Cookiecutter fix ruff

pyproject.toml

@@ -102,14 +102,14 @@ lint.select = [
     # "D",      # Docstring guidelines
     # "NPY",    # Check all numpy related deprecations
     # "D417",   # Missing argument descriptions in the docstring
-    # "PT",     # Pytest style
-    # "A",      # Avoid builtin function and type shadowing
-    # "ERA",    # No commented out code
-    # "NPY",    # Check all numpy related deprecations
-    # "COM",    # trailing comma rules
-    # "I002",   # missing required import
-    # "TID252", # Use absolute over relative imports
-    # "FIX",    # Code should not contain FIXME, TODO, etc
+    "PT",     # Pytest style
+    "A",      # Avoid builtin function and type shadowing
+    "ERA",    # No commented out code
+    "NPY",    # Check all numpy related deprecations
+    "COM",    # trailing comma rules
+    "I002",   # missing required import
+    "TID252", # Use absolute over relative imports
+    "FIX",    # Code should not contain FIXME, TODO, etc
 ]
 
 # Ignore missing docstrings in tests

pyrato/__init__.py

@@ -8,7 +8,7 @@
 
 
 from .rap import (
-    reverberation_time_linear_regression
+    reverberation_time_linear_regression,
 )
 from .roomacoustics import (
     reverberation_time_energy_decay_curve,

pyrato/analytic/__init__.py

@@ -1,9 +1,9 @@
 from .analytic import (
     rectangular_room_rigid_walls,
-    eigenfrequencies_rectangular_room_rigid
+    eigenfrequencies_rectangular_room_rigid,
 )
 
 __all__ = (
     'rectangular_room_rigid_walls',
-    'eigenfrequencies_rectangular_room_rigid'
+    'eigenfrequencies_rectangular_room_rigid',
 )

pyrato/analytic/analytic.py

@@ -76,7 +76,7 @@ def eigenfrequencies_rectangular_room_rigid(
         for n_y in range(0, n_y_max):
             n_modes += int(np.floor(np.real(
                 np.sqrt(
-                    (2*f_max/c)**2 - (n_x/L_x)**2 - (n_y/L_y)**2
+                    (2*f_max/c)**2 - (n_x/L_x)**2 - (n_y/L_y)**2,
                 ) * L_z))) + 1
 
     n = np.zeros((3, n_modes), dtype=int)
@@ -89,7 +89,7 @@ def eigenfrequencies_rectangular_room_rigid(
         for n_y in range(0, n_y_max):
             n_z_max = int(np.floor(np.real(
                 np.sqrt(
-                    (2*f_max/c)**2 - (n_x/L_x)**2 - (n_y/L_y)**2
+                    (2*f_max/c)**2 - (n_x/L_x)**2 - (n_y/L_y)**2,
                 ) * L_z))) + 1
 
             idx_end = idx + n_z_max

pyrato/analytic/impedance.py

@@ -284,7 +284,7 @@ def eigenfrequencies_rectangular_room_impedance(
     mask = ks >= 0.02
     ks_search = ks[mask]
     k_ns = normal_eigenfrequencies_rectangular_room_impedance(
-        L, ks_search, k_max, zeta
+        L, ks_search, k_max, zeta,
     )
     for idx in range(0, len(L)):
         k_ns[idx] = np.hstack((
@@ -394,7 +394,7 @@ def pressure_modal_superposition(
     k_ns_xyz = np.array([
         k_ns[0][mode_indices[:, 0]],
         k_ns[1][mode_indices[:, 1]],
-        k_ns[2][mode_indices[:, 2]]
+        k_ns[2][mode_indices[:, 2]],
         ])
 
     phi = np.arctanh(ks/(zeta_0 * k_ns_xyz.T).T)
@@ -491,7 +491,7 @@ def rectangular_room_impedance(
     k_ns_xyz = np.array([
         k_ns[0][mode_indices[:, 0]],
         k_ns[1][mode_indices[:, 1]],
-        k_ns[2][mode_indices[:, 2]]
+        k_ns[2][mode_indices[:, 2]],
         ])
 
     return rir, spectrum, k_ns_xyz

tests/test_analytic_impedance.py

@@ -12,7 +12,7 @@ def test_eigenfreq_impedance_1d_real():
     k_max = 1e3*2*np.pi/c
 
     k_ns = analytic.eigenfrequencies_rectangular_room_1d(
-        L, k, k_max, zeta
+        L, k, k_max, zeta,
     )
 
     truth = np.array([
@@ -35,7 +35,7 @@ def test_eigenfreq_impedance_1d_real_jac():
     k_max = 1e3*2*np.pi/c
 
     k_ns = analytic.eigenfrequencies_rectangular_room_1d(
-        L, k, k_max, zeta, gradient=True
+        L, k, k_max, zeta, gradient=True,
     )
 
     truth = np.array([
@@ -59,7 +59,7 @@ def test_analytic_shoebox_eigenfreqs_impedance_multi_k():
     k = np.linspace(0, k_max*1.1, 2**10)
 
     k_ns, _ = analytic.eigenfrequencies_rectangular_room_impedance(
-            L, k, k_max, zetas
+            L, k, k_max, zetas,
         )
 
     truth = np.array([
@@ -103,7 +103,7 @@ def test_analytic_shoebox_eigenfreqs_impedance():
     k_max = 1e3*2*np.pi/c
 
     k_ns, _ = analytic.eigenfrequencies_rectangular_room_impedance(
-            L, k, k_max, zetas
+            L, k, k_max, zetas,
         )
 
     truth = np.array([
@@ -148,7 +148,7 @@ def test_analytic_eigenfrequencies_impedance_cplx():
     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
+            L, k, k_max, zetas, only_normal=True,
         )
 
     k_ns_x = np.loadtxt(
@@ -180,7 +180,7 @@ def test_analytic_eigenfrequencies_impedance_zeta15():
     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
+            L, k, k_max, zetas, only_normal=True,
         )
 
     k_ns_x = np.loadtxt(

tests/test_data/generate_test_data.py

@@ -27,34 +27,6 @@
 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_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]))
-
-# psnr = 50
-
-# # 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))
-
-# rms = 10**(-(psnr-10) / 20)
-# noise = pf.signals.noise(
-#     n_samples*2, rms=rms, sampling_rate=sampling_rate, seed=1)
-
-# rir_array += noise
-
 # %%
 # Use existing RIR to avoid re-generating data for all tests
 
@@ -130,11 +102,6 @@
     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(
-#     edc_lundeby_chu_1D, intersection_time_1D[0], sampling_rate)
-# noise_energy_from_edc_2D = pyrato.edc.estimate_noise_energy_from_edc(
-#     edc_lundeby_chu_2D, intersection_time_2D[0], sampling_rate)
-
 # %%
 pf.plot.time(edc_lundeby_2D, dB=True, log_prefix=10)
 # %%
@@ -205,7 +172,4 @@
 np.savetxt("intersection_time_1D.csv", intersection_time_1D, delimiter=",")
 np.savetxt("intersection_time_2D.csv", intersection_time_2D, delimiter=",")
 
-#     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=",")
-
 # %%

tests/test_dsp.py

@@ -28,7 +28,8 @@ def test_start_ir_insufficient_snr():
 
     snr = 15
 
-    noise = np.random.randn(n_samples)
+    rng = np.random.default_rng()
+    noise = rng.standard_normal(n_samples)
     noise = noise / np.sqrt(np.mean(np.abs(noise**2))) * 10**(-snr/20)
     noise = pf.Signal(noise, 44100)
 
@@ -42,8 +43,8 @@ def test_start_ir():
     n_samples = 2**10
     ir = np.zeros(n_samples)
     snr = 60
-
-    noise = pf.Signal(np.random.randn(n_samples) * 10**(-snr/20), 44100)
+    rng = np.random.default_rng()
+    noise = pf.Signal(rng.standard_normal(n_samples) * 10**(-snr/20), 44100)
 
     start_sample = 24
     ir[start_sample] = 1
@@ -77,10 +78,11 @@ def test_start_ir_multidim():
     n_channels = 3
     ir = np.zeros((n_channels, n_samples))
 
+    rng = np.random.default_rng()
     snr = 60
 
     noise = pf.Signal(
-        np.random.randn(n_channels, n_samples) * 10**(-snr/20), 44100)
+        rng.standard_normal(n_channels, n_samples) * 10**(-snr/20), 44100)
 
     start_sample = [24, 5, 43]
     ir[[0, 1, 2], start_sample] = 1
@@ -94,7 +96,7 @@ def test_start_ir_multidim():
 
     ir = np.zeros((2, n_channels, n_samples))
     noise = pf.Signal(
-        np.random.randn(2, n_channels, n_samples) * 10**(-snr/20), 44100)
+        rng.standard_normal(2, n_channels, n_samples) * 10**(-snr/20), 44100)
 
     start_sample_1 = [24, 5, 43]
     ir[0, [0, 1, 2], start_sample_1] = 1
@@ -144,9 +146,9 @@ def test_max_ir():
     ir = np.zeros(n_samples)
 
     snr = 60
-
+    rng = np.random.default_rng()
     noise = pf.Signal(
-        np.random.randn(n_samples) * 10**(-snr/20), 44100)
+        rng.standard_normal(n_samples) * 10**(-snr/20), 44100)
 
     start_sample = 24
     ir[start_sample] = 1

tests/test_rt.py

@@ -2,8 +2,6 @@
 # -*- coding: utf-8 -*-
 
 """ Tests for reverberation time related things. """
-from pytest import raises
-
 import numpy as np
 import numpy.testing as npt
 
@@ -62,5 +60,5 @@ def test_rt_from_edc_error():
     edc_exp = pf.TimeData(10**(edc/10), times)
     T = 'Bla'
 
-    with raises(ValueError, match='is not a valid interval.'):
+    with pytest.raises(ValueError, match='is not a valid interval.'):
         ra.reverberation_time_linear_regression(edc_exp, T=T)