diff --git a/TESTS/unitTests.py b/TESTS/unitTests.py index 4a90787..1068acd 100755 --- a/TESTS/unitTests.py +++ b/TESTS/unitTests.py @@ -213,7 +213,7 @@ class pointOpTests(unittest.TestCase): def test1(self): matImg = scipy.io.loadmat(op.join(matfiles_path, 'pointOp1.mat')) img = pt.synthetic_images.ramp((200,200)) - filt = np.array([0.2, 0.5, 1.0, 0.4, 0.1]); + filt = np.asarray([0.2, 0.5, 1.0, 0.4, 0.1]); #foo = pointOp(200, 200, img, 5, filt, 0, 1, 0); foo = pt.pointOp(img, filt, 0, 1); foo = np.reshape(foo,(200,200)) @@ -249,15 +249,15 @@ def test13(self): class binomialFilterTests(unittest.TestCase): def test1(self): - target = np.array([[0.5],[0.5]]) + target = np.asarray([[0.5],[0.5]]) #target = target / np.sqrt(np.sum(target ** 2)) self.assertTrue((pt.binomial_filter(2) == target).all() ) def test2(self): - target = np.array([[0.25], [0.5], [0.25]]) + target = np.asarray([[0.25], [0.5], [0.25]]) #target = target / np.sqrt(np.sum(target ** 2)) self.assertTrue((pt.binomial_filter(3) == target).all()) def test3(self): - target = np.array([[0.0625], [0.25], [0.3750], [0.25], [0.0625]]) + target = np.asarray([[0.0625], [0.25], [0.3750], [0.25], [0.0625]]) #target = target / np.sqrt(np.sum(target ** 2)) self.assertTrue((pt.binomial_filter(5) == target).all()) @@ -269,13 +269,13 @@ def test1(self): self.assertTrue(pt.comparePyr(matPyr['pyr'], pyPyr)) def test2(self): matPyr = scipy.io.loadmat(op.join(matfiles_path, 'buildGpyr2row.mat')) - img = np.array(list(range(256))).astype(float) + img = np.asarray(list(range(256))).astype(float) img = img.reshape(1, 256) pyPyr = pt.pyramids.GaussianPyramid(img) self.assertTrue(pt.comparePyr(matPyr['pyr'], pyPyr)) def test3(self): matPyr = scipy.io.loadmat(op.join(matfiles_path, 'buildGpyr2col.mat')) - img = np.array(list(range(256))).astype(float) + img = np.asarray(list(range(256))).astype(float) img = img.reshape(256, 1) pyPyr = pt.pyramids.GaussianPyramid(img) self.assertTrue(pt.comparePyr(matPyr['pyr'], pyPyr)) @@ -318,17 +318,17 @@ def test4(self): self.assertTrue(pt.comparePyr(matPyr['pyr'], pyPyr)) def test5(self): matPyr = scipy.io.loadmat(op.join(matfiles_path, 'buildLpyr5.mat')) - pyRamp = np.array(list(range(200))).reshape(1, 200) + pyRamp = np.asarray(list(range(200))).reshape(1, 200) pyPyr = pt.pyramids.LaplacianPyramid(pyRamp) self.assertTrue(pt.comparePyr(matPyr['pyr'], pyPyr)) def test5bis(self): matPyr = scipy.io.loadmat(op.join(matfiles_path, 'buildLpyr5.mat')) - pyRamp = np.array(list(range(200))) + pyRamp = np.asarray(list(range(200))) pyPyr = pt.pyramids.LaplacianPyramid(pyRamp) self.assertTrue(pt.comparePyr(matPyr['pyr'], pyPyr)) def test6(self): matPyr = scipy.io.loadmat(op.join(matfiles_path, 'buildLpyr6.mat')) - pyRamp = np.array(list(range(200))) + pyRamp = np.asarray(list(range(200))) pyPyr = pt.pyramids.LaplacianPyramid(pyRamp) self.assertTrue(pt.comparePyr(matPyr['pyr'], pyPyr)) def test7(self): @@ -1145,16 +1145,16 @@ def test0(self): matPyr = scipy.io.loadmat(op.join(matfiles_path, 'imGradient0.mat')) ramp = pt.synthetic_images.ramp(10) [dx,dy] = pt.image_gradient(ramp) - dx = np.array(dx) - dy = np.array(dy) + dx = np.asarray(dx) + dy = np.asarray(dy) self.assertTrue(pt.compareRecon(matPyr['res'][:,:,0], dx)) self.assertTrue(pt.compareRecon(matPyr['res'][:,:,1], dy)) def test1(self): matPyr = scipy.io.loadmat(op.join(matfiles_path, 'imGradient1.mat')) ramp = pt.synthetic_images.ramp(10) [dx,dy] = pt.image_gradient(ramp, 'reflect1') - dx = np.array(dx) - dy = np.array(dy) + dx = np.asarray(dx) + dy = np.asarray(dy) self.assertTrue(pt.compareRecon(matPyr['res'][:,:,0], dx)) self.assertTrue(pt.compareRecon(matPyr['res'][:,:,1], dy)) diff --git a/src/pyrtools/pyramids/SteerablePyramidFreq.py b/src/pyrtools/pyramids/SteerablePyramidFreq.py index eb6dffc..607c61b 100644 --- a/src/pyrtools/pyramids/SteerablePyramidFreq.py +++ b/src/pyrtools/pyramids/SteerablePyramidFreq.py @@ -114,8 +114,8 @@ def __init__(self, image, height='auto', order=3, twidth=1, is_complex=False): raise ValueError("twidth must be positive.") twidth = int(twidth) - dims = np.array(self.image.shape) - ctr = np.ceil((np.array(dims)+0.5)/2).astype(int) + dims = np.asarray(self.image.shape) + ctr = np.ceil((np.asarray(dims)+0.5)/2).astype(int) (xramp, yramp) = np.meshgrid(np.linspace(-1, 1, dims[1]+1)[:-1], np.linspace(-1, 1, dims[0]+1)[:-1]) @@ -126,7 +126,7 @@ def __init__(self, image, height='auto', order=3, twidth=1, is_complex=False): log_rad = np.log2(log_rad) # Radial transition function (a raised cosine in log-frequency): - (Xrcos, Yrcos) = rcosFn(twidth, (-twidth/2.0), np.array([0, 1])) + (Xrcos, Yrcos) = rcosFn(twidth, (-twidth/2.0), np.asarray([0, 1])) Yrcos = np.sqrt(Yrcos) YIrcos = np.sqrt(1.0 - Yrcos**2) @@ -191,7 +191,7 @@ def __init__(self, image, height='auto', order=3, twidth=1, is_complex=False): self.pyr_size[(i, b)] = band.shape self._anglemasks.append(anglemasks) - dims = np.array(lodft.shape) + dims = np.asarray(lodft.shape) ctr = np.ceil((dims+0.5)/2).astype(int) lodims = np.ceil((dims-0.5)/2).astype(int) loctr = np.ceil((lodims+0.5)/2).astype(int) @@ -210,7 +210,7 @@ def __init__(self, image, height='auto', order=3, twidth=1, is_complex=False): lodft = lodft * lomask lodft = np.fft.ifft2(np.fft.ifftshift(lodft)) - self.pyr_coeffs['residual_lowpass'] = np.real(np.array(lodft).copy()) + self.pyr_coeffs['residual_lowpass'] = np.real(np.asarray(lodft).copy()) self.pyr_size['residual_lowpass'] = lodft.shape def recon_pyr(self, levels='all', bands='all', twidth=1): @@ -248,7 +248,7 @@ def recon_pyr(self, levels='all', bands='all', twidth=1): if dims in dim_list: continue dim_list.append(dims) - dims = np.array(dims) + dims = np.asarray(dims) ctr = np.ceil((dims+0.5)/2).astype(int) lodims = np.ceil((dims-0.5)/2).astype(int) loctr = np.ceil((lodims+0.5)/2).astype(int) @@ -260,7 +260,7 @@ def recon_pyr(self, levels='all', bands='all', twidth=1): dim_list.append((dim_list[-1][0], dim_list[-1][1])) # matlab code starts here - dims = np.array(self.pyr_size['residual_highpass']) + dims = np.asarray(self.pyr_size['residual_highpass']) ctr = np.ceil((dims+0.5)/2.0).astype(int) (xramp, yramp) = np.meshgrid((np.arange(1, dims[1]+1)-ctr[1]) / (dims[1]/2.), @@ -271,7 +271,7 @@ def recon_pyr(self, levels='all', bands='all', twidth=1): log_rad = np.log2(log_rad) # Radial transition function (a raised cosine in log-frequency): - (Xrcos, Yrcos) = rcosFn(twidth, (-twidth/2.0), np.array([0, 1])) + (Xrcos, Yrcos) = rcosFn(twidth, (-twidth/2.0), np.asarray([0, 1])) Yrcos = np.sqrt(Yrcos) YIrcos = np.sqrt(1.0 - Yrcos**2) diff --git a/src/pyrtools/pyramids/SteerablePyramidSpace.py b/src/pyrtools/pyramids/SteerablePyramidSpace.py index f3db3ac..5cc488b 100644 --- a/src/pyrtools/pyramids/SteerablePyramidSpace.py +++ b/src/pyrtools/pyramids/SteerablePyramidSpace.py @@ -85,7 +85,7 @@ def __init__(self, image, height='auto', order=1, edge_type='reflect1'): for b in range(self.num_orientations): filt = self.filters['bfilts'][:, b].reshape(bfiltsz, bfiltsz).T band = corrDn(image=lo, filt=filt, edge_type=self.edge_type) - self.pyr_coeffs[(i, b)] = np.array(band) + self.pyr_coeffs[(i, b)] = np.asarray(band) self.pyr_size[(i, b)] = band.shape lo = corrDn(image=lo, filt=self.filters['lofilt'], edge_type=self.edge_type, step=(2, 2)) diff --git a/src/pyrtools/pyramids/c/wrapper.py b/src/pyrtools/pyramids/c/wrapper.py index ea744c8..994d7a6 100644 --- a/src/pyrtools/pyramids/c/wrapper.py +++ b/src/pyrtools/pyramids/c/wrapper.py @@ -254,4 +254,4 @@ def pointOp(image, lut, origin, increment, warnings=False): ctypes.c_double(origin), ctypes.c_double(increment), warnings) - return np.array(result) + return np.asarray(result) diff --git a/src/pyrtools/pyramids/filters.py b/src/pyrtools/pyramids/filters.py index 6aea10d..972c7f3 100644 --- a/src/pyrtools/pyramids/filters.py +++ b/src/pyrtools/pyramids/filters.py @@ -28,7 +28,7 @@ def parse_filter(filt, normalize=True): filt = named_filter(filt) elif isinstance(filt, np.ndarray) or isinstance(filt, list) or isinstance(filt, tuple): - filt = np.array(filt) + filt = np.asarray(filt) if filt.ndim == 1: filt = np.reshape(filt, (filt.shape[0], 1)) elif filt.ndim == 2 and filt.shape[0] == 1: @@ -45,9 +45,9 @@ def binomial_filter(order_plus_one): if order_plus_one < 2: raise Exception("Error: order_plus_one argument must be at least 2") - kernel = np.array([[0.5], [0.5]]) + kernel = np.asarray([[0.5], [0.5]]) for i in range(order_plus_one - 2): - kernel = convolve(np.array([[0.5], [0.5]]), kernel) + kernel = convolve(np.asarray([[0.5], [0.5]]), kernel) return kernel @@ -90,44 +90,44 @@ def named_filter(name): kernel = steerable_filters(name) elif name == "qmf5": - kernel = np.array([[-0.076103], [0.3535534], [0.8593118], [0.3535534], [-0.076103]]) + kernel = np.asarray([[-0.076103], [0.3535534], [0.8593118], [0.3535534], [-0.076103]]) elif name == "qmf9": - kernel = np.array([[0.02807382], [-0.060944743], [-0.073386624], [0.41472545], [0.7973934], - [0.41472545], [-0.073386624], [-0.060944743], [0.02807382]]) + kernel = np.asarray([[0.02807382], [-0.060944743], [-0.073386624], [0.41472545], [0.7973934], + [0.41472545], [-0.073386624], [-0.060944743], [0.02807382]]) elif name == "qmf13": - kernel = np.array([[-0.014556438], [0.021651438], [0.039045125], [-0.09800052], - [-0.057827797], [0.42995453], [0.7737113], [0.42995453], [-0.057827797], - [-0.09800052], [0.039045125], [0.021651438], [-0.014556438]]) + kernel = np.asarray([[-0.014556438], [0.021651438], [0.039045125], [-0.09800052], + [-0.057827797], [0.42995453], [0.7737113], [0.42995453], [-0.057827797], + [-0.09800052], [0.039045125], [0.021651438], [-0.014556438]]) elif name == "qmf8": - kernel = np.sqrt(2) * np.array([[0.00938715], [-0.07065183], [0.06942827], [0.4899808], - [0.4899808], [0.06942827], [-0.07065183], [0.00938715]]) + kernel = np.sqrt(2) * np.asarray([[0.00938715], [-0.07065183], [0.06942827], [0.4899808], + [0.4899808], [0.06942827], [-0.07065183], [0.00938715]]) elif name == "qmf12": - kernel = np.array([[-0.003809699], [0.01885659], [-0.002710326], [-0.08469594], - [0.08846992], [0.4843894], [0.4843894], [0.08846992], - [-0.08469594], [-0.002710326], [0.01885659], [-0.003809699]]) + kernel = np.asarray([[-0.003809699], [0.01885659], [-0.002710326], [-0.08469594], + [0.08846992], [0.4843894], [0.4843894], [0.08846992], + [-0.08469594], [-0.002710326], [0.01885659], [-0.003809699]]) kernel *= np.sqrt(2) elif name == "qmf16": - kernel = np.array([[0.001050167], [-0.005054526], [-0.002589756], [0.0276414], - [-0.009666376], [-0.09039223], [0.09779817], [0.4810284], [0.4810284], - [0.09779817], [-0.09039223], [-0.009666376], [0.0276414], - [-0.002589756], [-0.005054526], [0.001050167]]) + kernel = np.asarray([[0.001050167], [-0.005054526], [-0.002589756], [0.0276414], + [-0.009666376], [-0.09039223], [0.09779817], [0.4810284], [0.4810284], + [0.09779817], [-0.09039223], [-0.009666376], [0.0276414], + [-0.002589756], [-0.005054526], [0.001050167]]) kernel *= np.sqrt(2) elif name == "haar": - kernel = np.array([[1], [1]]) / np.sqrt(2) + kernel = np.asarray([[1], [1]]) / np.sqrt(2) elif name == "daub2": - kernel = np.array([[0.482962913145], [0.836516303738], [0.224143868042], - [-0.129409522551]]) + kernel = np.asarray([[0.482962913145], [0.836516303738], [0.224143868042], + [-0.129409522551]]) elif name == "daub3": - kernel = np.array([[0.332670552950], [0.806891509311], [0.459877502118], [-0.135011020010], - [-0.085441273882], [0.035226291882]]) + kernel = np.asarray([[0.332670552950], [0.806891509311], [0.459877502118], [-0.135011020010], + [-0.085441273882], [0.035226291882]]) elif name == "daub4": - kernel = np.array([[0.230377813309], [0.714846570553], [0.630880767930], - [-0.027983769417], [-0.187034811719], [0.030841381836], - [0.032883011667], [-0.010597401785]]) + kernel = np.asarray([[0.230377813309], [0.714846570553], [0.630880767930], + [-0.027983769417], [-0.187034811719], [0.030841381836], + [0.032883011667], [-0.010597401785]]) elif name == "gauss5": # for backward-compatibility - kernel = np.sqrt(2) * np.array([[0.0625], [0.25], [0.375], [0.25], [0.0625]]) + kernel = np.sqrt(2) * np.asarray([[0.0625], [0.25], [0.375], [0.25], [0.0625]]) elif name == "gauss3": # for backward-compatibility - kernel = np.sqrt(2) * np.array([[0.25], [0.5], [0.25]]) + kernel = np.sqrt(2) * np.asarray([[0.25], [0.5], [0.25]]) else: raise Exception("Error: Unknown filter name: %s" % (name)) @@ -163,802 +163,802 @@ def steerable_filters(filter_name): def _sp0_filters(): filters = {} - filters['harmonics'] = np.array([0]) + filters['harmonics'] = np.asarray([0]) filters['lo0filt'] = ( - np.array([[-4.514000e-04, -1.137100e-04, -3.725800e-04, -3.743860e-03, - -3.725800e-04, -1.137100e-04, -4.514000e-04], - [-1.137100e-04, -6.119520e-03, -1.344160e-02, -7.563200e-03, - -1.344160e-02, -6.119520e-03, -1.137100e-04], - [-3.725800e-04, -1.344160e-02, 6.441488e-02, 1.524935e-01, - 6.441488e-02, -1.344160e-02, -3.725800e-04], - [-3.743860e-03, -7.563200e-03, 1.524935e-01, 3.153017e-01, - 1.524935e-01, -7.563200e-03, -3.743860e-03], - [-3.725800e-04, -1.344160e-02, 6.441488e-02, 1.524935e-01, - 6.441488e-02, -1.344160e-02, -3.725800e-04], - [-1.137100e-04, -6.119520e-03, -1.344160e-02, -7.563200e-03, - -1.344160e-02, -6.119520e-03, -1.137100e-04], - [-4.514000e-04, -1.137100e-04, -3.725800e-04, -3.743860e-03, - -3.725800e-04, -1.137100e-04, -4.514000e-04]])) + np.asarray([[-4.514000e-04, -1.137100e-04, -3.725800e-04, -3.743860e-03, + -3.725800e-04, -1.137100e-04, -4.514000e-04], + [-1.137100e-04, -6.119520e-03, -1.344160e-02, -7.563200e-03, + -1.344160e-02, -6.119520e-03, -1.137100e-04], + [-3.725800e-04, -1.344160e-02, 6.441488e-02, 1.524935e-01, + 6.441488e-02, -1.344160e-02, -3.725800e-04], + [-3.743860e-03, -7.563200e-03, 1.524935e-01, 3.153017e-01, + 1.524935e-01, -7.563200e-03, -3.743860e-03], + [-3.725800e-04, -1.344160e-02, 6.441488e-02, 1.524935e-01, + 6.441488e-02, -1.344160e-02, -3.725800e-04], + [-1.137100e-04, -6.119520e-03, -1.344160e-02, -7.563200e-03, + -1.344160e-02, -6.119520e-03, -1.137100e-04], + [-4.514000e-04, -1.137100e-04, -3.725800e-04, -3.743860e-03, + -3.725800e-04, -1.137100e-04, -4.514000e-04]])) filters['lofilt'] = ( - np.array([[-2.257000e-04, -8.064400e-04, -5.686000e-05, 8.741400e-04, - -1.862800e-04, -1.031640e-03, -1.871920e-03, -1.031640e-03, - -1.862800e-04, 8.741400e-04, -5.686000e-05, -8.064400e-04, - -2.257000e-04], - [-8.064400e-04, 1.417620e-03, -1.903800e-04, -2.449060e-03, - -4.596420e-03, -7.006740e-03, -6.948900e-03, -7.006740e-03, - -4.596420e-03, -2.449060e-03, -1.903800e-04, 1.417620e-03, - -8.064400e-04], - [-5.686000e-05, -1.903800e-04, -3.059760e-03, -6.401000e-03, - -6.720800e-03, -5.236180e-03, -3.781600e-03, -5.236180e-03, - -6.720800e-03, -6.401000e-03, -3.059760e-03, -1.903800e-04, - -5.686000e-05], - [8.741400e-04, -2.449060e-03, -6.401000e-03, -5.260020e-03, - 3.938620e-03, 1.722078e-02, 2.449600e-02, 1.722078e-02, - 3.938620e-03, -5.260020e-03, -6.401000e-03, -2.449060e-03, - 8.741400e-04], - [-1.862800e-04, -4.596420e-03, -6.720800e-03, 3.938620e-03, - 3.220744e-02, 6.306262e-02, 7.624674e-02, 6.306262e-02, - 3.220744e-02, 3.938620e-03, -6.720800e-03, -4.596420e-03, - -1.862800e-04], - [-1.031640e-03, -7.006740e-03, -5.236180e-03, 1.722078e-02, - 6.306262e-02, 1.116388e-01, 1.348999e-01, 1.116388e-01, - 6.306262e-02, 1.722078e-02, -5.236180e-03, -7.006740e-03, - -1.031640e-03], - [-1.871920e-03, -6.948900e-03, -3.781600e-03, 2.449600e-02, - 7.624674e-02, 1.348999e-01, 1.576508e-01, 1.348999e-01, - 7.624674e-02, 2.449600e-02, -3.781600e-03, -6.948900e-03, - -1.871920e-03], - [-1.031640e-03, -7.006740e-03, -5.236180e-03, 1.722078e-02, - 6.306262e-02, 1.116388e-01, 1.348999e-01, 1.116388e-01, - 6.306262e-02, 1.722078e-02, -5.236180e-03, -7.006740e-03, - -1.031640e-03], - [-1.862800e-04, -4.596420e-03, -6.720800e-03, 3.938620e-03, - 3.220744e-02, 6.306262e-02, 7.624674e-02, 6.306262e-02, - 3.220744e-02, 3.938620e-03, -6.720800e-03, -4.596420e-03, - -1.862800e-04], - [8.741400e-04, -2.449060e-03, -6.401000e-03, -5.260020e-03, - 3.938620e-03, 1.722078e-02, 2.449600e-02, 1.722078e-02, - 3.938620e-03, -5.260020e-03, -6.401000e-03, -2.449060e-03, - 8.741400e-04], - [-5.686000e-05, -1.903800e-04, -3.059760e-03, -6.401000e-03, - -6.720800e-03, -5.236180e-03, -3.781600e-03, -5.236180e-03, - -6.720800e-03, -6.401000e-03, -3.059760e-03, -1.903800e-04, - -5.686000e-05], - [-8.064400e-04, 1.417620e-03, -1.903800e-04, -2.449060e-03, - -4.596420e-03, -7.006740e-03, -6.948900e-03, -7.006740e-03, - -4.596420e-03, -2.449060e-03, -1.903800e-04, 1.417620e-03, - -8.064400e-04], - [-2.257000e-04, -8.064400e-04, -5.686000e-05, 8.741400e-04, - -1.862800e-04, -1.031640e-03, -1.871920e-03, -1.031640e-03, - -1.862800e-04, 8.741400e-04, -5.686000e-05, -8.064400e-04, - -2.257000e-04]])) - filters['mtx'] = np.array([1.000000]) + np.asarray([[-2.257000e-04, -8.064400e-04, -5.686000e-05, 8.741400e-04, + -1.862800e-04, -1.031640e-03, -1.871920e-03, -1.031640e-03, + -1.862800e-04, 8.741400e-04, -5.686000e-05, -8.064400e-04, + -2.257000e-04], + [-8.064400e-04, 1.417620e-03, -1.903800e-04, -2.449060e-03, + -4.596420e-03, -7.006740e-03, -6.948900e-03, -7.006740e-03, + -4.596420e-03, -2.449060e-03, -1.903800e-04, 1.417620e-03, + -8.064400e-04], + [-5.686000e-05, -1.903800e-04, -3.059760e-03, -6.401000e-03, + -6.720800e-03, -5.236180e-03, -3.781600e-03, -5.236180e-03, + -6.720800e-03, -6.401000e-03, -3.059760e-03, -1.903800e-04, + -5.686000e-05], + [8.741400e-04, -2.449060e-03, -6.401000e-03, -5.260020e-03, + 3.938620e-03, 1.722078e-02, 2.449600e-02, 1.722078e-02, + 3.938620e-03, -5.260020e-03, -6.401000e-03, -2.449060e-03, + 8.741400e-04], + [-1.862800e-04, -4.596420e-03, -6.720800e-03, 3.938620e-03, + 3.220744e-02, 6.306262e-02, 7.624674e-02, 6.306262e-02, + 3.220744e-02, 3.938620e-03, -6.720800e-03, -4.596420e-03, + -1.862800e-04], + [-1.031640e-03, -7.006740e-03, -5.236180e-03, 1.722078e-02, + 6.306262e-02, 1.116388e-01, 1.348999e-01, 1.116388e-01, + 6.306262e-02, 1.722078e-02, -5.236180e-03, -7.006740e-03, + -1.031640e-03], + [-1.871920e-03, -6.948900e-03, -3.781600e-03, 2.449600e-02, + 7.624674e-02, 1.348999e-01, 1.576508e-01, 1.348999e-01, + 7.624674e-02, 2.449600e-02, -3.781600e-03, -6.948900e-03, + -1.871920e-03], + [-1.031640e-03, -7.006740e-03, -5.236180e-03, 1.722078e-02, + 6.306262e-02, 1.116388e-01, 1.348999e-01, 1.116388e-01, + 6.306262e-02, 1.722078e-02, -5.236180e-03, -7.006740e-03, + -1.031640e-03], + [-1.862800e-04, -4.596420e-03, -6.720800e-03, 3.938620e-03, + 3.220744e-02, 6.306262e-02, 7.624674e-02, 6.306262e-02, + 3.220744e-02, 3.938620e-03, -6.720800e-03, -4.596420e-03, + -1.862800e-04], + [8.741400e-04, -2.449060e-03, -6.401000e-03, -5.260020e-03, + 3.938620e-03, 1.722078e-02, 2.449600e-02, 1.722078e-02, + 3.938620e-03, -5.260020e-03, -6.401000e-03, -2.449060e-03, + 8.741400e-04], + [-5.686000e-05, -1.903800e-04, -3.059760e-03, -6.401000e-03, + -6.720800e-03, -5.236180e-03, -3.781600e-03, -5.236180e-03, + -6.720800e-03, -6.401000e-03, -3.059760e-03, -1.903800e-04, + -5.686000e-05], + [-8.064400e-04, 1.417620e-03, -1.903800e-04, -2.449060e-03, + -4.596420e-03, -7.006740e-03, -6.948900e-03, -7.006740e-03, + -4.596420e-03, -2.449060e-03, -1.903800e-04, 1.417620e-03, + -8.064400e-04], + [-2.257000e-04, -8.064400e-04, -5.686000e-05, 8.741400e-04, + -1.862800e-04, -1.031640e-03, -1.871920e-03, -1.031640e-03, + -1.862800e-04, 8.741400e-04, -5.686000e-05, -8.064400e-04, + -2.257000e-04]])) + filters['mtx'] = np.asarray([1.000000]) filters['hi0filt'] = ( - np.array([[5.997200e-04, -6.068000e-05, -3.324900e-04, -3.325600e-04, - -2.406600e-04, -3.325600e-04, -3.324900e-04, -6.068000e-05, - 5.997200e-04], - [-6.068000e-05, 1.263100e-04, 4.927100e-04, 1.459700e-04, - -3.732100e-04, 1.459700e-04, 4.927100e-04, 1.263100e-04, - -6.068000e-05], - [-3.324900e-04, 4.927100e-04, -1.616650e-03, -1.437358e-02, - -2.420138e-02, -1.437358e-02, -1.616650e-03, 4.927100e-04, - -3.324900e-04], - [-3.325600e-04, 1.459700e-04, -1.437358e-02, -6.300923e-02, - -9.623594e-02, -6.300923e-02, -1.437358e-02, 1.459700e-04, - -3.325600e-04], - [-2.406600e-04, -3.732100e-04, -2.420138e-02, -9.623594e-02, - 8.554893e-01, -9.623594e-02, -2.420138e-02, -3.732100e-04, - -2.406600e-04], - [-3.325600e-04, 1.459700e-04, -1.437358e-02, -6.300923e-02, - -9.623594e-02, -6.300923e-02, -1.437358e-02, 1.459700e-04, - -3.325600e-04], - [-3.324900e-04, 4.927100e-04, -1.616650e-03, -1.437358e-02, - -2.420138e-02, -1.437358e-02, -1.616650e-03, 4.927100e-04, - -3.324900e-04], - [-6.068000e-05, 1.263100e-04, 4.927100e-04, 1.459700e-04, - -3.732100e-04, 1.459700e-04, 4.927100e-04, 1.263100e-04, - -6.068000e-05], - [5.997200e-04, -6.068000e-05, -3.324900e-04, -3.325600e-04, - -2.406600e-04, -3.325600e-04, -3.324900e-04, -6.068000e-05, - 5.997200e-04]])) + np.asarray([[5.997200e-04, -6.068000e-05, -3.324900e-04, -3.325600e-04, + -2.406600e-04, -3.325600e-04, -3.324900e-04, -6.068000e-05, + 5.997200e-04], + [-6.068000e-05, 1.263100e-04, 4.927100e-04, 1.459700e-04, + -3.732100e-04, 1.459700e-04, 4.927100e-04, 1.263100e-04, + -6.068000e-05], + [-3.324900e-04, 4.927100e-04, -1.616650e-03, -1.437358e-02, + -2.420138e-02, -1.437358e-02, -1.616650e-03, 4.927100e-04, + -3.324900e-04], + [-3.325600e-04, 1.459700e-04, -1.437358e-02, -6.300923e-02, + -9.623594e-02, -6.300923e-02, -1.437358e-02, 1.459700e-04, + -3.325600e-04], + [-2.406600e-04, -3.732100e-04, -2.420138e-02, -9.623594e-02, + 8.554893e-01, -9.623594e-02, -2.420138e-02, -3.732100e-04, + -2.406600e-04], + [-3.325600e-04, 1.459700e-04, -1.437358e-02, -6.300923e-02, + -9.623594e-02, -6.300923e-02, -1.437358e-02, 1.459700e-04, + -3.325600e-04], + [-3.324900e-04, 4.927100e-04, -1.616650e-03, -1.437358e-02, + -2.420138e-02, -1.437358e-02, -1.616650e-03, 4.927100e-04, + -3.324900e-04], + [-6.068000e-05, 1.263100e-04, 4.927100e-04, 1.459700e-04, + -3.732100e-04, 1.459700e-04, 4.927100e-04, 1.263100e-04, + -6.068000e-05], + [5.997200e-04, -6.068000e-05, -3.324900e-04, -3.325600e-04, + -2.406600e-04, -3.325600e-04, -3.324900e-04, -6.068000e-05, + 5.997200e-04]])) filters['bfilts'] = ( - np.array([-9.066000e-05, -1.738640e-03, -4.942500e-03, -7.889390e-03, - -1.009473e-02, -7.889390e-03, -4.942500e-03, -1.738640e-03, - -9.066000e-05, -1.738640e-03, -4.625150e-03, -7.272540e-03, - -7.623410e-03, -9.091950e-03, -7.623410e-03, -7.272540e-03, - -4.625150e-03, -1.738640e-03, -4.942500e-03, -7.272540e-03, - -2.129540e-02, -2.435662e-02, -3.487008e-02, -2.435662e-02, - -2.129540e-02, -7.272540e-03, -4.942500e-03, -7.889390e-03, - -7.623410e-03, -2.435662e-02, -1.730466e-02, -3.158605e-02, - -1.730466e-02, -2.435662e-02, -7.623410e-03, -7.889390e-03, - -1.009473e-02, -9.091950e-03, -3.487008e-02, -3.158605e-02, - 9.464195e-01, -3.158605e-02, -3.487008e-02, -9.091950e-03, - -1.009473e-02, -7.889390e-03, -7.623410e-03, -2.435662e-02, - -1.730466e-02, -3.158605e-02, -1.730466e-02, -2.435662e-02, - -7.623410e-03, -7.889390e-03, -4.942500e-03, -7.272540e-03, - -2.129540e-02, -2.435662e-02, -3.487008e-02, -2.435662e-02, - -2.129540e-02, -7.272540e-03, -4.942500e-03, -1.738640e-03, - -4.625150e-03, -7.272540e-03, -7.623410e-03, -9.091950e-03, - -7.623410e-03, -7.272540e-03, -4.625150e-03, -1.738640e-03, - -9.066000e-05, -1.738640e-03, -4.942500e-03, -7.889390e-03, - -1.009473e-02, -7.889390e-03, -4.942500e-03, -1.738640e-03, - -9.066000e-05])) + np.asarray([-9.066000e-05, -1.738640e-03, -4.942500e-03, -7.889390e-03, + -1.009473e-02, -7.889390e-03, -4.942500e-03, -1.738640e-03, + -9.066000e-05, -1.738640e-03, -4.625150e-03, -7.272540e-03, + -7.623410e-03, -9.091950e-03, -7.623410e-03, -7.272540e-03, + -4.625150e-03, -1.738640e-03, -4.942500e-03, -7.272540e-03, + -2.129540e-02, -2.435662e-02, -3.487008e-02, -2.435662e-02, + -2.129540e-02, -7.272540e-03, -4.942500e-03, -7.889390e-03, + -7.623410e-03, -2.435662e-02, -1.730466e-02, -3.158605e-02, + -1.730466e-02, -2.435662e-02, -7.623410e-03, -7.889390e-03, + -1.009473e-02, -9.091950e-03, -3.487008e-02, -3.158605e-02, + 9.464195e-01, -3.158605e-02, -3.487008e-02, -9.091950e-03, + -1.009473e-02, -7.889390e-03, -7.623410e-03, -2.435662e-02, + -1.730466e-02, -3.158605e-02, -1.730466e-02, -2.435662e-02, + -7.623410e-03, -7.889390e-03, -4.942500e-03, -7.272540e-03, + -2.129540e-02, -2.435662e-02, -3.487008e-02, -2.435662e-02, + -2.129540e-02, -7.272540e-03, -4.942500e-03, -1.738640e-03, + -4.625150e-03, -7.272540e-03, -7.623410e-03, -9.091950e-03, + -7.623410e-03, -7.272540e-03, -4.625150e-03, -1.738640e-03, + -9.066000e-05, -1.738640e-03, -4.942500e-03, -7.889390e-03, + -1.009473e-02, -7.889390e-03, -4.942500e-03, -1.738640e-03, + -9.066000e-05])) filters['bfilts'] = filters['bfilts'].reshape(len(filters['bfilts']), 1) return filters def _sp1_filters(): filters = {} - filters['harmonics'] = np.array([1]) + filters['harmonics'] = np.asarray([1]) filters['mtx'] = np.eye(2) filters['lo0filt'] = ( - np.array([[-8.701000e-05, -1.354280e-03, -1.601260e-03, -5.033700e-04, - 2.524010e-03, -5.033700e-04, -1.601260e-03, -1.354280e-03, - -8.701000e-05], - [-1.354280e-03, 2.921580e-03, 7.522720e-03, 8.224420e-03, - 1.107620e-03, 8.224420e-03, 7.522720e-03, 2.921580e-03, - -1.354280e-03], - [-1.601260e-03, 7.522720e-03, -7.061290e-03, -3.769487e-02, - -3.297137e-02, -3.769487e-02, -7.061290e-03, 7.522720e-03, - -1.601260e-03], - [-5.033700e-04, 8.224420e-03, -3.769487e-02, 4.381320e-02, - 1.811603e-01, 4.381320e-02, -3.769487e-02, 8.224420e-03, - -5.033700e-04], - [2.524010e-03, 1.107620e-03, -3.297137e-02, 1.811603e-01, - 4.376250e-01, 1.811603e-01, -3.297137e-02, 1.107620e-03, - 2.524010e-03], - [-5.033700e-04, 8.224420e-03, -3.769487e-02, 4.381320e-02, - 1.811603e-01, 4.381320e-02, -3.769487e-02, 8.224420e-03, - -5.033700e-04], - [-1.601260e-03, 7.522720e-03, -7.061290e-03, -3.769487e-02, - -3.297137e-02, -3.769487e-02, -7.061290e-03, 7.522720e-03, - -1.601260e-03], - [-1.354280e-03, 2.921580e-03, 7.522720e-03, 8.224420e-03, - 1.107620e-03, 8.224420e-03, 7.522720e-03, 2.921580e-03, - -1.354280e-03], - [-8.701000e-05, -1.354280e-03, -1.601260e-03, -5.033700e-04, - 2.524010e-03, -5.033700e-04, -1.601260e-03, -1.354280e-03, - -8.701000e-05]])) + np.asarray([[-8.701000e-05, -1.354280e-03, -1.601260e-03, -5.033700e-04, + 2.524010e-03, -5.033700e-04, -1.601260e-03, -1.354280e-03, + -8.701000e-05], + [-1.354280e-03, 2.921580e-03, 7.522720e-03, 8.224420e-03, + 1.107620e-03, 8.224420e-03, 7.522720e-03, 2.921580e-03, + -1.354280e-03], + [-1.601260e-03, 7.522720e-03, -7.061290e-03, -3.769487e-02, + -3.297137e-02, -3.769487e-02, -7.061290e-03, 7.522720e-03, + -1.601260e-03], + [-5.033700e-04, 8.224420e-03, -3.769487e-02, 4.381320e-02, + 1.811603e-01, 4.381320e-02, -3.769487e-02, 8.224420e-03, + -5.033700e-04], + [2.524010e-03, 1.107620e-03, -3.297137e-02, 1.811603e-01, + 4.376250e-01, 1.811603e-01, -3.297137e-02, 1.107620e-03, + 2.524010e-03], + [-5.033700e-04, 8.224420e-03, -3.769487e-02, 4.381320e-02, + 1.811603e-01, 4.381320e-02, -3.769487e-02, 8.224420e-03, + -5.033700e-04], + [-1.601260e-03, 7.522720e-03, -7.061290e-03, -3.769487e-02, + -3.297137e-02, -3.769487e-02, -7.061290e-03, 7.522720e-03, + -1.601260e-03], + [-1.354280e-03, 2.921580e-03, 7.522720e-03, 8.224420e-03, + 1.107620e-03, 8.224420e-03, 7.522720e-03, 2.921580e-03, + -1.354280e-03], + [-8.701000e-05, -1.354280e-03, -1.601260e-03, -5.033700e-04, + 2.524010e-03, -5.033700e-04, -1.601260e-03, -1.354280e-03, + -8.701000e-05]])) filters['lofilt'] = ( - np.array([[-4.350000e-05, 1.207800e-04, -6.771400e-04, -1.243400e-04, - -8.006400e-04, -1.597040e-03, -2.516800e-04, -4.202000e-04, - 1.262000e-03, -4.202000e-04, -2.516800e-04, -1.597040e-03, - -8.006400e-04, -1.243400e-04, -6.771400e-04, 1.207800e-04, - -4.350000e-05], - [1.207800e-04, 4.460600e-04, -5.814600e-04, 5.621600e-04, - -1.368800e-04, 2.325540e-03, 2.889860e-03, 4.287280e-03, - 5.589400e-03, 4.287280e-03, 2.889860e-03, 2.325540e-03, - -1.368800e-04, 5.621600e-04, -5.814600e-04, 4.460600e-04, - 1.207800e-04], - [-6.771400e-04, -5.814600e-04, 1.460780e-03, 2.160540e-03, - 3.761360e-03, 3.080980e-03, 4.112200e-03, 2.221220e-03, - 5.538200e-04, 2.221220e-03, 4.112200e-03, 3.080980e-03, - 3.761360e-03, 2.160540e-03, 1.460780e-03, -5.814600e-04, - -6.771400e-04], - [-1.243400e-04, 5.621600e-04, 2.160540e-03, 3.175780e-03, - 3.184680e-03, -1.777480e-03, -7.431700e-03, -9.056920e-03, - -9.637220e-03, -9.056920e-03, -7.431700e-03, -1.777480e-03, - 3.184680e-03, 3.175780e-03, 2.160540e-03, 5.621600e-04, - -1.243400e-04], - [-8.006400e-04, -1.368800e-04, 3.761360e-03, 3.184680e-03, - -3.530640e-03, -1.260420e-02, -1.884744e-02, -1.750818e-02, - -1.648568e-02, -1.750818e-02, -1.884744e-02, -1.260420e-02, - -3.530640e-03, 3.184680e-03, 3.761360e-03, -1.368800e-04, - -8.006400e-04], - [-1.597040e-03, 2.325540e-03, 3.080980e-03, -1.777480e-03, - -1.260420e-02, -2.022938e-02, -1.109170e-02, 3.955660e-03, - 1.438512e-02, 3.955660e-03, -1.109170e-02, -2.022938e-02, - -1.260420e-02, -1.777480e-03, 3.080980e-03, 2.325540e-03, - -1.597040e-03], - [-2.516800e-04, 2.889860e-03, 4.112200e-03, -7.431700e-03, - -1.884744e-02, -1.109170e-02, 2.190660e-02, 6.806584e-02, - 9.058014e-02, 6.806584e-02, 2.190660e-02, -1.109170e-02, - -1.884744e-02, -7.431700e-03, 4.112200e-03, 2.889860e-03, - -2.516800e-04], - [-4.202000e-04, 4.287280e-03, 2.221220e-03, -9.056920e-03, - -1.750818e-02, 3.955660e-03, 6.806584e-02, 1.445500e-01, - 1.773651e-01, 1.445500e-01, 6.806584e-02, 3.955660e-03, - -1.750818e-02, -9.056920e-03, 2.221220e-03, 4.287280e-03, - -4.202000e-04], - [1.262000e-03, 5.589400e-03, 5.538200e-04, -9.637220e-03, - -1.648568e-02, 1.438512e-02, 9.058014e-02, 1.773651e-01, - 2.120374e-01, 1.773651e-01, 9.058014e-02, 1.438512e-02, - -1.648568e-02, -9.637220e-03, 5.538200e-04, 5.589400e-03, - 1.262000e-03], - [-4.202000e-04, 4.287280e-03, 2.221220e-03, -9.056920e-03, - -1.750818e-02, 3.955660e-03, 6.806584e-02, 1.445500e-01, - 1.773651e-01, 1.445500e-01, 6.806584e-02, 3.955660e-03, - -1.750818e-02, -9.056920e-03, 2.221220e-03, 4.287280e-03, - -4.202000e-04], - [-2.516800e-04, 2.889860e-03, 4.112200e-03, -7.431700e-03, - -1.884744e-02, -1.109170e-02, 2.190660e-02, 6.806584e-02, - 9.058014e-02, 6.806584e-02, 2.190660e-02, -1.109170e-02, - -1.884744e-02, -7.431700e-03, 4.112200e-03, 2.889860e-03, - -2.516800e-04], - [-1.597040e-03, 2.325540e-03, 3.080980e-03, -1.777480e-03, - -1.260420e-02, -2.022938e-02, -1.109170e-02, 3.955660e-03, - 1.438512e-02, 3.955660e-03, -1.109170e-02, -2.022938e-02, - -1.260420e-02, -1.777480e-03, 3.080980e-03, 2.325540e-03, - -1.597040e-03], - [-8.006400e-04, -1.368800e-04, 3.761360e-03, 3.184680e-03, - -3.530640e-03, -1.260420e-02, -1.884744e-02, -1.750818e-02, - -1.648568e-02, -1.750818e-02, -1.884744e-02, -1.260420e-02, - -3.530640e-03, 3.184680e-03, 3.761360e-03, -1.368800e-04, - -8.006400e-04], - [-1.243400e-04, 5.621600e-04, 2.160540e-03, 3.175780e-03, - 3.184680e-03, -1.777480e-03, -7.431700e-03, -9.056920e-03, - -9.637220e-03, -9.056920e-03, -7.431700e-03, -1.777480e-03, - 3.184680e-03, 3.175780e-03, 2.160540e-03, 5.621600e-04, - -1.243400e-04], - [-6.771400e-04, -5.814600e-04, 1.460780e-03, 2.160540e-03, - 3.761360e-03, 3.080980e-03, 4.112200e-03, 2.221220e-03, - 5.538200e-04, 2.221220e-03, 4.112200e-03, 3.080980e-03, - 3.761360e-03, 2.160540e-03, 1.460780e-03, -5.814600e-04, - -6.771400e-04], - [1.207800e-04, 4.460600e-04, -5.814600e-04, 5.621600e-04, - -1.368800e-04, 2.325540e-03, 2.889860e-03, 4.287280e-03, - 5.589400e-03, 4.287280e-03, 2.889860e-03, 2.325540e-03, - -1.368800e-04, 5.621600e-04, -5.814600e-04, 4.460600e-04, - 1.207800e-04], - [-4.350000e-05, 1.207800e-04, -6.771400e-04, -1.243400e-04, - -8.006400e-04, -1.597040e-03, -2.516800e-04, -4.202000e-04, - 1.262000e-03, -4.202000e-04, -2.516800e-04, -1.597040e-03, - -8.006400e-04, -1.243400e-04, -6.771400e-04, 1.207800e-04, - -4.350000e-05]])) + np.asarray([[-4.350000e-05, 1.207800e-04, -6.771400e-04, -1.243400e-04, + -8.006400e-04, -1.597040e-03, -2.516800e-04, -4.202000e-04, + 1.262000e-03, -4.202000e-04, -2.516800e-04, -1.597040e-03, + -8.006400e-04, -1.243400e-04, -6.771400e-04, 1.207800e-04, + -4.350000e-05], + [1.207800e-04, 4.460600e-04, -5.814600e-04, 5.621600e-04, + -1.368800e-04, 2.325540e-03, 2.889860e-03, 4.287280e-03, + 5.589400e-03, 4.287280e-03, 2.889860e-03, 2.325540e-03, + -1.368800e-04, 5.621600e-04, -5.814600e-04, 4.460600e-04, + 1.207800e-04], + [-6.771400e-04, -5.814600e-04, 1.460780e-03, 2.160540e-03, + 3.761360e-03, 3.080980e-03, 4.112200e-03, 2.221220e-03, + 5.538200e-04, 2.221220e-03, 4.112200e-03, 3.080980e-03, + 3.761360e-03, 2.160540e-03, 1.460780e-03, -5.814600e-04, + -6.771400e-04], + [-1.243400e-04, 5.621600e-04, 2.160540e-03, 3.175780e-03, + 3.184680e-03, -1.777480e-03, -7.431700e-03, -9.056920e-03, + -9.637220e-03, -9.056920e-03, -7.431700e-03, -1.777480e-03, + 3.184680e-03, 3.175780e-03, 2.160540e-03, 5.621600e-04, + -1.243400e-04], + [-8.006400e-04, -1.368800e-04, 3.761360e-03, 3.184680e-03, + -3.530640e-03, -1.260420e-02, -1.884744e-02, -1.750818e-02, + -1.648568e-02, -1.750818e-02, -1.884744e-02, -1.260420e-02, + -3.530640e-03, 3.184680e-03, 3.761360e-03, -1.368800e-04, + -8.006400e-04], + [-1.597040e-03, 2.325540e-03, 3.080980e-03, -1.777480e-03, + -1.260420e-02, -2.022938e-02, -1.109170e-02, 3.955660e-03, + 1.438512e-02, 3.955660e-03, -1.109170e-02, -2.022938e-02, + -1.260420e-02, -1.777480e-03, 3.080980e-03, 2.325540e-03, + -1.597040e-03], + [-2.516800e-04, 2.889860e-03, 4.112200e-03, -7.431700e-03, + -1.884744e-02, -1.109170e-02, 2.190660e-02, 6.806584e-02, + 9.058014e-02, 6.806584e-02, 2.190660e-02, -1.109170e-02, + -1.884744e-02, -7.431700e-03, 4.112200e-03, 2.889860e-03, + -2.516800e-04], + [-4.202000e-04, 4.287280e-03, 2.221220e-03, -9.056920e-03, + -1.750818e-02, 3.955660e-03, 6.806584e-02, 1.445500e-01, + 1.773651e-01, 1.445500e-01, 6.806584e-02, 3.955660e-03, + -1.750818e-02, -9.056920e-03, 2.221220e-03, 4.287280e-03, + -4.202000e-04], + [1.262000e-03, 5.589400e-03, 5.538200e-04, -9.637220e-03, + -1.648568e-02, 1.438512e-02, 9.058014e-02, 1.773651e-01, + 2.120374e-01, 1.773651e-01, 9.058014e-02, 1.438512e-02, + -1.648568e-02, -9.637220e-03, 5.538200e-04, 5.589400e-03, + 1.262000e-03], + [-4.202000e-04, 4.287280e-03, 2.221220e-03, -9.056920e-03, + -1.750818e-02, 3.955660e-03, 6.806584e-02, 1.445500e-01, + 1.773651e-01, 1.445500e-01, 6.806584e-02, 3.955660e-03, + -1.750818e-02, -9.056920e-03, 2.221220e-03, 4.287280e-03, + -4.202000e-04], + [-2.516800e-04, 2.889860e-03, 4.112200e-03, -7.431700e-03, + -1.884744e-02, -1.109170e-02, 2.190660e-02, 6.806584e-02, + 9.058014e-02, 6.806584e-02, 2.190660e-02, -1.109170e-02, + -1.884744e-02, -7.431700e-03, 4.112200e-03, 2.889860e-03, + -2.516800e-04], + [-1.597040e-03, 2.325540e-03, 3.080980e-03, -1.777480e-03, + -1.260420e-02, -2.022938e-02, -1.109170e-02, 3.955660e-03, + 1.438512e-02, 3.955660e-03, -1.109170e-02, -2.022938e-02, + -1.260420e-02, -1.777480e-03, 3.080980e-03, 2.325540e-03, + -1.597040e-03], + [-8.006400e-04, -1.368800e-04, 3.761360e-03, 3.184680e-03, + -3.530640e-03, -1.260420e-02, -1.884744e-02, -1.750818e-02, + -1.648568e-02, -1.750818e-02, -1.884744e-02, -1.260420e-02, + -3.530640e-03, 3.184680e-03, 3.761360e-03, -1.368800e-04, + -8.006400e-04], + [-1.243400e-04, 5.621600e-04, 2.160540e-03, 3.175780e-03, + 3.184680e-03, -1.777480e-03, -7.431700e-03, -9.056920e-03, + -9.637220e-03, -9.056920e-03, -7.431700e-03, -1.777480e-03, + 3.184680e-03, 3.175780e-03, 2.160540e-03, 5.621600e-04, + -1.243400e-04], + [-6.771400e-04, -5.814600e-04, 1.460780e-03, 2.160540e-03, + 3.761360e-03, 3.080980e-03, 4.112200e-03, 2.221220e-03, + 5.538200e-04, 2.221220e-03, 4.112200e-03, 3.080980e-03, + 3.761360e-03, 2.160540e-03, 1.460780e-03, -5.814600e-04, + -6.771400e-04], + [1.207800e-04, 4.460600e-04, -5.814600e-04, 5.621600e-04, + -1.368800e-04, 2.325540e-03, 2.889860e-03, 4.287280e-03, + 5.589400e-03, 4.287280e-03, 2.889860e-03, 2.325540e-03, + -1.368800e-04, 5.621600e-04, -5.814600e-04, 4.460600e-04, + 1.207800e-04], + [-4.350000e-05, 1.207800e-04, -6.771400e-04, -1.243400e-04, + -8.006400e-04, -1.597040e-03, -2.516800e-04, -4.202000e-04, + 1.262000e-03, -4.202000e-04, -2.516800e-04, -1.597040e-03, + -8.006400e-04, -1.243400e-04, -6.771400e-04, 1.207800e-04, + -4.350000e-05]])) filters['hi0filt'] = ( - np.array([[-9.570000e-04, -2.424100e-04, -1.424720e-03, -8.742600e-04, - -1.166810e-03, -8.742600e-04, -1.424720e-03, -2.424100e-04, - -9.570000e-04], - [-2.424100e-04, -4.317530e-03, 8.998600e-04, 9.156420e-03, - 1.098012e-02, 9.156420e-03, 8.998600e-04, -4.317530e-03, - -2.424100e-04], - [-1.424720e-03, 8.998600e-04, 1.706347e-02, 1.094866e-02, - -5.897780e-03, 1.094866e-02, 1.706347e-02, 8.998600e-04, - -1.424720e-03], - [-8.742600e-04, 9.156420e-03, 1.094866e-02, -7.841370e-02, - -1.562827e-01, -7.841370e-02, 1.094866e-02, 9.156420e-03, - -8.742600e-04], - [-1.166810e-03, 1.098012e-02, -5.897780e-03, -1.562827e-01, - 7.282593e-01, -1.562827e-01, -5.897780e-03, 1.098012e-02, - -1.166810e-03], - [-8.742600e-04, 9.156420e-03, 1.094866e-02, -7.841370e-02, - -1.562827e-01, -7.841370e-02, 1.094866e-02, 9.156420e-03, - -8.742600e-04], - [-1.424720e-03, 8.998600e-04, 1.706347e-02, 1.094866e-02, - -5.897780e-03, 1.094866e-02, 1.706347e-02, 8.998600e-04, - -1.424720e-03], - [-2.424100e-04, -4.317530e-03, 8.998600e-04, 9.156420e-03, - 1.098012e-02, 9.156420e-03, 8.998600e-04, -4.317530e-03, - -2.424100e-04], - [-9.570000e-04, -2.424100e-04, -1.424720e-03, -8.742600e-04, - -1.166810e-03, -8.742600e-04, -1.424720e-03, -2.424100e-04, - -9.570000e-04]])) + np.asarray([[-9.570000e-04, -2.424100e-04, -1.424720e-03, -8.742600e-04, + -1.166810e-03, -8.742600e-04, -1.424720e-03, -2.424100e-04, + -9.570000e-04], + [-2.424100e-04, -4.317530e-03, 8.998600e-04, 9.156420e-03, + 1.098012e-02, 9.156420e-03, 8.998600e-04, -4.317530e-03, + -2.424100e-04], + [-1.424720e-03, 8.998600e-04, 1.706347e-02, 1.094866e-02, + -5.897780e-03, 1.094866e-02, 1.706347e-02, 8.998600e-04, + -1.424720e-03], + [-8.742600e-04, 9.156420e-03, 1.094866e-02, -7.841370e-02, + -1.562827e-01, -7.841370e-02, 1.094866e-02, 9.156420e-03, + -8.742600e-04], + [-1.166810e-03, 1.098012e-02, -5.897780e-03, -1.562827e-01, + 7.282593e-01, -1.562827e-01, -5.897780e-03, 1.098012e-02, + -1.166810e-03], + [-8.742600e-04, 9.156420e-03, 1.094866e-02, -7.841370e-02, + -1.562827e-01, -7.841370e-02, 1.094866e-02, 9.156420e-03, + -8.742600e-04], + [-1.424720e-03, 8.998600e-04, 1.706347e-02, 1.094866e-02, + -5.897780e-03, 1.094866e-02, 1.706347e-02, 8.998600e-04, + -1.424720e-03], + [-2.424100e-04, -4.317530e-03, 8.998600e-04, 9.156420e-03, + 1.098012e-02, 9.156420e-03, 8.998600e-04, -4.317530e-03, + -2.424100e-04], + [-9.570000e-04, -2.424100e-04, -1.424720e-03, -8.742600e-04, + -1.166810e-03, -8.742600e-04, -1.424720e-03, -2.424100e-04, + -9.570000e-04]])) filters['bfilts'] = ( - np.array([[6.125880e-03, -8.052600e-03, -2.103714e-02, -1.536890e-02, - -1.851466e-02, -1.536890e-02, -2.103714e-02, -8.052600e-03, - 6.125880e-03, -1.287416e-02, -9.611520e-03, 1.023569e-02, - 6.009450e-03, 1.872620e-03, 6.009450e-03, 1.023569e-02, - -9.611520e-03, -1.287416e-02, -5.641530e-03, 4.168400e-03, - -2.382180e-02, -5.375324e-02, -2.076086e-02, -5.375324e-02, - -2.382180e-02, 4.168400e-03, -5.641530e-03, -8.957260e-03, - -1.751170e-03, -1.836909e-02, 1.265655e-01, 2.996168e-01, - 1.265655e-01, -1.836909e-02, -1.751170e-03, -8.957260e-03, - 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, - 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, - 0.000000e+00, 8.957260e-03, 1.751170e-03, 1.836909e-02, - -1.265655e-01, -2.996168e-01, -1.265655e-01, 1.836909e-02, - 1.751170e-03, 8.957260e-03, 5.641530e-03, -4.168400e-03, - 2.382180e-02, 5.375324e-02, 2.076086e-02, 5.375324e-02, - 2.382180e-02, -4.168400e-03, 5.641530e-03, 1.287416e-02, - 9.611520e-03, -1.023569e-02, -6.009450e-03, -1.872620e-03, - -6.009450e-03, -1.023569e-02, 9.611520e-03, 1.287416e-02, - -6.125880e-03, 8.052600e-03, 2.103714e-02, 1.536890e-02, - 1.851466e-02, 1.536890e-02, 2.103714e-02, 8.052600e-03, - -6.125880e-03], - [-6.125880e-03, 1.287416e-02, 5.641530e-03, 8.957260e-03, - 0.000000e+00, -8.957260e-03, -5.641530e-03, -1.287416e-02, - 6.125880e-03, 8.052600e-03, 9.611520e-03, -4.168400e-03, - 1.751170e-03, 0.000000e+00, -1.751170e-03, 4.168400e-03, - -9.611520e-03, -8.052600e-03, 2.103714e-02, -1.023569e-02, - 2.382180e-02, 1.836909e-02, 0.000000e+00, -1.836909e-02, - -2.382180e-02, 1.023569e-02, -2.103714e-02, 1.536890e-02, - -6.009450e-03, 5.375324e-02, -1.265655e-01, 0.000000e+00, - 1.265655e-01, -5.375324e-02, 6.009450e-03, -1.536890e-02, - 1.851466e-02, -1.872620e-03, 2.076086e-02, -2.996168e-01, - 0.000000e+00, 2.996168e-01, -2.076086e-02, 1.872620e-03, - -1.851466e-02, 1.536890e-02, -6.009450e-03, 5.375324e-02, - -1.265655e-01, 0.000000e+00, 1.265655e-01, -5.375324e-02, - 6.009450e-03, -1.536890e-02, 2.103714e-02, -1.023569e-02, - 2.382180e-02, 1.836909e-02, 0.000000e+00, -1.836909e-02, - -2.382180e-02, 1.023569e-02, -2.103714e-02, 8.052600e-03, - 9.611520e-03, -4.168400e-03, 1.751170e-03, 0.000000e+00, - -1.751170e-03, 4.168400e-03, -9.611520e-03, -8.052600e-03, - -6.125880e-03, 1.287416e-02, 5.641530e-03, 8.957260e-03, - 0.000000e+00, -8.957260e-03, -5.641530e-03, -1.287416e-02, - 6.125880e-03]]).T) + np.asarray([[6.125880e-03, -8.052600e-03, -2.103714e-02, -1.536890e-02, + -1.851466e-02, -1.536890e-02, -2.103714e-02, -8.052600e-03, + 6.125880e-03, -1.287416e-02, -9.611520e-03, 1.023569e-02, + 6.009450e-03, 1.872620e-03, 6.009450e-03, 1.023569e-02, + -9.611520e-03, -1.287416e-02, -5.641530e-03, 4.168400e-03, + -2.382180e-02, -5.375324e-02, -2.076086e-02, -5.375324e-02, + -2.382180e-02, 4.168400e-03, -5.641530e-03, -8.957260e-03, + -1.751170e-03, -1.836909e-02, 1.265655e-01, 2.996168e-01, + 1.265655e-01, -1.836909e-02, -1.751170e-03, -8.957260e-03, + 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, + 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, + 0.000000e+00, 8.957260e-03, 1.751170e-03, 1.836909e-02, + -1.265655e-01, -2.996168e-01, -1.265655e-01, 1.836909e-02, + 1.751170e-03, 8.957260e-03, 5.641530e-03, -4.168400e-03, + 2.382180e-02, 5.375324e-02, 2.076086e-02, 5.375324e-02, + 2.382180e-02, -4.168400e-03, 5.641530e-03, 1.287416e-02, + 9.611520e-03, -1.023569e-02, -6.009450e-03, -1.872620e-03, + -6.009450e-03, -1.023569e-02, 9.611520e-03, 1.287416e-02, + -6.125880e-03, 8.052600e-03, 2.103714e-02, 1.536890e-02, + 1.851466e-02, 1.536890e-02, 2.103714e-02, 8.052600e-03, + -6.125880e-03], + [-6.125880e-03, 1.287416e-02, 5.641530e-03, 8.957260e-03, + 0.000000e+00, -8.957260e-03, -5.641530e-03, -1.287416e-02, + 6.125880e-03, 8.052600e-03, 9.611520e-03, -4.168400e-03, + 1.751170e-03, 0.000000e+00, -1.751170e-03, 4.168400e-03, + -9.611520e-03, -8.052600e-03, 2.103714e-02, -1.023569e-02, + 2.382180e-02, 1.836909e-02, 0.000000e+00, -1.836909e-02, + -2.382180e-02, 1.023569e-02, -2.103714e-02, 1.536890e-02, + -6.009450e-03, 5.375324e-02, -1.265655e-01, 0.000000e+00, + 1.265655e-01, -5.375324e-02, 6.009450e-03, -1.536890e-02, + 1.851466e-02, -1.872620e-03, 2.076086e-02, -2.996168e-01, + 0.000000e+00, 2.996168e-01, -2.076086e-02, 1.872620e-03, + -1.851466e-02, 1.536890e-02, -6.009450e-03, 5.375324e-02, + -1.265655e-01, 0.000000e+00, 1.265655e-01, -5.375324e-02, + 6.009450e-03, -1.536890e-02, 2.103714e-02, -1.023569e-02, + 2.382180e-02, 1.836909e-02, 0.000000e+00, -1.836909e-02, + -2.382180e-02, 1.023569e-02, -2.103714e-02, 8.052600e-03, + 9.611520e-03, -4.168400e-03, 1.751170e-03, 0.000000e+00, + -1.751170e-03, 4.168400e-03, -9.611520e-03, -8.052600e-03, + -6.125880e-03, 1.287416e-02, 5.641530e-03, 8.957260e-03, + 0.000000e+00, -8.957260e-03, -5.641530e-03, -1.287416e-02, + 6.125880e-03]]).T) filters['bfilts'] = np.negative(filters['bfilts']) return filters def _sp3_filters(): filters = {} - filters['harmonics'] = np.array([1, 3]) + filters['harmonics'] = np.asarray([1, 3]) filters['mtx'] = ( - np.array([[0.5000, 0.3536, 0, -0.3536], - [-0.0000, 0.3536, 0.5000, 0.3536], - [0.5000, -0.3536, 0, 0.3536], - [-0.0000, 0.3536, -0.5000, 0.3536]])) + np.asarray([[0.5000, 0.3536, 0, -0.3536], + [-0.0000, 0.3536, 0.5000, 0.3536], + [0.5000, -0.3536, 0, 0.3536], + [-0.0000, 0.3536, -0.5000, 0.3536]])) filters['hi0filt'] = ( - np.array([[-4.0483998600E-4, -6.2596000498E-4, -3.7829999201E-5, - 8.8387000142E-4, 1.5450799838E-3, 1.9235999789E-3, - 2.0687500946E-3, 2.0898699295E-3, 2.0687500946E-3, - 1.9235999789E-3, 1.5450799838E-3, 8.8387000142E-4, - -3.7829999201E-5, -6.2596000498E-4, -4.0483998600E-4], - [-6.2596000498E-4, -3.2734998967E-4, 7.7435001731E-4, - 1.5874400269E-3, 2.1750701126E-3, 2.5626500137E-3, - 2.2892199922E-3, 1.9755100366E-3, 2.2892199922E-3, - 2.5626500137E-3, 2.1750701126E-3, 1.5874400269E-3, - 7.7435001731E-4, -3.2734998967E-4, -6.2596000498E-4], - [-3.7829999201E-5, 7.7435001731E-4, 1.1793200392E-3, - 1.4050999889E-3, 2.2253401112E-3, 2.1145299543E-3, - 3.3578000148E-4, -8.3368999185E-4, 3.3578000148E-4, - 2.1145299543E-3, 2.2253401112E-3, 1.4050999889E-3, - 1.1793200392E-3, 7.7435001731E-4, -3.7829999201E-5], - [8.8387000142E-4, 1.5874400269E-3, 1.4050999889E-3, - 1.2960999738E-3, -4.9274001503E-4, -3.1295299996E-3, - -4.5751798898E-3, -5.1014497876E-3, -4.5751798898E-3, - -3.1295299996E-3, -4.9274001503E-4, 1.2960999738E-3, - 1.4050999889E-3, 1.5874400269E-3, 8.8387000142E-4], - [1.5450799838E-3, 2.1750701126E-3, 2.2253401112E-3, - -4.9274001503E-4, -6.3222697936E-3, -2.7556000277E-3, - 5.3632198833E-3, 7.3032598011E-3, 5.3632198833E-3, - -2.7556000277E-3, -6.3222697936E-3, -4.9274001503E-4, - 2.2253401112E-3, 2.1750701126E-3, 1.5450799838E-3], - [1.9235999789E-3, 2.5626500137E-3, 2.1145299543E-3, - -3.1295299996E-3, -2.7556000277E-3, 1.3962360099E-2, - 7.8046298586E-3, -9.3812197447E-3, 7.8046298586E-3, - 1.3962360099E-2, -2.7556000277E-3, -3.1295299996E-3, - 2.1145299543E-3, 2.5626500137E-3, 1.9235999789E-3], - [2.0687500946E-3, 2.2892199922E-3, 3.3578000148E-4, - -4.5751798898E-3, 5.3632198833E-3, 7.8046298586E-3, - -7.9501636326E-2, -0.1554141641, -7.9501636326E-2, - 7.8046298586E-3, 5.3632198833E-3, -4.5751798898E-3, - 3.3578000148E-4, 2.2892199922E-3, 2.0687500946E-3], - [2.0898699295E-3, 1.9755100366E-3, -8.3368999185E-4, - -5.1014497876E-3, 7.3032598011E-3, -9.3812197447E-3, - -0.1554141641, 0.7303866148, -0.1554141641, - -9.3812197447E-3, 7.3032598011E-3, -5.1014497876E-3, - -8.3368999185E-4, 1.9755100366E-3, 2.0898699295E-3], - [2.0687500946E-3, 2.2892199922E-3, 3.3578000148E-4, - -4.5751798898E-3, 5.3632198833E-3, 7.8046298586E-3, - -7.9501636326E-2, -0.1554141641, -7.9501636326E-2, - 7.8046298586E-3, 5.3632198833E-3, -4.5751798898E-3, - 3.3578000148E-4, 2.2892199922E-3, 2.0687500946E-3], - [1.9235999789E-3, 2.5626500137E-3, 2.1145299543E-3, - -3.1295299996E-3, -2.7556000277E-3, 1.3962360099E-2, - 7.8046298586E-3, -9.3812197447E-3, 7.8046298586E-3, - 1.3962360099E-2, -2.7556000277E-3, -3.1295299996E-3, - 2.1145299543E-3, 2.5626500137E-3, 1.9235999789E-3], - [1.5450799838E-3, 2.1750701126E-3, 2.2253401112E-3, - -4.9274001503E-4, -6.3222697936E-3, -2.7556000277E-3, - 5.3632198833E-3, 7.3032598011E-3, 5.3632198833E-3, - -2.7556000277E-3, -6.3222697936E-3, -4.9274001503E-4, - 2.2253401112E-3, 2.1750701126E-3, 1.5450799838E-3], - [8.8387000142E-4, 1.5874400269E-3, 1.4050999889E-3, - 1.2960999738E-3, -4.9274001503E-4, -3.1295299996E-3, - -4.5751798898E-3, -5.1014497876E-3, -4.5751798898E-3, - -3.1295299996E-3, -4.9274001503E-4, 1.2960999738E-3, - 1.4050999889E-3, 1.5874400269E-3, 8.8387000142E-4], - [-3.7829999201E-5, 7.7435001731E-4, 1.1793200392E-3, - 1.4050999889E-3, 2.2253401112E-3, 2.1145299543E-3, - 3.3578000148E-4, -8.3368999185E-4, 3.3578000148E-4, - 2.1145299543E-3, 2.2253401112E-3, 1.4050999889E-3, - 1.1793200392E-3, 7.7435001731E-4, -3.7829999201E-5], - [-6.2596000498E-4, -3.2734998967E-4, 7.7435001731E-4, - 1.5874400269E-3, 2.1750701126E-3, 2.5626500137E-3, - 2.2892199922E-3, 1.9755100366E-3, 2.2892199922E-3, - 2.5626500137E-3, 2.1750701126E-3, 1.5874400269E-3, - 7.7435001731E-4, -3.2734998967E-4, -6.2596000498E-4], - [-4.0483998600E-4, -6.2596000498E-4, -3.7829999201E-5, - 8.8387000142E-4, 1.5450799838E-3, 1.9235999789E-3, - 2.0687500946E-3, 2.0898699295E-3, 2.0687500946E-3, - 1.9235999789E-3, 1.5450799838E-3, 8.8387000142E-4, - -3.7829999201E-5, -6.2596000498E-4, -4.0483998600E-4]])) + np.asarray([[-4.0483998600E-4, -6.2596000498E-4, -3.7829999201E-5, + 8.8387000142E-4, 1.5450799838E-3, 1.9235999789E-3, + 2.0687500946E-3, 2.0898699295E-3, 2.0687500946E-3, + 1.9235999789E-3, 1.5450799838E-3, 8.8387000142E-4, + -3.7829999201E-5, -6.2596000498E-4, -4.0483998600E-4], + [-6.2596000498E-4, -3.2734998967E-4, 7.7435001731E-4, + 1.5874400269E-3, 2.1750701126E-3, 2.5626500137E-3, + 2.2892199922E-3, 1.9755100366E-3, 2.2892199922E-3, + 2.5626500137E-3, 2.1750701126E-3, 1.5874400269E-3, + 7.7435001731E-4, -3.2734998967E-4, -6.2596000498E-4], + [-3.7829999201E-5, 7.7435001731E-4, 1.1793200392E-3, + 1.4050999889E-3, 2.2253401112E-3, 2.1145299543E-3, + 3.3578000148E-4, -8.3368999185E-4, 3.3578000148E-4, + 2.1145299543E-3, 2.2253401112E-3, 1.4050999889E-3, + 1.1793200392E-3, 7.7435001731E-4, -3.7829999201E-5], + [8.8387000142E-4, 1.5874400269E-3, 1.4050999889E-3, + 1.2960999738E-3, -4.9274001503E-4, -3.1295299996E-3, + -4.5751798898E-3, -5.1014497876E-3, -4.5751798898E-3, + -3.1295299996E-3, -4.9274001503E-4, 1.2960999738E-3, + 1.4050999889E-3, 1.5874400269E-3, 8.8387000142E-4], + [1.5450799838E-3, 2.1750701126E-3, 2.2253401112E-3, + -4.9274001503E-4, -6.3222697936E-3, -2.7556000277E-3, + 5.3632198833E-3, 7.3032598011E-3, 5.3632198833E-3, + -2.7556000277E-3, -6.3222697936E-3, -4.9274001503E-4, + 2.2253401112E-3, 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1.4917500317E-2, 8.6204400286E-3, - 0.0000000000, -8.2846998703E-4, -5.7109999034E-5, - 4.0110000555E-5, 4.6670897864E-3, 8.0871898681E-3, - 1.4807609841E-2, 8.6204400286E-3, -3.1221499667E-3]]).T) + np.asarray([[-8.1125000725E-4, 4.4451598078E-3, 1.2316980399E-2, + 1.3955879956E-2, 1.4179450460E-2, 1.3955879956E-2, + 1.2316980399E-2, 4.4451598078E-3, -8.1125000725E-4, + 3.9103501476E-3, 4.4565401040E-3, -5.8724298142E-3, + -2.8760801069E-3, 8.5267601535E-3, -2.8760801069E-3, + -5.8724298142E-3, 4.4565401040E-3, 3.9103501476E-3, + 1.3462699717E-3, -3.7740699481E-3, 8.2581602037E-3, + 3.9442278445E-2, 5.3605638444E-2, 3.9442278445E-2, + 8.2581602037E-3, -3.7740699481E-3, 1.3462699717E-3, + 7.4700999539E-4, -3.6522001028E-4, -2.2522680461E-2, + -0.1105690673, -0.1768419296, -0.1105690673, + -2.2522680461E-2, -3.6522001028E-4, 7.4700999539E-4, + 0.0000000000, 0.0000000000, 0.0000000000, + 0.0000000000, 0.0000000000, 0.0000000000, + 0.0000000000, 0.0000000000, 0.0000000000, + -7.4700999539E-4, 3.6522001028E-4, 2.2522680461E-2, + 0.1105690673, 0.1768419296, 0.1105690673, + 2.2522680461E-2, 3.6522001028E-4, -7.4700999539E-4, + -1.3462699717E-3, 3.7740699481E-3, -8.2581602037E-3, + -3.9442278445E-2, -5.3605638444E-2, -3.9442278445E-2, + -8.2581602037E-3, 3.7740699481E-3, -1.3462699717E-3, + -3.9103501476E-3, -4.4565401040E-3, 5.8724298142E-3, + 2.8760801069E-3, -8.5267601535E-3, 2.8760801069E-3, + 5.8724298142E-3, -4.4565401040E-3, -3.9103501476E-3, + 8.1125000725E-4, -4.4451598078E-3, -1.2316980399E-2, + -1.3955879956E-2, -1.4179450460E-2, -1.3955879956E-2, + -1.2316980399E-2, -4.4451598078E-3, 8.1125000725E-4], + [0.0000000000, -8.2846998703E-4, -5.7109999034E-5, + 4.0110000555E-5, 4.6670897864E-3, 8.0871898681E-3, + 1.4807609841E-2, 8.6204400286E-3, -3.1221499667E-3, + 8.2846998703E-4, 0.0000000000, -9.7479997203E-4, + -6.9718998857E-3, -2.0865600090E-3, 2.3298799060E-3, + -4.4814897701E-3, 1.4917500317E-2, 8.6204400286E-3, + 5.7109999034E-5, 9.7479997203E-4, 0.0000000000, + -1.2145539746E-2, -2.4427289143E-2, 5.0797060132E-2, + 3.2785870135E-2, -4.4814897701E-3, 1.4807609841E-2, + -4.0110000555E-5, 6.9718998857E-3, 1.2145539746E-2, + 0.0000000000, -0.1510555595, -8.2495503128E-2, + 5.0797060132E-2, 2.3298799060E-3, 8.0871898681E-3, + -4.6670897864E-3, 2.0865600090E-3, 2.4427289143E-2, + 0.1510555595, 0.0000000000, -0.1510555595, + -2.4427289143E-2, -2.0865600090E-3, 4.6670897864E-3, + -8.0871898681E-3, -2.3298799060E-3, -5.0797060132E-2, + 8.2495503128E-2, 0.1510555595, 0.0000000000, + -1.2145539746E-2, -6.9718998857E-3, 4.0110000555E-5, + -1.4807609841E-2, 4.4814897701E-3, -3.2785870135E-2, + -5.0797060132E-2, 2.4427289143E-2, 1.2145539746E-2, + 0.0000000000, -9.7479997203E-4, -5.7109999034E-5, + -8.6204400286E-3, -1.4917500317E-2, 4.4814897701E-3, + -2.3298799060E-3, 2.0865600090E-3, 6.9718998857E-3, + 9.7479997203E-4, 0.0000000000, -8.2846998703E-4, + 3.1221499667E-3, -8.6204400286E-3, -1.4807609841E-2, + -8.0871898681E-3, -4.6670897864E-3, -4.0110000555E-5, + 5.7109999034E-5, 8.2846998703E-4, 0.0000000000], + [8.1125000725E-4, -3.9103501476E-3, -1.3462699717E-3, + -7.4700999539E-4, 0.0000000000, 7.4700999539E-4, + 1.3462699717E-3, 3.9103501476E-3, -8.1125000725E-4, + -4.4451598078E-3, -4.4565401040E-3, 3.7740699481E-3, + 3.6522001028E-4, 0.0000000000, -3.6522001028E-4, + -3.7740699481E-3, 4.4565401040E-3, 4.4451598078E-3, + -1.2316980399E-2, 5.8724298142E-3, -8.2581602037E-3, + 2.2522680461E-2, 0.0000000000, -2.2522680461E-2, + 8.2581602037E-3, -5.8724298142E-3, 1.2316980399E-2, + -1.3955879956E-2, 2.8760801069E-3, -3.9442278445E-2, + 0.1105690673, 0.0000000000, -0.1105690673, + 3.9442278445E-2, -2.8760801069E-3, 1.3955879956E-2, + -1.4179450460E-2, -8.5267601535E-3, -5.3605638444E-2, + 0.1768419296, 0.0000000000, -0.1768419296, + 5.3605638444E-2, 8.5267601535E-3, 1.4179450460E-2, + -1.3955879956E-2, 2.8760801069E-3, -3.9442278445E-2, + 0.1105690673, 0.0000000000, -0.1105690673, + 3.9442278445E-2, -2.8760801069E-3, 1.3955879956E-2, + -1.2316980399E-2, 5.8724298142E-3, -8.2581602037E-3, + 2.2522680461E-2, 0.0000000000, -2.2522680461E-2, + 8.2581602037E-3, -5.8724298142E-3, 1.2316980399E-2, + -4.4451598078E-3, -4.4565401040E-3, 3.7740699481E-3, + 3.6522001028E-4, 0.0000000000, -3.6522001028E-4, + -3.7740699481E-3, 4.4565401040E-3, 4.4451598078E-3, + 8.1125000725E-4, -3.9103501476E-3, -1.3462699717E-3, + -7.4700999539E-4, 0.0000000000, 7.4700999539E-4, + 1.3462699717E-3, 3.9103501476E-3, -8.1125000725E-4], + [3.1221499667E-3, -8.6204400286E-3, -1.4807609841E-2, + -8.0871898681E-3, -4.6670897864E-3, -4.0110000555E-5, + 5.7109999034E-5, 8.2846998703E-4, 0.0000000000, + -8.6204400286E-3, -1.4917500317E-2, 4.4814897701E-3, + -2.3298799060E-3, 2.0865600090E-3, 6.9718998857E-3, + 9.7479997203E-4, -0.0000000000, -8.2846998703E-4, + -1.4807609841E-2, 4.4814897701E-3, -3.2785870135E-2, + -5.0797060132E-2, 2.4427289143E-2, 1.2145539746E-2, + 0.0000000000, -9.7479997203E-4, -5.7109999034E-5, + -8.0871898681E-3, -2.3298799060E-3, -5.0797060132E-2, + 8.2495503128E-2, 0.1510555595, -0.0000000000, + -1.2145539746E-2, -6.9718998857E-3, 4.0110000555E-5, + -4.6670897864E-3, 2.0865600090E-3, 2.4427289143E-2, + 0.1510555595, 0.0000000000, -0.1510555595, + -2.4427289143E-2, -2.0865600090E-3, 4.6670897864E-3, + -4.0110000555E-5, 6.9718998857E-3, 1.2145539746E-2, + 0.0000000000, -0.1510555595, -8.2495503128E-2, + 5.0797060132E-2, 2.3298799060E-3, 8.0871898681E-3, + 5.7109999034E-5, 9.7479997203E-4, -0.0000000000, + -1.2145539746E-2, -2.4427289143E-2, 5.0797060132E-2, + 3.2785870135E-2, -4.4814897701E-3, 1.4807609841E-2, + 8.2846998703E-4, -0.0000000000, -9.7479997203E-4, + -6.9718998857E-3, -2.0865600090E-3, 2.3298799060E-3, + -4.4814897701E-3, 1.4917500317E-2, 8.6204400286E-3, + 0.0000000000, -8.2846998703E-4, -5.7109999034E-5, + 4.0110000555E-5, 4.6670897864E-3, 8.0871898681E-3, + 1.4807609841E-2, 8.6204400286E-3, -3.1221499667E-3]]).T) return filters def _sp5_filters(): filters = {} - filters['harmonics'] = np.array([1, 3, 5]) + filters['harmonics'] = np.asarray([1, 3, 5]) filters['mtx'] = ( - np.array([[0.3333, 0.2887, 0.1667, 0.0000, -0.1667, -0.2887], - [0.0000, 0.1667, 0.2887, 0.3333, 0.2887, 0.1667], - [0.3333, -0.0000, -0.3333, -0.0000, 0.3333, -0.0000], - [0.0000, 0.3333, 0.0000, -0.3333, 0.0000, 0.3333], - [0.3333, -0.2887, 0.1667, -0.0000, -0.1667, 0.2887], - [-0.0000, 0.1667, -0.2887, 0.3333, -0.2887, 0.1667]])) + np.asarray([[0.3333, 0.2887, 0.1667, 0.0000, -0.1667, -0.2887], + [0.0000, 0.1667, 0.2887, 0.3333, 0.2887, 0.1667], + [0.3333, -0.0000, -0.3333, -0.0000, 0.3333, -0.0000], + [0.0000, 0.3333, 0.0000, -0.3333, 0.0000, 0.3333], + [0.3333, -0.2887, 0.1667, -0.0000, -0.1667, 0.2887], + [-0.0000, 0.1667, -0.2887, 0.3333, -0.2887, 0.1667]])) filters['hi0filt'] = ( - np.array([[-0.00033429, -0.00113093, -0.00171484, - -0.00133542, -0.00080639, -0.00133542, - -0.00171484, -0.00113093, -0.00033429], - [-0.00113093, -0.00350017, -0.00243812, - 0.00631653, 0.01261227, 0.00631653, - -0.00243812, -0.00350017, -0.00113093], - [-0.00171484, -0.00243812, -0.00290081, - -0.00673482, -0.00981051, -0.00673482, - -0.00290081, -0.00243812, -0.00171484], - [-0.00133542, 0.00631653, -0.00673482, - -0.07027679, -0.11435863, -0.07027679, - -0.00673482, 0.00631653, -0.00133542], - [-0.00080639, 0.01261227, -0.00981051, - -0.11435863, 0.81380200, -0.11435863, - -0.00981051, 0.01261227, -0.00080639], - [-0.00133542, 0.00631653, -0.00673482, - -0.07027679, -0.11435863, -0.07027679, - -0.00673482, 0.00631653, -0.00133542], - [-0.00171484, -0.00243812, -0.00290081, - -0.00673482, -0.00981051, -0.00673482, - -0.00290081, -0.00243812, -0.00171484], - [-0.00113093, -0.00350017, -0.00243812, - 0.00631653, 0.01261227, 0.00631653, - -0.00243812, -0.00350017, -0.00113093], - [-0.00033429, -0.00113093, -0.00171484, - -0.00133542, -0.00080639, -0.00133542, - -0.00171484, -0.00113093, -0.00033429]])) + np.asarray([[-0.00033429, -0.00113093, -0.00171484, + -0.00133542, -0.00080639, -0.00133542, + -0.00171484, -0.00113093, -0.00033429], + [-0.00113093, -0.00350017, -0.00243812, + 0.00631653, 0.01261227, 0.00631653, + -0.00243812, -0.00350017, -0.00113093], + [-0.00171484, -0.00243812, -0.00290081, + -0.00673482, -0.00981051, -0.00673482, + -0.00290081, -0.00243812, -0.00171484], + [-0.00133542, 0.00631653, -0.00673482, + -0.07027679, -0.11435863, -0.07027679, + -0.00673482, 0.00631653, -0.00133542], + [-0.00080639, 0.01261227, -0.00981051, + -0.11435863, 0.81380200, -0.11435863, + -0.00981051, 0.01261227, -0.00080639], + [-0.00133542, 0.00631653, -0.00673482, + -0.07027679, -0.11435863, -0.07027679, + -0.00673482, 0.00631653, -0.00133542], + [-0.00171484, -0.00243812, -0.00290081, + -0.00673482, -0.00981051, -0.00673482, + -0.00290081, -0.00243812, -0.00171484], + [-0.00113093, -0.00350017, -0.00243812, + 0.00631653, 0.01261227, 0.00631653, + -0.00243812, -0.00350017, -0.00113093], + [-0.00033429, -0.00113093, -0.00171484, + -0.00133542, -0.00080639, -0.00133542, + -0.00171484, -0.00113093, -0.00033429]])) filters['lo0filt'] = ( - np.array([[0.00341614, -0.01551246, -0.03848215, -0.01551246, - 0.00341614], - [-0.01551246, 0.05586982, 0.15925570, 0.05586982, - -0.01551246], - [-0.03848215, 0.15925570, 0.40304148, 0.15925570, - -0.03848215], - [-0.01551246, 0.05586982, 0.15925570, 0.05586982, - -0.01551246], - [0.00341614, -0.01551246, -0.03848215, -0.01551246, - 0.00341614]])) + np.asarray([[0.00341614, -0.01551246, -0.03848215, -0.01551246, + 0.00341614], + [-0.01551246, 0.05586982, 0.15925570, 0.05586982, + -0.01551246], + [-0.03848215, 0.15925570, 0.40304148, 0.15925570, + -0.03848215], + [-0.01551246, 0.05586982, 0.15925570, 0.05586982, + -0.01551246], + [0.00341614, -0.01551246, -0.03848215, -0.01551246, + 0.00341614]])) filters['lofilt'] = ( - 2 * np.array([[0.00085404, -0.00244917, -0.00387812, -0.00944432, - -0.00962054, -0.00944432, -0.00387812, -0.00244917, - 0.00085404], - [-0.00244917, -0.00523281, -0.00661117, 0.00410600, - 0.01002988, 0.00410600, -0.00661117, -0.00523281, - -0.00244917], - [-0.00387812, -0.00661117, 0.01396746, 0.03277038, - 0.03981393, 0.03277038, 0.01396746, -0.00661117, - -0.00387812], - [-0.00944432, 0.00410600, 0.03277038, 0.06426333, - 0.08169618, 0.06426333, 0.03277038, 0.00410600, - -0.00944432], - [-0.00962054, 0.01002988, 0.03981393, 0.08169618, - 0.10096540, 0.08169618, 0.03981393, 0.01002988, - -0.00962054], - [-0.00944432, 0.00410600, 0.03277038, 0.06426333, - 0.08169618, 0.06426333, 0.03277038, 0.00410600, - -0.00944432], - [-0.00387812, -0.00661117, 0.01396746, 0.03277038, - 0.03981393, 0.03277038, 0.01396746, -0.00661117, - -0.00387812], - [-0.00244917, -0.00523281, -0.00661117, 0.00410600, - 0.01002988, 0.00410600, -0.00661117, -0.00523281, - -0.00244917], - [0.00085404, -0.00244917, -0.00387812, -0.00944432, - -0.00962054, -0.00944432, -0.00387812, -0.00244917, - 0.00085404]])) + 2 * np.asarray([[0.00085404, -0.00244917, -0.00387812, -0.00944432, + -0.00962054, -0.00944432, -0.00387812, -0.00244917, + 0.00085404], + [-0.00244917, -0.00523281, -0.00661117, 0.00410600, + 0.01002988, 0.00410600, -0.00661117, -0.00523281, + -0.00244917], + [-0.00387812, -0.00661117, 0.01396746, 0.03277038, + 0.03981393, 0.03277038, 0.01396746, -0.00661117, + -0.00387812], + [-0.00944432, 0.00410600, 0.03277038, 0.06426333, + 0.08169618, 0.06426333, 0.03277038, 0.00410600, + -0.00944432], + [-0.00962054, 0.01002988, 0.03981393, 0.08169618, + 0.10096540, 0.08169618, 0.03981393, 0.01002988, + -0.00962054], + [-0.00944432, 0.00410600, 0.03277038, 0.06426333, + 0.08169618, 0.06426333, 0.03277038, 0.00410600, + -0.00944432], + [-0.00387812, -0.00661117, 0.01396746, 0.03277038, + 0.03981393, 0.03277038, 0.01396746, -0.00661117, + -0.00387812], + [-0.00244917, -0.00523281, -0.00661117, 0.00410600, + 0.01002988, 0.00410600, -0.00661117, -0.00523281, + -0.00244917], + [0.00085404, -0.00244917, -0.00387812, -0.00944432, + -0.00962054, -0.00944432, -0.00387812, -0.00244917, + 0.00085404]])) filters['bfilts'] = ( - np.array([[0.00277643, 0.00496194, 0.01026699, 0.01455399, 0.01026699, - 0.00496194, 0.00277643, -0.00986904, -0.00893064, - 0.01189859, 0.02755155, 0.01189859, -0.00893064, - -0.00986904, -0.01021852, -0.03075356, -0.08226445, - -0.11732297, -0.08226445, -0.03075356, -0.01021852, - 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, - 0.00000000, 0.00000000, 0.01021852, 0.03075356, 0.08226445, - 0.11732297, 0.08226445, 0.03075356, 0.01021852, 0.00986904, - 0.00893064, -0.01189859, -0.02755155, -0.01189859, - 0.00893064, 0.00986904, -0.00277643, -0.00496194, - -0.01026699, -0.01455399, -0.01026699, -0.00496194, - -0.00277643], - [-0.00343249, -0.00640815, -0.00073141, 0.01124321, - 0.00182078, 0.00285723, 0.01166982, -0.00358461, - -0.01977507, -0.04084211, -0.00228219, 0.03930573, - 0.01161195, 0.00128000, 0.01047717, 0.01486305, - -0.04819057, -0.12227230, -0.05394139, 0.00853965, - -0.00459034, 0.00790407, 0.04435647, 0.09454202, - -0.00000000, -0.09454202, -0.04435647, -0.00790407, - 0.00459034, -0.00853965, 0.05394139, 0.12227230, - 0.04819057, -0.01486305, -0.01047717, -0.00128000, - -0.01161195, -0.03930573, 0.00228219, 0.04084211, - 0.01977507, 0.00358461, -0.01166982, -0.00285723, - -0.00182078, -0.01124321, 0.00073141, 0.00640815, - 0.00343249], - [0.00343249, 0.00358461, -0.01047717, -0.00790407, - -0.00459034, 0.00128000, 0.01166982, 0.00640815, - 0.01977507, -0.01486305, -0.04435647, 0.00853965, - 0.01161195, 0.00285723, 0.00073141, 0.04084211, 0.04819057, - -0.09454202, -0.05394139, 0.03930573, 0.00182078, - -0.01124321, 0.00228219, 0.12227230, -0.00000000, - -0.12227230, -0.00228219, 0.01124321, -0.00182078, - -0.03930573, 0.05394139, 0.09454202, -0.04819057, - -0.04084211, -0.00073141, -0.00285723, -0.01161195, - -0.00853965, 0.04435647, 0.01486305, -0.01977507, - -0.00640815, -0.01166982, -0.00128000, 0.00459034, - 0.00790407, 0.01047717, -0.00358461, -0.00343249], - [-0.00277643, 0.00986904, 0.01021852, -0.00000000, - -0.01021852, -0.00986904, 0.00277643, -0.00496194, - 0.00893064, 0.03075356, -0.00000000, -0.03075356, - -0.00893064, 0.00496194, -0.01026699, -0.01189859, - 0.08226445, -0.00000000, -0.08226445, 0.01189859, - 0.01026699, -0.01455399, -0.02755155, 0.11732297, - -0.00000000, -0.11732297, 0.02755155, 0.01455399, - -0.01026699, -0.01189859, 0.08226445, -0.00000000, - -0.08226445, 0.01189859, 0.01026699, -0.00496194, - 0.00893064, 0.03075356, -0.00000000, -0.03075356, - -0.00893064, 0.00496194, -0.00277643, 0.00986904, - 0.01021852, -0.00000000, -0.01021852, -0.00986904, - 0.00277643], - [-0.01166982, -0.00128000, 0.00459034, 0.00790407, - 0.01047717, -0.00358461, -0.00343249, -0.00285723, - -0.01161195, -0.00853965, 0.04435647, 0.01486305, - -0.01977507, -0.00640815, -0.00182078, -0.03930573, - 0.05394139, 0.09454202, -0.04819057, -0.04084211, - -0.00073141, -0.01124321, 0.00228219, 0.12227230, - -0.00000000, -0.12227230, -0.00228219, 0.01124321, - 0.00073141, 0.04084211, 0.04819057, -0.09454202, - -0.05394139, 0.03930573, 0.00182078, 0.00640815, - 0.01977507, -0.01486305, -0.04435647, 0.00853965, - 0.01161195, 0.00285723, 0.00343249, 0.00358461, - -0.01047717, -0.00790407, -0.00459034, 0.00128000, - 0.01166982], - [-0.01166982, -0.00285723, -0.00182078, -0.01124321, - 0.00073141, 0.00640815, 0.00343249, -0.00128000, - -0.01161195, -0.03930573, 0.00228219, 0.04084211, - 0.01977507, 0.00358461, 0.00459034, -0.00853965, - 0.05394139, 0.12227230, 0.04819057, -0.01486305, - -0.01047717, 0.00790407, 0.04435647, 0.09454202, - -0.00000000, -0.09454202, -0.04435647, -0.00790407, - 0.01047717, 0.01486305, -0.04819057, -0.12227230, - -0.05394139, 0.00853965, -0.00459034, -0.00358461, - -0.01977507, -0.04084211, -0.00228219, 0.03930573, - 0.01161195, 0.00128000, -0.00343249, -0.00640815, - -0.00073141, 0.01124321, 0.00182078, 0.00285723, - 0.01166982]]).T) + np.asarray([[0.00277643, 0.00496194, 0.01026699, 0.01455399, 0.01026699, + 0.00496194, 0.00277643, -0.00986904, -0.00893064, + 0.01189859, 0.02755155, 0.01189859, -0.00893064, + -0.00986904, -0.01021852, -0.03075356, -0.08226445, + -0.11732297, -0.08226445, -0.03075356, -0.01021852, + 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, + 0.00000000, 0.00000000, 0.01021852, 0.03075356, 0.08226445, + 0.11732297, 0.08226445, 0.03075356, 0.01021852, 0.00986904, + 0.00893064, -0.01189859, -0.02755155, -0.01189859, + 0.00893064, 0.00986904, -0.00277643, -0.00496194, + -0.01026699, -0.01455399, -0.01026699, -0.00496194, + -0.00277643], + [-0.00343249, -0.00640815, -0.00073141, 0.01124321, + 0.00182078, 0.00285723, 0.01166982, -0.00358461, + -0.01977507, -0.04084211, -0.00228219, 0.03930573, + 0.01161195, 0.00128000, 0.01047717, 0.01486305, + -0.04819057, -0.12227230, -0.05394139, 0.00853965, + -0.00459034, 0.00790407, 0.04435647, 0.09454202, + -0.00000000, -0.09454202, -0.04435647, -0.00790407, + 0.00459034, -0.00853965, 0.05394139, 0.12227230, + 0.04819057, -0.01486305, -0.01047717, -0.00128000, + -0.01161195, -0.03930573, 0.00228219, 0.04084211, + 0.01977507, 0.00358461, -0.01166982, -0.00285723, + -0.00182078, -0.01124321, 0.00073141, 0.00640815, + 0.00343249], + [0.00343249, 0.00358461, -0.01047717, -0.00790407, + -0.00459034, 0.00128000, 0.01166982, 0.00640815, + 0.01977507, -0.01486305, -0.04435647, 0.00853965, + 0.01161195, 0.00285723, 0.00073141, 0.04084211, 0.04819057, + -0.09454202, -0.05394139, 0.03930573, 0.00182078, + -0.01124321, 0.00228219, 0.12227230, -0.00000000, + -0.12227230, -0.00228219, 0.01124321, -0.00182078, + -0.03930573, 0.05394139, 0.09454202, -0.04819057, + -0.04084211, -0.00073141, -0.00285723, -0.01161195, + -0.00853965, 0.04435647, 0.01486305, -0.01977507, + -0.00640815, -0.01166982, -0.00128000, 0.00459034, + 0.00790407, 0.01047717, -0.00358461, -0.00343249], + [-0.00277643, 0.00986904, 0.01021852, -0.00000000, + -0.01021852, -0.00986904, 0.00277643, -0.00496194, + 0.00893064, 0.03075356, -0.00000000, -0.03075356, + -0.00893064, 0.00496194, -0.01026699, -0.01189859, + 0.08226445, -0.00000000, -0.08226445, 0.01189859, + 0.01026699, -0.01455399, -0.02755155, 0.11732297, + -0.00000000, -0.11732297, 0.02755155, 0.01455399, + -0.01026699, -0.01189859, 0.08226445, -0.00000000, + -0.08226445, 0.01189859, 0.01026699, -0.00496194, + 0.00893064, 0.03075356, -0.00000000, -0.03075356, + -0.00893064, 0.00496194, -0.00277643, 0.00986904, + 0.01021852, -0.00000000, -0.01021852, -0.00986904, + 0.00277643], + [-0.01166982, -0.00128000, 0.00459034, 0.00790407, + 0.01047717, -0.00358461, -0.00343249, -0.00285723, + -0.01161195, -0.00853965, 0.04435647, 0.01486305, + -0.01977507, -0.00640815, -0.00182078, -0.03930573, + 0.05394139, 0.09454202, -0.04819057, -0.04084211, + -0.00073141, -0.01124321, 0.00228219, 0.12227230, + -0.00000000, -0.12227230, -0.00228219, 0.01124321, + 0.00073141, 0.04084211, 0.04819057, -0.09454202, + -0.05394139, 0.03930573, 0.00182078, 0.00640815, + 0.01977507, -0.01486305, -0.04435647, 0.00853965, + 0.01161195, 0.00285723, 0.00343249, 0.00358461, + -0.01047717, -0.00790407, -0.00459034, 0.00128000, + 0.01166982], + [-0.01166982, -0.00285723, -0.00182078, -0.01124321, + 0.00073141, 0.00640815, 0.00343249, -0.00128000, + -0.01161195, -0.03930573, 0.00228219, 0.04084211, + 0.01977507, 0.00358461, 0.00459034, -0.00853965, + 0.05394139, 0.12227230, 0.04819057, -0.01486305, + -0.01047717, 0.00790407, 0.04435647, 0.09454202, + -0.00000000, -0.09454202, -0.04435647, -0.00790407, + 0.01047717, 0.01486305, -0.04819057, -0.12227230, + -0.05394139, 0.00853965, -0.00459034, -0.00358461, + -0.01977507, -0.04084211, -0.00228219, 0.03930573, + 0.01161195, 0.00128000, -0.00343249, -0.00640815, + -0.00073141, 0.01124321, 0.00182078, 0.00285723, + 0.01166982]]).T) return filters diff --git a/src/pyrtools/pyramids/pyramid.py b/src/pyrtools/pyramids/pyramid.py index 5f216e6..046882f 100644 --- a/src/pyrtools/pyramids/pyramid.py +++ b/src/pyrtools/pyramids/pyramid.py @@ -49,7 +49,7 @@ class Pyramid: def __init__(self, image, edge_type): - self.image = np.array(image).astype(float) + self.image = np.asarray(image).astype(float) if self.image.ndim == 1: self.image = self.image.reshape(-1, 1) assert self.image.ndim == 2, "Error: Input signal must be 1D or 2D." @@ -128,7 +128,7 @@ def _recon_levels_check(self, levels): if not hasattr(levels, '__iter__') or isinstance(levels, str): # then it's a single int or string levels = [levels] - levs_nums = np.array([int(i) for i in levels if isinstance(i, int) or i.isdigit()]) + levs_nums = np.asarray([int(i) for i in levels if isinstance(i, int) or i.isdigit()]) assert (levs_nums >= 0).all(), "Level numbers must be non-negative." assert (levs_nums < self.num_scales).all(), "Level numbers must be in the range [0, %d]" % (self.num_scales-1) levs_tmp = list(np.sort(levs_nums)) # we want smallest first diff --git a/src/pyrtools/pyramids/steer.py b/src/pyrtools/pyramids/steer.py index 79ecd2c..11ec488 100644 --- a/src/pyrtools/pyramids/steer.py +++ b/src/pyrtools/pyramids/steer.py @@ -87,7 +87,7 @@ def steer(basis, angle, harmonics=None, steermtx=None, return_weights=False, eve num = basis.shape[1] if isinstance(angle, (int, float)): - angle = np.array([angle]) + angle = np.asarray([angle]) else: if angle.shape[0] != basis.shape[0] or angle.shape[1] != 1: raise Exception("""ANGLE must be a scalar, or a column vector @@ -130,6 +130,6 @@ def steer(basis, angle, harmonics=None, steermtx=None, return_weights=False, eve res = np.dot(basis, steervect.T) if return_weights: - return res, np.array(steervect).reshape(num) + return res, np.asarray(steervect).reshape(num) else: return res diff --git a/src/pyrtools/tools/compare_matpyrtools.py b/src/pyrtools/tools/compare_matpyrtools.py index 9e758d1..8e2ea9d 100644 --- a/src/pyrtools/tools/compare_matpyrtools.py +++ b/src/pyrtools/tools/compare_matpyrtools.py @@ -15,9 +15,9 @@ def comparePyr(matPyr, pyPyr, rtol=1e-5, atol=1e-8): # correct number of elements? matSz = sum(matPyr.shape) try: - pySz = 1 + sum([np.array(size).prod() for size in pyPyr.pyr_size.values()]) + pySz = 1 + sum([np.asarray(size).prod() for size in pyPyr.pyr_size.values()]) except AttributeError: - pySz = 1 + sum([np.array(size).prod() for size in pyPyr.pyrSize]) + pySz = 1 + sum([np.asarray(size).prod() for size in pyPyr.pyrSize]) if(matSz != pySz): print("size difference: %d != %d, returning False" % (matSz, pySz)) diff --git a/src/pyrtools/tools/convolutions.py b/src/pyrtools/tools/convolutions.py index 811241d..c294093 100644 --- a/src/pyrtools/tools/convolutions.py +++ b/src/pyrtools/tools/convolutions.py @@ -287,8 +287,8 @@ def image_gradient(image, edge_type="dont-compute"): # kernels from Farid & Simoncelli, IEEE Trans Image Processing, # 13(4):496-508, April 2004. - gp = np.array([0.037659, 0.249153, 0.426375, 0.249153, 0.037659]).reshape(5, 1) - gd = np.array([-0.109604, -0.276691, 0.000000, 0.276691, 0.109604]).reshape(5, 1) + gp = np.asarray([0.037659, 0.249153, 0.426375, 0.249153, 0.037659]).reshape(5, 1) + gd = np.asarray([-0.109604, -0.276691, 0.000000, 0.276691, 0.109604]).reshape(5, 1) dx = corrDn(corrDn(image, gp, edge_type), gd.T, edge_type) dy = corrDn(corrDn(image, gd, edge_type), gp.T, edge_type) @@ -384,8 +384,8 @@ def rconv2(mtx1, mtx2, ctr=0): # print 'where center parameter is optional' # return # else: -# a = np.array(args[0]) -# b = np.array(args[1]) +# a = np.asarray(args[0]) +# b = np.asarray(args[1]) # # if len(args) == 3: # ctr = args[2] @@ -459,8 +459,8 @@ def rconv2(mtx1, mtx2, ctr=0): # print 'first two input parameters are required' # return # else: -# a = np.array(args[0]) -# b = np.array(args[1]) +# a = np.asarray(args[0]) +# b = np.asarray(args[1]) # # # OPTIONAL ARGUMENT # #---------------------------------------------------------------- diff --git a/src/pyrtools/tools/display.py b/src/pyrtools/tools/display.py index ad3dc7d..39b8daa 100644 --- a/src/pyrtools/tools/display.py +++ b/src/pyrtools/tools/display.py @@ -556,7 +556,7 @@ def _process_signal(signal, title, plot_complex, video=False): else: title_tmp.extend([None, None]) else: - signal_tmp.append(np.array(sig)) + signal_tmp.append(np.asarray(sig)) title_tmp.append(t) return signal_tmp, title_tmp, contains_rgb @@ -605,8 +605,8 @@ def _check_zooms(signal, zoom, contains_rgb, video=False): else: # then we have multiple images/videos that are different shapes zooms, max_shape = find_zooms(signal, video) - max_shape = np.array(max_shape) - zooms = zoom * np.array(zooms) + max_shape = np.asarray(max_shape) + zooms = zoom * np.asarray(zooms) if not ((zoom * max_shape).astype(int) == zoom * max_shape).all(): raise Exception("zoom * signal.shape must result in integers!") return zooms, max_shape @@ -837,7 +837,7 @@ def animshow(video, framerate=2., as_html5=True, repeat=False, raise ImportError("Unable to import IPython.display.HTML, animshow must be called with " "as_html5=False") video = _convert_signal_to_list(video) - video_n_frames = np.array([v.shape[0] for v in video]) + video_n_frames = np.asarray([v.shape[0] for v in video]) if (video_n_frames != video_n_frames[0]).any(): raise Exception("All videos must have the same number of frames! But you " "passed videos with {} frames".format(video_n_frames)) @@ -934,8 +934,8 @@ def pyrshow(pyr_coeffs, is_complex=False, vrange='indep1', col_wrap=None, zoom=1 # pasting all coefficients into a giant array. # and the steerable pyramids have a num_orientations attribute - num_scales = np.max(np.array([k for k in pyr_coeffs.keys() if isinstance(k, tuple)])[:,0]) + 1 - num_orientations = np.max(np.array([k for k in pyr_coeffs.keys() if isinstance(k, tuple)])[:,1]) + 1 + num_scales = np.max(np.asarray([k for k in pyr_coeffs.keys() if isinstance(k, tuple)])[:,0]) + 1 + num_orientations = np.max(np.asarray([k for k in pyr_coeffs.keys() if isinstance(k, tuple)])[:,1]) + 1 col_wrap_new = num_orientations if is_complex: diff --git a/src/pyrtools/tools/synthetic_images.py b/src/pyrtools/tools/synthetic_images.py index 88cf62b..7feb270 100644 --- a/src/pyrtools/tools/synthetic_images.py +++ b/src/pyrtools/tools/synthetic_images.py @@ -178,8 +178,6 @@ def polar_angle(size, phase=0, origin=None, direction='clockwise'): xramp, yramp = np.meshgrid(np.arange(1, size[1]+1)-origin[1], np.arange(1, size[0]+1)-origin[0]) - xramp = np.array(xramp) - yramp = np.array(yramp) if direction == 'counter-clockwise': yramp = np.flip(yramp, 0) @@ -234,7 +232,7 @@ def disk(size, radius=None, origin=None, twidth=2, vals=(1, 0)): [Xtbl, Ytbl] = rcosFn(twidth, radius, [vals[0], vals[1]]) res = pointOp(res, Ytbl, Xtbl[0], Xtbl[1]-Xtbl[0]) - return np.array(res) + return np.asarray(res) def gaussian(size, covariance=None, origin=None, amplitude='norm'): @@ -273,7 +271,7 @@ def gaussian(size, covariance=None, origin=None, amplitude='norm'): if covariance is None: covariance = (min([size[0], size[1]]) / 6.0) ** 2 - covariance = np.array(covariance) + covariance = np.asarray(covariance) if origin is None: origin = ((size[0]+1)/2., (size[1]+1)/2.)