1e3*2*np.pi/c - k_min = 150*2*np.pi/c - k = np.linspace(k_min, k_max*1.1, 2**10) - - k_ns, _ = analytic.eigenfrequencies_rectangular_room_impedance( - L, k, k_max, zetas, only_normal=True - ) - - k_ns_x = np.loadtxt( - 'tests/data/analytic_impedance/k_ns_x.csv', - delimiter=',', - dtype=complex) - k_ns_y = np.loadtxt( - 'tests/data/analytic_impedance/k_ns_y.csv', - delimiter=',', - dtype=complex) - k_ns_z = np.loadtxt( - 'tests/data/analytic_impedance/k_ns_z.csv', - delimiter=',', - dtype=complex) - - npt.assert_allclose(k_ns[0], k_ns_x, rtol=1e-6) - npt.assert_allclose(k_ns[1], k_ns_y, rtol=1e-6) - npt.assert_allclose(k_ns[2], k_ns_z, rtol=1e-6) - - -def test_analytic_eigenfrequencies_impedance_zeta15(): - L = np.array([8, 5, 3])/10 - zetas = np.ones((3, 2)) * 15 - - c = 343.9 - - k_max = 1e3*2*np.pi/c - k_min = 150*2*np.pi/c - k = np.linspace(k_min, k_max*1.1, 2**10) - - k_ns, _ = analytic.eigenfrequencies_rectangular_room_impedance( - L, k, k_max, zetas, only_normal=True - ) - - k_ns_x = np.loadtxt( - 'tests/data/analytic_impedance/k_ns_x_zeta15.csv', - delimiter=',', - dtype=complex) - k_ns_y = np.loadtxt( - 'tests/data/analytic_impedance/k_ns_y_zeta15.csv', - delimiter=',', - dtype=complex) - k_ns_z = np.loadtxt( - 'tests/data/analytic_impedance/k_ns_z_zeta15.csv', - delimiter=',', - dtype=complex) - - npt.assert_allclose(k_ns[0], k_ns_x, rtol=1e-6) - npt.assert_allclose(k_ns[1], k_ns_y, rtol=1e-6) - npt.assert_allclose(k_ns[2], k_ns_z, rtol=1e-6) - - -def test_analytic_pressure_shoebox_impedance(): - L = np.array([8, 5, 3])/10 - zetas = np.ones((3, 2)) * 1e10 - - c = 343.9 - - k_max = 1e3*2*np.pi/c - k_min = 150*2*np.pi/c - k = np.linspace(k_min, k_max*1.1, 2**10) - - k_ns_x = np.loadtxt( - 'tests/data/analytic_impedance/k_ns_x.csv', - delimiter=',', - dtype=complex) - k_ns_y = np.loadtxt( - 'tests/data/analytic_impedance/k_ns_y.csv', - delimiter=',', - dtype=complex) - k_ns_z = np.loadtxt( - 'tests/data/analytic_impedance/k_ns_z.csv', - delimiter=',', - dtype=complex) - - k_ns = list((k_ns_x, k_ns_y, k_ns_z)) - - mode_indices = np.loadtxt( - 'tests/data/analytic_impedance/mode_indices.csv', - delimiter=',', - dtype=int) - - r_R = np.array([3.3, 1.6, 1.8])/10 - r_S = np.array([5.3, 3.6, 1.2])/10 - - p_x = analytic.pressure_modal_superposition( - k, k*c, k_ns, mode_indices, r_R, r_S, L, zetas) - - truth = np.loadtxt( - 'tests/data/analytic_impedance/p_x.csv', - delimiter=',', - dtype=complex) - - npt.assert_allclose(p_x, truth, atol=1e-6, rtol=1e-6) - - -def test_analytic_pressure_shoebox_impedance_multi_R(): - L = np.array([8, 5, 3])/10 - zetas = np.ones((3, 2)) * 1e10 - - c = 343.9 - - k_max = 1e3*2*np.pi/c - k_min = 150*2*np.pi/c - k = np.linspace(k_min, k_max*1.1, 2**10) - - k_ns_x = np.loadtxt( - 'tests/data/analytic_impedance/k_ns_x.csv', - delimiter=',', - dtype=complex) - k_ns_y = np.loadtxt( - 'tests/data/analytic_impedance/k_ns_y.csv', - delimiter=',', - dtype=complex) - k_ns_z = np.loadtxt( - 'tests/data/analytic_impedance/k_ns_z.csv', - delimiter=',', - dtype=complex) - - k_ns = list((k_ns_x, k_ns_y, k_ns_z)) - - mode_indices = np.loadtxt( - 'tests/data/analytic_impedance/mode_indices.csv', - delimiter=',', - dtype=int) - - r_R = np.array([ - [3.3, 1.6, 1.8], - [3.3, 1.6, 1.8]])/10 - r_S = np.array([5.3, 3.6, 1.2])/10 - - p_x = analytic.pressure_modal_superposition( - k, k*c, k_ns, mode_indices, r_R, r_S, L, zetas) - - truth = np.loadtxt( - 'tests/data/analytic_impedance/p_x.csv', - delimiter=',', - dtype=complex) - - truth = np.vstack((truth, truth)) - - npt.assert_allclose(p_x, truth, atol=1e-6, rtol=1e-6) - - -def test_analytic_pressure_shoebox_impedance_zeta15(): - L = np.array([8, 5, 3])/10 - zetas = np.ones((3, 2)) * 15 - - c = 343.9 - - k_max = 1e3*2*np.pi/c - k_min = 150*2*np.pi/c - k = np.linspace(k_min, k_max*1.1, 2**10) - - k_ns_x = np.loadtxt( - 'tests/data/analytic_impedance/k_ns_x_zeta15.csv', - delimiter=',', - dtype=complex) - k_ns_y = np.loadtxt( - 'tests/data/analytic_impedance/k_ns_y_zeta15.csv', - delimiter=',', - dtype=complex) - k_ns_z = np.loadtxt( - 'tests/data/analytic_impedance/k_ns_z_zeta15.csv', - delimiter=',', - dtype=complex) - - k_ns = list((k_ns_x, k_ns_y, k_ns_z)) - - mode_indices = np.loadtxt( - 'tests/data/analytic_impedance/mode_indices_zeta15.csv', - delimiter=',', - dtype=int) - - r_R = np.array([3.3, 1.6, 1.8])/10 - r_S = np.array([5.3, 3.6, 1.2])/10 - - p_x = analytic.pressure_modal_superposition( - k, k*c, k_ns, mode_indices, r_R, r_S, L, zetas) - - truth = np.loadtxt( - 'tests/data/analytic_impedance/p_x_zeta15.csv', - delimiter=',', - dtype=complex) - - npt.assert_allclose(p_x, truth, atol=1e-4, rtol=1e-4) - - -def test_analytic_shoebox_spec_impedance(): - L = np.array([8, 5, 3])/10 - zetas = np.ones((3, 2)) * 100 - - c = 343.9 - - r_R = np.array([3.3, 1.6, 1.8])/10 - r_S = np.array([5.3, 3.6, 1.2])/10 - - f_max = 1e3 - - samplingrate = 2200 - n_samples = 2**10 - - rir, spec, k_ns = analytic.rectangular_room_impedance( - L, - r_S, - r_R, - zetas, - f_max, - samplingrate, - c, - n_samples, - remove_cavity_mode=False) - - truth_rtf = np.load('tests/data/analytic_rtf_impedance.npy') - truth_rir = np.load('tests/data/analytic_rir_impedance.npy') - - npt.assert_allclose(spec, truth_rtf, atol=1e-2, rtol=1e-2) - npt.assert_allclose(rir, truth_rir, atol=1e-2, rtol=1e-2) - - -def test_analytic_shoebox_spec_impedance_no_cavity_mode(): - L = np.array([8, 5, 3])/10 - zetas = np.ones((3, 2)) * 100 - - c = 343.9 - - r_R = np.array([3.3, 1.6, 1.8])/10 - r_S = np.array([5.3, 3.6, 1.2])/10 - - f_max = 1e3 - - samplingrate = 2200 - n_samples = 2**10 - - rir, spec, k_ns = analytic.rectangular_room_impedance( - L, - r_S, - r_R, - zetas, - f_max, - samplingrate, - c, - n_samples, - remove_cavity_mode=True) - - truth_rtf = np.load('tests/data/analytic_rtf_impedance_no_cav.npy') - truth_rir = np.load('tests/data/analytic_rir_impedance_no_cav.npy') - - npt.assert_allclose(spec, truth_rtf, atol=1e-2, rtol=1e-2) - npt.assert_allclose(rir, truth_rir, atol=1e-2, rtol=1e-2) diff --git a/tests/test_analytic_impedance.py b/tests/test_analytic_impedance.py new file mode 100644 index 0000000..8e625e7 --- /dev/null +++ b/tests/test_analytic_impedance.py @@ -0,0 +1,401 @@ +import numpy as np +import numpy.testing as npt +import pyrato.analytic.impedance as analytic + + +def test_eigenfreq_impedance_1d_real(): + L = 8/10 + zeta = np.ones(2) * 1e10 + c = 343.9 + + k = [0.1] + k_max = 1e3*2*np.pi/c + + k_ns = analytic.eigenfrequencies_rectangular_room_1d( + L, k, k_max, zeta + ) + + truth = np.array([ + 1e-6, + 2.149375000000000, + 4.298750000000000, + 6.448125000000000, + 8.597500000000000]) * 1e2 + + f_n = np.squeeze(c*k_ns.real/2/np.pi) + npt.assert_allclose(f_n, truth, rtol=1e-3, atol=1e-3) + + +def test_eigenfreq_impedance_1d_real_jac(): + L = 8/10 + zeta = np.ones(2) * 1e10 + c = 343.9 + + k = [0.1] + k_max = 1e3*2*np.pi/c + + k_ns = analytic.eigenfrequencies_rectangular_room_1d( + L, k, k_max, zeta, gradient=True + ) + + truth = np.array([ + 1e-6, + 2.149375000000000, + 4.298750000000000, + 6.448125000000000, + 8.597500000000000]) * 1e2 + + f_n = np.squeeze(c*k_ns.real/2/np.pi) + npt.assert_allclose(f_n, truth, rtol=1e-3, atol=1e-3) + + +def test_analytic_shoebox_eigenfreqs_impedance_multi_k(): + L = np.array([8, 5, 3])/10 + zetas = np.ones((3, 2)) * 1e10 + + c = 343.9 + + k_max = 1e3*2*np.pi/c + k = np.linspace(0, k_max*1.1, 2**10) + + k_ns, _ = analytic.eigenfrequencies_rectangular_room_impedance( + L, k, k_max, zetas + ) + + truth = np.array([ + 4.196434e-05, + 5.731666666666666, + 3.439000000000000, + 6.684214522124330, + 6.877999999999999, + 8.953149545147662, + 2.149375000000000, + 6.121422683363956, + 4.055432639142833, + 7.021291666666667, + 7.206018102296510, + 9.207534939841542, + 4.298750000000000, + 7.164583333333334, + 5.505086063132890, + 7.947199213576930, + 8.110865278285665, + 9.931673491425187, + 6.448125000000000, + 8.627300782597231, + 7.307874999999999, + 9.287466812506130, + 9.427894781743429, + 8.597500000000000, + 9.259790885867778]) * 1e2 + + f_n = np.mean(np.squeeze(c*k_ns.real/2/np.pi), axis=-1) + npt.assert_allclose(np.sort(f_n), np.sort(truth), atol=1e-3, rtol=1e-3) + + +def test_analytic_shoebox_eigenfreqs_impedance(): + L = np.array([8, 5, 3])/10 + zetas = np.ones((3, 2)) * 1e10 + + c = 343.9 + + k = [0.1] + k_max = 1e3*2*np.pi/c + + k_ns, _ = analytic.eigenfrequencies_rectangular_room_impedance( + L, k, k_max, zetas + ) + + truth = np.array([ + 4.440943e-06, + 5.731666666666666, + 3.439000000000000, + 6.684214522124330, + 6.877999999999999, + 8.953149545147662, + 2.149375000000000, + 6.121422683363956, + 4.055432639142833, + 7.021291666666667, + 7.206018102296510, + 9.207534939841542, + 4.298750000000000, + 7.164583333333334, + 5.505086063132890, + 7.947199213576930, + 8.110865278285665, + 9.931673491425187, + 6.448125000000000, + 8.627300782597231, + 7.307874999999999, + 9.287466812506130, + 9.427894781743429, + 8.597500000000000, + 9.259790885867778]) * 1e2 + + f_n = np.squeeze(c*k_ns.real/2/np.pi) + npt.assert_allclose(np.sort(f_n), np.sort(truth), atol=1e-3, rtol=1e-3) + + +def test_analytic_eigenfrequencies_impedance_cplx(): + L = np.array([8, 5, 3])/10 + zetas = np.ones((3, 2)) * 1e10 + + c = 343.9 + + k_max = 1e3*2*np.pi/c + k_min = 150*2*np.pi/c + k = np.linspace(k_min, k_max*1.1, 2**10) + + k_ns, _ = analytic.eigenfrequencies_rectangular_room_impedance( + L, k, k_max, zetas, only_normal=True + ) + + k_ns_x = np.loadtxt( + 'tests/data/analytic_impedance/k_ns_x.csv', + delimiter=',', + dtype=complex) + k_ns_y = np.loadtxt( + 'tests/data/analytic_impedance/k_ns_y.csv', + delimiter=',', + dtype=complex) + k_ns_z = np.loadtxt( + 'tests/data/analytic_impedance/k_ns_z.csv', + delimiter=',', + dtype=complex) + + npt.assert_allclose(k_ns[0], k_ns_x, rtol=1e-6) + npt.assert_allclose(k_ns[1], k_ns_y, rtol=1e-6) + npt.assert_allclose(k_ns[2], k_ns_z, rtol=1e-6) + + +def test_analytic_eigenfrequencies_impedance_zeta15(): + L = np.array([8, 5, 3])/10 + zetas = np.ones((3, 2)) * 15 + + c = 343.9 + + k_max = 1e3*2*np.pi/c + k_min = 150*2*np.pi/c + k = np.linspace(k_min, k_max*1.1, 2**10) + + k_ns, _ = analytic.eigenfrequencies_rectangular_room_impedance( + L, k, k_max, zetas, only_normal=True + ) + + k_ns_x = np.loadtxt( + 'tests/data/analytic_impedance/k_ns_x_zeta15.csv', + delimiter=',', + dtype=complex) + k_ns_y = np.loadtxt( + 'tests/data/analytic_impedance/k_ns_y_zeta15.csv', + delimiter=',', + dtype=complex) + k_ns_z = np.loadtxt( + 'tests/data/analytic_impedance/k_ns_z_zeta15.csv', + delimiter=',', + dtype=complex) + + npt.assert_allclose(k_ns[0], k_ns_x, rtol=1e-6) + npt.assert_allclose(k_ns[1], k_ns_y, rtol=1e-6) + npt.assert_allclose(k_ns[2], k_ns_z, rtol=1e-6) + + +def test_analytic_pressure_shoebox_impedance(): + L = np.array([8, 5, 3])/10 + zetas = np.ones((3, 2)) * 1e10 + + c = 343.9 + + k_max = 1e3*2*np.pi/c + k_min = 150*2*np.pi/c + k = np.linspace(k_min, k_max*1.1, 2**10) + + k_ns_x = np.loadtxt( + 'tests/data/analytic_impedance/k_ns_x.csv', + delimiter=',', + dtype=complex) + k_ns_y = np.loadtxt( + 'tests/data/analytic_impedance/k_ns_y.csv', + delimiter=',', + dtype=complex) + k_ns_z = np.loadtxt( + 'tests/data/analytic_impedance/k_ns_z.csv', + delimiter=',', + dtype=complex) + + k_ns = list((k_ns_x, k_ns_y, k_ns_z)) + + mode_indices = np.loadtxt( + 'tests/data/analytic_impedance/mode_indices.csv', + delimiter=',', + dtype=int) + + r_R = np.array([3.3, 1.6, 1.8])/10 + r_S = np.array([5.3, 3.6, 1.2])/10 + + p_x = analytic.pressure_modal_superposition( + k, k*c, k_ns, mode_indices, r_R, r_S, L, zetas) + + truth = np.loadtxt( + 'tests/data/analytic_impedance/p_x.csv', + delimiter=',', + dtype=complex) + + npt.assert_allclose(p_x, truth, atol=1e-6, rtol=1e-6) + + +def test_analytic_pressure_shoebox_impedance_multi_R(): + L = np.array([8, 5, 3])/10 + zetas = np.ones((3, 2)) * 1e10 + + c = 343.9 + + k_max = 1e3*2*np.pi/c + k_min = 150*2*np.pi/c + k = np.linspace(k_min, k_max*1.1, 2**10) + + k_ns_x = np.loadtxt( + 'tests/data/analytic_impedance/k_ns_x.csv', + delimiter=',', + dtype=complex) + k_ns_y = np.loadtxt( + 'tests/data/analytic_impedance/k_ns_y.csv', + delimiter=',', + dtype=complex) + k_ns_z = np.loadtxt( + 'tests/data/analytic_impedance/k_ns_z.csv', + delimiter=',', + dtype=complex) + + k_ns = list((k_ns_x, k_ns_y, k_ns_z)) + + mode_indices = np.loadtxt( + 'tests/data/analytic_impedance/mode_indices.csv', + delimiter=',', + dtype=int) + + r_R = np.array([ + [3.3, 1.6, 1.8], + [3.3, 1.6, 1.8]])/10 + r_S = np.array([5.3, 3.6, 1.2])/10 + + p_x = analytic.pressure_modal_superposition( + k, k*c, k_ns, mode_indices, r_R, r_S, L, zetas) + + truth = np.loadtxt( + 'tests/data/analytic_impedance/p_x.csv', + delimiter=',', + dtype=complex) + + truth = np.vstack((truth, truth)) + + npt.assert_allclose(p_x, truth, atol=1e-6, rtol=1e-6) + + +def test_analytic_pressure_shoebox_impedance_zeta15(): + L = np.array([8, 5, 3])/10 + zetas = np.ones((3, 2)) * 15 + + c = 343.9 + + k_max = 1e3*2*np.pi/c + k_min = 150*2*np.pi/c + k = np.linspace(k_min, k_max*1.1, 2**10) + + k_ns_x = np.loadtxt( + 'tests/data/analytic_impedance/k_ns_x_zeta15.csv', + delimiter=',', + dtype=complex) + k_ns_y = np.loadtxt( + 'tests/data/analytic_impedance/k_ns_y_zeta15.csv', + delimiter=',', + dtype=complex) + k_ns_z = np.loadtxt( + 'tests/data/analytic_impedance/k_ns_z_zeta15.csv', + delimiter=',', + dtype=complex) + + k_ns = list((k_ns_x, k_ns_y, k_ns_z)) + + mode_indices = np.loadtxt( + 'tests/data/analytic_impedance/mode_indices_zeta15.csv', + delimiter=',', + dtype=int) + + r_R = np.array([3.3, 1.6, 1.8])/10 + r_S = np.array([5.3, 3.6, 1.2])/10 + + p_x = analytic.pressure_modal_superposition( + k, k*c, k_ns, mode_indices, r_R, r_S, L, zetas) + + truth = np.loadtxt( + 'tests/data/analytic_impedance/p_x_zeta15.csv', + delimiter=',', + dtype=complex) + + npt.assert_allclose(p_x, truth, atol=1e-4, rtol=1e-4) + + +def test_analytic_shoebox_spec_impedance(): + L = np.array([8, 5, 3])/10 + zetas = np.ones((3, 2)) * 100 + + c = 343.9 + + r_R = np.array([3.3, 1.6, 1.8])/10 + r_S = np.array([5.3, 3.6, 1.2])/10 + + f_max = 1e3 + + samplingrate = 2200 + n_samples = 2**10 + + rir, spec, k_ns = analytic.rectangular_room_impedance( + L, + r_S, + r_R, + zetas, + f_max, + samplingrate, + c, + n_samples, + remove_cavity_mode=False) + + truth_rtf = np.load('tests/data/analytic_rtf_impedance.npy') + truth_rir = np.load('tests/data/analytic_rir_impedance.npy') + + npt.assert_allclose(spec, truth_rtf, atol=1e-2, rtol=1e-2) + npt.assert_allclose(rir, truth_rir, atol=1e-2, rtol=1e-2) + + +def test_analytic_shoebox_spec_impedance_no_cavity_mode(): + L = np.array([8, 5, 3])/10 + zetas = np.ones((3, 2)) * 100 + + c = 343.9 + + r_R = np.array([3.3, 1.6, 1.8])/10 + r_S = np.array([5.3, 3.6, 1.2])/10 + + f_max = 1e3 + + samplingrate = 2200 + n_samples = 2**10 + + rir, spec, k_ns = analytic.rectangular_room_impedance( + L, + r_S, + r_R, + zetas, + f_max, + samplingrate, + c, + n_samples, + remove_cavity_mode=True) + + truth_rtf = np.load('tests/data/analytic_rtf_impedance_no_cav.npy') + truth_rir = np.load('tests/data/analytic_rir_impedance_no_cav.npy') + + npt.assert_allclose(spec, truth_rtf, atol=1e-2, rtol=1e-2) + npt.assert_allclose(rir, truth_rir, atol=1e-2, rtol=1e-2) From f3f19b4ae30824587511a71fbe045dc744c96462 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Tue, 6 Sep 2022 13:29:50 +0200 Subject: [PATCH 31/66] remove monkeypatches from time shift, currently not required --- tests/test_dsp.py | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/tests/test_dsp.py b/tests/test_dsp.py index fb705d2..7643f54 100644 --- a/tests/test_dsp.py +++ b/tests/test_dsp.py @@ -325,10 +325,10 @@ def test_preprocessing_2D(): def test_preprocessing_time_shift_1D(monkeypatch): # Patch the RIR start finding to always return same number of samples - monkeypatch.setattr( - dsp, - "find_impulse_response_start", - mock_shift_samples_1d) + # monkeypatch.setattr( + # dsp, + # "find_impulse_response_start", + # mock_shift_samples_1d) rir = genfromtxt( os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), @@ -349,10 +349,10 @@ def test_preprocessing_time_shift_1D(monkeypatch): def test_preprocessing_time_shift_2D(monkeypatch): # Patch the RIR start finding to always return same number of samples - monkeypatch.setattr( - dsp, - "find_impulse_response_start", - mock_shift_samples_2d) + # monkeypatch.setattr( + # dsp, + # "find_impulse_response_start", + # mock_shift_samples_2d) rir = pf.Signal( genfromtxt( @@ -374,10 +374,10 @@ def test_preprocessing_time_shift_2D(monkeypatch): def test_preprocessing_time_shift_channel_independent_1D(monkeypatch): # Patch the RIR start finding to always return same number of samples - monkeypatch.setattr( - dsp, - "find_impulse_response_start", - mock_shift_samples_1d) + # monkeypatch.setattr( + # dsp, + # "find_impulse_response_start", + # mock_shift_samples_1d) rir = pf.Signal( genfromtxt( @@ -400,10 +400,10 @@ def test_preprocessing_time_shift_channel_independent_1D(monkeypatch): def test_preprocessing_time_shift_channel_independent_2D(monkeypatch): # Patch the RIR start finding to always return same number of samples - monkeypatch.setattr( - dsp, - "find_impulse_response_start", - mock_shift_samples_2d) + # monkeypatch.setattr( + # dsp, + # "find_impulse_response_start", + # mock_shift_samples_2d) rir = pf.Signal(genfromtxt( os.path.join(test_data_path, 'analytic_rir_psnr50_2D.csv'), From 1165de87dac6eb7802491946d8ee708a63de902e Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Tue, 6 Sep 2022 13:34:43 +0200 Subject: [PATCH 32/66] deprecation fixes --- pyrato/dsp.py | 2 +- pyrato/edc.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pyrato/dsp.py b/pyrato/dsp.py index 8d7413c..1b89381 100644 --- a/pyrato/dsp.py +++ b/pyrato/dsp.py @@ -43,7 +43,7 @@ def find_impulse_response_start( """ ir_squared = np.abs(impulse_response.time)**2 - mask_start = np.int(0.9*ir_squared.shape[-1]) + mask_start = int(0.9*ir_squared.shape[-1]) if noise_energy == 'auto': mask = np.arange(mask_start, ir_squared.shape[-1]) diff --git a/pyrato/edc.py b/pyrato/edc.py index 59669fd..1152840 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -676,7 +676,7 @@ def energy_decay_curve_chu_lundeby( for idx_channel in range(0, n_channels): intersection_time_idx = np.argmin(np.abs( time_vector - intersection_time[idx_channel])) - if noise_level == 'auto': + if type(noise_level) is str and noise_level == 'auto': p_square_at_intersection = dsp.estimate_noise_energy( energy_data.time[idx_channel], is_energy=True) else: From 29bf9640b39d3fc09e758f3c9adcd9a8cf0c1e43 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Tue, 6 Sep 2022 13:39:28 +0200 Subject: [PATCH 33/66] flake8 fixes --- pyrato/edc.py | 4 ++-- pyrato/roomacoustics.py | 5 +++-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/pyrato/edc.py b/pyrato/edc.py index 1152840..d76bee1 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -300,7 +300,7 @@ def energy_decay_curve_lundeby( channel_independent=False, normalize=True, plot=False): - """ Lundeby et al. [#]_ proposed a correction term to prevent the truncation + """Lundeby et al. [#]_ proposed a correction term to prevent the truncation error. The missing signal energy from truncation time to infinity is estimated and added to the truncated integral. @@ -915,7 +915,7 @@ def intersection_time_lundeby( old_crossing_point = 11+crossing_point loop_counter = 0 - while(True): + while True: # (7) ESTIMATE BACKGROUND LEVEL corresponding_decay = 10 # 5...10 dB idx_last_10_percent = np.round( diff --git a/pyrato/roomacoustics.py b/pyrato/roomacoustics.py index 2632bb4..14eb554 100644 --- a/pyrato/roomacoustics.py +++ b/pyrato/roomacoustics.py @@ -7,8 +7,9 @@ def reverberation_time_energy_decay_curve( energy_decay_curve, T='T20'): - """Estimate the reverberation time from a given energy decay curve according - to the ISO standard 3382 [#]_. + """Estimate the reverberation time from a given energy decay curve. + The linear regression is performed using least squares error minimization + according to the ISO standard 3382 [#]_. Parameters ---------- From e38d941e4fa6f0e1ecb80efde4aa238bcc285323 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Wed, 5 Oct 2022 15:26:12 +0200 Subject: [PATCH 34/66] deprecation warnings for dsp functions --- pyrato/dsp.py | 215 ++++++++++++++++++++--------- tests/test_deprecation_warnings.py | 38 +++++ tests/test_dsp.py | 32 +---- 3 files changed, 193 insertions(+), 92 deletions(-) create mode 100644 tests/test_deprecation_warnings.py diff --git a/pyrato/dsp.py b/pyrato/dsp.py index 1b89381..5ff7b27 100644 --- a/pyrato/dsp.py +++ b/pyrato/dsp.py @@ -10,97 +10,120 @@ def find_impulse_response_start( impulse_response, - threshold=20, - noise_energy='auto'): - """Find the first sample of an impulse response in a accordance with the - ISO standard ISO 3382 [#]_. The start sample is identified as the first - sample that varies significantly from the noise floor but still has a level - of at least 20 dB below the maximum of the impulse response. The function - further tries to consider oscillations before the time below the threshold - value. + threshold=20): + """Find the start sample of an impulse response. + The start sample is identified as the first sample which is below the + ``threshold`` level relative to the maximum level of the impulse response. + For room impulse responses, ISO 3382 [#]_ specifies a threshold of 20 dB. + This function is primary intended to be used when processing room impulse + responses. Parameters ---------- impulse_response : pyfar.Signal The impulse response - threshold : double, optional - Threshold according to ISO3382 in dB - + threshold : float, optional + The threshold level in dB, by default 20, which complies with ISO 3382. Returns ------- start_sample : numpy.ndarray, int Sample at which the impulse response starts - - Note - ---- - The function tries to estimate the SNR in the IR based on the signal energy - in the last 10 percent of the IR. - + Notes + ----- + The function tries to estimate the PSNR in the IR based on the signal + power in the last 10 percent of the IR. The automatic estimation may fail + if the noise spectrum is not white or the impulse response contains + non-linear distortions. If the PSNR is lower than the specified threshold, + the function will issue a warning. References ---------- .. [#] ISO 3382-1:2009-10, Acoustics - Measurement of the reverberation time of rooms with reference to other acoustical parameters. pp. 22 + Examples + -------- + Create a band-limited impulse shifted by 0.5 samples and estimate the + starting sample of the impulse and plot. + .. plot:: + >>> import pyfar as pf + >>> import numpy as np + >>> n_samples = 256 + >>> delay_samples = n_samples // 2 + 1/2 + >>> ir = pf.signals.impulse(n_samples) + >>> ir = pf.dsp.linear_phase(ir, delay_samples, unit='samples') + >>> start_samples = pf.dsp.find_impulse_response_start(ir) + >>> ax = pf.plot.time(ir, unit='ms', label='impulse response', dB=True) + >>> ax.axvline( + ... start_samples/ir.sampling_rate*1e3, + ... color='k', linestyle='-.', label='start sample') + >>> ax.axhline( + ... 20*np.log10(np.max(np.abs(ir.time)))-20, + ... color='k', linestyle=':', label='threshold') + >>> ax.legend() + Create a train of weighted impulses with levels below and above the + threshold, serving as a very abstract room impulse response. The starting + sample is identified as the last sample below the threshold relative to the + maximum of the impulse response. + .. plot:: + >>> import pyfar as pf + >>> import numpy as np + >>> n_samples = 64 + >>> delays = np.array([14, 22, 26, 30, 33]) + >>> amplitudes = np.array([-35, -22, -6, 0, -9], dtype=float) + >>> ir = pf.signals.impulse(n_samples, delays, 10**(amplitudes/20)) + >>> ir.time = np.sum(ir.time, axis=0) + >>> start_sample_est = pf.dsp.find_impulse_response_start( + ... ir, threshold=20) + >>> ax = pf.plot.time( + ... ir, dB=True, unit='samples', + ... label=f'peak samples: {delays}') + >>> ax.axvline( + ... start_sample_est, linestyle='-.', color='k', + ... label=f'ir start sample: {start_sample_est}') + >>> ax.axhline( + ... 20*np.log10(np.max(np.abs(ir.time)))-20, + ... color='k', linestyle=':', label='threshold') + >>> ax.legend() """ + warnings.warn( + "This function will be deprecated in version 0.5.0 " + "Use pyfar.dsp.find_impulse_response_start instead", + DeprecationWarning) + ir_squared = np.abs(impulse_response.time)**2 - mask_start = int(0.9*ir_squared.shape[-1]) + mask_start = int(0.9*impulse_response.n_samples) - if noise_energy == 'auto': - mask = np.arange(mask_start, ir_squared.shape[-1]) - noise = np.mean(np.take(ir_squared, mask, axis=-1), axis=-1) - else: - noise = noise_energy + mask = np.arange(mask_start, ir_squared.shape[-1]) + noise = np.mean(np.take(ir_squared, mask, axis=-1), axis=-1) max_sample = np.argmax(ir_squared, axis=-1) max_value = np.max(ir_squared, axis=-1) if np.any(max_value < 10**(threshold/10) * noise) or \ np.any(max_sample > mask_start): - raise ValueError( - "The SNR is lower than the defined threshold. Check " + warnings.warn( + "The SNR seems lower than the specified threshold value. Check " "if this is a valid impulse response with sufficient SNR.") - start_sample_shape = max_sample.shape - n_samples = ir_squared.shape[-1] - ir_squared = np.reshape(ir_squared, (-1, n_samples)) - n_channels = ir_squared.shape[0] - max_sample = np.reshape(max_sample, n_channels) - max_value = np.reshape(max_value, n_channels) - start_sample = max_sample.copy() - for idx in range(0, n_channels): + + for ch in np.ndindex(impulse_response.cshape): # Only look for the start sample if the maximum index is bigger than 0 - if start_sample[idx] > 0: - ir_before_max = ir_squared[idx, :max_sample[idx]+1] \ - / max_value[idx] - # Last value before peak lower than the peak/threshold - idx_last_below_thresh = np.argwhere( - ir_before_max < 10**(-threshold/10)) - if idx_last_below_thresh.size > 0: - start_sample[idx] = idx_last_below_thresh[-1] + if start_sample[ch] > 0: + # Check samples before maximum + ir_before_max = np.squeeze( + ir_squared[ch][:max_sample[ch]+1] / max_value[ch]) + # First sample above or at the threshold level + idx_first_above_thresh = np.where( + ir_before_max >= 10**(-threshold/10))[0] + if idx_first_above_thresh.size > 0: + # The start sample is the last sample below the threshold + start_sample[ch] = np.min(idx_first_above_thresh) - 1 else: - start_sample[idx] = 0 + start_sample[ch] = 0 warnings.warn( - 'No values below threshold found before the maximum value,\ - defaulting to 0') - - idx_6dB_above_threshold = np.argwhere( - ir_before_max[:start_sample[idx]+1] > - 10**((-threshold+6)/10)) - if idx_6dB_above_threshold.size > 0: - idx_6dB_above_threshold = int(idx_6dB_above_threshold[0]) - tmp = np.argwhere( - ir_before_max[:idx_6dB_above_threshold+1] < - 10**(-threshold/10)) - if tmp.size == 0: - start_sample[idx] = 0 - warnings.warn( - 'Oscillations detected in the impulse response. \ - No clear starting sample found, defaulting to 0') - else: - start_sample[idx] = tmp[-1] - - start_sample = np.reshape(start_sample, start_sample_shape) + f'No values below threshold found found for channel {ch}', + 'defaulting to 0') return np.squeeze(start_sample) @@ -451,3 +474,71 @@ def preprocess_rir( energy_data = pf.TimeData(energy_data, times) return energy_data, n_channels, data_shape + + +def filter_fractional_octave_bands( + signal, num_fractions, + freq_range=(20.0, 20e3), order=6): + """Apply a fractional octave filter to a signal. + Filter bank implementation using second order sections of butterworth + filters for increased numeric accuracy and stability. + + Parameters + ---------- + signal : ndarray + input signal to be filtered + num_fractions : integer + number of octave fractions + order : integer, optional + order of the butterworth filter + + Returns + ------- + signal_filtered : ndarray + Signal filtered into fractional octave bands. + """ + warnings.warn( + "This function will be deprecated in version 0.5.0 " + "Use pyfar.dsp.filter.fractional_octave_bands instead", + DeprecationWarning) + + return pf.dsp.filter.fractional_octave_bands( + signal, num_fractions, freq_range=freq_range, order=order) + + +def center_frequencies_octaves(): + """Return the octave center frequencies according to the IEC 61260:1:2014 + standard. + Returns + ------- + frequencies : ndarray, float + Octave center frequencies + """ + warnings.warn( + "This function will be deprecated in version 0.5.0 " + "Use pyfar.dsp.filter.fractional_octave_frequencies instead", + DeprecationWarning) + + nominal, exact = pf.dsp.filter.fractional_octave_frequencies( + 1, (20, 20e3), return_cutoff=False) + + return nominal, exact + + +def center_frequencies_third_octaves(): + """Return the third octave center frequencies according + to the ICE 61260:1:2014 standard. + Returns + ------- + frequencies : ndarray, float + third octave center frequencies + """ + warnings.warn( + "This function will be deprecated in version 0.5.0 " + "Use pyfar.dsp.filter.fractional_octave_frequencies instead", + DeprecationWarning) + + nominal, exact = pf.dsp.filter.fractional_octave_frequencies( + 3, (20, 20e3), return_cutoff=False) + + return nominal, exact diff --git a/tests/test_deprecation_warnings.py b/tests/test_deprecation_warnings.py new file mode 100644 index 0000000..8753e9e --- /dev/null +++ b/tests/test_deprecation_warnings.py @@ -0,0 +1,38 @@ +import pytest +import warnings +import pyfar as pf +import pyrato +import numpy as np + + +def test_warning_fractional_octave_bands(): + + with pytest.warns(DeprecationWarning, match='0.5.0'): + sig = pf.Signal([1, 2, 3], 44100) + pyrato.dsp.filter_fractional_octave_bands(sig, 1) + + +def test_warning_center_frequencies_thirds(): + + nominal = np.array([ + 25, 31.5, 40, 50, 63, 80, 100, 125, 160, + 200, 250, 315, 400, 500, 630, 800, 1000, + 1250, 1600, 2000, 2500, 3150, 4000, 5000, + 6300, 8000, 10000, 12500, 16000, 20000], dtype=float) + + with pytest.warns(DeprecationWarning, match='0.5.0'): + nom = pyrato.dsp.center_frequencies_third_octaves()[0] + + np.testing.assert_allclose(nom, nominal) + + +def test_warning_center_frequencies_octaves(): + with pytest.warns(DeprecationWarning, match='0.5.0'): + nom, exact = pyrato.dsp.center_frequencies_octaves() + + +def test_warning_start_ir(): + with pytest.warns(DeprecationWarning, match='0.5.0'): + + sig = pf.Signal([0, 0, 1, 0, 0], 44100) + pyrato.dsp.find_impulse_response_start(sig) diff --git a/tests/test_dsp.py b/tests/test_dsp.py index 7643f54..6ace112 100644 --- a/tests/test_dsp.py +++ b/tests/test_dsp.py @@ -7,10 +7,8 @@ import pytest import pyfar as pf from numpy import genfromtxt -from scipy.fftpack import shift - import pyrato.dsp as dsp -import pyrato + test_data_path = os.path.join(os.path.dirname(__file__), 'test_data') @@ -36,7 +34,7 @@ def test_start_ir_insufficient_snr(): ir_noise = ir + noise - with pytest.raises(ValueError): + with pytest.warns(match='SNR seems lower'): dsp.find_impulse_response_start(ir_noise) @@ -141,32 +139,6 @@ def test_start_room_impulse_response_shfted(monkeypatch): npt.assert_allclose(actual, expected) -def test_start_ir_thresh_invalid(): - n_samples = 2**10 - ir = np.zeros(n_samples) - - start_sample = 24 - ir[start_sample] = 1 - # ir[start_sample-4:start_sample] = 10**(-10/10) - ir[0:start_sample] = 10**(-5/10) - ir = pf.Signal(ir, 44100) - start_sample_est = dsp.find_impulse_response_start(ir, threshold=20) - assert start_sample_est == 0 - - -def test_start_ir_thresh_invalid_osci(): - n_samples = 2**10 - ir = np.zeros(n_samples) - - start_sample = 24 - ir[start_sample] = 1 - ir[start_sample-4:start_sample] = 10**(-30/10) - ir[0:start_sample-4] = 10**(-5/10) - ir = pf.Signal(ir, 44100) - start_sample_est = dsp.find_impulse_response_start(ir, threshold=20) - assert start_sample_est == 0 - - def test_max_ir(): n_samples = 2**10 ir = np.zeros(n_samples) From 3f5718d6dcdd5440331181ef14b415db79e735e9 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Wed, 5 Oct 2022 16:35:28 +0200 Subject: [PATCH 35/66] rename to reverberation_time_linear_regression --- pyrato/__init__.py | 5 +- pyrato/rap.py | 117 ++++++++++++++++++++++++++++++++++++++++ pyrato/roomacoustics.py | 42 +-------------- tests/test_rt.py | 34 +++++++++++- 4 files changed, 155 insertions(+), 43 deletions(-) create mode 100644 pyrato/rap.py diff --git a/pyrato/__init__.py b/pyrato/__init__.py index ceabc94..b00a8e8 100644 --- a/pyrato/__init__.py +++ b/pyrato/__init__.py @@ -5,7 +5,9 @@ __email__ = 'marco.berzborn@akustik.rwth-aachen.de' __version__ = '0.3.1' - +from .rap import ( + reverberation_time_linear_regression +) from .roomacoustics import ( reverberation_time_energy_decay_curve, energy_decay_curve_analytic, @@ -28,6 +30,7 @@ ) __all__ = [ + 'reverberation_time_linear_regression', 'reverberation_time_energy_decay_curve', 'schroeder_integration', 'energy_decay_curve_analytic', diff --git a/pyrato/rap.py b/pyrato/rap.py new file mode 100644 index 0000000..9127a12 --- /dev/null +++ b/pyrato/rap.py @@ -0,0 +1,117 @@ +"""Room acoustic parameters +""" + +import re +import numpy as np + + +def reverberation_time_linear_regression( + energy_decay_curve, T='T20', return_intercept=False): + """Estimate the reverberation time from a given energy decay curve. + The linear regression is performed using least squares error minimization + according to the ISO standard 3382 [#]_. + + Parameters + ---------- + energy_decay_curve : ndarray, double + Energy decay curve. The time needs to be the arrays last dimension. + times : ndarray, double + Time vector corresponding to each sample of the EDC. + T : 'T20', 'T30', 'T40', 'T50', 'T60', 'EDT', 'LDT' + Decay interval to be used for the reverberation time extrapolation. EDT + corresponds to the early decay time extrapolated from the interval + [0, -10] dB, LDT corresponds to the late decay time extrapolated from + the interval [-25, -35] dB. + normalize : bool, True + Normalize the EDC to the steady state energy level + + Returns + ------- + reverberation_time : double + The reverberation time + + References + ---------- + .. [#] ISO 3382, Acoustics - Measurement of the reverberation time of + rooms with reference to other acoustical parameters. + + Examples + -------- + + Estimate the reverberation time from an energy decay curve. + + >>> import numpy as np + >>> import pyfar as pf + >>> import pyrato as ra + >>> from pyrato.analytic import rectangular_room_rigid_walls + ... + >>> L = np.array([8, 5, 3])/10 + >>> source_pos = np.array([5, 3, 1.2])/10 + >>> receiver_pos = np.array([1, 1, 1.2])/10 + >>> rir, _ = rectangular_room_rigid_walls( + ... L, source_pos, receiver_pos, + ... reverberation_time=1, max_freq=1.5e3, n_samples=2**12, + ... speed_of_sound=343.9, samplingrate=3e3) + >>> rir = rir/rir.time.max() + ... + >>> awgn = pf.signals.noise( + ... rir.n_samples, rms=10**(-50/20), + ... sampling_rate=rir.sampling_rate) + >>> rir = rir + awgn + ... + >>> edc = ra.energy_decay_curve_chu_lundeby(rir) + >>> t_20 = ra.reverberation_time_linear_regression(edc, 'T20') + >>> t_20 + ... array([0.99526253]) + + """ + intervals = [20, 30, 40, 50, 60] + + if T == 'EDT': + upper = -0.1 + lower = -10.1 + elif T == 'LDT': + upper = -25. + lower = -35. + else: + try: + (int(re.findall(r'\d+', T)[0]) in intervals) + except IndexError: + raise ValueError( + "{} is not a valid interval for the regression.".format(T)) + + upper = -5 + lower = -np.double(re.findall(r'\d+', T)) + upper + + edc = energy_decay_curve.time.copy() + edc = edc.reshape((-1, energy_decay_curve.n_samples)) + times = energy_decay_curve.times + + amplitude_steady_state = edc[..., 0].copy() + edc /= np.atleast_2d(amplitude_steady_state).T + + edcs_db = 10*np.log10(np.abs(edc)) + + reverberation_times = np.zeros( + np.prod(energy_decay_curve.cshape), dtype=float) + intercepts = np.zeros( + np.prod(energy_decay_curve.cshape), dtype=float) + + for ch, edc_db in enumerate(edcs_db): + idx_upper = np.nanargmin(np.abs(upper - edc_db)) + idx_lower = np.nanargmin(np.abs(lower - edc_db)) + + A = np.vstack( + [times[idx_upper:idx_lower], np.ones(idx_lower - idx_upper)]).T + gradient, const = np.linalg.lstsq( + A, edc_db[..., idx_upper:idx_lower], rcond=None)[0] + + reverberation_times[ch] = -60 / gradient + intercepts[ch] = 10**(const/10) + + intercepts *= amplitude_steady_state + + if return_intercept is True: + return reverberation_times, intercepts + else: + return reverberation_times diff --git a/pyrato/roomacoustics.py b/pyrato/roomacoustics.py index 14eb554..ec63a4c 100644 --- a/pyrato/roomacoustics.py +++ b/pyrato/roomacoustics.py @@ -2,6 +2,7 @@ import re import numpy as np +from pyrato.rap import reverberation_time_linear_regression def reverberation_time_energy_decay_curve( @@ -65,46 +66,7 @@ def reverberation_time_energy_decay_curve( ... array([0.99526253]) """ - intervals = [20, 30, 40, 50, 60] - - if T == 'EDT': - upper = -0.1 - lower = -10.1 - elif T == 'LDT': - upper = -25. - lower = -35. - else: - try: - (int(re.findall(r'\d+', T)[0]) in intervals) - except IndexError: - raise ValueError( - "{} is not a valid interval for the regression.".format(T)) - - upper = -5 - lower = -np.double(re.findall(r'\d+', T)) + upper - - edc = energy_decay_curve.time.copy() - edc = edc.reshape((-1, energy_decay_curve.n_samples)) - times = energy_decay_curve.times - edc /= np.atleast_2d(edc[..., 0]).T - - edcs_db = 10*np.log10(np.abs(edc)) - - reverberation_times = np.zeros( - np.prod(energy_decay_curve.cshape), dtype=float) - - for ch, edc_db in enumerate(edcs_db): - idx_upper = np.nanargmin(np.abs(upper - edc_db)) - idx_lower = np.nanargmin(np.abs(lower - edc_db)) - - A = np.vstack( - [times[idx_upper:idx_lower], np.ones(idx_lower - idx_upper)]).T - gradient, const = np.linalg.lstsq( - A, edc_db[..., idx_upper:idx_lower], rcond=None)[0] - - reverberation_times[ch] = -60 / gradient - - return reverberation_times + return reverberation_time_energy_decay_curve(energy_decay_curve, T) def energy_decay_curve_analytic( diff --git a/tests/test_rt.py b/tests/test_rt.py index 4013709..2d59d0f 100644 --- a/tests/test_rt.py +++ b/tests/test_rt.py @@ -19,11 +19,41 @@ def test_rt_from_edc(tx): m = -60 edc = times * m edc_exp = pf.TimeData(10**(edc/10), times) - RT_est = ra.reverberation_time_energy_decay_curve( + RT_est = ra.reverberation_time_linear_regression( edc_exp, T=tx) npt.assert_allclose(RT_est, 1.) +@pytest.mark.parametrize( + 'tx', ['T20', 'T30', 'T40', 'T50', 'T60', 'LDT', 'EDT']) +def test_rt_from_edc_mulitchannel(tx): + times = np.linspace(0, 1.5, 2**9) + Ts = np.array([1, 2, 1.5]) + m = -60 + edc = np.atleast_2d(m/Ts).T @ np.atleast_2d(times) + edc_exp = pf.TimeData(10**(edc/10), times) + RT_est = ra.reverberation_time_linear_regression( + edc_exp, T=tx) + npt.assert_allclose(RT_est, Ts) + + +@pytest.mark.parametrize( + 'tx', ['T20', 'T30', 'T40', 'T50', 'T60', 'LDT', 'EDT']) +def test_rt_from_edc_mulitchannel_amplitude(tx): + times = np.linspace(0, 1.5, 2**9) + Ts = np.array([1, 2, 1.5]) + As = np.array([0, 3, 6]) + m = -60 + edc = np.atleast_2d(m/Ts).T @ np.atleast_2d(times) + edc += np.atleast_2d(As).T + + edc_exp = pf.TimeData(10**(edc/10), times) + RT_est, A_est = ra.reverberation_time_linear_regression( + edc_exp, T=tx, return_intercept=True) + npt.assert_allclose(RT_est, Ts) + npt.assert_allclose(A_est, 10**(As/10)) + + def test_rt_from_edc_error(): times = np.linspace(0, 1.5, 2**9) m = -60 @@ -32,4 +62,4 @@ def test_rt_from_edc_error(): T = 'Bla' with raises(ValueError, match='is not a valid interval.'): - ra.reverberation_time_energy_decay_curve(edc_exp, T=T) + ra.reverberation_time_linear_regression(edc_exp, T=T) From 916e4d9a91634fd5f0f94a81e8b065d234437868 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Wed, 5 Oct 2022 16:39:44 +0200 Subject: [PATCH 36/66] try to avoid merge conflicts with main --- pyrato/dsp.py | 136 +++++++++++++++++++++++++------------------------- 1 file changed, 68 insertions(+), 68 deletions(-) diff --git a/pyrato/dsp.py b/pyrato/dsp.py index 5ff7b27..17ba55c 100644 --- a/pyrato/dsp.py +++ b/pyrato/dsp.py @@ -293,6 +293,74 @@ def time_shift(signal, shift, circular_shift=True, unit='samples'): return shifted +def center_frequencies_octaves(): + """Return the octave center frequencies according to the IEC 61260:1:2014 + standard. + Returns + ------- + frequencies : ndarray, float + Octave center frequencies + """ + warnings.warn( + "This function will be deprecated in version 0.5.0 " + "Use pyfar.dsp.filter.fractional_octave_frequencies instead", + DeprecationWarning) + + nominal, exact = pf.dsp.filter.fractional_octave_frequencies( + 1, (20, 20e3), return_cutoff=False) + + return nominal, exact + + +def center_frequencies_third_octaves(): + """Return the third octave center frequencies according + to the ICE 61260:1:2014 standard. + Returns + ------- + frequencies : ndarray, float + third octave center frequencies + """ + warnings.warn( + "This function will be deprecated in version 0.5.0 " + "Use pyfar.dsp.filter.fractional_octave_frequencies instead", + DeprecationWarning) + + nominal, exact = pf.dsp.filter.fractional_octave_frequencies( + 3, (20, 20e3), return_cutoff=False) + + return nominal, exact + + +def filter_fractional_octave_bands( + signal, num_fractions, + freq_range=(20.0, 20e3), order=6): + """Apply a fractional octave filter to a signal. + Filter bank implementation using second order sections of butterworth + filters for increased numeric accuracy and stability. + + Parameters + ---------- + signal : ndarray + input signal to be filtered + num_fractions : integer + number of octave fractions + order : integer, optional + order of the butterworth filter + + Returns + ------- + signal_filtered : ndarray + Signal filtered into fractional octave bands. + """ + warnings.warn( + "This function will be deprecated in version 0.5.0 " + "Use pyfar.dsp.filter.fractional_octave_bands instead", + DeprecationWarning) + + return pf.dsp.filter.fractional_octave_bands( + signal, num_fractions, freq_range=freq_range, order=order) + + def estimate_noise_energy( data, interval=[0.9, 1.0], @@ -474,71 +542,3 @@ def preprocess_rir( energy_data = pf.TimeData(energy_data, times) return energy_data, n_channels, data_shape - - -def filter_fractional_octave_bands( - signal, num_fractions, - freq_range=(20.0, 20e3), order=6): - """Apply a fractional octave filter to a signal. - Filter bank implementation using second order sections of butterworth - filters for increased numeric accuracy and stability. - - Parameters - ---------- - signal : ndarray - input signal to be filtered - num_fractions : integer - number of octave fractions - order : integer, optional - order of the butterworth filter - - Returns - ------- - signal_filtered : ndarray - Signal filtered into fractional octave bands. - """ - warnings.warn( - "This function will be deprecated in version 0.5.0 " - "Use pyfar.dsp.filter.fractional_octave_bands instead", - DeprecationWarning) - - return pf.dsp.filter.fractional_octave_bands( - signal, num_fractions, freq_range=freq_range, order=order) - - -def center_frequencies_octaves(): - """Return the octave center frequencies according to the IEC 61260:1:2014 - standard. - Returns - ------- - frequencies : ndarray, float - Octave center frequencies - """ - warnings.warn( - "This function will be deprecated in version 0.5.0 " - "Use pyfar.dsp.filter.fractional_octave_frequencies instead", - DeprecationWarning) - - nominal, exact = pf.dsp.filter.fractional_octave_frequencies( - 1, (20, 20e3), return_cutoff=False) - - return nominal, exact - - -def center_frequencies_third_octaves(): - """Return the third octave center frequencies according - to the ICE 61260:1:2014 standard. - Returns - ------- - frequencies : ndarray, float - third octave center frequencies - """ - warnings.warn( - "This function will be deprecated in version 0.5.0 " - "Use pyfar.dsp.filter.fractional_octave_frequencies instead", - DeprecationWarning) - - nominal, exact = pf.dsp.filter.fractional_octave_frequencies( - 3, (20, 20e3), return_cutoff=False) - - return nominal, exact From 04b1aa3378253d9f44ff2e50ea271049a01cd902 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Wed, 5 Oct 2022 16:50:42 +0200 Subject: [PATCH 37/66] remove support for python 3.7 --- .circleci/config.yml | 2 -- CONTRIBUTING.rst | 2 +- README.rst | 2 +- 3 files changed, 2 insertions(+), 4 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 80c5652..9427f2e 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -128,7 +128,6 @@ workflows: matrix: parameters: version: - - "3.7" - "3.8" - "3.9" - "3.10" @@ -156,7 +155,6 @@ workflows: matrix: parameters: version: - - "3.7" - "3.8" - "3.9" - "3.10" diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst index adb1139..9405699 100644 --- a/CONTRIBUTING.rst +++ b/CONTRIBUTING.rst @@ -102,7 +102,7 @@ Before you submit a pull request, check that it meets these guidelines: 2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst. -3. The pull request should work for Python 3.5 to 3.9. Check +3. The pull request should work for Python 3.8 to 3.10. Check https://app.travis-ci.com/github/mberz/pyrato/pull_requests and make sure that the tests pass for all supported Python versions. diff --git a/README.rst b/README.rst index de2b108..7febc89 100644 --- a/README.rst +++ b/README.rst @@ -13,7 +13,7 @@ Use pip to install pyrato $ pip install pyrato -(Requires Python >= 3.5) +(Requires Python >= 3.8) Getting Started From a7c8187e90e1d5b6d8c417b06a7c18602bc56a14 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Wed, 5 Oct 2022 16:52:05 +0200 Subject: [PATCH 38/66] remove 3.7 support from setup --- setup.py | 1 - 1 file changed, 1 deletion(-) diff --git a/setup.py b/setup.py index 51e868b..a3c3db5 100644 --- a/setup.py +++ b/setup.py @@ -40,7 +40,6 @@ 'License :: OSI Approved :: MIT License', 'Natural Language :: English', 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', From d6d0b247582be08f7e8561ef493e8d0d3045a105 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Wed, 5 Oct 2022 16:53:01 +0200 Subject: [PATCH 39/66] fix typo --- pyrato/roomacoustics.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyrato/roomacoustics.py b/pyrato/roomacoustics.py index ec63a4c..2b37897 100644 --- a/pyrato/roomacoustics.py +++ b/pyrato/roomacoustics.py @@ -66,7 +66,7 @@ def reverberation_time_energy_decay_curve( ... array([0.99526253]) """ - return reverberation_time_energy_decay_curve(energy_decay_curve, T) + return reverberation_time_linear_regression(energy_decay_curve, T) def energy_decay_curve_analytic( From c4a621254939dfa284d3bce49186fe7aa2dbe111 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Wed, 5 Oct 2022 16:53:37 +0200 Subject: [PATCH 40/66] remove unused import --- pyrato/roomacoustics.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pyrato/roomacoustics.py b/pyrato/roomacoustics.py index 2b37897..1dff510 100644 --- a/pyrato/roomacoustics.py +++ b/pyrato/roomacoustics.py @@ -1,6 +1,5 @@ # -*- coding: utf-8 -*- -import re import numpy as np from pyrato.rap import reverberation_time_linear_regression From 218a1eb33ab7f512a168fb9e69499ae8d836869f Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Wed, 5 Oct 2022 16:57:29 +0200 Subject: [PATCH 41/66] don't pass dtype to TimeData constructor --- pyrato/dsp.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/pyrato/dsp.py b/pyrato/dsp.py index 1b89381..3cb707b 100644 --- a/pyrato/dsp.py +++ b/pyrato/dsp.py @@ -254,8 +254,7 @@ def time_shift(signal, shift, circular_shift=True, unit='samples'): shifted = pf.TimeData( shifted.time, shifted.times, - comment=shifted.comment, - dtype=shifted.dtype) + comment=shifted.comment) shifted = shifted.flatten() shift_samples = shift_samples.flatten() From a328b80a34f0c453f0442374242e8236a24dff24 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Wed, 5 Oct 2022 17:08:37 +0200 Subject: [PATCH 42/66] add deprecation warning --- pyrato/roomacoustics.py | 6 ++++++ tests/test_deprecation_warnings.py | 6 ++++++ 2 files changed, 12 insertions(+) diff --git a/pyrato/roomacoustics.py b/pyrato/roomacoustics.py index 1dff510..64f5fea 100644 --- a/pyrato/roomacoustics.py +++ b/pyrato/roomacoustics.py @@ -2,6 +2,7 @@ import numpy as np from pyrato.rap import reverberation_time_linear_regression +import warnings def reverberation_time_energy_decay_curve( @@ -65,6 +66,11 @@ def reverberation_time_energy_decay_curve( ... array([0.99526253]) """ + warnings.warn( + "This function will be deprecated in version 0.5.0 " + "Use pyrato.reverberation_time_linear_regression instead", + DeprecationWarning) + return reverberation_time_linear_regression(energy_decay_curve, T) diff --git a/tests/test_deprecation_warnings.py b/tests/test_deprecation_warnings.py index 8753e9e..c8c60b0 100644 --- a/tests/test_deprecation_warnings.py +++ b/tests/test_deprecation_warnings.py @@ -36,3 +36,9 @@ def test_warning_start_ir(): sig = pf.Signal([0, 0, 1, 0, 0], 44100) pyrato.dsp.find_impulse_response_start(sig) + + +def test_warning_rt_edc(): + with pytest.warns(DeprecationWarning, match='0.5.0'): + edc = pf.TimeData([-5., -60.], [0.1, 1]) + pyrato.reverberation_time_energy_decay_curve(edc) From 60b4a1a976bfd0729ed2b36466dcda183871cbff Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Wed, 5 Oct 2022 18:16:35 +0200 Subject: [PATCH 43/66] use array concatenation instead of passing nested lists to np.nanmax --- pyrato/edc.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/pyrato/edc.py b/pyrato/edc.py index d76bee1..d97d05c 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -920,10 +920,11 @@ def intersection_time_lundeby( corresponding_decay = 10 # 5...10 dB idx_last_10_percent = np.round( time_window_data_current_channel.shape[-1]*0.9) - idx_10dB_below_crosspoint = np.nanmax([1, np.round( - ((crossing_point - - corresponding_decay / slope[1]) - * sampling_rate / n_samples_per_block))]) + + t_block = n_samples_per_block / sampling_rate + rel_decay = corresponding_decay / slope[1] + idx_10dB_below_crosspoint = np.nanmax( + np.r_[1, np.round(((crossing_point - rel_decay) / t_block))]) noise_estimation_current_channel = np.nanmean( time_window_data_current_channel[int(np.nanmin( From ac74c2fc9715f7627da9af6303a897862b823cf5 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 10 Oct 2022 17:13:31 +0200 Subject: [PATCH 44/66] Refactor channel indexing for dsp functions - Use ndindex instead of reshaping - preprocess does no longer return number of channels and shape --- pyrato/dsp.py | 19 ++++--------------- tests/test_dsp.py | 6 +++--- 2 files changed, 7 insertions(+), 18 deletions(-) diff --git a/pyrato/dsp.py b/pyrato/dsp.py index 7d64a06..dc11b3d 100644 --- a/pyrato/dsp.py +++ b/pyrato/dsp.py @@ -279,16 +279,12 @@ def time_shift(signal, shift, circular_shift=True, unit='samples'): shifted.times, comment=shifted.comment) - shifted = shifted.flatten() - shift_samples = shift_samples.flatten() - for ch in range(shifted.cshape[0]): + for ch in np.ndindex(shifted.cshape): if shift[ch] < 0: shifted.time[ch, shift_samples[ch]:] = np.nan else: shifted.time[ch, :shift_samples[ch]] = np.nan - shifted = shifted.reshape(signal.cshape) - return shifted @@ -387,7 +383,7 @@ def estimate_noise_energy( data, is_energy=is_energy, shift=False, - channel_independent=False)[0] + channel_independent=False) return _estimate_noise_energy(energy_data.time, interval=interval) @@ -506,18 +502,11 @@ def preprocess_rir( ------- energy_data : ndarray, double The preprocessed RIR - n_channels : integer - The number of channels of the RIR - data_shape : list, integer - The original data shape. """ times = data.times n_channels = np.prod(data.cshape) - data_shape = list(data.cshape) - data = data.reshape((-1,)) - if shift: rir_start_idx = find_impulse_response_start(data) @@ -526,7 +515,7 @@ def preprocess_rir( else: min_shift = np.amin(rir_start_idx) shift_samples = np.asarray( - -min_shift * np.ones(n_channels), dtype=int) + -min_shift * np.ones(data.cshape), dtype=int) result = time_shift( data, shift_samples, circular_shift=False) @@ -540,4 +529,4 @@ def preprocess_rir( energy_data = pf.TimeData(energy_data, times) - return energy_data, n_channels, data_shape + return energy_data diff --git a/tests/test_dsp.py b/tests/test_dsp.py index 6ace112..810f349 100644 --- a/tests/test_dsp.py +++ b/tests/test_dsp.py @@ -287,7 +287,7 @@ def test_preprocessing_2D(): rir, is_energy=False, shift=False, - channel_independent=False)[0] + channel_independent=False) expected = genfromtxt( os.path.join(test_data_path, 'preprocessing_2D.csv'), @@ -340,7 +340,7 @@ def test_preprocessing_time_shift_2D(monkeypatch): rir, is_energy=False, shift=True, - channel_independent=False)[0] + channel_independent=False) npt.assert_allclose(actual.time, expected) @@ -390,7 +390,7 @@ def test_preprocessing_time_shift_channel_independent_2D(monkeypatch): rir, is_energy=False, shift=True, - channel_independent=True)[0] + channel_independent=True) npt.assert_allclose(actual.time, expected) From f5f384b6881d055184ec9d818aa54e800a3da4c5 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 10 Oct 2022 17:33:14 +0200 Subject: [PATCH 45/66] Use ndindex instead of reshaping the data in all edc calculation functions --- pyrato/edc.py | 93 +++++++++++++++++++++++++-------------------------- 1 file changed, 46 insertions(+), 47 deletions(-) diff --git a/pyrato/edc.py b/pyrato/edc.py index d97d05c..503f1ff 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -241,7 +241,7 @@ def energy_decay_curve_truncation( >>> ax.legend() """ - energy_data, n_channels, data_shape = dsp.preprocess_rir( + energy_data = dsp.preprocess_rir( data, is_energy=is_energy, shift=time_shift, @@ -259,13 +259,13 @@ def energy_decay_curve_truncation( intersection_time_idx = np.rint(intersection_time * data.sampling_rate) - energy_decay_curve = np.zeros([n_channels, n_samples]) - for idx_channel in range(0, n_channels): + energy_decay_curve = np.zeros([*data.cshape, n_samples]) + for ch in np.ndindex(data.cshape): energy_decay_curve[ - idx_channel, :int(intersection_time_idx[idx_channel])] = \ + ch, :int(intersection_time_idx[ch])] = \ _schroeder_integration( energy_data.time[ - idx_channel, :int(intersection_time_idx[idx_channel])], + ch, :int(intersection_time_idx[ch])], is_energy=True) if normalize: @@ -288,7 +288,7 @@ def energy_decay_curve_truncation( ax.set_ylim(-65, 5) ax.legend() - return edc.reshape(data.cshape) + return edc def energy_decay_curve_lundeby( @@ -373,7 +373,7 @@ def energy_decay_curve_lundeby( """ - energy_data, n_channels, data_shape = dsp.preprocess_rir( + energy_data = dsp.preprocess_rir( data, is_energy=is_energy, shift=time_shift, @@ -392,25 +392,25 @@ def energy_decay_curve_lundeby( plot=False) time_vector = data.times - energy_decay_curve = np.zeros([n_channels, n_samples]) + energy_decay_curve = np.zeros([*data.cshape, n_samples]) - for idx_channel in range(0, n_channels): + for ch in np.ndindex(data.cshape): intersection_time_idx = np.argmin( - np.abs(time_vector - intersection_time[idx_channel])) - p_square_at_intersection = noise_estimation[idx_channel] + np.abs(time_vector - intersection_time[ch])) + p_square_at_intersection = noise_estimation[ch] # Calculate correction term according to DIN EN ISO 3382 # TO-DO: check reference! correction = (p_square_at_intersection - * late_reverberation_time[idx_channel] + * late_reverberation_time[ch] * (1 / (6*np.log(10))) * sampling_rate) - energy_decay_curve[idx_channel, :intersection_time_idx] = \ + energy_decay_curve[ch, :intersection_time_idx] = \ _schroeder_integration( - energy_data.time[idx_channel, :intersection_time_idx], + energy_data.time[ch, :intersection_time_idx], is_energy=True) - energy_decay_curve[idx_channel] += correction + energy_decay_curve[ch] += correction if normalize: # Normalize the EDC... @@ -434,7 +434,7 @@ def energy_decay_curve_lundeby( ax.set_ylim(-65, 5) ax.legend() - return edc.reshape(data.cshape) + return edc def energy_decay_curve_chu( @@ -514,8 +514,7 @@ def energy_decay_curve_chu( >>> ax.legend() """ - cshape = data.cshape - energy_data, n_channels, data_shape = dsp.preprocess_rir( + energy_data = dsp.preprocess_rir( data, is_energy=is_energy, shift=time_shift, @@ -540,8 +539,8 @@ def energy_decay_curve_chu( mask = edc.time <= 2*np.finfo(float).eps if np.any(mask): first_zero = np.nanargmax(mask, axis=-1) - for channel in range(n_channels): - edc.time[channel, first_zero[channel]:] = np.nan + for ch in np.ndindex(edc.cshape): + edc.time[ch, first_zero[ch]:] = np.nan if plot: plt.figure(figsize=(15, 3)) @@ -556,7 +555,7 @@ def energy_decay_curve_chu( pf.plot.time(edc, dB=True, log_prefix=10) plt.ylabel('EDC in dB') - return edc.reshape(cshape) + return edc def energy_decay_curve_chu_lundeby( @@ -649,7 +648,7 @@ def energy_decay_curve_chu_lundeby( """ - energy_data, n_channels, data_shape = dsp.preprocess_rir( + energy_data = dsp.preprocess_rir( data, is_energy=is_energy, shift=time_shift, @@ -671,28 +670,28 @@ def energy_decay_curve_chu_lundeby( plot=False) time_vector = data.times - energy_decay_curve = np.zeros([n_channels, n_samples]) + energy_decay_curve = np.zeros([*data.cshape, n_samples]) - for idx_channel in range(0, n_channels): + for ch in np.ndindex(data.cshape): intersection_time_idx = np.argmin(np.abs( - time_vector - intersection_time[idx_channel])) + time_vector - intersection_time[ch])) if type(noise_level) is str and noise_level == 'auto': p_square_at_intersection = dsp.estimate_noise_energy( - energy_data.time[idx_channel], is_energy=True) + energy_data.time[ch], is_energy=True) else: - p_square_at_intersection = noise_level[idx_channel] + p_square_at_intersection = noise_level[ch] # calculate correction term according to DIN EN ISO 3382 correction = (p_square_at_intersection - * late_reverberation_time[idx_channel] + * late_reverberation_time[ch] * (1 / (6*np.log(10))) * data.sampling_rate) - energy_decay_curve[idx_channel, :intersection_time_idx] = \ + energy_decay_curve[ch, :intersection_time_idx] = \ _schroeder_integration( - subtraction.time[idx_channel, :intersection_time_idx], + subtraction.time[ch, :intersection_time_idx], is_energy=True) - energy_decay_curve[idx_channel] += correction + energy_decay_curve[ch] += correction if normalize: # Normalize the EDC... @@ -715,7 +714,7 @@ def energy_decay_curve_chu_lundeby( ax.set_ylim(-65, 5) ax.legend() - return edc.reshape(data.cshape) + return edc def intersection_time_lundeby( @@ -812,7 +811,7 @@ def intersection_time_lundeby( # Dynamic range 10 ... 20 dB use_dyn_range_for_regression = 20 - energy_data, n_channels, data_shape = dsp.preprocess_rir( + energy_data = dsp.preprocess_rir( data, is_energy=is_energy, shift=time_shift, @@ -841,18 +840,18 @@ def intersection_time_lundeby( noise_estimation = initial_noise_power.copy() # (3) REGRESSION - reverberation_time = np.zeros(n_channels) - noise_level = np.zeros(n_channels) - intersection_time = np.zeros(n_channels) - noise_peak_level = np.zeros(n_channels) + reverberation_time = np.zeros(data.cshape, data.time.dtype) + noise_level = np.zeros(data.cshape, data.time.dtype) + intersection_time = np.zeros(data.cshape, data.time.dtype) + noise_peak_level = np.zeros(data.cshape, data.time.dtype) - for idx_channel in range(0, n_channels): - time_window_data_current_channel = time_window_data[idx_channel] + for ch in np.ndindex(data.cshape): + time_window_data_current_channel = time_window_data[ch] start_idx = np.nanargmax(time_window_data_current_channel, axis=-1) try: stop_idx = (np.argwhere(10*np.log10( time_window_data_current_channel[start_idx+1:-1]) > - (10*np.log10(noise_estimation[idx_channel]) + + (10*np.log10(noise_estimation[ch]) + dB_above_noise))[-1, 0] + start_idx) except IndexError: raise Exception( @@ -887,7 +886,7 @@ def intersection_time_lundeby( # (4) PRELIMINARY CROSSING POINT crossing_point = \ - (10*np.log10(noise_estimation[idx_channel]) - slope[0]) / slope[1] + (10*np.log10(noise_estimation[ch]) - slope[0]) / slope[1] preliminary_crossing_point = crossing_point # (5) NEW LOCAL TIME INTERVAL LENGTH @@ -906,7 +905,7 @@ def intersection_time_lundeby( time_window_data_current_channel, \ time_vector_window_current_channel, \ time_vector_current_channel = dsp._smooth_rir( - energy_data[idx_channel], sampling_rate, window_time) + energy_data[ch], sampling_rate, window_time) time_window_data_current_channel = np.squeeze( time_window_data_current_channel) idx_max = np.nanargmax(time_window_data_current_channel) @@ -983,10 +982,10 @@ def intersection_time_lundeby( "Lundeby algorithm was terminated after 30 iterations.") break - reverberation_time[idx_channel] = -60/slope[1] - noise_level[idx_channel] = noise_estimation_current_channel - intersection_time[idx_channel] = crossing_point - noise_peak_level[idx_channel] = 10 * np.log10( + reverberation_time[ch] = -60/slope[1] + noise_level[ch] = noise_estimation_current_channel + intersection_time[ch] = crossing_point + noise_peak_level[ch] = 10 * np.log10( np.nanmax(time_window_data_current_channel[int(np.nanmin( [idx_last_10_percent, idx_10dB_below_crosspoint])):])) @@ -1023,7 +1022,7 @@ def intersection_time_lundeby( label='regression') plt.plot( preliminary_crossing_point, - 10*np.log10(noise_estimation[idx_channel]), + 10*np.log10(noise_estimation[ch]), marker='o', color='C3', label='preliminary crosspoint') From 0f17169d394e77cc90916680514726becb401b9a Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 10 Oct 2022 17:34:31 +0200 Subject: [PATCH 46/66] use ndindex in rt calculation instead of reshaping the input data final reshape to the original shape was also missing --- pyrato/rap.py | 20 +++++--------------- tests/test_rt.py | 11 ++++++----- 2 files changed, 11 insertions(+), 20 deletions(-) diff --git a/pyrato/rap.py b/pyrato/rap.py index 9127a12..06acb0c 100644 --- a/pyrato/rap.py +++ b/pyrato/rap.py @@ -1,6 +1,5 @@ """Room acoustic parameters """ - import re import numpy as np @@ -83,21 +82,14 @@ def reverberation_time_linear_regression( upper = -5 lower = -np.double(re.findall(r'\d+', T)) + upper - edc = energy_decay_curve.time.copy() - edc = edc.reshape((-1, energy_decay_curve.n_samples)) + edcs_db = 10*np.log10(np.abs(energy_decay_curve.time)) times = energy_decay_curve.times - amplitude_steady_state = edc[..., 0].copy() - edc /= np.atleast_2d(amplitude_steady_state).T - - edcs_db = 10*np.log10(np.abs(edc)) + reverberation_times = np.zeros(energy_decay_curve.cshape, dtype=float) + intercepts = np.zeros(energy_decay_curve.cshape, dtype=float) - reverberation_times = np.zeros( - np.prod(energy_decay_curve.cshape), dtype=float) - intercepts = np.zeros( - np.prod(energy_decay_curve.cshape), dtype=float) - - for ch, edc_db in enumerate(edcs_db): + for ch in np.ndindex(energy_decay_curve.cshape): + edc_db = edcs_db[ch] idx_upper = np.nanargmin(np.abs(upper - edc_db)) idx_lower = np.nanargmin(np.abs(lower - edc_db)) @@ -109,8 +101,6 @@ def reverberation_time_linear_regression( reverberation_times[ch] = -60 / gradient intercepts[ch] = 10**(const/10) - intercepts *= amplitude_steady_state - if return_intercept is True: return reverberation_times, intercepts else: diff --git a/tests/test_rt.py b/tests/test_rt.py index 2d59d0f..bb2ca57 100644 --- a/tests/test_rt.py +++ b/tests/test_rt.py @@ -40,12 +40,13 @@ def test_rt_from_edc_mulitchannel(tx): @pytest.mark.parametrize( 'tx', ['T20', 'T30', 'T40', 'T50', 'T60', 'LDT', 'EDT']) def test_rt_from_edc_mulitchannel_amplitude(tx): - times = np.linspace(0, 1.5, 2**9) - Ts = np.array([1, 2, 1.5]) - As = np.array([0, 3, 6]) + times = np.linspace(0, 5/2, 2**9) + Ts = np.array([[1, 2, 1.5], [3, 4, 5]]) + As = np.array([[0, 3, 6], [1, 1, 1]]) m = -60 - edc = np.atleast_2d(m/Ts).T @ np.atleast_2d(times) - edc += np.atleast_2d(As).T + edc = np.zeros((*Ts.shape, times.size)) + for idx in np.ndindex(Ts.shape): + edc[idx] = As[idx] + m*times/Ts[idx] edc_exp = pf.TimeData(10**(edc/10), times) RT_est, A_est = ra.reverberation_time_linear_regression( From 21ed4725fe5c6ebd519ef6acdbdfc99688452c55 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Thu, 13 Oct 2022 14:10:48 +0200 Subject: [PATCH 47/66] internally use pyfar's start finding function, require pyfar>=0.5.0 --- pyrato/dsp.py | 38 +------------------------------------- requirements_dev.txt | 2 +- setup.py | 2 +- 3 files changed, 3 insertions(+), 39 deletions(-) diff --git a/pyrato/dsp.py b/pyrato/dsp.py index dc11b3d..d6d6268 100644 --- a/pyrato/dsp.py +++ b/pyrato/dsp.py @@ -89,43 +89,7 @@ def find_impulse_response_start( "Use pyfar.dsp.find_impulse_response_start instead", DeprecationWarning) - ir_squared = np.abs(impulse_response.time)**2 - - mask_start = int(0.9*impulse_response.n_samples) - - mask = np.arange(mask_start, ir_squared.shape[-1]) - noise = np.mean(np.take(ir_squared, mask, axis=-1), axis=-1) - - max_sample = np.argmax(ir_squared, axis=-1) - max_value = np.max(ir_squared, axis=-1) - - if np.any(max_value < 10**(threshold/10) * noise) or \ - np.any(max_sample > mask_start): - warnings.warn( - "The SNR seems lower than the specified threshold value. Check " - "if this is a valid impulse response with sufficient SNR.") - - start_sample = max_sample.copy() - - for ch in np.ndindex(impulse_response.cshape): - # Only look for the start sample if the maximum index is bigger than 0 - if start_sample[ch] > 0: - # Check samples before maximum - ir_before_max = np.squeeze( - ir_squared[ch][:max_sample[ch]+1] / max_value[ch]) - # First sample above or at the threshold level - idx_first_above_thresh = np.where( - ir_before_max >= 10**(-threshold/10))[0] - if idx_first_above_thresh.size > 0: - # The start sample is the last sample below the threshold - start_sample[ch] = np.min(idx_first_above_thresh) - 1 - else: - start_sample[ch] = 0 - warnings.warn( - f'No values below threshold found found for channel {ch}', - 'defaulting to 0') - - return np.squeeze(start_sample) + return pf.dsp.find_impulse_response_start(impulse_response, threshold) def find_impulse_response_maximum( diff --git a/requirements_dev.txt b/requirements_dev.txt index c82fc14..cf9a14e 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -15,4 +15,4 @@ scipy>=1.5.0 matplotlib autodocsumm insipid-sphinx-theme -pyfar>=0.3.0 +pyfar>=0.5.0 diff --git a/setup.py b/setup.py index 5adf3f0..a63e092 100644 --- a/setup.py +++ b/setup.py @@ -12,7 +12,7 @@ history = history_file.read() requirements = [ - 'pyfar>=0.3.0', + 'pyfar>=0.5.0', 'numpy>=1.14.0', 'scipy>=1.5.0', 'matplotlib'] From c456c92990c69b0c0cf185333e80b6d9fd49e927 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 7 Nov 2022 11:14:16 +0100 Subject: [PATCH 48/66] let find_max_ir also only warn instead of raising an error --- pyrato/dsp.py | 5 +++-- tests/test_dsp.py | 4 ++++ 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/pyrato/dsp.py b/pyrato/dsp.py index d6d6268..a488955 100644 --- a/pyrato/dsp.py +++ b/pyrato/dsp.py @@ -131,8 +131,9 @@ def find_impulse_response_maximum( if np.any(max_value < 10**(threshold/10) * noise) or \ np.any(max_sample > mask_start): - raise ValueError("The SNR is lower than the defined threshold. Check \ - if this is a valid impulse response with sufficient SNR.") + warnings.warn( + "The SNR seems lower than the specified threshold value. Check " + "if this is a valid impulse response with sufficient SNR.") return np.squeeze(max_sample) diff --git a/tests/test_dsp.py b/tests/test_dsp.py index 810f349..dc6e0a9 100644 --- a/tests/test_dsp.py +++ b/tests/test_dsp.py @@ -158,6 +158,10 @@ def test_max_ir(): start_sample_est = dsp.find_impulse_response_maximum(ir_awgn) assert start_sample_est == start_sample + with pytest.warns(match='SNR seems lower'): + start_sample_est = dsp.find_impulse_response_maximum( + ir_awgn, threshold=200) + # ------------------ # Time shift # ------------------ From 679084fda9d7758eac8e0690d090bfeaf2caa8a7 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 7 Nov 2022 11:20:23 +0100 Subject: [PATCH 49/66] add requirement python>=3.8 to setup.py --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index a3c3db5..ae56f7f 100644 --- a/setup.py +++ b/setup.py @@ -58,4 +58,5 @@ url='https://github.com/mberz/pyrato', version='0.3.1', zip_safe=False, + python_requires='>=3.8' ) From 4cd5dedee0bc6658b3dc345fe81d7034fb1f8ff1 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 7 Nov 2022 11:22:22 +0100 Subject: [PATCH 50/66] Contributing don't exclude recent versions --- CONTRIBUTING.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst index 9405699..2abff58 100644 --- a/CONTRIBUTING.rst +++ b/CONTRIBUTING.rst @@ -102,7 +102,7 @@ Before you submit a pull request, check that it meets these guidelines: 2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst. -3. The pull request should work for Python 3.8 to 3.10. Check +3. The pull request should work for Python versions 3.8 or later. Check https://app.travis-ci.com/github/mberz/pyrato/pull_requests and make sure that the tests pass for all supported Python versions. From 06753de849310a4616edb29bb521c19744eef157 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Wed, 9 Nov 2022 13:16:58 +0100 Subject: [PATCH 51/66] add edc truncation function for pyfar.TimeData --- pyrato/edc.py | 51 +++++++++++++++++++++- tests/test_edc_noise_handling.py | 73 ++++++++++++++++++++++++++------ 2 files changed, 111 insertions(+), 13 deletions(-) diff --git a/pyrato/edc.py b/pyrato/edc.py index 503f1ff..b6a25ce 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -173,6 +173,7 @@ def energy_decay_curve_truncation( time_shift=True, channel_independent=False, normalize=True, + threshold=15, plot=False): """ This function truncates a given room impulse response by the intersection time after Lundeby and calculates the energy decay curve. @@ -199,13 +200,18 @@ def energy_decay_curve_truncation( normalize : boolean Defines, if the energy decay curve should be normalized in the end or not. + threshold : float + Defines a peak-signal-to-noise ratio based threshold in dB for final + truncation of the EDC. Values below the sum of the threshold level and + the peak-signal-to-noise ratio in dB are discarded. The default is + 15 dB, which is in correspondence with ISO 3382-1:2009 [#]_. plot: Boolean Specifies, whether the results should be visualized or not. Returns ------- pyfar.TimeData - Returns the noise handeled edc. + Returns the noise compensated EDC. Examples -------- @@ -240,6 +246,12 @@ def energy_decay_curve_truncation( >>> ax.set_ylim(-65, 5) >>> ax.legend() + + References + ---------- + .. [#] International Organization for Standardization, “EN ISO 3382-1:2009 + Acoustics - Measurement of room acoustic parameters,” 2009. + """ energy_data = dsp.preprocess_rir( data, @@ -259,6 +271,10 @@ def energy_decay_curve_truncation( intersection_time_idx = np.rint(intersection_time * data.sampling_rate) + psnr = dsp.peak_signal_to_noise_ratio( + data, noise_level, is_energy=is_energy) + trunc_levels = 10*np.log10((psnr)) - threshold + energy_decay_curve = np.zeros([*data.cshape, n_samples]) for ch in np.ndindex(data.cshape): energy_decay_curve[ @@ -268,6 +284,9 @@ def energy_decay_curve_truncation( ch, :int(intersection_time_idx[ch])], is_energy=True) + energy_decay_curve = _truncate_energy_decay_curve( + energy_decay_curve, trunc_levels) + if normalize: # Normalize the EDC... if not channel_independent: @@ -1067,3 +1086,33 @@ def intersection_time_lundeby( plt.grid(True) return intersection_time, reverberation_time, noise_level + + +def _truncate_energy_decay_curve(energy_decay_curve, threshold_level): + + edc = np.atleast_2d(energy_decay_curve) + threshold_level = np.atleast_2d(threshold_level) + e = edc.T[0] + t = e / 10**(threshold_level/10) + + mask = edc.T < np.broadcast_to(t, edc.T.shape) + edc[mask.T] = np.nan + + return edc + + +def truncate_energy_decay_curve(energy_decay_curve, threshold): + """Truncate an energy decay curve, discarding values below the threshold. + + Parameters + ---------- + energy_decay_curve : pyfar.TimeData + The energy decay curve + threshold : float + The threshold level in dB. The data below the threshold level are set + to numpy.nan values. + """ + return pf.TimeData( + _truncate_energy_decay_curve(energy_decay_curve.time, threshold), + energy_decay_curve.times, + energy_decay_curve.comment) diff --git a/tests/test_edc_noise_handling.py b/tests/test_edc_noise_handling.py index 3142099..d28ef9e 100644 --- a/tests/test_edc_noise_handling.py +++ b/tests/test_edc_noise_handling.py @@ -45,7 +45,7 @@ def test_substracted_2D(): npt.assert_allclose(actual, expected) -def test_edc_truncation_1D(monkeypatch): +def test_edc_truncation_1D(): rir = pf.Signal(genfromtxt( os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), delimiter=','), 3000) @@ -53,10 +53,18 @@ def test_edc_truncation_1D(monkeypatch): os.path.join(test_data_path, 'edc_truncation_1D.csv'), delimiter=',')) - # monkeypatch.setattr( - # dsp, - # "find_impulse_response_start", - # mock_shift_samples_1d) + actual = enh.energy_decay_curve_truncation( + rir, + freq='broadband', + is_energy=False, + time_shift=True, + channel_independent=False, + normalize=True, + threshold=-np.inf) + + pf.plot.time(actual, dB=True, log_prefix=10) + + npt.assert_allclose(actual.time, expected) actual = enh.energy_decay_curve_truncation( rir, @@ -64,7 +72,14 @@ def test_edc_truncation_1D(monkeypatch): is_energy=False, time_shift=True, channel_independent=False, - normalize=True) + normalize=True, + threshold=15) + + mask = expected < 10**((-40+15)/10) + expected[mask] = np.nan + + pf.plot.time(actual, dB=True, log_prefix=10) + npt.assert_allclose(actual.time, expected) @@ -76,18 +91,14 @@ def test_edc_truncation_2D(monkeypatch): os.path.join(test_data_path, 'edc_truncation_2D.csv'), delimiter=',')) - # monkeypatch.setattr( - # dsp, - # "find_impulse_response_start", - # mock_shift_samples_2d) - actual = enh.energy_decay_curve_truncation( rir, freq='broadband', is_energy=False, time_shift=True, channel_independent=False, - normalize=True) + normalize=True, + threshold=-np.inf) npt.assert_allclose(actual.time, expected) @@ -277,3 +288,41 @@ def test_intersection_time_2D(monkeypatch): channel_independent=False, plot=False) npt.assert_allclose(actual, expected) + + +def test_energy_decay_curve_threshold(): + + t_60 = 1 + m = -60/t_60 + + n_samples = 10 + times = np.linspace(0, 1, n_samples) + edc_log = np.atleast_2d(times * m) + + edc_log = np.tile(edc_log, (2, 3, 1)) + + edc = enh._truncate_energy_decay_curve(10**(edc_log.copy()/10), 30) + + edc_ref = 10**(edc_log.copy()/10) + edc_ref[..., n_samples//2:] = np.nan + + npt.assert_allclose(edc, edc_ref) + + +def test_truncate_energy_decay_curve(): + t_60 = 1 + m = -60/t_60 + + n_samples = 10 + times = np.linspace(0, 1, n_samples) + edc_log = np.atleast_2d(times * m) + + edc_log = np.tile(edc_log, (2, 3, 1)) + + edc = pf.TimeData(10**(edc_log.copy()/10), times) + edc_trunc = enh.truncate_energy_decay_curve(edc, 30) + + edc_ref = 10**(edc_log.copy()/10) + edc_ref[..., n_samples//2:] = np.nan + + npt.assert_allclose(edc_trunc.time, edc_ref) From 82a4846405dd0f5569b2e13c39ca0f890f8742e8 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Wed, 9 Nov 2022 14:11:21 +0100 Subject: [PATCH 52/66] add thresholding to chu's method --- pyrato/edc.py | 13 ++++++++++++- tests/test_edc_noise_handling.py | 24 ++++++++++++++++++++---- 2 files changed, 32 insertions(+), 5 deletions(-) diff --git a/pyrato/edc.py b/pyrato/edc.py index b6a25ce..230ea13 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -463,8 +463,8 @@ def energy_decay_curve_chu( time_shift=True, channel_independent=False, normalize=True, + threshold=10, plot=False): - """ Implementation of the "subtraction of noise"-method after Chu [#] The noise level is estimated and subtracted from the impulse response before backward integration. @@ -487,6 +487,11 @@ def energy_decay_curve_chu( normalize : boolean Defines, if the energy decay curve should be normalized in the end or not. + threshold : float, None + Defines a peak-signal-to-noise ratio based threshold in dB for final + truncation of the EDC. Values below the sum of the threshold level and + the peak-signal-to-noise ratio in dB are discarded. The default is + 10 dB. If `None`, the decay curve will not be truncated further. plot: Boolean Specifies, whether the results should be visualized or not. @@ -561,6 +566,12 @@ def energy_decay_curve_chu( for ch in np.ndindex(edc.cshape): edc.time[ch, first_zero[ch]:] = np.nan + if threshold is not None: + psnr = dsp.peak_signal_to_noise_ratio( + data, noise_level, is_energy=is_energy) + trunc_levels = 10*np.log10((psnr)) - threshold + edc = truncate_energy_decay_curve(edc, trunc_levels) + if plot: plt.figure(figsize=(15, 3)) pf.plot.use('light') diff --git a/tests/test_edc_noise_handling.py b/tests/test_edc_noise_handling.py index d28ef9e..a4fe5d0 100644 --- a/tests/test_edc_noise_handling.py +++ b/tests/test_edc_noise_handling.py @@ -206,18 +206,33 @@ def test_edc_chu_1D(monkeypatch): os.path.join(test_data_path, 'edc_chu_1D.csv'), delimiter=',')) - # monkeypatch.setattr( - # dsp, - # "find_impulse_response_start", - # mock_shift_samples_1d) + actual = enh.energy_decay_curve_chu( + rir, + is_energy=False, + time_shift=True, + channel_independent=False, + normalize=True, + threshold=None, + plot=False) + npt.assert_allclose(actual.time, expected) + # Test with a sufficiently high threshold to ensure exact matching of nans + threshold = 15 actual = enh.energy_decay_curve_chu( rir, is_energy=False, time_shift=True, channel_independent=False, normalize=True, + threshold=threshold, plot=False) + + mask = expected <= 10**((-40+threshold)/10) + expected[mask] = np.nan + + pf.plot.time(actual, dB=True, log_prefix=10) + + pf.plot.time(pf.TimeData(expected, actual.times), dB=True, log_prefix=10) npt.assert_allclose(actual.time, expected) @@ -240,6 +255,7 @@ def test_edc_chu_2D(monkeypatch): time_shift=True, channel_independent=False, normalize=True, + threshold=None, plot=False) npt.assert_allclose(actual.time, expected) From 6993e6a1b677d0ad146042b4ec2dfa3244df9487 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Wed, 9 Nov 2022 14:28:10 +0100 Subject: [PATCH 53/66] add function for PSNR estimation --- pyrato/dsp.py | 31 +++++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/pyrato/dsp.py b/pyrato/dsp.py index a488955..e21014b 100644 --- a/pyrato/dsp.py +++ b/pyrato/dsp.py @@ -495,3 +495,34 @@ def preprocess_rir( energy_data = pf.TimeData(energy_data, times) return energy_data + + +def peak_signal_to_noise_ratio( + impulse_response, + noise_power='auto', + is_energy=False): + """Calculate the peak-signal-to-noise-ratio of an impulse response. + + Parameters + ---------- + impulse_response : pyfar.Signal + The impulse response + noise_power : float, str, optional + The noise power. The default is 'auto', in which case the noise power + is estimated from the last 10 % of the impulse response. + + Returns + ------- + float, array-like + The estimated peak-signal-to-noise-ratio for each channel of the + impulse response. + + """ + data = impulse_response.time + if is_energy is False: + data = data**2 + + if noise_power == 'auto': + noise_power = _estimate_noise_energy(data) + + return np.max(data, axis=-1) / noise_power From 57bc1009adf063d5c8fed38e2c4cef661f78c7dd Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Thu, 10 Nov 2022 10:47:30 +0100 Subject: [PATCH 54/66] remove output from example notebook which are affected by changes in the default behavior made in this PR --- examples/energy_decay_curves_and_reverberation_time.ipynb | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/examples/energy_decay_curves_and_reverberation_time.ipynb b/examples/energy_decay_curves_and_reverberation_time.ipynb index f74623d..13e3cab 100644 --- a/examples/energy_decay_curves_and_reverberation_time.ipynb +++ b/examples/energy_decay_curves_and_reverberation_time.ipynb @@ -509,7 +509,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.12 ('pyrato')", "language": "python", "name": "python3" }, @@ -524,6 +524,11 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" + }, + "vscode": { + "interpreter": { + "hash": "af8819fd7f8a66ab9d7215237632d6e1478b14f767e5cd6cf228e02c2933a175" + } } }, "nbformat": 4, From be23155fe35a6ec8c4ba10a12b681b66bc50f590 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Thu, 10 Nov 2022 11:48:33 +0100 Subject: [PATCH 55/66] add test for psnr estimation --- tests/test_dsp.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/tests/test_dsp.py b/tests/test_dsp.py index dc6e0a9..6d2b82a 100644 --- a/tests/test_dsp.py +++ b/tests/test_dsp.py @@ -264,6 +264,21 @@ def test_noise_energy_2D(): npt.assert_allclose(actual, expected) +def test_psnr(): + n_samples = 2**20 + peak_levels = np.array([0, -6, -10]) + noise_level = np.array([-20, -30, -40]) + imp = pf.signals.impulse(n_samples, amplitude=10**(peak_levels/20)) + awgn = pf.signals.noise(n_samples, rms=10**(noise_level/20), seed=7) + psnr = dsp.peak_signal_to_noise_ratio(imp+awgn) + + npt.assert_allclose( + 1/psnr, 10**((peak_levels + noise_level)/10), rtol=1e-2, atol=1e-2) + +# ---------------- +# RIR preprocessing +# ---------------- + def test_preprocessing_1D(): rir = genfromtxt( os.path.join(test_data_path, 'analytic_rir_psnr50_1D.csv'), From f7c67eeafd89aed982cf11d38b1920e20578f438 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 14 Nov 2022 16:17:59 +0100 Subject: [PATCH 56/66] bugifx: channel-correct intersection sample truncation for lundeby edc variants --- pyrato/edc.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyrato/edc.py b/pyrato/edc.py index 230ea13..c4f33cf 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -430,6 +430,7 @@ def energy_decay_curve_lundeby( energy_data.time[ch, :intersection_time_idx], is_energy=True) energy_decay_curve[ch] += correction + energy_decay_curve[..., intersection_time_idx:] = np.nan if normalize: # Normalize the EDC... @@ -443,7 +444,6 @@ def energy_decay_curve_lundeby( max_start_value = np.amax(energy_decay_curve[..., 0]) energy_decay_curve /= max_start_value - energy_decay_curve[..., intersection_time_idx:] = np.nan edc = pf.TimeData( energy_decay_curve, data.times, comment=data.comment) @@ -722,6 +722,7 @@ def energy_decay_curve_chu_lundeby( subtraction.time[ch, :intersection_time_idx], is_energy=True) energy_decay_curve[ch] += correction + energy_decay_curve[..., intersection_time_idx:] = np.nan if normalize: # Normalize the EDC... @@ -734,7 +735,6 @@ def energy_decay_curve_chu_lundeby( max_start_value = np.amax(energy_decay_curve[..., 0]) energy_decay_curve /= max_start_value - energy_decay_curve[..., intersection_time_idx:] = np.nan edc = pf.TimeData( energy_decay_curve, data.times, comment=data.comment) From f8fd63fb161207c8c56b6c75f38ce45d263ba1fb Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 14 Nov 2022 18:20:32 +0100 Subject: [PATCH 57/66] update test data --- tests/test_data/edc_lundeby_2D.csv | 4 +- tests/test_data/edc_lundeby_chu_2D.csv | 4 +- tests/test_data/generate_test_data.py | 146 ++++++++++++++++--------- 3 files changed, 97 insertions(+), 57 deletions(-) diff --git a/tests/test_data/edc_lundeby_2D.csv b/tests/test_data/edc_lundeby_2D.csv index ce3fdaa..29c9a7e 100644 --- a/tests/test_data/edc_lundeby_2D.csv +++ b/tests/test_data/edc_lundeby_2D.csv @@ -1,2 +1,2 @@ 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diff --git a/tests/test_data/edc_lundeby_chu_2D.csv b/tests/test_data/edc_lundeby_chu_2D.csv index 3bef544..b17fc0f 100644 --- a/tests/test_data/edc_lundeby_chu_2D.csv +++ b/tests/test_data/edc_lundeby_chu_2D.csv @@ -1,2 +1,2 @@ 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diff --git a/tests/test_data/generate_test_data.py b/tests/test_data/generate_test_data.py index 48562b5..bdf138b 100644 --- a/tests/test_data/generate_test_data.py +++ b/tests/test_data/generate_test_data.py @@ -16,6 +16,7 @@ import numpy as np import pyrato from pyrato import analytic +import pyfar as pf sampling_rate = 3000 @@ -28,27 +29,35 @@ rir_1 = analytic.rectangular_room_rigid_walls( dimensions=room_dimensions, source=src_pos, receiver=rec_pos, reverberation_time=t_60, max_freq=max_freq, - samplingrate=sampling_rate, speed_of_sound=343.9, n_samples=n_samples)[0] + samplingrate=sampling_rate, speed_of_sound=343.9, n_samples=n_samples*2)[0] rir_2 = analytic.rectangular_room_rigid_walls( dimensions=room_dimensions, source=src_pos, receiver=rec_pos, reverberation_time=t_60*2, max_freq=max_freq, samplingrate=sampling_rate, speed_of_sound=343.9, n_samples=n_samples*2)[0] -rir_array = np.zeros(([2, rir_2.size])) +# %% + +rir_array = np.zeros(([2, rir_2.time.size])) psnr = 50 -rir_1 /= np.amax(np.abs(rir_1)) -rir_2 /= np.amax(np.abs(rir_2)) +# rir_1 /= np.amax(np.abs(rir_1)) +# rir_2 /= np.amax(np.abs(rir_2)) + +rir_array[0] = rir_1.time +rir_array[1] = rir_2.time + +rir_array = pf.dsp.normalize(pf.Signal(rir_array, sampling_rate)) -rir_array[0,:rir_1.shape[0]] = rir_1 -rir_array[1,:] = rir_2 # fix the seed np.random.seed(1) -noise = np.random.normal(0, 1, rir_2.size) -rir_array += 10**(-(psnr-10) / 20) * noise +rms = 10**(-(psnr-10) / 20) +noise = pf.signals.noise( + n_samples*2, rms=rms, sampling_rate=sampling_rate, seed=1) + +rir_array += noise # %% noise_energy_1D = pyrato.estimate_noise_energy( @@ -56,60 +65,62 @@ noise_energy_2D = pyrato.estimate_noise_energy( rir_array, interval=[0.9, 1.0], is_energy=False) -preprocessing_1D = pyrato.edc.preprocess_rir( - rir_array[0], is_energy=False, time_shift=False, channel_independent=False) -preprocessing_2D = pyrato.edc.preprocess_rir( - rir_array, is_energy=False, time_shift=False, channel_independent=False) +preprocessing_1D = pyrato.dsp.preprocess_rir( + rir_array[0], is_energy=False, shift=False, channel_independent=False) +preprocessing_2D = pyrato.dsp.preprocess_rir( + rir_array, is_energy=False, shift=False, channel_independent=False) -preprocessing_time_shift_1D = pyrato.edc.preprocess_rir( - rir_array[0], is_energy=False, time_shift=True, channel_independent=False) -preprocessing_time_shift_2D = pyrato.edc.preprocess_rir( - rir_array, is_energy=False, time_shift=True, channel_independent=False) +preprocessing_time_shift_1D = pyrato.dsp.preprocess_rir( + rir_array[0], is_energy=False, shift=True, channel_independent=False) +preprocessing_time_shift_2D = pyrato.dsp.preprocess_rir( + rir_array, is_energy=False, shift=True, channel_independent=False) -preprocessing_time_shift_channel_independent_1D = pyrato.edc.preprocess_rir( - rir_array[0], is_energy=False, time_shift=True, channel_independent=True) -preprocessing_time_shift_channel_independent_2D = pyrato.edc.preprocess_rir( - rir_array, is_energy=False, time_shift=True, channel_independent=True) +preprocessing_time_shift_channel_independent_1D = pyrato.dsp.preprocess_rir( + rir_array[0], is_energy=False, shift=True, channel_independent=True) +preprocessing_time_shift_channel_independent_2D = pyrato.dsp.preprocess_rir( + rir_array, is_energy=False, shift=True, channel_independent=True) -smoothed_rir_1D = pyrato.edc.smooth_rir(rir_array[0], sampling_rate, smooth_block_length=0.075) -smoothed_rir_2D = pyrato.edc.smooth_rir(rir_array, sampling_rate, smooth_block_length=0.075) +smoothed_rir_1D = pyrato.dsp._smooth_rir( + rir_array[0].time, sampling_rate, smooth_block_length=0.075) +smoothed_rir_2D = pyrato.dsp._smooth_rir( + rir_array.time, sampling_rate, smooth_block_length=0.075) substracted_1D = pyrato.edc.subtract_noise_from_squared_rir(rir_array[0]**2) substracted_2D = pyrato.edc.subtract_noise_from_squared_rir(rir_array**2) edc_truncation_1D = pyrato.energy_decay_curve_truncation( - rir_array[0], sampling_rate, freq='broadband', is_energy=False, time_shift=True, + rir_array[0], freq='broadband', is_energy=False, time_shift=True, channel_independent=False, normalize=True) edc_truncation_2D = pyrato.energy_decay_curve_truncation( - rir_array, sampling_rate, freq='broadband', is_energy=False, time_shift=True, + rir_array, freq='broadband', is_energy=False, time_shift=True, channel_independent=False, normalize=True) edc_lundeby_1D = pyrato.energy_decay_curve_lundeby( - rir_array[0], sampling_rate, freq='broadband', is_energy=False, time_shift=True, + rir_array[0], freq='broadband', is_energy=False, time_shift=True, channel_independent=False, normalize=True, plot=False) edc_lundeby_2D = pyrato.energy_decay_curve_lundeby( - rir_array, sampling_rate, freq='broadband', is_energy=False, time_shift=True, + rir_array, freq='broadband', is_energy=False, time_shift=True, channel_independent=False, normalize=True, plot=False) edc_lundeby_chu_1D = pyrato.energy_decay_curve_chu_lundeby( - rir_array[0], sampling_rate, freq='broadband', is_energy=False, time_shift=True, + rir_array[0], freq='broadband', is_energy=False, time_shift=True, channel_independent=False, normalize=True, plot=False) edc_lundeby_chu_2D = pyrato.energy_decay_curve_chu_lundeby( - rir_array, sampling_rate, freq='broadband', is_energy=False, time_shift=True, + rir_array, freq='broadband', is_energy=False, time_shift=True, channel_independent=False, normalize=True, plot=False) edc_chu_1D = pyrato.energy_decay_curve_chu( - rir_array[0], sampling_rate, freq='broadband', is_energy=False, time_shift=True, + rir_array[0], is_energy=False, time_shift=True, channel_independent=False, normalize=True, plot=False) edc_chu_2D = pyrato.energy_decay_curve_chu( - rir_array, sampling_rate, freq='broadband', is_energy=False, time_shift=True, + rir_array, is_energy=False, time_shift=True, channel_independent=False, normalize=True, plot=False) intersection_time_1D = pyrato.intersection_time_lundeby( - rir_array[0], sampling_rate, freq='broadband', is_energy=False, time_shift=False, + rir_array[0], freq='broadband', is_energy=False, time_shift=False, channel_independent=False, plot=False) intersection_time_2D = pyrato.intersection_time_lundeby( - rir_array, sampling_rate, freq='broadband', is_energy=False, time_shift=False, + rir_array, freq='broadband', is_energy=False, time_shift=False, channel_independent=False, plot=False) # noise_energy_from_edc_1D = pyrato.edc.estimate_noise_energy_from_edc( @@ -120,8 +131,14 @@ # %% -np.savetxt("analytic_rir_psnr50_1D.csv", rir_array[0], delimiter=",") -np.savetxt("analytic_rir_psnr50_2D.csv", rir_array, delimiter=",") +np.savetxt( + "analytic_rir_psnr50_1D.csv", + np.squeeze(rir_array[0].time), + delimiter=",") +np.savetxt( + "analytic_rir_psnr50_2D.csv", + np.squeeze(rir_array.time), + delimiter=",") np.savetxt("noise_energy_1D.csv", noise_energy_1D, delimiter=",") np.savetxt("noise_energy_2D.csv", noise_energy_2D, delimiter=",") @@ -129,29 +146,52 @@ np.savetxt("preprocessing_1D.csv", preprocessing_1D[0], delimiter=",") np.savetxt("preprocessing_2D.csv", preprocessing_2D[0], delimiter=",") -np.savetxt("preprocessing_time_shift_1D.csv", preprocessing_time_shift_1D[0], delimiter=",") -np.savetxt("preprocessing_time_shift_2D.csv", preprocessing_time_shift_2D[0], delimiter=",") - -np.savetxt("preprocessing_time_shift_channel_independent_1D.csv", preprocessing_time_shift_channel_independent_1D[0], delimiter=",") -np.savetxt("preprocessing_time_shift_channel_independent_2D.csv", preprocessing_time_shift_channel_independent_2D[0], delimiter=",") +np.savetxt( + "preprocessing_time_shift_1D.csv", + preprocessing_time_shift_1D[0], + delimiter=",") +np.savetxt( + "preprocessing_time_shift_2D.csv", + preprocessing_time_shift_2D[0], + delimiter=",") + +np.savetxt( + "preprocessing_time_shift_channel_independent_1D.csv", + preprocessing_time_shift_channel_independent_1D[0], + delimiter=",") +np.savetxt( + "preprocessing_time_shift_channel_independent_2D.csv", + preprocessing_time_shift_channel_independent_2D[0], delimiter=",") np.savetxt("smoothed_rir_1D.csv", smoothed_rir_1D[0], delimiter=",") np.savetxt("smoothed_rir_2D.csv", smoothed_rir_2D[0], delimiter=",") -np.savetxt("substracted_1D.csv", substracted_1D, delimiter=",") -np.savetxt("substracted_2D.csv", substracted_2D, delimiter=",") - -np.savetxt("edc_truncation_1D.csv", edc_truncation_1D, delimiter=",") -np.savetxt("edc_truncation_2D.csv", edc_truncation_2D, delimiter=",") - -np.savetxt("edc_lundeby_1D.csv", edc_lundeby_1D, delimiter=",") -np.savetxt("edc_lundeby_2D.csv", edc_lundeby_2D, delimiter=",") - -np.savetxt("edc_lundeby_chu_1D.csv", edc_lundeby_chu_1D, delimiter=",") -np.savetxt("edc_lundeby_chu_2D.csv", edc_lundeby_chu_2D, delimiter=",") - -np.savetxt("edc_chu_1D.csv", edc_chu_1D, delimiter=",") -np.savetxt("edc_chu_2D.csv", edc_chu_2D, delimiter=",") +np.savetxt( + "substracted_1D.csv", np.squeeze(substracted_1D.time), delimiter=",") +np.savetxt( + "substracted_2D.csv", np.squeeze(substracted_2D.time), delimiter=",") + +np.savetxt( + "edc_truncation_1D.csv", np.squeeze(edc_truncation_1D.time), delimiter=",") +np.savetxt( + "edc_truncation_2D.csv", np.squeeze(edc_truncation_2D.time), delimiter=",") + +np.savetxt( + "edc_lundeby_1D.csv", np.squeeze(edc_lundeby_1D.time), delimiter=",") +np.savetxt( + "edc_lundeby_2D.csv", np.squeeze(edc_lundeby_2D.time), delimiter=",") + +np.savetxt( + "edc_lundeby_chu_1D.csv", + np.squeeze(edc_lundeby_chu_1D.time), + delimiter=",") +np.savetxt( + "edc_lundeby_chu_2D.csv", + np.squeeze(edc_lundeby_chu_2D.time), + delimiter=",") + +np.savetxt("edc_chu_1D.csv", np.squeeze(edc_chu_1D.time), delimiter=",") +np.savetxt("edc_chu_2D.csv", np.squeeze(edc_chu_2D.time), delimiter=",") np.savetxt("intersection_time_1D.csv", intersection_time_1D, delimiter=",") np.savetxt("intersection_time_2D.csv", intersection_time_2D, delimiter=",") From d1cad9f84bcc18e78bb2195f25b710c0cf0ac6dc Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 14 Nov 2022 19:02:00 +0100 Subject: [PATCH 58/66] Read existing RIR from disk when generating test data to avoid re-writing all files --- tests/test_data/edc_lundeby_2D.csv | 4 +- tests/test_data/edc_lundeby_chu_2D.csv | 4 +- tests/test_data/generate_test_data.py | 55 +++++++++++++++----------- 3 files changed, 37 insertions(+), 26 deletions(-) diff --git a/tests/test_data/edc_lundeby_2D.csv b/tests/test_data/edc_lundeby_2D.csv index 29c9a7e..e14c311 100644 --- a/tests/test_data/edc_lundeby_2D.csv +++ b/tests/test_data/edc_lundeby_2D.csv @@ -1,2 +1,2 @@ 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diff --git a/tests/test_data/edc_lundeby_chu_2D.csv b/tests/test_data/edc_lundeby_chu_2D.csv index b17fc0f..c5200eb 100644 --- a/tests/test_data/edc_lundeby_chu_2D.csv +++ b/tests/test_data/edc_lundeby_chu_2D.csv @@ -1,2 +1,2 @@ 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diff --git a/tests/test_data/generate_test_data.py b/tests/test_data/generate_test_data.py index bdf138b..2d67743 100644 --- a/tests/test_data/generate_test_data.py +++ b/tests/test_data/generate_test_data.py @@ -27,37 +27,44 @@ max_freq = 1000 n_samples = 2**13 -rir_1 = analytic.rectangular_room_rigid_walls( - dimensions=room_dimensions, source=src_pos, receiver=rec_pos, reverberation_time=t_60, max_freq=max_freq, - samplingrate=sampling_rate, speed_of_sound=343.9, n_samples=n_samples*2)[0] +# rir_1 = analytic.rectangular_room_rigid_walls( +# dimensions=room_dimensions, source=src_pos, receiver=rec_pos, reverberation_time=t_60, max_freq=max_freq, +# samplingrate=sampling_rate, speed_of_sound=343.9, n_samples=n_samples*2)[0] -rir_2 = analytic.rectangular_room_rigid_walls( - dimensions=room_dimensions, source=src_pos, receiver=rec_pos, reverberation_time=t_60*2, max_freq=max_freq, - samplingrate=sampling_rate, speed_of_sound=343.9, n_samples=n_samples*2)[0] +# rir_2 = analytic.rectangular_room_rigid_walls( +# dimensions=room_dimensions, source=src_pos, receiver=rec_pos, reverberation_time=t_60*2, max_freq=max_freq, +# samplingrate=sampling_rate, speed_of_sound=343.9, n_samples=n_samples*2)[0] -# %% +# # %% + +# rir_array = np.zeros(([2, rir_2.time.size])) -rir_array = np.zeros(([2, rir_2.time.size])) +# psnr = 50 -psnr = 50 +# # rir_1 /= np.amax(np.abs(rir_1)) +# # rir_2 /= np.amax(np.abs(rir_2)) -# rir_1 /= np.amax(np.abs(rir_1)) -# rir_2 /= np.amax(np.abs(rir_2)) +# rir_array[0] = rir_1.time +# rir_array[1] = rir_2.time -rir_array[0] = rir_1.time -rir_array[1] = rir_2.time +# rir_array = pf.dsp.normalize(pf.Signal(rir_array, sampling_rate)) -rir_array = pf.dsp.normalize(pf.Signal(rir_array, sampling_rate)) +# rms = 10**(-(psnr-10) / 20) +# noise = pf.signals.noise( +# n_samples*2, rms=rms, sampling_rate=sampling_rate, seed=1) +# rir_array += noise -# fix the seed -np.random.seed(1) +# %% +# Use existing RIR to avoid re-generating data for all tests -rms = 10**(-(psnr-10) / 20) -noise = pf.signals.noise( - n_samples*2, rms=rms, sampling_rate=sampling_rate, seed=1) -rir_array += noise +rir_array = pf.Signal(np.genfromtxt( + 'analytic_rir_psnr50_2D.csv', + delimiter=','), 3000) + +# %% +pf.plot.time(rir_array, dB=True, alpha=0.5) # %% noise_energy_1D = pyrato.estimate_noise_energy( @@ -117,10 +124,10 @@ channel_independent=False, normalize=True, plot=False) intersection_time_1D = pyrato.intersection_time_lundeby( - rir_array[0], freq='broadband', is_energy=False, time_shift=False, + rir_array[0], freq='broadband', is_energy=False, time_shift=True, channel_independent=False, plot=False) intersection_time_2D = pyrato.intersection_time_lundeby( - rir_array, freq='broadband', is_energy=False, time_shift=False, + rir_array, freq='broadband', is_energy=False, time_shift=True, channel_independent=False, plot=False) # noise_energy_from_edc_1D = pyrato.edc.estimate_noise_energy_from_edc( @@ -129,6 +136,8 @@ # edc_lundeby_chu_2D, intersection_time_2D[0], sampling_rate) # %% +pf.plot.time(edc_lundeby_2D, dB=True, log_prefix=10) +# %% np.savetxt( @@ -198,3 +207,5 @@ # np.savetxt("noise_energy_from_edc_1D.csv", noise_energy_from_edc_1D, delimiter=",") # np.savetxt("noise_energy_from_edc_2D.csv", noise_energy_from_edc_2D, delimiter=",") + +# %% From 6c5008c48033df5d5a07109e224347abb69f0fe0 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Mon, 14 Nov 2022 19:08:31 +0100 Subject: [PATCH 59/66] explicit ch indexing --- pyrato/edc.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyrato/edc.py b/pyrato/edc.py index c4f33cf..080e6f3 100644 --- a/pyrato/edc.py +++ b/pyrato/edc.py @@ -430,7 +430,7 @@ def energy_decay_curve_lundeby( energy_data.time[ch, :intersection_time_idx], is_energy=True) energy_decay_curve[ch] += correction - energy_decay_curve[..., intersection_time_idx:] = np.nan + energy_decay_curve[ch, intersection_time_idx:] = np.nan if normalize: # Normalize the EDC... @@ -722,7 +722,7 @@ def energy_decay_curve_chu_lundeby( subtraction.time[ch, :intersection_time_idx], is_energy=True) energy_decay_curve[ch] += correction - energy_decay_curve[..., intersection_time_idx:] = np.nan + energy_decay_curve[ch, intersection_time_idx:] = np.nan if normalize: # Normalize the EDC... From 2bc73e1e70fb9cda2421dc770e78b999f0f9b4b2 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Tue, 5 Dec 2023 10:24:14 +0100 Subject: [PATCH 60/66] update readthedocs config to new format --- .readthedocs.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.readthedocs.yml b/.readthedocs.yml index 4fc878e..9a269eb 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -5,9 +5,10 @@ # Required version: 2 -# Add libsndfile to the image build: - image: latest + os: ubuntu-22.04 + tools: + python: "3.10" apt_packages: - libsndfile1 @@ -21,6 +22,5 @@ formats: # Optionally set the version of Python and requirements required to build your docs python: - version: 3.7 install: - requirements: requirements_dev.txt From 82981f9053f96f669073274f46f5180ceeea9806 Mon Sep 17 00:00:00 2001 From: Anne Heimes Date: Fri, 15 Mar 2024 14:59:07 +0100 Subject: [PATCH 61/66] apply new doc style + fix old links to mberz --- CONTRIBUTING.rst | 8 +- README.rst | 12 +- docs/_static/css/custom.css | 8 + docs/_static/favicon.ico | Bin 0 -> 15406 bytes docs/_templates/navbar-nav.html | 41 ++++ docs/conf.py | 220 ++++++++++-------- docs/index.rst | 2 +- .../logos/pyfar_logos_fixed_size_pyrato.png | Bin 0 -> 9135 bytes requirements_dev.txt | 4 +- setup.py | 2 +- 10 files changed, 181 insertions(+), 116 deletions(-) create mode 100644 docs/_static/css/custom.css create mode 100644 docs/_static/favicon.ico create mode 100644 docs/_templates/navbar-nav.html mode change 100755 => 100644 docs/conf.py create mode 100644 docs/resources/logos/pyfar_logos_fixed_size_pyrato.png diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst index adb1139..561b98c 100644 --- a/CONTRIBUTING.rst +++ b/CONTRIBUTING.rst @@ -15,7 +15,7 @@ Types of Contributions Report Bugs ~~~~~~~~~~~ -Report bugs at https://github.com/mberz/pyrato/issues. +Report bugs at https://github.com/pyfar/pyrato/issues. If you are reporting a bug, please include: @@ -45,7 +45,7 @@ articles, and such. Submit Feedback ~~~~~~~~~~~~~~~ -The best way to send feedback is to file an issue at https://github.com/mberz/pyrato/issues. +The best way to send feedback is to file an issue at https://github.com/pyfar/pyrato/issues. If you are proposing a feature: @@ -62,7 +62,7 @@ Ready to contribute? Here's how to set up `pyrato` for local development. 1. Fork the `pyrato` repo on GitHub. 2. Clone your fork locally:: - $ git clone https://github.com/mberz/pyrato.git + $ git clone https://github.com/pyfar/pyrato.git 3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:: @@ -103,7 +103,7 @@ Before you submit a pull request, check that it meets these guidelines: your new functionality into a function with a docstring, and add the feature to the list in README.rst. 3. The pull request should work for Python 3.5 to 3.9. Check - https://app.travis-ci.com/github/mberz/pyrato/pull_requests + https://app.travis-ci.com/github/pyfar/pyrato/pull_requests and make sure that the tests pass for all supported Python versions. Tips diff --git a/README.rst b/README.rst index 915cb8a..f82cfc1 100644 --- a/README.rst +++ b/README.rst @@ -6,11 +6,11 @@ pyrato .. image:: https://readthedocs.org/projects/pyrato/badge/?version=latest :target: https://pyrato.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status -.. image:: https://circleci.com/gh/mberz/pyrato.svg?style=shield - :target: https://circleci.com/gh/mberz/pyrato +.. image:: https://circleci.com/gh/pyfar/pyrato.svg?style=shield + :target: https://circleci.com/gh/pyfar/pyrato .. image:: https://mybinder.org/badge_logo.svg - :target: https://mybinder.org/v2/gh/mberz/pyrato/HEAD?filepath=examples%2Fenergy_decay_curves_and_reverberation_time.ipynb - + :target: https://mybinder.org/v2/gh/pyfar/pyrato/HEAD?filepath=examples%2Fenergy_decay_curves_and_reverberation_time.ipynb + Python Room Acoustics Tools - A collection of functions for commonly used operations in room acoustics Installation @@ -36,6 +36,6 @@ Contributing Refer to the `contribution guidelines`_ for more information. -.. _contribution guidelines: https://github.com/mberz/pyrato/blob/main/CONTRIBUTING.rst -.. _examples notebook: https://mybinder.org/v2/gh/mberz/pyrato/HEAD?filepath=examples%2Fenergy_decay_curves_and_reverberation_time.ipynb +.. _contribution guidelines: https://github.com/pyfar/pyrato/blob/main/CONTRIBUTING.rst +.. _examples notebook: https://mybinder.org/v2/gh/pyfar/pyrato/HEAD?filepath=examples%2Fenergy_decay_curves_and_reverberation_time.ipynb .. _read the docs: https://pyrato.readthedocs.io/en/latest diff --git a/docs/_static/css/custom.css b/docs/_static/css/custom.css new file mode 100644 index 0000000..cbf825f --- /dev/null +++ b/docs/_static/css/custom.css @@ -0,0 +1,8 @@ + +html[data-theme="light"] { + --pst-color-primary: #18609d; /* pyfar blue as primary color */ +} + +html[data-theme="dark"] { + --pst-color-primary: #18609d; /* pyfar blue as primary color */ +} diff --git a/docs/_static/favicon.ico b/docs/_static/favicon.ico new file mode 100644 index 0000000000000000000000000000000000000000..0c96ea3a00a4706507435f6ad5b082f4d0a9ab72 GIT binary patch literal 15406 zcmeHO2~1qa8GepaB~?>3O3)|@4cD%zxn5%e`fxFmSJWwvzdngx=XitnC%cJ%v` z3{&?_hItChU=^$}@*We_pUdU?a#yD*IX62?a60Ut2|TBTcrVYtXS3NJuR(a;2o z7yb#%U7djSwG+o3^$NU)>Mz3jg%>Zt2NmT|cDV$~%SxfWwMA~L$9NUG8>{5!Q9gFU zE7ecfxdRe+?-FTy+%|Y|@gk`E?_{qgf03+vXbhRpP||EWIV z+p=jRWTd6)^-&fRmA@=Mjq)e`xp<*K)F0BJLd?Is^hHsZtfodNIk*H06JLPVkFubx zItO^pgX1zX{#_j=*t;hY_PnuMrliC-AT9MAaI6Q+h8oC?{|RJmeFnHD>eJ#c>WkKkI`F3cwa>#~C&aLN7s{a@u-HVeyq zK3+Q>M;|m@J_&XCZ$n+)kH{k;py;`w`xLo{htP-@yJm zLQN+T(Y9QEG{T3+*NW6x|6#y)soY4)w_)m# z-bwN&{#s~d_$$!ZaMOo}x#=pLi+W6?rm~SWMruDWO8i$vtVEm5$}*S={?y;U9yu)XUx8zw^Vj7s%>iBiCU1X& z&3}FV>Go&3?Jw;OX#H8aJbcRSzujgP&!EJA{n|Al=TGhK{!0QJI{2n4tz8`>?@vyq zB#Ub=ottXT?u5&SUy}D9MM-~v%D+d;bmjexcs_B+Yc-y;#@v4phVf|epBIW}GKbwJ zxA(!Z7@4=U2U4c#YvzF4-Y1v$=TG}N@-bX2$cGF0d1F!$`Xd@{){AFA$?j9y+g1-Z zvX00Ys#4;ixguSrmddQr^PzMu6#H*gwlUfK#MU1?cL?0I$?{az_u*f9Rv~Mt&x(}e zD!pDVL0NyYc^ZRrQQsEFpyFsGI4vFEarBN-pU+(3J)W(DgRV!AC;5xU$Exu=oxgM9 z=F2*e8vQuP6!TrMb&Q@XIo3>PUOIaSX@oD*85>r;`vv4f21->t%mEG&J-m z0L&t+{y%(wpCAY?pr7YbYfJMG`do%<(CNao-qbaxnae*kUbMfA`7hOjE} z(=*zuNNnGObLh_L6Di{QPkR8OO7t&If_EtE-r?z7FR)9cK43Zv?cMLtuO`|VIzu0S z_n5rK(t1j3-xmbGgu^BXo?j65!NI{t&~_9ezB`0*hT#7X&WZHfw{L$9=j$&~=LaPD z&Jg@G4njZ6&c`{iAM^6MFoW>l=@;1TI3LK@C+U@{{$Q*NI!w2Gh+;^F#s*(%Y6Mez z8(8~#1NMMQ9+anjG3`NYod%KY=2qZYiV@t!5;QOH=h414Da-lGpZ3ZTE0#k{RHP3@ zt$GzAUyTSLDi^;k7S5hN1?KLqG4Z(V7PykU33B4+iBzM{#f70ZpCii1TDs9RZMPc50oAWm-k~iaX$kq`e>C{g5^>6S@J4 zk@d^vbil5J*X1#wm>|Uo{dHj~-^c*nT-z#`dyl^r_|IzB1GZw&!NK1Ma)q89s4I4Fh@9l0tf z5mxdyNZ2Xem$V1uSUldxYpu$K{GHE1_O`i@zBWw8pNcWi?AT{8Mv8GsKPpIg9-Mt< zSq73WYQ?X_qcumsUdINo_YWOKOnvVC# zH{kt^H8IgZe%dJX(`qHE^ZJh0;Ov=Gh`rSZJK=ZRdht$rB*uV$4(aRW$o)u-`5xrP zEr7h&o(rJD-G2lZ`b(8^pu8G>dQblEPV|?XZnX#1dH;Dqbu2w*@L+t{pP%@QI^^u> z0E_WDlpI(h%FvfzDFqZmz#P+XH;|tcF+h&1s z{C|>UbJExS5L&O~0PC~^?{T5MR}#jR6nCLV+U7sQ*yFFfUhiWVf26ziQ#uB8AEx^B zcJU63)CaOjRmof6YHETn;eS|Y4l86bjCWFshurQ*3knKm_w#%>e*355yRz)P$)h}t zm6?#S{t0;uM*2Z_jQlA2JJ5a8Ck7%Q=7mxW-eR#lfZ=zFqYj4Zj*S*suudC%eEtx| z?-oGj#wT$-{((B7ZkZcvNRD_9g1aqno*iH?d`Wo+Li}%^$Fmx3k^%ehZ!P>oQ3mf1 zL0@|VwAJK8Q^`qayp$41#V2sh=p;<_0?S5NEENwZu}k^d+S>aCr*jV8L)kQX|7|D~ q7&W(nIz|5(xJ&q-Gmag^wW}KK%!Yx1f$yWv%$D%#sV;%ZO5nfOzM}yE literal 0 HcmV?d00001 diff --git a/docs/_templates/navbar-nav.html b/docs/_templates/navbar-nav.html new file mode 100644 index 0000000..afcdd67 --- /dev/null +++ b/docs/_templates/navbar-nav.html @@ -0,0 +1,41 @@ +
+
diff --git a/docs/conf.py b/docs/conf.py old mode 100755 new mode 100644 index 146c74f..08a6811 --- a/docs/conf.py +++ b/docs/conf.py @@ -1,37 +1,20 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- +# Configuration file for the Sphinx documentation builder. # -# pyrato documentation build configuration file, created by -# sphinx-quickstart on Fri Jun 9 13:47:02 2017. -# -# This file is execfile()d with the current directory set to its -# containing dir. -# -# Note that not all possible configuration values are present in this -# autogenerated file. -# -# All configuration values have a default; values that are commented out -# serve to show the default. +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information -# If extensions (or modules to document with autodoc) are in another -# directory, add these directories to sys.path here. If the directory is -# relative to the documentation root, use os.path.abspath to make it -# absolute, like shown here. -# import os import sys sys.path.insert(0, os.path.abspath('..')) -import pyrato +import pyrato # noqa -# -- General configuration --------------------------------------------- +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration -# If your documentation needs a minimal Sphinx version, state it here. -# -# needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.viewcode', @@ -39,7 +22,10 @@ 'sphinx.ext.autosummary', 'matplotlib.sphinxext.plot_directive', 'sphinx.ext.imgmath', - 'autodocsumm'] + 'autodocsumm', + 'sphinx_design', + 'sphinx_favicon', +] # show tocs for classes and functions of modules using the autodocsumm # package @@ -50,7 +36,7 @@ plot_include_source = True # Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] +# templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: @@ -80,99 +66,127 @@ # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. -language = None +language = 'en' + # List of patterns, relative to source directory, that match files and +templates_path = ['_templates'] # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path + + exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' +# The name of the Pygments (syntax highlighting) style to use (Not defining +# uses the default style of the html_theme). +# pygments_style = 'sphinx' + +# If true, '()' will be appended to :func: etc. cross-reference text. +add_function_parentheses = False # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False +# default language for highlighting in source code +highlight_language = "python3" -# -- Options for HTML output ------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = 'insipid' +# intersphinx mapping +intersphinx_mapping = { + 'numpy': ('https://numpy.org/doc/stable/', None), + 'scipy': ('https://docs.scipy.org/doc/scipy/', None), + 'matplotlib': ('https://matplotlib.org/stable/', None), + 'spharpy': ('https://spharpy.readthedocs.io/en/stable/', None) + } -# Theme options are theme-specific and customize the look and feel of a -# theme further. For a list of options available for each theme, see the -# documentation. -# -# html_theme_options = {} +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". +html_theme = 'pydata_sphinx_theme' html_static_path = ['_static'] - - -# -- Options for HTMLHelp output --------------------------------------- - -# Output file base name for HTML help builder. -htmlhelp_basename = 'pyratodoc' - - -# -- Options for LaTeX output ------------------------------------------ - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - # - # 'papersize': 'letterpaper', - - # The font size ('10pt', '11pt' or '12pt'). - # - # 'pointsize': '10pt', - - # Additional stuff for the LaTeX preamble. - # - # 'preamble': '', - - # Latex figure (float) alignment - # - # 'figure_align': 'htbp', +html_css_files = ['css/custom.css'] +html_logo = 'resources/logos/pyfar_logos_fixed_size_pyrato.png' +html_title = "pyfar" +html_favicon = '_static/favicon.ico' + + +# -- HTML theme options +# https://pydata-sphinx-theme.readthedocs.io/en/stable/user_guide/layout.html + +html_theme_options = { + "navbar_start": ["navbar-logo"], + "navbar_end": ["navbar-icon-links"], + "navbar_align": "content", + "icon_links": [ + { + "name": "GitHub", + "url": "https://github.com/pyfar", + "icon": "fa-brands fa-square-github", + "type": "fontawesome", + }, + { + "name": "CC-BY", + "url": "https://creativecommons.org/licenses/by/4.0/deed.de", + "icon": "fa-brands fa-creative-commons-by", + "type": "fontawesome", + } + ], + + # Configure secondary (right) side bar + "show_toc_level": 3, # Show all subsections of notebooks + "secondary_sidebar_items": ["page-toc"], # Omit 'show source' link that that + # shows notebook in json format + "navigation_with_keys": True, } -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, author, documentclass -# [howto, manual, or own class]). -latex_documents = [ - (master_doc, 'pyrato.tex', - u'pyrato Documentation', - u'Marco Berzborn - Institute of Technical Acoustics', 'manual'), -] - - -# -- Options for manual page output ------------------------------------ - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - (master_doc, 'pyrato', - u'pyrato Documentation', - [author], 1) -] - - +html_context = { + "default_mode": "light" +} # -- Options for Texinfo output ---------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) -texinfo_documents = [ - (master_doc, 'pyrato', - u'pyrato Documentation', - author, - 'pyrato', - 'One line description of project.', - 'Miscellaneous'), -] - - - +# texinfo_documents = [ +# (master_doc, 'pyfar', +# u'pyfar Gallery', +# author, +# 'pyfar', +# 'One line description of project.', +# 'Miscellaneous'), +# ] + +# -- Options for nbsphinx ------------------------------------------------- +nbsphinx_prolog = r""" +{% set docname = 'doc/' + env.doc2path(env.docname, base=None) %} + +.. raw:: html + +
+ Open an interactive online version by clicking the badge + Binder badge + or + download + the notebook. + +
+ +""" + +# -- manage thumbnails -------------------------------------------------------- +# must be located in 'docs/_static' +nbsphinx_thumbnails = { +} diff --git a/docs/index.rst b/docs/index.rst index ade9482..398d111 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -11,7 +11,7 @@ Documentation ============= .. toctree:: - :maxdepth: 2 + :maxdepth: 3 modules diff --git a/docs/resources/logos/pyfar_logos_fixed_size_pyrato.png b/docs/resources/logos/pyfar_logos_fixed_size_pyrato.png new file mode 100644 index 0000000000000000000000000000000000000000..d3ea015c4e31fa5b36ac620760ab3bfa9eadeb23 GIT binary patch literal 9135 zcmdUVbyQT*_wOA_L`eyyC8WC>2^l~_q#U|OkWN8h07XI=5dmqG5JVKDq!Ca$qy#Ag zq=qhu8QvNE{@#0kzV%!0{qfdfao3!C_dfgXv(Mh2{W&+*P+yCjjFAk2AaZTEh7kl2 zwn7k0ij)A1y!K-?fFO2ATjRQMP}bUv=RMlMu+FU}exJ{Yzmv-+Q4h%*_}hjDl%eIS zc=aRbHW556FN>CfRZ1z#h1g6}e%(jQgz;HcM@g&Fg`ozXgy2qIcAo{Je3V4JCZR7<_td61(N^2LY{qKffn!#y7D)yM6#1yRDnMceQb z{=cx|ziIsce`2jng7k_eWu~2-z1EG9c9%U!t&Qm(6Xbu%{0*avyEIT7512m{J(+wJb=ytt>KQ=c?sY2l3%e5j)Y|BE5!q~UJnG<7mPSu~bXy_MCnbme=(&P4*k?yfF=`0sc1+FR>kq#a4A;t@J)AF0i z=1c~$x+5)jizL!-3TKi#rXl!0Q7&BAXgXF;QeQ4Kf3T;eq4lgg;n&n4wjJ$3VMe0G z(~`5#M`QP>IVY#Ig-@N5wPlm1#@%{qcHh}-5mA{hHB<3@k*>+d6SHu8JEp*KAR~(!n46KMkMw!9yU~*8M>z)ui5U z*@vUzT3Ub8!7L?qx<~3FXZp0cu!`UPEc>r<&ZwF=&4Y8@3qCZ9G^HB}q28sVDkt!K z7I8tw;LV_=PrpkKI_)dWd*2kzSjhPKzniJ_R}Vc$J)2(EKgS#fo-Gb9ph{nAUN|59h#+j%3!L|nJAl<2@{(pJ z>c?r8B1-F2ep*Ve;@G3|lV2a*Xsd9Eegr@8!`*MbvfAWz5LGL)HiwgWu(tjvwxrIt zR>y2TLGw^z@!N>+BF<3$;PG%xzZ-ge$1930BJUC5ExS8~x#1`Ax!4njq%G7p8g0)C zbHQA+Wi)-ABPVO1T$6-Friu@49UbPMyvuBAwt>bjnaKpQZZ!CK3a+?aT``{P8u%Rw zbpK+?tbch99ll$dxe<;_@f_LQguxiU8lk`DW}Woa5F^A>D~3*~&2Bk=9GuG%IKpH<}pI9v{9i z+vfK+BcAYm_Obe1V8?;uh$xWA36ZydPNQ^5yWm2<%98uuY(I=ZTXM2JCz2xqXtvUE27bE0qR|hYzZs;C^cL z@z_|mT>W^B!+q!$Z{?-BRuVGIgj)OdyID2|^k#}q%9V9yFSHmE|1^VDz26r+G;e%Q1bw=KDhvReaKVRDwn3)gMQagofp2o+#l6ln(wU$~o2G!Lbl#dQO_B}aX zp7lzbOhBg*@|CZ$g>}#)1nJ#8-DYd+7{*fcyVa4td zkGq%Ohf?3{*CH@)qNNy}Ycl)H@&(_j%}U3%eCp)QSVYhkskJr@l+$%1BRiMcMKuyr zd!63Tc`48viBu*tlHSs5rn(t1^P`w2T;!dn`}&HdOTb(Gz_*E??N?66K87C`VW(vH zyf_jrGiPVL$Y5f&LK5vPd9wmvy`N@syBCXFvt375pIZH~`TO;~N4;$I#$T+#f#iwH z)YPosk(QumjugoHhxt~^?*{z$Y}hn!-t6C_R5|)(+8v);VdNAIm9Y@`Xu3V__J4- zlCXbP(25_l)caJ`&FU;`Y7aIa?b!HjO#J-JZP^lLwTT1)%)xj2ItX8V6)Uj6bT8Rk zAcaaZ!cIq&Tqv5*~uy*ZDxY)qGW<1OYqPFIzsMs|#p8iv0m zuDRbC;|jXD&N)gAV+_{iqG@V+tfF+N%~5e=C?Ls-Tcr*1I!X}>DaN;#U*zj*++42aKruULc2@7!`|0W3tCxfqF5+Fw9B!t z(nUYBXmk8gm#s(k=#nNcn>MeJZ5sToH^SBoXjH5>$ZR?0e8oMx+t98svi8iAEOSWr zJnm?8F=sEX0N5Hi=VPe(o|8aQtxfdFpt4eWKiR!qVg4RE8?YxIeIQ3TN^+<5%V9Z< zd>^k`WR9KA%0(i6>3y)NJ~otpNj+I%#j;_CjhQ<_a;kC~<4O+7;reCCYe&euSAU&; 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The array has a new axis - with dimension corresponding to the number of frequency bands: - [num_fractions, *signal.shape] + with dimension corresponding to the number of frequency bands + (num_fractions, signal.shape) """ From 96f962e645f1f0e6eb01880f596604c9ea206a62 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Wed, 20 Mar 2024 15:20:28 +0100 Subject: [PATCH 63/66] update links and copyrights --- LICENSE | 3 ++- README.rst | 6 +++--- docs/_templates/navbar-nav.html | 10 +++++----- docs/conf.py | 4 ++-- docs/index.rst | 2 +- 5 files changed, 13 insertions(+), 12 deletions(-) diff --git a/LICENSE b/LICENSE index 9aec10f..63dc56f 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,7 @@ MIT License -Copyright (c) 2018, Marco Berzborn - Institute of Technical Acoustics +Copyright (c) 2021-2023, Marco Berzborn - Institute of Technical Acoustics +Copyright (c) 2023, The pyfar developers Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/README.rst b/README.rst index 29fc334..def0187 100644 --- a/README.rst +++ b/README.rst @@ -3,8 +3,8 @@ pyrato ====== .. image:: https://badge.fury.io/py/pyrato.svg :target: https://badge.fury.io/py/pyrato -.. image:: https://readthedocs.org/projects/pyrato/badge/?version=latest - :target: https://pyrato.readthedocs.io/en/latest/?badge=latest +.. image:: https://readthedocs.org/projects/pyrato/badge/?version=stable + :target: https://pyrato.readthedocs.io/en/stable/?badge=stable :alt: Documentation Status .. image:: https://circleci.com/gh/pyfar/pyrato.svg?style=shield :target: https://circleci.com/gh/pyfar/pyrato @@ -38,4 +38,4 @@ Refer to the `contribution guidelines`_ for more information. .. _contribution guidelines: https://github.com/pyfar/pyrato/blob/main/CONTRIBUTING.rst .. _examples notebook: https://mybinder.org/v2/gh/pyfar/pyrato/HEAD?filepath=examples%2Fenergy_decay_curves_and_reverberation_time.ipynb -.. _read the docs: https://pyrato.readthedocs.io/en/latest +.. _read the docs: https://pyrato.readthedocs.io/en/stable diff --git a/docs/_templates/navbar-nav.html b/docs/_templates/navbar-nav.html index afcdd67..69beba4 100644 --- a/docs/_templates/navbar-nav.html +++ b/docs/_templates/navbar-nav.html @@ -3,7 +3,7 @@
  • - + Home
    • @@ -15,25 +15,25 @@
  • - + pyfar
  • - + sofar
  • - + spharpy
  • - + pyrato
    • diff --git a/docs/conf.py b/docs/conf.py index 08a6811..1f8c972 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -49,8 +49,8 @@ # General information about the project. project = u'pyrato' -copyright = u"2021, Marco Berzborn - Institute for Hearing Technology and Acoustics" -author = u"Marco Berzborn - Institute for Hearing Technology and Acoustics" +copyright = u"2021-2023, Marco Berzborn; 2023, The pyfar developers" +author = u"The pyfar developers" # The version info for the project you're documenting, acts as replacement # for |version| and |release|, also used in various other places throughout diff --git a/docs/index.rst b/docs/index.rst index 398d111..ade9482 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -11,7 +11,7 @@ Documentation ============= .. toctree:: - :maxdepth: 3 + :maxdepth: 2 modules From 176e3dca35e7f5dc3b66a38a98b388a1c8f23ff4 Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Wed, 20 Mar 2024 15:26:06 +0100 Subject: [PATCH 64/66] add python 3.11 and 3.12 to testing --- .circleci/config.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.circleci/config.yml b/.circleci/config.yml index 9427f2e..d4f7e9b 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -131,6 +131,8 @@ workflows: - "3.8" - "3.9" - "3.10" + - "3.11" + - "3.12" - flake: matrix: parameters: @@ -158,6 +160,8 @@ workflows: - "3.8" - "3.9" - "3.10" + - "3.11" + - "3.12" filters: branches: ignore: /.*/ From 2cc8e95012ce33b5cc731c23fcccca386334b41e Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Wed, 20 Mar 2024 15:26:44 +0100 Subject: [PATCH 65/66] add supported versions to setup.py --- setup.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/setup.py b/setup.py index 4f30d84..defef73 100644 --- a/setup.py +++ b/setup.py @@ -43,6 +43,8 @@ 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', + 'Programming Language :: Python :: 3.11', + 'Programming Language :: Python :: 3.12', ], description="Collection of functions commonly used in room acoustics", install_requires=requirements, From 5da9f1cf6d851da7861b2161e803b4ee326adeef Mon Sep 17 00:00:00 2001 From: Marco Berzborn Date: Wed, 20 Mar 2024 15:28:54 +0100 Subject: [PATCH 66/66] update author information --- pyrato/__init__.py | 4 ++-- setup.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pyrato/__init__.py b/pyrato/__init__.py index 8472f80..2384d1f 100644 --- a/pyrato/__init__.py +++ b/pyrato/__init__.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- __author__ = \ - """Marco Berzborn - Institute for Hearing Technology and Acoustics""" -__email__ = 'marco.berzborn@akustik.rwth-aachen.de' + """The pyfar developers""" +__email__ = 'info@pyfar.org' __version__ = '0.3.2' from .rap import ( diff --git a/setup.py b/setup.py index defef73..7c5bfcd 100644 --- a/setup.py +++ b/setup.py @@ -32,8 +32,8 @@ ] setup( - author="Marco Berzborn - Institute for Hearing Technology and Acoustics", - author_email='marco.berzborn@akustik.rwth-aachen.de', + author="The pyfar developers", + author_email='info@pyfar.org', classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Science/Research',