├── .gitignore ├── LICENSE ├── README.md ├── StreamingWavelet ├── MakeCDJVFilter.m ├── MakeCDJVFilter.py ├── StreamingWavelet.py ├── __init__.py └── wavelets_coeff │ ├── __init__.py │ ├── bior1_1.py │ ├── bior1_3.py │ ├── bior1_5.py │ ├── bior2_2.py │ ├── bior2_4.py │ ├── bior2_6.py │ ├── bior2_8.py │ ├── bior3_1.py │ ├── bior3_3.py │ ├── bior3_5.py │ ├── bior3_7.py │ ├── bior3_9.py │ ├── bior4_4.py │ ├── bior5_5.py │ ├── bior6_8.py │ ├── coif1.py │ ├── coif2.py │ ├── coif3.py │ ├── coif4.py │ ├── coif5.py │ ├── db10.py │ ├── db11.py │ ├── db12.py │ ├── db13.py │ ├── db14.py │ ├── db15.py │ ├── db16.py │ ├── db17.py │ ├── db18.py │ ├── db19.py │ ├── db2.py │ ├── db20.py │ ├── db3.py │ ├── db4.py │ ├── db5.py │ ├── db6.py │ ├── db7.py │ ├── db8.py │ ├── db9.py │ ├── dmey.py │ ├── haar.py │ ├── sym10.py │ ├── sym11.py │ ├── sym12.py │ ├── sym13.py │ ├── sym14.py │ ├── sym15.py │ ├── sym16.py │ ├── sym17.py │ ├── sym18.py │ ├── sym19.py │ ├── sym2.py │ ├── sym20.py │ ├── sym3.py │ ├── sym4.py │ ├── sym5.py │ ├── sym6.py │ ├── sym7.py │ ├── sym8.py │ └── sym9.py └── setup.py /.gitignore: -------------------------------------------------------------------------------- 1 | /StreamingWavelet.egg-info/ 2 | /build/ 3 | /dist/ 4 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023-2024 Yu-Yang Qian 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Python implementation for _Streaming Wavelet Operator_ 2 | 3 | This is the python implementation for the Streaming Wavelet Operator, which **sequentially** 4 | applies wavelet transform to a sequence efficiently in an online manner (instead of recalculation in each round). 5 | 6 | The **speed** of the Streaming Wavelet Operator is much faster than the traditional wavelet transform for streaming data, 7 | especially for long signals, due to its use of lazy updates and bit-wise operations in the implementation. 8 | 9 | You can install the `StreamingWavelet` package in [https://pypi.org/project/StreamingWavelet/](https://pypi.org/project/StreamingWavelet/). 10 | 11 | We will first introduce the structure and requirements of the code, followed by a brief instruction for a quick start. 12 | 13 | ## Reference: 14 | 15 | - Qian et al., Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation. In Proceedings of the 41st International Conference on Machine Learning (ICML 2024). 16 | 17 | ## Install: 18 | 19 | ``` 20 | pip install StreamingWavelet 21 | ``` 22 | 23 | ## Code Structure: 24 | 25 | - `StreamingWavelet/StreamingWavelet.py`: The main file for the Streaming Wavelet Operator, which supports dozens types of wavelet bases. 26 | - `StreamingWavelet/MakeCDJVFilter.py`: The file for generating the filters of different wavelet transforms, thanks to the MatLab code of _Cohen, Daubechies, Jawerth and Vial, 1992_. 27 | - `StreamingWavelet/wavelet_coeff`: The folder for storing the different wavelet coefficients. 28 | 29 | ## Requirements: 30 | 31 | * numpy>=1.19.0 32 | 33 | ## Quick Start & Demos: 34 | 35 | We provide a concrete demo here. 36 | For example, one can use the following code to generate the following Streaming Wavelet Operator 37 | for a sequence of `dim=128`, `max_length=10000`, and using the Haar wavelets (`order=1`) as wavelet basis. 38 | 39 | ```python 40 | import StreamingWavelet 41 | 42 | SW = StreamingWavelet.Operator(128, 10000, 1) 43 | ``` 44 | 45 | Then, for a sequence of length 10000, one can use the following code to sequentially calculate the wavelet coefficients in an online manner: 46 | 47 | ```python 48 | import numpy as np 49 | import StreamingWavelet 50 | 51 | SW = StreamingWavelet.Operator(128, 10000, 1, get_coeff=False) # Initialize the Streaming Wavelet Operator 52 | 53 | # Generate a sequence of length 10000 54 | x_list = [] 55 | for i in range(10000): 56 | x_list.append(np.random.randn(128)) # Generate a random element of dim=128, and add it to the sequence 57 | 58 | for i in range(10000): 59 | SW.add_signal(x_list[i]) # Update the wavelet coefficients by adding the new element 60 | current_norm = SW.get_norm() # Get the norm of the wavelet coefficients 61 | print('Norm of Wavelet Coefficients of x_list[0:{}]:'.format(i), current_norm) # Print the current norm of the wavelet coefficients 62 | ``` 63 | 64 | which will output the norm of the wavelet coefficients in each round. 65 | 66 | --- 67 | 68 | Note that the default mode is `get_coeff=False`, which will only maintain the **2-norm** of the wavelet coefficients. 69 | If you want to get the wavelet coefficients, you can set `get_coeff=True` when initializing the Streaming Wavelet Operator (this will take more storage): 70 | 71 | ```python 72 | import numpy as np 73 | import StreamingWavelet 74 | 75 | SW = StreamingWavelet.Operator(128, 10000, 1, get_coeff=True) # Initialize the Streaming Wavelet Operator 76 | 77 | # Generate a sequence of length 10000 78 | x_list = [] 79 | for i in range(10000): 80 | x_list.append(np.random.randn(128)) # Generate a random element of dim=128, and add it to the sequence 81 | 82 | for i in range(10000): 83 | SW.add_signal(x_list[i]) # Update the wavelet coefficients by adding the new element 84 | current_norm = SW.get_norm() # Get the norm of the wavelet coefficients 85 | print('Norm of Wavelet Coefficients of x_list[0:{}]:'.format(i), current_norm) # Print the current norm of the wavelet coefficients 86 | if (i + 1) % 1000 == 0: 87 | print('Wavelet Coefficients of x_list[0:{}]:'.format(i), SW.all_coeff_arrs[:5]) # Print the wavelet coefficients 88 | ``` 89 | 90 | ## Parameters in `StreamingWavelet.Operator`: 91 | 92 | - `dim`: The dimension of the input signal. 93 | - `max_length`: The maximum length of the sequence. 94 | - `order`: The order of the wavelet transform (e.g., order=1 means Haar wavelets; order>=2 means various Daubechies wavelets). 95 | - `get_coeff`: Whether to maintain the whole wavelet coefficients (default: False). 96 | - `axis`: The axis to apply the wavelet transform (default: -1). 97 | - `verbose`: Whether to print the running information (default: False). -------------------------------------------------------------------------------- /StreamingWavelet/MakeCDJVFilter.m: -------------------------------------------------------------------------------- 1 | function [a,b,c] = MakeCDJVFilter(request,degree) 2 | % MakeCDJVFilter -- Set up filters for CDJV Wavelet Transform 3 | % Usage 4 | % [a,b,c] = MakeCDJVFilter(request,degree) 5 | % Inputs 6 | % request string: 'HighPass', 'LowPass', 'Precondition', 'Postcondition' 7 | % degree integer: 2 or 3 (number of vanishing moments) 8 | % Outputs 9 | % a,b,c filter, left edge filter, right edge filter 10 | % ('HighPass', 'LowPass') 11 | % a conditioning matrix ('Precondition', 'Postcondition') 12 | % 13 | % Description 14 | % CDJV have developed an algorithm for wavelets on the interval which 15 | % preserves the orthogonality, vanishing moments, smoothness, and compact 16 | % support of Daubechies wavelets on the line. 17 | % 18 | % The algorithm for wavelets on the interval of CDJV involves four objects 19 | % not present in the usual periodized algorithm: right edge filters, left 20 | % edge filters, and pre- and post- conditioning operators. 21 | % 22 | % These objects are supplied by appropriate requests to MakeCDJVFilter. 23 | % 24 | % See Also 25 | % IWT_CDJV, FWT_CDJV, CDJVDyadDown 26 | % 27 | % References 28 | % Cohen, Daubechies, Jawerth and Vial, 1992. 29 | % 30 | F2 = [ 0.482962913145 0.836516303738 0.224143868042 -0.129409522551]; 31 | F3 = [ .332670552950 .806891509311 .459877502118 -.135011020010 ... 32 | -.085441273882 .035226291882 ]; 33 | if strcmp(request,'HighPass'), 34 | if degree==2, 35 | LEHI2 = [ -.7965435153E+00 .5463927105 -.2587922607 0 0; ... 36 | .01003722199 .1223510414 .2274281035 -.8366029193 .4830129294 ]; 37 | REHI2 = [ -.2575129317 .8014229647 -.5398224908 0 0 ; ... 38 | .3717189691 -.3639069552 -.7175800176 .4010694996 .2315575756 ]; 39 | a = reverse(MirrorFilt(F2)); b = LEHI2; c = REHI2; 40 | end 41 | if degree==3, 42 | LEHI3 = [ .5837810161 .7936188102 .1609551602 -.05884169984 0 0 0 0 ; ... 43 | -.3493401755 .2989205708 -.3283012959 -.3322637280 ... 44 | .6982497314 -.2878790040 0 0; ... 45 | .001015059936 -.00003930151414 -.03451437279 -.08486981368 ... 46 | .1337306925 .4604064313 -.8068932340 .3326712638 ]; 47 | REHI3 = zeros(3,8); 48 | REHI3(1,1:4) = [ 0.07221947896 -0.42656220040000 ... 49 | 0.80423313630 -0.40747772770000 ]; 50 | REHI3(2,1:6) = [ -0.1535052177 0.5223942253 -0.09819804815 ... 51 | -0.7678795675 0.2985152672 0.1230738394]; 52 | REHI3(3,:) = [ 0.2294775468 -0.4451794532 -0.2558698634 ... 53 | 0.001694456403 0.7598761492 0.1391503023 ... 54 | -0.2725472621 -0.1123675794]; 55 | a = reverse(MirrorFilt(F3)); b = LEHI3; c = REHI3; 56 | end 57 | end 58 | if strcmp(request,'LowPass'), 59 | if degree==2, 60 | LELO2 = [.6033325147 .6908955290 -.3983129985 0 0 ; ... 61 | .3751745208e-01 .4573276687 .8500881006 .2238203490 -.1292227411 ] ; 62 | RELO2 = [ .8705087515 .4348970037 .2303890399 0 0; 63 | -.1942333944 .1901514021 .3749553135 .7675566880 .4431490452 ]; 64 | a = F2; b = LELO2; c = RELO2; 65 | end 66 | if degree==3, 67 | LELO3 = zeros(3,8); 68 | LELO3(1,1:4) = [.3888996730 -.08820780195 -.8478413443 .3494874575]; 69 | LELO3(2,1:6) = [ -.6211483347 .5225274354 -.2000079353 ... 70 | .3378673010 -.3997707643 .1648201271]; 71 | LELO3(3,:) = [ -.009587872354 .0003712272422 .3260097151 ... 72 | .8016481698 .4720552497 -.1400420768 -.08542510419 .03521962531 ]; 73 | RELO3 = zeros(3,8); 74 | RELO3(1,1:4) = [ 0.9096849932 0.3823606566 0.1509872202 0.0589610111]; 75 | RELO3(2,1:6) = [ -0.2904078626 0.4189992458 0.4969643833 ... 76 | 0.4907578162 0.4643627531 0.1914505327 ]; 77 | RELO3(3,:) = [ 0.08183542639 -0.1587582353 -0.09124735588 ... 78 | 0.00060427071940 0.0770293676 0.520060179 0.7642591949 ... 79 | 0.3150938119 ]; 80 | a = F3; b = LELO3; c = RELO3; 81 | end 82 | end 83 | if strcmp(request,'PreCondition'), 84 | if degree==2, 85 | LPREMAT2 = [ .03715799299 .3248940464 ; ... 86 | 1.001445417 .0000000000 ]; 87 | RPREMAT2 = [ -.8008131776 1.089843048 ; ... 88 | 2.096292891 .00000000 ]; 89 | a = LPREMAT2; b = flipud(RPREMAT2)'; c = []; 90 | end 91 | if degree==3, 92 | LPREMAT3 = [ -.01509646707 -.5929309617 .1007941548 ; ... 93 | .03068539981 .2137256750 .0000000000 ; ... 94 | 1.000189315 .0000000000 .0000000000 ]; 95 | RPREMAT3 = [2.417783369 -.4658799095 1.055782523 ;... 96 | -6.66336795000000 1.73763183100000 0 ;... 97 | 5.64221225900000 0 0 ]; 98 | a = fliplr(LPREMAT3); b = flipud(RPREMAT3)'; c = []; 99 | end 100 | end 101 | if strcmp(request,'PostCondition'), 102 | if degree==2, 103 | RPOSTMAT2 = [ .0000000000 .4770325771 ; 104 | .9175633147 .3505220082 ]; 105 | LPOSTMAT2 = [ 0.000000000 .9985566693; ... 106 | 3.077926515 -.1142044987 ]; 107 | a = LPOSTMAT2; b = flipud(RPOSTMAT2'); c = []; 108 | end 109 | if degree==3, 110 | LPOSTMAT3 = [ .0000000000 .000000000 .9998107210 ;... 111 | .0000000000 4.678895037 -.1435465894 ;... 112 | 9.921210229 27.52403389 -.6946792478 ]; 113 | RPOSTMAT3 = [ 0 0 0.1772354449 ;... 114 | 0 0.5754959032 0.6796520196 ;... 115 | 0.9471647601 0.2539462185 -0.1059694467 ]; 116 | a = flipud(LPOSTMAT3); b = flipud(RPOSTMAT3'); c = []; 117 | end 118 | end 119 | 120 | % 121 | % Copyright (c) 1993. David L. Donoho 122 | % 123 | 124 | 125 | 126 | 127 | 128 | 129 | % 130 | % Part of Wavelab Version 850 131 | % Built Tue Jan 3 13:20:40 EST 2006 132 | % This is Copyrighted Material 133 | % For Copying permissions see COPYING.m 134 | % Comments? e-mail wavelab@stat.stanford.edu 135 | -------------------------------------------------------------------------------- /StreamingWavelet/MakeCDJVFilter.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def mirror_filt(x): 5 | y = -((-1) ** (np.arange(1, len(x) + 1))) * x 6 | return y 7 | 8 | 9 | def MakeCDJVFilter(request, degree=2): 10 | ''' 11 | MakeCDJVFilter -- Set up filters for CDJV Wavelet Transform 12 | 13 | Usage 14 | [a,b,c] = MakeCDJVFilter(request,degree) 15 | 16 | Inputs 17 | request string: 'HighPass', 'LowPass', 'Precondition', 'Postcondition' 18 | degree integer: 2 or 3 (number of vanishing moments) 19 | 20 | Outputs 21 | a,b,c filter, left edge filter, right edge filter 22 | ('HighPass', 'LowPass') 23 | a conditioning matrix ('Precondition', 'Postcondition') 24 | 25 | Description 26 | CDJV have developed an algorithm for wavelets on the interval which 27 | preserves the orthogonality, vanishing moments, smoothness, and compact 28 | support of Daubechies wavelets on the line. 29 | 30 | The algorithm for wavelets on the interval of CDJV involves four objects 31 | not present in the usual periodized algorithm: right edge filters, left 32 | edge filters, and pre- and post- conditioning operators. 33 | These objects are supplied by appropriate requests to MakeCDJVFilter. 34 | 35 | References 36 | Cohen, Daubechies, Jawerth and Vial, 1992. 37 | ''' 38 | 39 | F2 = np.array([0.482962913145, 0.836516303738, 0.224143868042, - 0.129409522551]) 40 | F3 = np.array([0.33267055295, 0.806891509311, 0.459877502118, - 0.13501102001, - 0.085441273882, 0.035226291882]) 41 | if str(request) == str('HighPass'): 42 | if degree == 2: 43 | LEHI2 = np.array([[- 0.7965435153, 0.5463927105, - 0.2587922607, 0, 0], [0.01003722199, 0.1223510414, 0.2274281035, - 0.8366029193, 0.4830129294]]) 44 | REHI2 = np.array([[- 0.2575129317, 0.8014229647, - 0.5398224908, 0, 0], [0.3717189691, - 0.3639069552, - 0.7175800176, 0.4010694996, 0.2315575756]]) 45 | a = np.flip(mirror_filt(F2)) 46 | b = LEHI2 47 | c = REHI2 48 | if degree == 3: 49 | LEHI3 = np.array([[0.5837810161, 0.7936188102, 0.1609551602, - 0.05884169984, 0, 0, 0, 0], [- 0.3493401755, 0.2989205708, - 0.3283012959, - 0.332263728, 0.6982497314, - 0.287879004, 0, 0], [0.001015059936, - 3.930151414e-05, - 0.03451437279, - 0.08486981368, 0.1337306925, 0.4604064313, - 0.806893234, 0.3326712638]]) 50 | REHI3 = np.zeros((3, 8)) 51 | REHI3[0, 0:4] = np.array([0.07221947896, - 0.4265622004, 0.8042331363, - 0.4074777277]) 52 | REHI3[1, 0:6] = np.array([- 0.1535052177, 0.5223942253, - 0.09819804815, - 0.7678795675, 0.2985152672, 0.1230738394]) 53 | REHI3[2, :] = np.array([0.2294775468, - 0.4451794532, - 0.2558698634, 0.001694456403, 0.7598761492, 0.1391503023, - 0.2725472621, - 0.1123675794]) 54 | a = np.flip(mirror_filt(F3)) 55 | b = LEHI3 56 | c = REHI3 57 | 58 | if str(request) == str('LowPass'): 59 | if degree == 2: 60 | LELO2 = np.array([[0.6033325147, 0.690895529, - 0.3983129985, 0, 0], [0.03751745208, 0.4573276687, 0.8500881006, 0.223820349, - 0.1292227411]]) 61 | RELO2 = np.array([[0.8705087515, 0.4348970037, 0.2303890399, 0, 0], [- 0.1942333944, 0.1901514021, 0.3749553135, 0.767556688, 0.4431490452]]) 62 | a = F2 63 | b = LELO2 64 | c = RELO2 65 | if degree == 3: 66 | LELO3 = np.zeros((3, 8)) 67 | LELO3[0, 0:4] = np.array([0.388899673, - 0.08820780195, - 0.8478413443, 0.3494874575]) 68 | LELO3[1, 0:6] = np.array([- 0.6211483347, 0.5225274354, - 0.2000079353, 0.337867301, - 0.3997707643, 0.1648201271]) 69 | LELO3[2, :] = np.array([- 0.009587872354, 0.0003712272422, 0.3260097151, 0.8016481698, 0.4720552497, - 0.1400420768, - 0.08542510419, 0.03521962531]) 70 | RELO3 = np.zeros((3, 8)) 71 | RELO3[0, 0:4] = np.array([0.9096849932, 0.3823606566, 0.1509872202, 0.0589610111]) 72 | RELO3[1, 0:6] = np.array([- 0.2904078626, 0.4189992458, 0.4969643833, 0.4907578162, 0.4643627531, 0.1914505327]) 73 | RELO3[2, :] = np.array([0.08183542639, - 0.1587582353, - 0.09124735588, 0.0006042707194, 0.0770293676, 0.520060179, 0.7642591949, 0.3150938119]) 74 | a = F3 75 | b = LELO3 76 | c = RELO3 77 | 78 | if str(request) == str('PreCondition'): 79 | if degree == 2: 80 | LPREMAT2 = np.array([[0.03715799299, 0.3248940464], [1.001445417, 0.0]]) 81 | RPREMAT2 = np.array([[- 0.8008131776, 1.089843048], [2.096292891, 0.0]]) 82 | a = LPREMAT2 83 | b = np.flipud(RPREMAT2).T 84 | c = [] 85 | if degree == 3: 86 | LPREMAT3 = np.array([[- 0.01509646707, - 0.5929309617, 0.1007941548], [0.03068539981, 0.213725675, 0.0], [1.000189315, 0.0, 0.0]]) 87 | RPREMAT3 = np.array([[2.417783369, - 0.4658799095, 1.055782523], [- 6.66336795, 1.737631831, 0], [5.642212259, 0, 0]]) 88 | a = LPREMAT3 89 | b = np.flipud(RPREMAT3).T 90 | c = [] 91 | 92 | if str(request) == str('PostCondition'): 93 | if degree == 2: 94 | RPOSTMAT2 = np.array([[0.0, 0.4770325771], [0.9175633147, 0.3505220082]]) 95 | LPOSTMAT2 = np.array([[0.0, 0.9985566693], [3.077926515, - 0.1142044987]]) 96 | a = LPOSTMAT2 97 | b = np.flipud(np.transpose(RPOSTMAT2)) 98 | c = [] 99 | if degree == 3: 100 | LPOSTMAT3 = np.array([[0.0, 0.0, 0.999810721], [0.0, 4.678895037, - 0.1435465894], [9.921210229, 27.52403389, - 0.6946792478]]) 101 | RPOSTMAT3 = np.array([[0, 0, 0.1772354449], [0, 0.5754959032, 0.6796520196], [0.9471647601, 0.2539462185, - 0.1059694467]]) 102 | a = np.flipud(LPOSTMAT3) 103 | b = np.flipud(np.transpose(RPOSTMAT3)) 104 | c = [] 105 | 106 | return a, b, c 107 | 108 | # Copyright (c) 1993. David L. Donoho 109 | 110 | # Part of Wavelab Version 850 111 | # Built Tue Jan 3 13:20:40 EST 2006 112 | # This is Copyrighted Material 113 | # For Copying permissions see COPYING.m 114 | # Comments? e-mail wavelab@stat.stanford.edu 115 | 116 | 117 | # Demo: 118 | if __name__ == '__main__': 119 | a, b, c = MakeCDJVFilter(request='HighPass', degree=2) 120 | -------------------------------------------------------------------------------- /StreamingWavelet/__init__.py: -------------------------------------------------------------------------------- 1 | name = "StreamingWavelet" 2 | 3 | from StreamingWavelet.StreamingWavelet import StreamingWavelet as Operator 4 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZinYY/StreamingWavelet/4b1bf3fdff94b3901d8d0382215696a5433de2cc/StreamingWavelet/wavelets_coeff/__init__.py -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/bior1_1.py: -------------------------------------------------------------------------------- 1 | """ Biorthogonal 1.1 wavelet """ 2 | 3 | 4 | class Biorthogonal11: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, not orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/bior1.1/ 11 | """ 12 | __name__ = "Biorthogonal Wavelet 1.1" 13 | __motherWaveletLength__ = 2 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.7071067811865476, 20 | 0.7071067811865476, 21 | ] 22 | 23 | # high-pass 24 | decompositionHighFilter = [ 25 | -0.7071067811865476, 26 | 0.7071067811865476, 27 | ] 28 | 29 | # reconstruction filters 30 | # low pass 31 | reconstructionLowFilter = [ 32 | 0.7071067811865476, 33 | 0.7071067811865476, 34 | ] 35 | 36 | # high-pass 37 | reconstructionHighFilter = [ 38 | 0.7071067811865476, 39 | -0.7071067811865476, 40 | ] 41 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/bior1_3.py: -------------------------------------------------------------------------------- 1 | """ Biorthogonal 1.3 wavelet """ 2 | 3 | 4 | class Biorthogonal13: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, not orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/bior1.3/ 11 | """ 12 | __name__ = "Biorthogonal Wavelet 1.3" 13 | __motherWaveletLength__ = 6 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -0.08838834764831845, 20 | 0.08838834764831845, 21 | 0.7071067811865476, 22 | 0.7071067811865476, 23 | 0.08838834764831845, 24 | -0.08838834764831845, 25 | ] 26 | 27 | # high-pass 28 | decompositionHighFilter = [ 29 | 0.0, 30 | 0.0, 31 | -0.7071067811865476, 32 | 0.7071067811865476, 33 | 0.0, 34 | 0.0, 35 | ] 36 | 37 | # reconstruction filters 38 | # low pass 39 | reconstructionLowFilter = [ 40 | 0.0, 41 | 0.0, 42 | 0.7071067811865476, 43 | 0.7071067811865476, 44 | 0.0, 45 | 0.0, 46 | ] 47 | 48 | # high-pass 49 | reconstructionHighFilter = [ 50 | -0.08838834764831845, 51 | -0.08838834764831845, 52 | 0.7071067811865476, 53 | -0.7071067811865476, 54 | 0.08838834764831845, 55 | 0.08838834764831845, 56 | ] 57 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/bior1_5.py: -------------------------------------------------------------------------------- 1 | """ Biorthogonal 1.5 wavelet """ 2 | 3 | 4 | class Biorthogonal15: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, not orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/bior1.5/ 11 | """ 12 | __name__ = "Biorthogonal Wavelet 1.5" 13 | __motherWaveletLength__ = 10 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.01657281518405971, 20 | -0.01657281518405971, 21 | -0.12153397801643787, 22 | 0.12153397801643787, 23 | 0.7071067811865476, 24 | 0.7071067811865476, 25 | 0.12153397801643787, 26 | -0.12153397801643787, 27 | -0.01657281518405971, 28 | 0.01657281518405971, 29 | ] 30 | 31 | # high-pass 32 | decompositionHighFilter = [ 33 | 0.0, 34 | 0.0, 35 | 0.0, 36 | 0.0, 37 | -0.7071067811865476, 38 | 0.7071067811865476, 39 | 0.0, 40 | 0.0, 41 | 0.0, 42 | 0.0, 43 | ] 44 | 45 | # reconstruction filters 46 | # low pass 47 | reconstructionLowFilter = [ 48 | 0.0, 49 | 0.0, 50 | 0.0, 51 | 0.0, 52 | 0.7071067811865476, 53 | 0.7071067811865476, 54 | 0.0, 55 | 0.0, 56 | 0.0, 57 | 0.0, 58 | ] 59 | 60 | # high-pass 61 | reconstructionHighFilter = [ 62 | 0.01657281518405971, 63 | 0.01657281518405971, 64 | -0.12153397801643787, 65 | -0.12153397801643787, 66 | 0.7071067811865476, 67 | -0.7071067811865476, 68 | 0.12153397801643787, 69 | 0.12153397801643787, 70 | -0.01657281518405971, 71 | -0.01657281518405971, 72 | ] 73 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/bior2_2.py: -------------------------------------------------------------------------------- 1 | """ Biorthogonal 2.2 wavelet """ 2 | 3 | 4 | class Biorthogonal22: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, not orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/bior2.2/ 11 | """ 12 | __name__ = "Biorthogonal Wavelet 2.2" 13 | __motherWaveletLength__ = 6 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.0, 20 | -0.1767766952966369, 21 | 0.3535533905932738, 22 | 1.0606601717798214, 23 | 0.3535533905932738, 24 | -0.1767766952966369, 25 | ] 26 | 27 | # high-pass 28 | decompositionHighFilter = [ 29 | 0.0, 30 | 0.3535533905932738, 31 | -0.7071067811865476, 32 | 0.3535533905932738, 33 | 0.0, 34 | 0.0, 35 | ] 36 | 37 | # reconstruction filters 38 | # low pass 39 | reconstructionLowFilter = [ 40 | 0.0, 41 | 0.3535533905932738, 42 | 0.7071067811865476, 43 | 0.3535533905932738, 44 | 0.0, 45 | 0.0, 46 | ] 47 | 48 | # high-pass 49 | reconstructionHighFilter = [ 50 | 0.0, 51 | 0.1767766952966369, 52 | 0.3535533905932738, 53 | -1.0606601717798214, 54 | 0.3535533905932738, 55 | 0.1767766952966369, 56 | ] 57 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/bior2_4.py: -------------------------------------------------------------------------------- 1 | """ Biorthogonal 2.4 wavelet """ 2 | 3 | 4 | class Biorthogonal24: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, not orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/bior2.4/ 11 | """ 12 | __name__ = "Biorthogonal Wavelet 1.1" 13 | __motherWaveletLength__ = 10 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.0, 20 | 0.03314563036811942, 21 | -0.06629126073623884, 22 | -0.1767766952966369, 23 | 0.4198446513295126, 24 | 0.9943689110435825, 25 | 0.4198446513295126, 26 | -0.1767766952966369, 27 | -0.06629126073623884, 28 | 0.03314563036811942, 29 | ] 30 | 31 | # high-pass 32 | decompositionHighFilter = [ 33 | 0.0, 34 | 0.0, 35 | 0.0, 36 | 0.3535533905932738, 37 | -0.7071067811865476, 38 | 0.3535533905932738, 39 | 0.0, 40 | 0.0, 41 | 0.0, 42 | 0.0, 43 | ] 44 | 45 | # reconstruction filters 46 | # low pass 47 | reconstructionLowFilter = [ 48 | 0.0, 49 | 0.0, 50 | 0.0, 51 | 0.3535533905932738, 52 | 0.7071067811865476, 53 | 0.3535533905932738, 54 | 0.0, 55 | 0.0, 56 | 0.0, 57 | 0.0, 58 | ] 59 | 60 | # high-pass 61 | reconstructionHighFilter = [ 62 | 0.0, 63 | -0.03314563036811942, 64 | -0.06629126073623884, 65 | 0.1767766952966369, 66 | 0.4198446513295126, 67 | -0.9943689110435825, 68 | 0.4198446513295126, 69 | 0.1767766952966369, 70 | -0.06629126073623884, 71 | -0.03314563036811942, 72 | ] 73 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/bior2_6.py: -------------------------------------------------------------------------------- 1 | """ Biorthogonal 2.6 wavelet """ 2 | 3 | 4 | class Biorthogonal26: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, not orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/bior2.6/ 11 | """ 12 | __name__ = "Biorthogonal Wavelet 2.6" 13 | __motherWaveletLength__ = 14 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.0, 20 | -0.006905339660024878, 21 | 0.013810679320049757, 22 | 0.046956309688169176, 23 | -0.10772329869638811, 24 | -0.16987135563661201, 25 | 0.4474660099696121, 26 | 0.966747552403483, 27 | 0.4474660099696121, 28 | -0.16987135563661201, 29 | -0.10772329869638811, 30 | 0.046956309688169176, 31 | 0.013810679320049757, 32 | -0.006905339660024878, 33 | ] 34 | 35 | # high-pass 36 | decompositionHighFilter = [ 37 | 0.0, 38 | 0.0, 39 | 0.0, 40 | 0.0, 41 | 0.0, 42 | 0.3535533905932738, 43 | -0.7071067811865476, 44 | 0.3535533905932738, 45 | 0.0, 46 | 0.0, 47 | 0.0, 48 | 0.0, 49 | 0.0, 50 | 0.0, 51 | ] 52 | 53 | # reconstruction filters 54 | # low pass 55 | reconstructionLowFilter = [ 56 | 0.0, 57 | 0.0, 58 | 0.0, 59 | 0.0, 60 | 0.0, 61 | 0.3535533905932738, 62 | 0.7071067811865476, 63 | 0.3535533905932738, 64 | 0.0, 65 | 0.0, 66 | 0.0, 67 | 0.0, 68 | 0.0, 69 | 0.0, 70 | ] 71 | 72 | # high-pass 73 | reconstructionHighFilter = [ 74 | 0.0, 75 | 0.006905339660024878, 76 | 0.013810679320049757, 77 | -0.046956309688169176, 78 | -0.10772329869638811, 79 | 0.16987135563661201, 80 | 0.4474660099696121, 81 | -0.966747552403483, 82 | 0.4474660099696121, 83 | 0.16987135563661201, 84 | -0.10772329869638811, 85 | -0.046956309688169176, 86 | 0.013810679320049757, 87 | 0.006905339660024878, 88 | ] 89 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/bior2_8.py: -------------------------------------------------------------------------------- 1 | """ Biorthogonal 2.8 wavelet """ 2 | 3 | 4 | class Biorthogonal28: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, not orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/bior2.8/ 11 | """ 12 | __name__ = "Biorthogonal Wavelet 2.8" 13 | __motherWaveletLength__ = 18 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.0, 20 | 0.0015105430506304422, 21 | -0.0030210861012608843, 22 | -0.012947511862546647, 23 | 0.02891610982635418, 24 | 0.052998481890690945, 25 | -0.13491307360773608, 26 | -0.16382918343409025, 27 | 0.4625714404759166, 28 | 0.9516421218971786, 29 | 0.4625714404759166, 30 | -0.16382918343409025, 31 | -0.13491307360773608, 32 | 0.052998481890690945, 33 | 0.02891610982635418, 34 | -0.012947511862546647, 35 | -0.0030210861012608843, 36 | 0.0015105430506304422, 37 | ] 38 | 39 | # high-pass 40 | decompositionHighFilter = [ 41 | 0.0, 42 | 0.0, 43 | 0.0, 44 | 0.0, 45 | 0.0, 46 | 0.0, 47 | 0.0, 48 | 0.3535533905932738, 49 | -0.7071067811865476, 50 | 0.3535533905932738, 51 | 0.0, 52 | 0.0, 53 | 0.0, 54 | 0.0, 55 | 0.0, 56 | 0.0, 57 | 0.0, 58 | 0.0, 59 | ] 60 | 61 | # reconstruction filters 62 | # low pass 63 | reconstructionLowFilter = [ 64 | 0.0, 65 | 0.0, 66 | 0.0, 67 | 0.0, 68 | 0.0, 69 | 0.0, 70 | 0.0, 71 | 0.3535533905932738, 72 | 0.7071067811865476, 73 | 0.3535533905932738, 74 | 0.0, 75 | 0.0, 76 | 0.0, 77 | 0.0, 78 | 0.0, 79 | 0.0, 80 | 0.0, 81 | 0.0, 82 | ] 83 | 84 | # high-pass 85 | reconstructionHighFilter = [ 86 | 0.0, 87 | -0.0015105430506304422, 88 | -0.0030210861012608843, 89 | 0.012947511862546647, 90 | 0.02891610982635418, 91 | -0.052998481890690945, 92 | -0.13491307360773608, 93 | 0.16382918343409025, 94 | 0.4625714404759166, 95 | -0.9516421218971786, 96 | 0.4625714404759166, 97 | 0.16382918343409025, 98 | -0.13491307360773608, 99 | -0.052998481890690945, 100 | 0.02891610982635418, 101 | 0.012947511862546647, 102 | -0.0030210861012608843, 103 | -0.0015105430506304422, 104 | ] 105 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/bior3_1.py: -------------------------------------------------------------------------------- 1 | """ Biorthogonal 3.1 wavelet """ 2 | 3 | 4 | class Biorthogonal31: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, not orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/bior3.1/ 11 | """ 12 | __name__ = "Biorthogonal Wavelet 3.1" 13 | __motherWaveletLength__ = 4 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -0.3535533905932738, 20 | 1.0606601717798214, 21 | 1.0606601717798214, 22 | -0.3535533905932738, 23 | ] 24 | 25 | # high-pass 26 | decompositionHighFilter = [ 27 | -0.1767766952966369, 28 | 0.5303300858899107, 29 | -0.5303300858899107, 30 | 0.1767766952966369, 31 | ] 32 | 33 | # reconstruction filters 34 | # low pass 35 | reconstructionLowFilter = [ 36 | 0.1767766952966369, 37 | 0.5303300858899107, 38 | 0.5303300858899107, 39 | 0.1767766952966369, 40 | ] 41 | 42 | # high-pass 43 | reconstructionHighFilter = [ 44 | -0.3535533905932738, 45 | -1.0606601717798214, 46 | 1.0606601717798214, 47 | 0.3535533905932738, 48 | ] 49 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/bior3_3.py: -------------------------------------------------------------------------------- 1 | """ Biorthogonal 3.3 wavelet """ 2 | 3 | 4 | class Biorthogonal33: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, not orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/bior3.3/ 11 | """ 12 | __name__ = "Biorthogonal Wavelet 3.3" 13 | __motherWaveletLength__ = 8 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.06629126073623884, 20 | -0.19887378220871652, 21 | -0.15467960838455727, 22 | 0.9943689110435825, 23 | 0.9943689110435825, 24 | -0.15467960838455727, 25 | -0.19887378220871652, 26 | 0.06629126073623884, 27 | ] 28 | 29 | # high-pass 30 | decompositionHighFilter = [ 31 | 0.0, 32 | 0.0, 33 | -0.1767766952966369, 34 | 0.5303300858899107, 35 | -0.5303300858899107, 36 | 0.1767766952966369, 37 | 0.0, 38 | 0.0, 39 | ] 40 | 41 | # reconstruction filters 42 | # low pass 43 | reconstructionLowFilter = [ 44 | 0.0, 45 | 0.0, 46 | 0.1767766952966369, 47 | 0.5303300858899107, 48 | 0.5303300858899107, 49 | 0.1767766952966369, 50 | 0.0, 51 | 0.0, 52 | ] 53 | 54 | # high-pass 55 | reconstructionHighFilter = [ 56 | 0.06629126073623884, 57 | 0.19887378220871652, 58 | -0.15467960838455727, 59 | -0.9943689110435825, 60 | 0.9943689110435825, 61 | 0.15467960838455727, 62 | -0.19887378220871652, 63 | -0.06629126073623884, 64 | ] 65 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/bior3_5.py: -------------------------------------------------------------------------------- 1 | """ Biorthogonal 3.5 wavelet """ 2 | 3 | 4 | class Biorthogonal35: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, not orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/bior3.5/ 11 | """ 12 | __name__ = "Biorthogonal Wavelet 3.5" 13 | __motherWaveletLength__ = 12 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -0.013810679320049757, 20 | 0.04143203796014927, 21 | 0.052480581416189075, 22 | -0.26792717880896527, 23 | -0.07181553246425874, 24 | 0.966747552403483, 25 | 0.966747552403483, 26 | -0.07181553246425874, 27 | -0.26792717880896527, 28 | 0.052480581416189075, 29 | 0.04143203796014927, 30 | -0.013810679320049757, 31 | ] 32 | 33 | # high-pass 34 | decompositionHighFilter = [ 35 | 0.0, 36 | 0.0, 37 | 0.0, 38 | 0.0, 39 | -0.1767766952966369, 40 | 0.5303300858899107, 41 | -0.5303300858899107, 42 | 0.1767766952966369, 43 | 0.0, 44 | 0.0, 45 | 0.0, 46 | 0.0, 47 | ] 48 | 49 | # reconstruction filters 50 | # low pass 51 | reconstructionLowFilter = [ 52 | 0.0, 53 | 0.0, 54 | 0.0, 55 | 0.0, 56 | 0.1767766952966369, 57 | 0.5303300858899107, 58 | 0.5303300858899107, 59 | 0.1767766952966369, 60 | 0.0, 61 | 0.0, 62 | 0.0, 63 | 0.0, 64 | ] 65 | 66 | # high-pass 67 | reconstructionHighFilter = [ 68 | -0.013810679320049757, 69 | -0.04143203796014927, 70 | 0.052480581416189075, 71 | 0.26792717880896527, 72 | -0.07181553246425874, 73 | -0.966747552403483, 74 | 0.966747552403483, 75 | 0.07181553246425874, 76 | -0.26792717880896527, 77 | -0.052480581416189075, 78 | 0.04143203796014927, 79 | 0.013810679320049757, 80 | ] 81 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/bior3_7.py: -------------------------------------------------------------------------------- 1 | """ Biorthogonal 3.7 wavelet """ 2 | 3 | 4 | class Biorthogonal37: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, not orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/bior3.7/ 11 | """ 12 | __name__ = "Biorthogonal Wavelet 3.7" 13 | __motherWaveletLength__ = 16 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -0.01565572813546454, 20 | 0.0030210861012608843, 21 | -0.009063258303782653, 22 | -0.01683176542131064, 23 | 0.074663985074019, 24 | 0.03133297870736289, 25 | -0.301159125922835, 26 | -0.026499240945345472, 27 | 0.9516421218971786, 28 | 0.9516421218971786, 29 | -0.026499240945345472, 30 | -0.301159125922835, 31 | 0.03133297870736289, 32 | 0.074663985074019, 33 | -0.01683176542131064, 34 | -0.009063258303782653, 35 | 0.0030210861012608843, 36 | ] 37 | 38 | # high-pass 39 | decompositionHighFilter = [ 40 | 0.0, 41 | 0.0, 42 | 0.0, 43 | 0.0, 44 | 0.0, 45 | 0.0, 46 | -0.1767766952966369, 47 | 0.5303300858899107, 48 | -0.5303300858899107, 49 | 0.1767766952966369, 50 | 0.0, 51 | 0.0, 52 | 0.0, 53 | 0.0, 54 | 0.0, 55 | 0.0, 56 | ] 57 | 58 | # reconstruction filters 59 | # low pass 60 | reconstructionLowFilter = [ 61 | 0.0, 62 | 0.0, 63 | 0.0, 64 | 0.0, 65 | 0.0, 66 | 0.0, 67 | 0.1767766952966369, 68 | 0.5303300858899107, 69 | 0.5303300858899107, 70 | 0.1767766952966369, 71 | 0.0, 72 | 0.0, 73 | 0.0, 74 | 0.0, 75 | 0.0, 76 | 0.0, 77 | ] 78 | 79 | # high-pass 80 | reconstructionHighFilter = [ 81 | 0.0030210861012608843, 82 | 0.009063258303782653, 83 | -0.01683176542131064, 84 | -0.074663985074019, 85 | 0.03133297870736289, 86 | 0.301159125922835, 87 | -0.026499240945345472, 88 | -0.9516421218971786, 89 | 0.9516421218971786, 90 | 0.026499240945345472, 91 | -0.301159125922835, 92 | -0.03133297870736289, 93 | 0.074663985074019, 94 | 0.01683176542131064, 95 | -0.009063258303782653, 96 | -0.0030210861012608843, 97 | ] 98 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/bior3_9.py: -------------------------------------------------------------------------------- 1 | """ Biorthogonal 3.9 wavelet """ 2 | 3 | 4 | class Biorthogonal39: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, not orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/bior3.9/ 11 | """ 12 | __name__ = "Biorthogonal Wavelet 3.9" 13 | __motherWaveletLength__ = 20 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -0.000679744372783699, 20 | 0.002039233118351097, 21 | 0.005060319219611981, 22 | -0.020618912641105536, 23 | -0.014112787930175846, 24 | 0.09913478249423216, 25 | 0.012300136269419315, 26 | -0.32019196836077857, 27 | 0.0020500227115698858, 28 | 0.9421257006782068, 29 | 0.9421257006782068, 30 | 0.0020500227115698858, 31 | -0.32019196836077857, 32 | 0.012300136269419315, 33 | 0.09913478249423216, 34 | -0.014112787930175846, 35 | -0.020618912641105536, 36 | 0.005060319219611981, 37 | 0.002039233118351097, 38 | -0.000679744372783699, 39 | ] 40 | 41 | # high-pass 42 | decompositionHighFilter = [ 43 | 0.0, 44 | 0.0, 45 | 0.0, 46 | 0.0, 47 | 0.0, 48 | 0.0, 49 | 0.0, 50 | 0.0, 51 | -0.1767766952966369, 52 | 0.5303300858899107, 53 | -0.5303300858899107, 54 | 0.1767766952966369, 55 | 0.0, 56 | 0.0, 57 | 0.0, 58 | 0.0, 59 | 0.0, 60 | 0.0, 61 | 0.0, 62 | 0.0, 63 | ] 64 | 65 | # reconstruction filters 66 | # low pass 67 | reconstructionLowFilter = [ 68 | 0.0, 69 | 0.0, 70 | 0.0, 71 | 0.0, 72 | 0.0, 73 | 0.0, 74 | 0.0, 75 | 0.0, 76 | 0.1767766952966369, 77 | 0.5303300858899107, 78 | 0.5303300858899107, 79 | 0.1767766952966369, 80 | 0.0, 81 | 0.0, 82 | 0.0, 83 | 0.0, 84 | 0.0, 85 | 0.0, 86 | 0.0, 87 | 0.0, 88 | ] 89 | 90 | # high-pass 91 | reconstructionHighFilter = [ 92 | -0.000679744372783699, 93 | -0.002039233118351097, 94 | 0.005060319219611981, 95 | 0.020618912641105536, 96 | -0.014112787930175846, 97 | -0.09913478249423216, 98 | 0.012300136269419315, 99 | 0.32019196836077857, 100 | 0.0020500227115698858, 101 | -0.9421257006782068, 102 | 0.9421257006782068, 103 | -0.0020500227115698858, 104 | -0.32019196836077857, 105 | -0.012300136269419315, 106 | 0.09913478249423216, 107 | 0.014112787930175846, 108 | -0.020618912641105536, 109 | -0.005060319219611981, 110 | 0.002039233118351097, 111 | 0.000679744372783699, 112 | ] 113 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/bior4_4.py: -------------------------------------------------------------------------------- 1 | """ Biorthogonal 4.4 wavelet """ 2 | 3 | 4 | class Biorthogonal44: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, not orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/bior4.4/ 11 | """ 12 | __name__ = "Biorthogonal Wavelet 4.4" 13 | __motherWaveletLength__ = 10 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.0, 20 | 0.03782845550726404, 21 | -0.023849465019556843, 22 | -0.11062440441843718, 23 | 0.37740285561283066, 24 | 0.8526986790088938, 25 | 0.37740285561283066, 26 | -0.11062440441843718, 27 | -0.023849465019556843, 28 | 0.03782845550726404, 29 | ] 30 | 31 | # high-pass 32 | decompositionHighFilter = [ 33 | 0.0, 34 | -0.06453888262869706, 35 | 0.04068941760916406, 36 | 0.41809227322161724, 37 | -0.7884856164055829, 38 | 0.41809227322161724, 39 | 0.04068941760916406, 40 | -0.06453888262869706, 41 | 0.0, 42 | 0.0, 43 | ] 44 | 45 | # reconstruction filters 46 | # low pass 47 | reconstructionLowFilter = [ 48 | 0.0, 49 | -0.06453888262869706, 50 | -0.04068941760916406, 51 | 0.41809227322161724, 52 | 0.7884856164055829, 53 | 0.41809227322161724, 54 | -0.04068941760916406, 55 | -0.06453888262869706, 56 | 0.0, 57 | 0.0, 58 | ] 59 | 60 | # high-pass 61 | reconstructionHighFilter = [ 62 | 0.0, 63 | -0.03782845550726404, 64 | -0.023849465019556843, 65 | 0.11062440441843718, 66 | 0.37740285561283066, 67 | -0.8526986790088938, 68 | 0.37740285561283066, 69 | 0.11062440441843718, 70 | -0.023849465019556843, 71 | -0.03782845550726404, 72 | ] 73 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/bior5_5.py: -------------------------------------------------------------------------------- 1 | """ Biorthogonal 5.5 wavelet """ 2 | 3 | 4 | class Biorthogonal55: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, not orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/bior5.5/ 11 | """ 12 | __name__ = "Biorthogonal Wavelet 5.5" 13 | __motherWaveletLength__ = 12 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.0, 20 | 0.0, 21 | 0.03968708834740544, 22 | 0.007948108637240322, 23 | -0.05446378846823691, 24 | 0.34560528195603346, 25 | 0.7366601814282105, 26 | 0.34560528195603346, 27 | -0.05446378846823691, 28 | 0.007948108637240322, 29 | 0.03968708834740544, 30 | 0.0, 31 | ] 32 | 33 | # high-pass 34 | decompositionHighFilter = [ 35 | -0.013456709459118716, 36 | -0.002694966880111507, 37 | 0.13670658466432914, 38 | -0.09350469740093886, 39 | -0.47680326579848425, 40 | 0.8995061097486484, 41 | -0.47680326579848425, 42 | -0.09350469740093886, 43 | 0.13670658466432914, 44 | -0.002694966880111507, 45 | -0.013456709459118716, 46 | 0.0, 47 | ] 48 | 49 | # reconstruction filters 50 | # low pass 51 | reconstructionLowFilter = [ 52 | 0.013456709459118716, 53 | -0.002694966880111507, 54 | -0.13670658466432914, 55 | -0.09350469740093886, 56 | 0.47680326579848425, 57 | 0.8995061097486484, 58 | 0.47680326579848425, 59 | -0.09350469740093886, 60 | -0.13670658466432914, 61 | -0.002694966880111507, 62 | 0.013456709459118716, 63 | 0.0, 64 | ] 65 | 66 | # high-pass 67 | reconstructionHighFilter = [ 68 | 0.0, 69 | 0.0, 70 | 0.03968708834740544, 71 | -0.007948108637240322, 72 | -0.05446378846823691, 73 | -0.34560528195603346, 74 | 0.7366601814282105, 75 | -0.34560528195603346, 76 | -0.05446378846823691, 77 | -0.007948108637240322, 78 | 0.03968708834740544, 79 | 0.0, 80 | ] 81 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/bior6_8.py: -------------------------------------------------------------------------------- 1 | """ Biorthogonal 6.8 wavelet """ 2 | 3 | 4 | class Biorthogonal68: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, not orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/bior6.8/ 11 | """ 12 | __name__ = "Biorthogonal Wavelet 6.8" 13 | __motherWaveletLength__ = 18 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.0, 20 | 0.0019088317364812906, 21 | -0.0019142861290887667, 22 | -0.016990639867602342, 23 | 0.01193456527972926, 24 | 0.04973290349094079, 25 | -0.07726317316720414, 26 | -0.09405920349573646, 27 | 0.4207962846098268, 28 | 0.8259229974584023, 29 | 0.4207962846098268, 30 | -0.09405920349573646, 31 | -0.07726317316720414, 32 | 0.04973290349094079, 33 | 0.01193456527972926, 34 | -0.016990639867602342, 35 | -0.0019142861290887667, 36 | 0.0019088317364812906, 37 | ] 38 | 39 | # high-pass 40 | decompositionHighFilter = [ 41 | 0.0, 42 | 0.0, 43 | 0.0, 44 | 0.014426282505624435, 45 | -0.014467504896790148, 46 | -0.07872200106262882, 47 | 0.04036797903033992, 48 | 0.41784910915027457, 49 | -0.7589077294536541, 50 | 0.41784910915027457, 51 | 0.04036797903033992, 52 | -0.07872200106262882, 53 | -0.014467504896790148, 54 | 0.014426282505624435, 55 | 0.0, 56 | 0.0, 57 | 0.0, 58 | 0.0, 59 | ] 60 | 61 | # reconstruction filters 62 | # low pass 63 | reconstructionLowFilter = [ 64 | 0.0, 65 | 0.0, 66 | 0.0, 67 | 0.014426282505624435, 68 | 0.014467504896790148, 69 | -0.07872200106262882, 70 | -0.04036797903033992, 71 | 0.41784910915027457, 72 | 0.7589077294536541, 73 | 0.41784910915027457, 74 | -0.04036797903033992, 75 | -0.07872200106262882, 76 | 0.014467504896790148, 77 | 0.014426282505624435, 78 | 0.0, 79 | 0.0, 80 | 0.0, 81 | 0.0, 82 | ] 83 | 84 | # high-pass 85 | reconstructionHighFilter = [ 86 | 0.0, 87 | -0.0019088317364812906, 88 | -0.0019142861290887667, 89 | 0.016990639867602342, 90 | 0.01193456527972926, 91 | -0.04973290349094079, 92 | -0.07726317316720414, 93 | 0.09405920349573646, 94 | 0.4207962846098268, 95 | -0.8259229974584023, 96 | 0.4207962846098268, 97 | 0.09405920349573646, 98 | -0.07726317316720414, 99 | -0.04973290349094079, 100 | 0.01193456527972926, 101 | 0.016990639867602342, 102 | -0.0019142861290887667, 103 | -0.0019088317364812906, 104 | ] 105 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/coif1.py: -------------------------------------------------------------------------------- 1 | """ Coiflets 1 wavelet """ 2 | import numpy as np 3 | 4 | 5 | class Coiflets1: 6 | """ 7 | Properties 8 | ---------- 9 | near symmetric, orthogonal, biorthogonal 10 | 11 | All values are from http://wavelets.pybytes.com/wavelet/coif1/ 12 | """ 13 | __name__ = "Coiflets Wavelet 1" 14 | __motherWaveletLength__ = 6 # length of the mother wavelet 15 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 16 | 17 | # decomposition filter 18 | # low-pass 19 | decompositionLowFilter = np.array([ 20 | -0.01565572813546454, 21 | -0.0727326195128539, 22 | 0.38486484686420286, 23 | 0.8525720202122554, 24 | 0.3378976624578092, 25 | -0.0727326195128539, 26 | ]) 27 | 28 | # high-pass 29 | decompositionHighFilter = np.array([ 30 | 0.0727326195128539, 31 | 0.3378976624578092, 32 | -0.8525720202122554, 33 | 0.38486484686420286, 34 | 0.0727326195128539, 35 | -0.01565572813546454, 36 | ]) 37 | 38 | # reconstruction filters 39 | # low pass 40 | reconstructionLowFilter = np.array([ 41 | -0.0727326195128539, 42 | 0.3378976624578092, 43 | 0.8525720202122554, 44 | 0.38486484686420286, 45 | -0.0727326195128539, 46 | -0.01565572813546454, 47 | ]) 48 | 49 | # high-pass 50 | reconstructionHighFilter = np.array([ 51 | -0.01565572813546454, 52 | 0.0727326195128539, 53 | 0.38486484686420286, 54 | -0.8525720202122554, 55 | 0.3378976624578092, 56 | 0.0727326195128539, 57 | ]) 58 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/coif2.py: -------------------------------------------------------------------------------- 1 | """ Coiflets 2 wavelet """ 2 | 3 | 4 | class Coiflets2: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/coif2/ 11 | """ 12 | __name__ = "Coiflets Wavelet 2" 13 | __motherWaveletLength__ = 12 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -0.0007205494453645122, 20 | -0.0018232088707029932, 21 | 0.0056114348193944995, 22 | 0.023680171946334084, 23 | -0.0594344186464569, 24 | -0.0764885990783064, 25 | 0.41700518442169254, 26 | 0.8127236354455423, 27 | 0.3861100668211622, 28 | -0.06737255472196302, 29 | -0.04146493678175915, 30 | 0.016387336463522112, 31 | ] 32 | 33 | # high-pass 34 | decompositionHighFilter = [ 35 | -0.016387336463522112, 36 | -0.04146493678175915, 37 | 0.06737255472196302, 38 | 0.3861100668211622, 39 | -0.8127236354455423, 40 | 0.41700518442169254, 41 | 0.0764885990783064, 42 | -0.0594344186464569, 43 | -0.023680171946334084, 44 | 0.0056114348193944995, 45 | 0.0018232088707029932, 46 | -0.0007205494453645122, 47 | ] 48 | 49 | # reconstruction filters 50 | # low pass 51 | reconstructionLowFilter = [ 52 | 0.016387336463522112, 53 | -0.04146493678175915, 54 | -0.06737255472196302, 55 | 0.3861100668211622, 56 | 0.8127236354455423, 57 | 0.41700518442169254, 58 | -0.0764885990783064, 59 | -0.0594344186464569, 60 | 0.023680171946334084, 61 | 0.0056114348193944995, 62 | -0.0018232088707029932, 63 | -0.0007205494453645122, 64 | ] 65 | 66 | # high-pass 67 | reconstructionHighFilter = [ 68 | -0.0007205494453645122, 69 | 0.0018232088707029932, 70 | 0.0056114348193944995, 71 | -0.023680171946334084, 72 | -0.0594344186464569, 73 | 0.0764885990783064, 74 | 0.41700518442169254, 75 | -0.8127236354455423, 76 | 0.3861100668211622, 77 | 0.06737255472196302, 78 | -0.04146493678175915, 79 | -0.016387336463522112, 80 | ] 81 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/coif3.py: -------------------------------------------------------------------------------- 1 | """ Coiflets 3 wavelet """ 2 | 3 | 4 | class Coiflets3: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/coif3/ 11 | """ 12 | __name__ = "Coiflets Wavelet 3" 13 | __motherWaveletLength__ = 18 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -3.459977283621256e-05, 20 | -7.098330313814125e-05, 21 | 0.0004662169601128863, 22 | 0.0011175187708906016, 23 | -0.0025745176887502236, 24 | -0.00900797613666158, 25 | 0.015880544863615904, 26 | 0.03455502757306163, 27 | -0.08230192710688598, 28 | -0.07179982161931202, 29 | 0.42848347637761874, 30 | 0.7937772226256206, 31 | 0.4051769024096169, 32 | -0.06112339000267287, 33 | -0.0657719112818555, 34 | 0.023452696141836267, 35 | 0.007782596427325418, 36 | -0.003793512864491014, 37 | ] 38 | 39 | # high-pass 40 | decompositionHighFilter = [ 41 | 0.003793512864491014, 42 | 0.007782596427325418, 43 | -0.023452696141836267, 44 | -0.0657719112818555, 45 | 0.06112339000267287, 46 | 0.4051769024096169, 47 | -0.7937772226256206, 48 | 0.42848347637761874, 49 | 0.07179982161931202, 50 | -0.08230192710688598, 51 | -0.03455502757306163, 52 | 0.015880544863615904, 53 | 0.00900797613666158, 54 | -0.0025745176887502236, 55 | -0.0011175187708906016, 56 | 0.0004662169601128863, 57 | 7.098330313814125e-05, 58 | -3.459977283621256e-05, 59 | ] 60 | 61 | # reconstruction filters 62 | # low pass 63 | reconstructionLowFilter = [ 64 | -0.003793512864491014, 65 | 0.007782596427325418, 66 | 0.023452696141836267, 67 | -0.0657719112818555, 68 | -0.06112339000267287, 69 | 0.4051769024096169, 70 | 0.7937772226256206, 71 | 0.42848347637761874, 72 | -0.07179982161931202, 73 | -0.08230192710688598, 74 | 0.03455502757306163, 75 | 0.015880544863615904, 76 | -0.00900797613666158, 77 | -0.0025745176887502236, 78 | 0.0011175187708906016, 79 | 0.0004662169601128863, 80 | -7.098330313814125e-05, 81 | -3.459977283621256e-05, 82 | ] 83 | 84 | # high-pass 85 | reconstructionHighFilter = [ 86 | -3.459977283621256e-05, 87 | 7.098330313814125e-05, 88 | 0.0004662169601128863, 89 | -0.0011175187708906016, 90 | -0.0025745176887502236, 91 | 0.00900797613666158, 92 | 0.015880544863615904, 93 | -0.03455502757306163, 94 | -0.08230192710688598, 95 | 0.07179982161931202, 96 | 0.42848347637761874, 97 | -0.7937772226256206, 98 | 0.4051769024096169, 99 | 0.06112339000267287, 100 | -0.0657719112818555, 101 | -0.023452696141836267, 102 | 0.007782596427325418, 103 | 0.003793512864491014, 104 | ] 105 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/coif4.py: -------------------------------------------------------------------------------- 1 | """ Coiflets 4 wavelet """ 2 | 3 | 4 | class Coiflets4: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/coif4/ 11 | """ 12 | __name__ = "Coiflets Wavelet 4" 13 | __motherWaveletLength__ = 24 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -1.7849850030882614e-06, 20 | -3.2596802368833675e-06, 21 | 3.1229875865345646e-05, 22 | 6.233903446100713e-05, 23 | -0.00025997455248771324, 24 | -0.0005890207562443383, 25 | 0.0012665619292989445, 26 | 0.003751436157278457, 27 | -0.00565828668661072, 28 | -0.015211731527946259, 29 | 0.025082261844864097, 30 | 0.03933442712333749, 31 | -0.09622044203398798, 32 | -0.06662747426342504, 33 | 0.4343860564914685, 34 | 0.782238930920499, 35 | 0.41530840703043026, 36 | -0.05607731331675481, 37 | -0.08126669968087875, 38 | 0.026682300156053072, 39 | 0.016068943964776348, 40 | -0.0073461663276420935, 41 | -0.0016294920126017326, 42 | 0.0008923136685823146, 43 | ] 44 | 45 | # high-pass 46 | decompositionHighFilter = [ 47 | -0.0008923136685823146, 48 | -0.0016294920126017326, 49 | 0.0073461663276420935, 50 | 0.016068943964776348, 51 | -0.026682300156053072, 52 | -0.08126669968087875, 53 | 0.05607731331675481, 54 | 0.41530840703043026, 55 | -0.782238930920499, 56 | 0.4343860564914685, 57 | 0.06662747426342504, 58 | -0.09622044203398798, 59 | -0.03933442712333749, 60 | 0.025082261844864097, 61 | 0.015211731527946259, 62 | -0.00565828668661072, 63 | -0.003751436157278457, 64 | 0.0012665619292989445, 65 | 0.0005890207562443383, 66 | -0.00025997455248771324, 67 | -6.233903446100713e-05, 68 | 3.1229875865345646e-05, 69 | 3.2596802368833675e-06, 70 | -1.7849850030882614e-06, 71 | ] 72 | 73 | # reconstruction filters 74 | # low pass 75 | reconstructionLowFilter = [ 76 | 0.0008923136685823146, 77 | -0.0016294920126017326, 78 | -0.0073461663276420935, 79 | 0.016068943964776348, 80 | 0.026682300156053072, 81 | -0.08126669968087875, 82 | -0.05607731331675481, 83 | 0.41530840703043026, 84 | 0.782238930920499, 85 | 0.4343860564914685, 86 | -0.06662747426342504, 87 | -0.09622044203398798, 88 | 0.03933442712333749, 89 | 0.025082261844864097, 90 | -0.015211731527946259, 91 | -0.00565828668661072, 92 | 0.003751436157278457, 93 | 0.0012665619292989445, 94 | -0.0005890207562443383, 95 | -0.00025997455248771324, 96 | 6.233903446100713e-05, 97 | 3.1229875865345646e-05, 98 | -3.2596802368833675e-06, 99 | -1.7849850030882614e-06, 100 | ] 101 | 102 | # high-pass 103 | reconstructionHighFilter = [ 104 | -1.7849850030882614e-06, 105 | 3.2596802368833675e-06, 106 | 3.1229875865345646e-05, 107 | -6.233903446100713e-05, 108 | -0.00025997455248771324, 109 | 0.0005890207562443383, 110 | 0.0012665619292989445, 111 | -0.003751436157278457, 112 | -0.00565828668661072, 113 | 0.015211731527946259, 114 | 0.025082261844864097, 115 | -0.03933442712333749, 116 | -0.09622044203398798, 117 | 0.06662747426342504, 118 | 0.4343860564914685, 119 | -0.782238930920499, 120 | 0.41530840703043026, 121 | 0.05607731331675481, 122 | -0.08126669968087875, 123 | -0.026682300156053072, 124 | 0.016068943964776348, 125 | 0.0073461663276420935, 126 | -0.0016294920126017326, 127 | -0.0008923136685823146, 128 | ] 129 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/coif5.py: -------------------------------------------------------------------------------- 1 | """ Coiflets 5 wavelet """ 2 | 3 | 4 | class Coiflets5: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/coif5/ 11 | """ 12 | __name__ = "Coiflets Wavelet 5" 13 | __motherWaveletLength__ = 30 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -9.517657273819165e-08, 20 | -1.6744288576823017e-07, 21 | 2.0637618513646814e-06, 22 | 3.7346551751414047e-06, 23 | -2.1315026809955787e-05, 24 | -4.134043227251251e-05, 25 | 0.00014054114970203437, 26 | 0.00030225958181306315, 27 | -0.0006381313430451114, 28 | -0.0016628637020130838, 29 | 0.0024333732126576722, 30 | 0.006764185448053083, 31 | -0.009164231162481846, 32 | -0.01976177894257264, 33 | 0.03268357426711183, 34 | 0.0412892087501817, 35 | -0.10557420870333893, 36 | -0.06203596396290357, 37 | 0.4379916261718371, 38 | 0.7742896036529562, 39 | 0.4215662066908515, 40 | -0.05204316317624377, 41 | -0.09192001055969624, 42 | 0.02816802897093635, 43 | 0.023408156785839195, 44 | -0.010131117519849788, 45 | -0.004159358781386048, 46 | 0.0021782363581090178, 47 | 0.00035858968789573785, 48 | -0.00021208083980379827, 49 | ] 50 | 51 | # high-pass 52 | decompositionHighFilter = [ 53 | 0.00021208083980379827, 54 | 0.00035858968789573785, 55 | -0.0021782363581090178, 56 | -0.004159358781386048, 57 | 0.010131117519849788, 58 | 0.023408156785839195, 59 | -0.02816802897093635, 60 | -0.09192001055969624, 61 | 0.05204316317624377, 62 | 0.4215662066908515, 63 | -0.7742896036529562, 64 | 0.4379916261718371, 65 | 0.06203596396290357, 66 | -0.10557420870333893, 67 | -0.0412892087501817, 68 | 0.03268357426711183, 69 | 0.01976177894257264, 70 | -0.009164231162481846, 71 | -0.006764185448053083, 72 | 0.0024333732126576722, 73 | 0.0016628637020130838, 74 | -0.0006381313430451114, 75 | -0.00030225958181306315, 76 | 0.00014054114970203437, 77 | 4.134043227251251e-05, 78 | -2.1315026809955787e-05, 79 | -3.7346551751414047e-06, 80 | 2.0637618513646814e-06, 81 | 1.6744288576823017e-07, 82 | -9.517657273819165e-08, 83 | ] 84 | 85 | # reconstruction filters 86 | # low pass 87 | reconstructionLowFilter = [ 88 | -0.00021208083980379827, 89 | 0.00035858968789573785, 90 | 0.0021782363581090178, 91 | -0.004159358781386048, 92 | -0.010131117519849788, 93 | 0.023408156785839195, 94 | 0.02816802897093635, 95 | -0.09192001055969624, 96 | -0.05204316317624377, 97 | 0.4215662066908515, 98 | 0.7742896036529562, 99 | 0.4379916261718371, 100 | -0.06203596396290357, 101 | -0.10557420870333893, 102 | 0.0412892087501817, 103 | 0.03268357426711183, 104 | -0.01976177894257264, 105 | -0.009164231162481846, 106 | 0.006764185448053083, 107 | 0.0024333732126576722, 108 | -0.0016628637020130838, 109 | -0.0006381313430451114, 110 | 0.00030225958181306315, 111 | 0.00014054114970203437, 112 | -4.134043227251251e-05, 113 | -2.1315026809955787e-05, 114 | 3.7346551751414047e-06, 115 | 2.0637618513646814e-06, 116 | -1.6744288576823017e-07, 117 | -9.517657273819165e-08, 118 | ] 119 | 120 | # high-pass 121 | reconstructionHighFilter = [ 122 | -9.517657273819165e-08, 123 | 1.6744288576823017e-07, 124 | 2.0637618513646814e-06, 125 | -3.7346551751414047e-06, 126 | -2.1315026809955787e-05, 127 | 4.134043227251251e-05, 128 | 0.00014054114970203437, 129 | -0.00030225958181306315, 130 | -0.0006381313430451114, 131 | 0.0016628637020130838, 132 | 0.0024333732126576722, 133 | -0.006764185448053083, 134 | -0.009164231162481846, 135 | 0.01976177894257264, 136 | 0.03268357426711183, 137 | -0.0412892087501817, 138 | -0.10557420870333893, 139 | 0.06203596396290357, 140 | 0.4379916261718371, 141 | -0.7742896036529562, 142 | 0.4215662066908515, 143 | 0.05204316317624377, 144 | -0.09192001055969624, 145 | -0.02816802897093635, 146 | 0.023408156785839195, 147 | 0.010131117519849788, 148 | -0.004159358781386048, 149 | -0.0021782363581090178, 150 | 0.00035858968789573785, 151 | 0.00021208083980379827, 152 | ] 153 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db10.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 10 wavelet """ 2 | 3 | 4 | class Daubechies10: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db10/ 11 | """ 12 | __name__ = "Daubechies Wavelet 10" 13 | __motherWaveletLength__ = 20 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -1.326420300235487e-05, 20 | 9.358867000108985e-05, 21 | - 0.0001164668549943862, 22 | - 0.0006858566950046825, 23 | 0.00199240529499085, 24 | 0.0013953517469940798, 25 | - 0.010733175482979604, 26 | 0.0036065535669883944, 27 | 0.03321267405893324, 28 | - 0.02945753682194567, 29 | - 0.07139414716586077, 30 | 0.09305736460380659, 31 | 0.12736934033574265, 32 | - 0.19594627437659665, 33 | - 0.24984642432648865, 34 | 0.2811723436604265, 35 | 0.6884590394525921, 36 | 0.5272011889309198, 37 | 0.18817680007762133, 38 | 0.026670057900950818 39 | ] 40 | 41 | # high-pass 42 | decompositionHighFilter = [ 43 | -0.026670057900950818, 44 | 0.18817680007762133, 45 | - 0.5272011889309198, 46 | 0.6884590394525921, 47 | - 0.2811723436604265, 48 | - 0.24984642432648865, 49 | 0.19594627437659665, 50 | 0.12736934033574265, 51 | - 0.09305736460380659, 52 | - 0.07139414716586077, 53 | 0.02945753682194567, 54 | 0.03321267405893324, 55 | - 0.0036065535669883944, 56 | - 0.010733175482979604, 57 | - 0.0013953517469940798, 58 | 0.00199240529499085, 59 | 0.0006858566950046825, 60 | - 0.0001164668549943862, 61 | - 9.358867000108985e-05, 62 | - 1.326420300235487e-05, 63 | ] 64 | 65 | # reconstruction filters 66 | # low pass 67 | reconstructionLowFilter = [ 68 | 0.026670057900950818, 69 | 0.18817680007762133, 70 | 0.5272011889309198, 71 | 0.6884590394525921, 72 | 0.2811723436604265, 73 | - 0.24984642432648865, 74 | - 0.19594627437659665, 75 | 0.12736934033574265, 76 | 0.09305736460380659, 77 | - 0.07139414716586077, 78 | - 0.02945753682194567, 79 | 0.03321267405893324, 80 | 0.0036065535669883944, 81 | - 0.010733175482979604, 82 | 0.0013953517469940798, 83 | 0.00199240529499085, 84 | - 0.0006858566950046825, 85 | - 0.0001164668549943862, 86 | 9.358867000108985e-05, 87 | - 1.326420300235487e-05, 88 | ] 89 | 90 | # high-pass 91 | reconstructionHighFilter = [ 92 | -1.326420300235487e-05, 93 | - 9.358867000108985e-05, 94 | - 0.0001164668549943862, 95 | 0.0006858566950046825, 96 | 0.00199240529499085, 97 | - 0.0013953517469940798, 98 | - 0.010733175482979604, 99 | - 0.0036065535669883944, 100 | 0.03321267405893324, 101 | 0.02945753682194567, 102 | - 0.07139414716586077, 103 | - 0.09305736460380659, 104 | 0.12736934033574265, 105 | 0.19594627437659665, 106 | - 0.24984642432648865, 107 | - 0.2811723436604265, 108 | 0.6884590394525921, 109 | - 0.5272011889309198, 110 | 0.18817680007762133, 111 | - 0.026670057900950818 112 | ] 113 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db11.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 11 wavelet """ 2 | 3 | 4 | class Daubechies11: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db11/ 11 | """ 12 | __name__ = "Daubechies Wavelet 11" 13 | __motherWaveletLength__ = 22 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 4.494274277236352e-06, 20 | - 3.463498418698379e-05, 21 | 5.443907469936638e-05, 22 | 0.00024915252355281426, 23 | - 0.0008930232506662366, 24 | - 0.00030859285881515924, 25 | 0.004928417656058778, 26 | - 0.0033408588730145018, 27 | - 0.015364820906201324, 28 | 0.02084090436018004, 29 | 0.03133509021904531, 30 | - 0.06643878569502022, 31 | - 0.04647995511667613, 32 | 0.14981201246638268, 33 | 0.06604358819669089, 34 | - 0.27423084681792875, 35 | - 0.16227524502747828, 36 | 0.41196436894789695, 37 | 0.6856867749161785, 38 | 0.44989976435603013, 39 | 0.1440670211506196, 40 | 0.01869429776147044 41 | ] 42 | 43 | # high-pass 44 | decompositionHighFilter = [ 45 | -0.01869429776147044, 46 | 0.1440670211506196, 47 | - 0.44989976435603013, 48 | 0.6856867749161785, 49 | - 0.41196436894789695, 50 | - 0.16227524502747828, 51 | 0.27423084681792875, 52 | 0.06604358819669089, 53 | - 0.14981201246638268, 54 | - 0.04647995511667613, 55 | 0.06643878569502022, 56 | 0.03133509021904531, 57 | - 0.02084090436018004, 58 | - 0.015364820906201324, 59 | 0.0033408588730145018, 60 | 0.004928417656058778, 61 | 0.00030859285881515924, 62 | - 0.0008930232506662366, 63 | - 0.00024915252355281426, 64 | 5.443907469936638e-05, 65 | 3.463498418698379e-05, 66 | 4.494274277236352e-06 67 | ] 68 | 69 | # reconstruction filters 70 | # low pass 71 | reconstructionLowFilter = [ 72 | 0.01869429776147044, 73 | 0.1440670211506196, 74 | 0.44989976435603013, 75 | 0.6856867749161785, 76 | 0.41196436894789695, 77 | - 0.16227524502747828, 78 | - 0.27423084681792875, 79 | 0.06604358819669089, 80 | 0.14981201246638268, 81 | - 0.04647995511667613, 82 | - 0.06643878569502022, 83 | 0.03133509021904531, 84 | 0.02084090436018004, 85 | - 0.015364820906201324, 86 | - 0.0033408588730145018, 87 | 0.004928417656058778, 88 | - 0.00030859285881515924, 89 | - 0.0008930232506662366, 90 | 0.00024915252355281426, 91 | 5.443907469936638e-05, 92 | - 3.463498418698379e-05, 93 | 4.494274277236352e-06 94 | ] 95 | 96 | # high-pass 97 | reconstructionHighFilter = [ 98 | 4.494274277236352e-06, 99 | 3.463498418698379e-05, 100 | 5.443907469936638e-05, 101 | - 0.00024915252355281426, 102 | - 0.0008930232506662366, 103 | 0.00030859285881515924, 104 | 0.004928417656058778, 105 | 0.0033408588730145018, 106 | - 0.015364820906201324, 107 | - 0.02084090436018004, 108 | 0.03133509021904531, 109 | 0.06643878569502022, 110 | - 0.04647995511667613, 111 | - 0.14981201246638268, 112 | 0.06604358819669089, 113 | 0.27423084681792875, 114 | - 0.16227524502747828, 115 | - 0.41196436894789695, 116 | 0.6856867749161785, 117 | - 0.44989976435603013, 118 | 0.1440670211506196, 119 | - 0.01869429776147044 120 | ] 121 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db12.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 12 wavelet """ 2 | 3 | 4 | class Daubechies12: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db12/ 11 | """ 12 | __name__ = "Daubechies Wavelet 12" 13 | __motherWaveletLength__ = 24 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -1.5290717580684923e-06, 20 | 1.2776952219379579e-05, 21 | - 2.4241545757030318e-05, 22 | - 8.850410920820318e-05, 23 | 0.0003886530628209267, 24 | 6.5451282125215034e-06, 25 | - 0.0021795036186277044, 26 | 0.0022486072409952287, 27 | 0.006711499008795549, 28 | - 0.012840825198299882, 29 | - 0.01221864906974642, 30 | 0.04154627749508764, 31 | 0.010849130255828966, 32 | - 0.09643212009649671, 33 | 0.0053595696743599965, 34 | 0.18247860592758275, 35 | - 0.023779257256064865, 36 | - 0.31617845375277914, 37 | - 0.04476388565377762, 38 | 0.5158864784278007, 39 | 0.6571987225792911, 40 | 0.3773551352142041, 41 | 0.10956627282118277, 42 | 0.013112257957229239 43 | ] 44 | 45 | # high-pass 46 | decompositionHighFilter = [ 47 | -0.013112257957229239, 48 | 0.10956627282118277, 49 | - 0.3773551352142041, 50 | 0.6571987225792911, 51 | - 0.5158864784278007, 52 | - 0.04476388565377762, 53 | 0.31617845375277914, 54 | - 0.023779257256064865, 55 | - 0.18247860592758275, 56 | 0.0053595696743599965, 57 | 0.09643212009649671, 58 | 0.010849130255828966, 59 | - 0.04154627749508764, 60 | - 0.01221864906974642, 61 | 0.012840825198299882, 62 | 0.006711499008795549, 63 | - 0.0022486072409952287, 64 | - 0.0021795036186277044, 65 | - 6.5451282125215034e-06, 66 | 0.0003886530628209267, 67 | 8.850410920820318e-05, 68 | - 2.4241545757030318e-05, 69 | - 1.2776952219379579e-05, 70 | - 1.5290717580684923e-06 71 | ] 72 | 73 | # reconstruction filters 74 | # low pass 75 | reconstructionLowFilter = [ 76 | 0.013112257957229239, 77 | 0.10956627282118277, 78 | 0.3773551352142041, 79 | 0.6571987225792911, 80 | 0.5158864784278007, 81 | - 0.04476388565377762, 82 | - 0.31617845375277914, 83 | - 0.023779257256064865, 84 | 0.18247860592758275, 85 | 0.0053595696743599965, 86 | - 0.09643212009649671, 87 | 0.010849130255828966, 88 | 0.04154627749508764, 89 | - 0.01221864906974642, 90 | - 0.012840825198299882, 91 | 0.006711499008795549, 92 | 0.0022486072409952287, 93 | - 0.0021795036186277044, 94 | 6.5451282125215034e-06, 95 | 0.0003886530628209267, 96 | - 8.850410920820318e-05, 97 | - 2.4241545757030318e-05, 98 | 1.2776952219379579e-05, 99 | - 1.5290717580684923e-06 100 | ] 101 | 102 | # high-pass 103 | reconstructionHighFilter = [ 104 | -1.5290717580684923e-06, 105 | - 1.2776952219379579e-05, 106 | - 2.4241545757030318e-05, 107 | 8.850410920820318e-05, 108 | 0.0003886530628209267, 109 | - 6.5451282125215034e-06, 110 | - 0.0021795036186277044, 111 | - 0.0022486072409952287, 112 | 0.006711499008795549, 113 | 0.012840825198299882, 114 | - 0.01221864906974642, 115 | - 0.04154627749508764, 116 | 0.010849130255828966, 117 | 0.09643212009649671, 118 | 0.0053595696743599965, 119 | - 0.18247860592758275, 120 | - 0.023779257256064865, 121 | 0.31617845375277914, 122 | - 0.04476388565377762, 123 | - 0.5158864784278007, 124 | 0.6571987225792911, 125 | - 0.3773551352142041, 126 | 0.10956627282118277, 127 | - 0.013112257957229239 128 | ] 129 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db13.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 13 wavelet """ 2 | 3 | 4 | class Daubechies13: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db13/ 11 | """ 12 | __name__ = "Daubechies Wavelet 13" 13 | __motherWaveletLength__ = 26 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 5.2200350984548e-07, 20 | - 4.700416479360808e-06, 21 | 1.0441930571407941e-05, 22 | 3.067853757932436e-05, 23 | - 0.0001651289885565057, 24 | 4.9251525126285676e-05, 25 | 0.000932326130867249, 26 | - 0.0013156739118922766, 27 | - 0.002761911234656831, 28 | 0.007255589401617119, 29 | 0.003923941448795577, 30 | - 0.02383142071032781, 31 | 0.002379972254052227, 32 | 0.056139477100276156, 33 | - 0.026488406475345658, 34 | - 0.10580761818792761, 35 | 0.07294893365678874, 36 | 0.17947607942935084, 37 | - 0.12457673075080665, 38 | - 0.31497290771138414, 39 | 0.086985726179645, 40 | 0.5888895704312119, 41 | 0.6110558511587811, 42 | 0.3119963221604349, 43 | 0.08286124387290195, 44 | 0.009202133538962279 45 | ] 46 | 47 | # high-pass 48 | decompositionHighFilter = [ 49 | -0.009202133538962279, 50 | 0.08286124387290195, 51 | - 0.3119963221604349, 52 | 0.6110558511587811, 53 | - 0.5888895704312119, 54 | 0.086985726179645, 55 | 0.31497290771138414, 56 | - 0.12457673075080665, 57 | - 0.17947607942935084, 58 | 0.07294893365678874, 59 | 0.10580761818792761, 60 | - 0.026488406475345658, 61 | - 0.056139477100276156, 62 | 0.002379972254052227, 63 | 0.02383142071032781, 64 | 0.003923941448795577, 65 | - 0.007255589401617119, 66 | - 0.002761911234656831, 67 | 0.0013156739118922766, 68 | 0.000932326130867249, 69 | - 4.9251525126285676e-05, 70 | - 0.0001651289885565057, 71 | - 3.067853757932436e-05, 72 | 1.0441930571407941e-05, 73 | 4.700416479360808e-06, 74 | 5.2200350984548e-07 75 | ] 76 | 77 | # reconstruction filters 78 | # low pass 79 | reconstructionLowFilter = [ 80 | 0.009202133538962279, 81 | 0.08286124387290195, 82 | 0.3119963221604349, 83 | 0.6110558511587811, 84 | 0.5888895704312119, 85 | 0.086985726179645, 86 | - 0.31497290771138414, 87 | - 0.12457673075080665, 88 | 0.17947607942935084, 89 | 0.07294893365678874, 90 | - 0.10580761818792761, 91 | - 0.026488406475345658, 92 | 0.056139477100276156, 93 | 0.002379972254052227, 94 | - 0.02383142071032781, 95 | 0.003923941448795577, 96 | 0.007255589401617119, 97 | - 0.002761911234656831, 98 | - 0.0013156739118922766, 99 | 0.000932326130867249, 100 | 4.9251525126285676e-05, 101 | - 0.0001651289885565057, 102 | 3.067853757932436e-05, 103 | 1.0441930571407941e-05, 104 | - 4.700416479360808e-06, 105 | 5.2200350984548e-07 106 | ] 107 | 108 | # high-pass 109 | reconstructionHighFilter = [ 110 | 5.2200350984548e-07, 111 | 4.700416479360808e-06, 112 | 1.0441930571407941e-05, 113 | - 3.067853757932436e-05, 114 | - 0.0001651289885565057, 115 | - 4.9251525126285676e-05, 116 | 0.000932326130867249, 117 | 0.0013156739118922766, 118 | - 0.002761911234656831, 119 | - 0.007255589401617119, 120 | 0.003923941448795577, 121 | 0.02383142071032781, 122 | 0.002379972254052227, 123 | - 0.056139477100276156, 124 | - 0.026488406475345658, 125 | 0.10580761818792761, 126 | 0.07294893365678874, 127 | - 0.17947607942935084, 128 | - 0.12457673075080665, 129 | 0.31497290771138414, 130 | 0.086985726179645, 131 | - 0.5888895704312119, 132 | 0.6110558511587811, 133 | - 0.3119963221604349, 134 | 0.08286124387290195, 135 | - 0.009202133538962279 136 | ] 137 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db14.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 14 wavelet """ 2 | 3 | 4 | class Daubechies14: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db14/ 11 | """ 12 | __name__ = "Daubechies Wavelet 14" 13 | __motherWaveletLength__ = 28 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -1.7871399683109222e-07, 20 | 1.7249946753674012e-06, 21 | - 4.389704901780418e-06, 22 | - 1.0337209184568496e-05, 23 | 6.875504252695734e-05, 24 | - 4.177724577037067e-05, 25 | - 0.00038683194731287514, 26 | 0.0007080211542354048, 27 | 0.001061691085606874, 28 | - 0.003849638868019787, 29 | - 0.0007462189892638753, 30 | 0.01278949326634007, 31 | - 0.0056150495303375755, 32 | - 0.030185351540353976, 33 | 0.02698140830794797, 34 | 0.05523712625925082, 35 | - 0.0715489555039835, 36 | - 0.0867484115681106, 37 | 0.13998901658445695, 38 | 0.13839521386479153, 39 | - 0.2180335299932165, 40 | - 0.27168855227867705, 41 | 0.21867068775886594, 42 | 0.6311878491047198, 43 | 0.5543056179407709, 44 | 0.25485026779256437, 45 | 0.062364758849384874, 46 | 0.0064611534600864905 47 | ] 48 | 49 | # high-pass 50 | decompositionHighFilter = [ 51 | -0.0064611534600864905, 52 | 0.062364758849384874, 53 | - 0.25485026779256437, 54 | 0.5543056179407709, 55 | - 0.6311878491047198, 56 | 0.21867068775886594, 57 | 0.27168855227867705, 58 | - 0.2180335299932165, 59 | - 0.13839521386479153, 60 | 0.13998901658445695, 61 | 0.0867484115681106, 62 | - 0.0715489555039835, 63 | - 0.05523712625925082, 64 | 0.02698140830794797, 65 | 0.030185351540353976, 66 | - 0.0056150495303375755, 67 | - 0.01278949326634007, 68 | - 0.0007462189892638753, 69 | 0.003849638868019787, 70 | 0.001061691085606874, 71 | - 0.0007080211542354048, 72 | - 0.00038683194731287514, 73 | 4.177724577037067e-05, 74 | 6.875504252695734e-05, 75 | 1.0337209184568496e-05, 76 | - 4.389704901780418e-06, 77 | - 1.7249946753674012e-06, 78 | - 1.7871399683109222e-07 79 | ] 80 | 81 | # reconstruction filters 82 | # low pass 83 | reconstructionLowFilter = [ 84 | 0.0064611534600864905, 85 | 0.062364758849384874, 86 | 0.25485026779256437, 87 | 0.5543056179407709, 88 | 0.6311878491047198, 89 | 0.21867068775886594, 90 | - 0.27168855227867705, 91 | - 0.2180335299932165, 92 | 0.13839521386479153, 93 | 0.13998901658445695, 94 | - 0.0867484115681106, 95 | - 0.0715489555039835, 96 | 0.05523712625925082, 97 | 0.02698140830794797, 98 | - 0.030185351540353976, 99 | - 0.0056150495303375755, 100 | 0.01278949326634007, 101 | - 0.0007462189892638753, 102 | - 0.003849638868019787, 103 | 0.001061691085606874, 104 | 0.0007080211542354048, 105 | - 0.00038683194731287514, 106 | - 4.177724577037067e-05, 107 | 6.875504252695734e-05, 108 | - 1.0337209184568496e-05, 109 | - 4.389704901780418e-06, 110 | 1.7249946753674012e-06, 111 | - 1.7871399683109222e-07 112 | ] 113 | 114 | # high-pass 115 | reconstructionHighFilter = [ 116 | -1.7871399683109222e-07, 117 | - 1.7249946753674012e-06, 118 | - 4.389704901780418e-06, 119 | 1.0337209184568496e-05, 120 | 6.875504252695734e-05, 121 | 4.177724577037067e-05, 122 | - 0.00038683194731287514, 123 | - 0.0007080211542354048, 124 | 0.001061691085606874, 125 | 0.003849638868019787, 126 | - 0.0007462189892638753, 127 | - 0.01278949326634007, 128 | - 0.0056150495303375755, 129 | 0.030185351540353976, 130 | 0.02698140830794797, 131 | - 0.05523712625925082, 132 | - 0.0715489555039835, 133 | 0.0867484115681106, 134 | 0.13998901658445695, 135 | - 0.13839521386479153, 136 | - 0.2180335299932165, 137 | 0.27168855227867705, 138 | 0.21867068775886594, 139 | - 0.6311878491047198, 140 | 0.5543056179407709, 141 | - 0.25485026779256437, 142 | 0.062364758849384874, 143 | - 0.0064611534600864905 144 | ] 145 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db15.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 15 wavelet """ 2 | 3 | 4 | class Daubechies15: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db15/ 11 | """ 12 | __name__ = "Daubechies Wavelet 15" 13 | __motherWaveletLength__ = 30 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 6.133359913303714e-08, 20 | - 6.316882325879451e-07, 21 | 1.8112704079399406e-06, 22 | 3.3629871817363823e-06, 23 | - 2.8133296266037558e-05, 24 | 2.579269915531323e-05, 25 | 0.00015589648992055726, 26 | - 0.00035956524436229364, 27 | - 0.0003734823541372647, 28 | 0.0019433239803823459, 29 | - 0.00024175649075894543, 30 | - 0.0064877345603061454, 31 | 0.005101000360422873, 32 | 0.015083918027862582, 33 | - 0.020810050169636805, 34 | - 0.02576700732836694, 35 | 0.054780550584559995, 36 | 0.033877143923563204, 37 | - 0.11112093603713753, 38 | - 0.0396661765557336, 39 | 0.19014671400708816, 40 | 0.06528295284876569, 41 | - 0.28888259656686216, 42 | - 0.19320413960907623, 43 | 0.33900253545462167, 44 | 0.6458131403572103, 45 | 0.4926317717079753, 46 | 0.20602386398692688, 47 | 0.04674339489275062, 48 | 0.004538537361577376 49 | ] 50 | 51 | # high-pass 52 | decompositionHighFilter = [ 53 | -0.004538537361577376, 54 | 0.04674339489275062, 55 | - 0.20602386398692688, 56 | 0.4926317717079753, 57 | - 0.6458131403572103, 58 | 0.33900253545462167, 59 | 0.19320413960907623, 60 | - 0.28888259656686216, 61 | - 0.06528295284876569, 62 | 0.19014671400708816, 63 | 0.0396661765557336, 64 | - 0.11112093603713753, 65 | - 0.033877143923563204, 66 | 0.054780550584559995, 67 | 0.02576700732836694, 68 | - 0.020810050169636805, 69 | - 0.015083918027862582, 70 | 0.005101000360422873, 71 | 0.0064877345603061454, 72 | - 0.00024175649075894543, 73 | - 0.0019433239803823459, 74 | - 0.0003734823541372647, 75 | 0.00035956524436229364, 76 | 0.00015589648992055726, 77 | - 2.579269915531323e-05, 78 | - 2.8133296266037558e-05, 79 | - 3.3629871817363823e-06, 80 | 1.8112704079399406e-06, 81 | 6.316882325879451e-07, 82 | 6.133359913303714e-08 83 | ] 84 | 85 | # reconstruction filters 86 | # low pass 87 | reconstructionLowFilter = [ 88 | 0.004538537361577376, 89 | 0.04674339489275062, 90 | 0.20602386398692688, 91 | 0.4926317717079753, 92 | 0.6458131403572103, 93 | 0.33900253545462167, 94 | - 0.19320413960907623, 95 | - 0.28888259656686216, 96 | 0.06528295284876569, 97 | 0.19014671400708816, 98 | - 0.0396661765557336, 99 | - 0.11112093603713753, 100 | 0.033877143923563204, 101 | 0.054780550584559995, 102 | - 0.02576700732836694, 103 | - 0.020810050169636805, 104 | 0.015083918027862582, 105 | 0.005101000360422873, 106 | - 0.0064877345603061454, 107 | - 0.00024175649075894543, 108 | 0.0019433239803823459, 109 | - 0.0003734823541372647, 110 | - 0.00035956524436229364, 111 | 0.00015589648992055726, 112 | 2.579269915531323e-05, 113 | - 2.8133296266037558e-05, 114 | 3.3629871817363823e-06, 115 | 1.8112704079399406e-06, 116 | - 6.316882325879451e-07, 117 | 6.133359913303714e-08 118 | ] 119 | 120 | # high-pass 121 | reconstructionHighFilter = [ 122 | 6.133359913303714e-08, 123 | 6.316882325879451e-07, 124 | 1.8112704079399406e-06, 125 | - 3.3629871817363823e-06, 126 | - 2.8133296266037558e-05, 127 | - 2.579269915531323e-05, 128 | 0.00015589648992055726, 129 | 0.00035956524436229364, 130 | - 0.0003734823541372647, 131 | - 0.0019433239803823459, 132 | - 0.00024175649075894543, 133 | 0.0064877345603061454, 134 | 0.005101000360422873, 135 | - 0.015083918027862582, 136 | - 0.020810050169636805, 137 | 0.02576700732836694, 138 | 0.054780550584559995, 139 | - 0.033877143923563204, 140 | - 0.11112093603713753, 141 | 0.0396661765557336, 142 | 0.19014671400708816, 143 | - 0.06528295284876569, 144 | - 0.28888259656686216, 145 | 0.19320413960907623, 146 | 0.33900253545462167, 147 | - 0.6458131403572103, 148 | 0.4926317717079753, 149 | - 0.20602386398692688, 150 | 0.04674339489275062, 151 | - 0.004538537361577376 152 | ] 153 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db16.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 16 wavelet """ 2 | 3 | 4 | class Daubechies16: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db16/ 11 | """ 12 | __name__ = "Daubechies Wavelet 16" 13 | __motherWaveletLength__ = 32 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -2.1093396300980412e-08, 20 | 2.3087840868545578e-07, 21 | - 7.363656785441815e-07, 22 | - 1.0435713423102517e-06, 23 | 1.133660866126152e-05, 24 | - 1.394566898819319e-05, 25 | - 6.103596621404321e-05, 26 | 0.00017478724522506327, 27 | 0.00011424152003843815, 28 | - 0.0009410217493585433, 29 | 0.00040789698084934395, 30 | 0.00312802338120381, 31 | - 0.0036442796214883506, 32 | - 0.006990014563390751, 33 | 0.013993768859843242, 34 | 0.010297659641009963, 35 | - 0.036888397691556774, 36 | - 0.007588974368642594, 37 | 0.07592423604445779, 38 | - 0.006239722752156254, 39 | - 0.13238830556335474, 40 | 0.027340263752899923, 41 | 0.21119069394696974, 42 | - 0.02791820813292813, 43 | - 0.3270633105274758, 44 | - 0.08975108940236352, 45 | 0.44029025688580486, 46 | 0.6373563320829833, 47 | 0.43031272284545874, 48 | 0.1650642834886438, 49 | 0.03490771432362905, 50 | 0.0031892209253436892 51 | ] 52 | 53 | # high-pass 54 | decompositionHighFilter = [ 55 | -0.0031892209253436892, 56 | 0.03490771432362905, 57 | - 0.1650642834886438, 58 | 0.43031272284545874, 59 | - 0.6373563320829833, 60 | 0.44029025688580486, 61 | 0.08975108940236352, 62 | - 0.3270633105274758, 63 | 0.02791820813292813, 64 | 0.21119069394696974, 65 | - 0.027340263752899923, 66 | - 0.13238830556335474, 67 | 0.006239722752156254, 68 | 0.07592423604445779, 69 | 0.007588974368642594, 70 | - 0.036888397691556774, 71 | - 0.010297659641009963, 72 | 0.013993768859843242, 73 | 0.006990014563390751, 74 | - 0.0036442796214883506, 75 | - 0.00312802338120381, 76 | 0.00040789698084934395, 77 | 0.0009410217493585433, 78 | 0.00011424152003843815, 79 | - 0.00017478724522506327, 80 | - 6.103596621404321e-05, 81 | 1.394566898819319e-05, 82 | 1.133660866126152e-05, 83 | 1.0435713423102517e-06, 84 | - 7.363656785441815e-07, 85 | - 2.3087840868545578e-07, 86 | - 2.1093396300980412e-08, 87 | ] 88 | 89 | # reconstruction filters 90 | # low pass 91 | reconstructionLowFilter = [ 92 | 0.0031892209253436892, 93 | 0.03490771432362905, 94 | 0.1650642834886438, 95 | 0.43031272284545874, 96 | 0.6373563320829833, 97 | 0.44029025688580486, 98 | - 0.08975108940236352, 99 | - 0.3270633105274758, 100 | - 0.02791820813292813, 101 | 0.21119069394696974, 102 | 0.027340263752899923, 103 | - 0.13238830556335474, 104 | - 0.006239722752156254, 105 | 0.07592423604445779, 106 | - 0.007588974368642594, 107 | - 0.036888397691556774, 108 | 0.010297659641009963, 109 | 0.013993768859843242, 110 | - 0.006990014563390751, 111 | - 0.0036442796214883506, 112 | 0.00312802338120381, 113 | 0.00040789698084934395, 114 | - 0.0009410217493585433, 115 | 0.00011424152003843815, 116 | 0.00017478724522506327, 117 | - 6.103596621404321e-05, 118 | - 1.394566898819319e-05, 119 | 1.133660866126152e-05, 120 | - 1.0435713423102517e-06, 121 | - 7.363656785441815e-07, 122 | 2.3087840868545578e-07, 123 | - 2.1093396300980412e-08 124 | ] 125 | 126 | # high-pass 127 | reconstructionHighFilter = [ 128 | -2.1093396300980412e-08, 129 | - 2.3087840868545578e-07, 130 | - 7.363656785441815e-07, 131 | 1.0435713423102517e-06, 132 | 1.133660866126152e-05, 133 | 1.394566898819319e-05, 134 | - 6.103596621404321e-05, 135 | - 0.00017478724522506327, 136 | 0.00011424152003843815, 137 | 0.0009410217493585433, 138 | 0.00040789698084934395, 139 | - 0.00312802338120381, 140 | - 0.0036442796214883506, 141 | 0.006990014563390751, 142 | 0.013993768859843242, 143 | - 0.010297659641009963, 144 | - 0.036888397691556774, 145 | 0.007588974368642594, 146 | 0.07592423604445779, 147 | 0.006239722752156254, 148 | - 0.13238830556335474, 149 | - 0.027340263752899923, 150 | 0.21119069394696974, 151 | 0.02791820813292813, 152 | - 0.3270633105274758, 153 | 0.08975108940236352, 154 | 0.44029025688580486, 155 | - 0.6373563320829833, 156 | 0.43031272284545874, 157 | - 0.1650642834886438, 158 | 0.03490771432362905, 159 | - 0.0031892209253436892 160 | ] 161 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db17.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 17 wavelet """ 2 | 3 | 4 | class Daubechies17: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db17/ 11 | """ 12 | __name__ = "Daubechies Wavelet 17" 13 | __motherWaveletLength__ = 34 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 7.26749296856637e-09, 20 | - 8.423948446008154e-08, 21 | 2.9577009333187617e-07, 22 | 3.0165496099963414e-07, 23 | - 4.505942477225963e-06, 24 | 6.990600985081294e-06, 25 | 2.318681379876164e-05, 26 | - 8.204803202458212e-05, 27 | - 2.5610109566546042e-05, 28 | 0.0004394654277689454, 29 | - 0.00032813251941022427, 30 | - 0.001436845304805, 31 | 0.0023012052421511474, 32 | 0.002967996691518064, 33 | - 0.008602921520347815, 34 | - 0.0030429899813869555, 35 | 0.022733676583919053, 36 | - 0.0032709555358783646, 37 | - 0.04692243838937891, 38 | 0.022312336178011833, 39 | 0.08110598665408082, 40 | - 0.05709141963185808, 41 | - 0.12681569177849797, 42 | 0.10113548917744287, 43 | 0.19731058956508457, 44 | - 0.12659975221599248, 45 | - 0.32832074836418546, 46 | 0.027314970403312946, 47 | 0.5183157640572823, 48 | 0.6109966156850273, 49 | 0.3703507241528858, 50 | 0.13121490330791097, 51 | 0.025985393703623173, 52 | 0.00224180700103879 53 | ] 54 | 55 | # high-pass 56 | decompositionHighFilter = [ 57 | -0.00224180700103879, 58 | 0.025985393703623173, 59 | - 0.13121490330791097, 60 | 0.3703507241528858, 61 | - 0.6109966156850273, 62 | 0.5183157640572823, 63 | - 0.027314970403312946, 64 | - 0.32832074836418546, 65 | 0.12659975221599248, 66 | 0.19731058956508457, 67 | - 0.10113548917744287, 68 | - 0.12681569177849797, 69 | 0.05709141963185808, 70 | 0.08110598665408082, 71 | - 0.022312336178011833, 72 | - 0.04692243838937891, 73 | 0.0032709555358783646, 74 | 0.022733676583919053, 75 | 0.0030429899813869555, 76 | - 0.008602921520347815, 77 | - 0.002967996691518064, 78 | 0.0023012052421511474, 79 | 0.001436845304805, 80 | - 0.00032813251941022427, 81 | - 0.0004394654277689454, 82 | - 2.5610109566546042e-05, 83 | 8.204803202458212e-05, 84 | 2.318681379876164e-05, 85 | - 6.990600985081294e-06, 86 | - 4.505942477225963e-06, 87 | - 3.0165496099963414e-07, 88 | 2.9577009333187617e-07, 89 | 8.423948446008154e-08, 90 | 7.26749296856637e-09 91 | ] 92 | 93 | # reconstruction filters 94 | # low pass 95 | reconstructionLowFilter = [ 96 | 0.00224180700103879, 97 | 0.025985393703623173, 98 | 0.13121490330791097, 99 | 0.3703507241528858, 100 | 0.6109966156850273, 101 | 0.5183157640572823, 102 | 0.027314970403312946, 103 | - 0.32832074836418546, 104 | - 0.12659975221599248, 105 | 0.19731058956508457, 106 | 0.10113548917744287, 107 | - 0.12681569177849797, 108 | - 0.05709141963185808, 109 | 0.08110598665408082, 110 | 0.022312336178011833, 111 | - 0.04692243838937891, 112 | - 0.0032709555358783646, 113 | 0.022733676583919053, 114 | - 0.0030429899813869555, 115 | - 0.008602921520347815, 116 | 0.002967996691518064, 117 | 0.0023012052421511474, 118 | - 0.001436845304805, 119 | - 0.00032813251941022427, 120 | 0.0004394654277689454, 121 | - 2.5610109566546042e-05, 122 | - 8.204803202458212e-05, 123 | 2.318681379876164e-05, 124 | 6.990600985081294e-06, 125 | - 4.505942477225963e-06, 126 | 3.0165496099963414e-07, 127 | 2.9577009333187617e-07, 128 | - 8.423948446008154e-08, 129 | 7.26749296856637e-09 130 | ] 131 | 132 | # high-pass 133 | reconstructionHighFilter = [ 134 | 7.26749296856637e-09, 135 | 8.423948446008154e-08, 136 | 2.9577009333187617e-07, 137 | - 3.0165496099963414e-07, 138 | - 4.505942477225963e-06, 139 | - 6.990600985081294e-06, 140 | 2.318681379876164e-05, 141 | 8.204803202458212e-05, 142 | - 2.5610109566546042e-05, 143 | - 0.0004394654277689454, 144 | - 0.00032813251941022427, 145 | 0.001436845304805, 146 | 0.0023012052421511474, 147 | - 0.002967996691518064, 148 | - 0.008602921520347815, 149 | 0.0030429899813869555, 150 | 0.022733676583919053, 151 | 0.0032709555358783646, 152 | - 0.04692243838937891, 153 | - 0.022312336178011833, 154 | 0.08110598665408082, 155 | 0.05709141963185808, 156 | - 0.12681569177849797, 157 | - 0.10113548917744287, 158 | 0.19731058956508457, 159 | 0.12659975221599248, 160 | - 0.32832074836418546, 161 | - 0.027314970403312946, 162 | 0.5183157640572823, 163 | - 0.6109966156850273, 164 | 0.3703507241528858, 165 | - 0.13121490330791097, 166 | 0.025985393703623173, 167 | - 0.00224180700103879 168 | ] 169 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db18.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 18 wavelet """ 2 | 3 | 4 | class Daubechies18: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db18/ 11 | """ 12 | __name__ = "Daubechies Wavelet 18" 13 | __motherWaveletLength__ = 36 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -2.507934454941929e-09, 20 | 3.06883586303703e-08, 21 | - 1.1760987670250871e-07, 22 | - 7.691632689865049e-08, 23 | 1.768712983622886e-06, 24 | - 3.3326344788769603e-06, 25 | - 8.520602537423464e-06, 26 | 3.741237880730847e-05, 27 | - 1.535917123021341e-07, 28 | - 0.00019864855231101547, 29 | 0.0002135815619103188, 30 | 0.0006284656829644715, 31 | - 0.0013405962983313922, 32 | - 0.0011187326669886426, 33 | 0.004943343605456594, 34 | 0.00011863003387493042, 35 | - 0.013051480946517112, 36 | 0.006262167954438661, 37 | 0.026670705926689853, 38 | - 0.023733210395336858, 39 | - 0.04452614190225633, 40 | 0.05705124773905827, 41 | 0.0648872162123582, 42 | - 0.10675224665906288, 43 | - 0.09233188415030412, 44 | 0.16708131276294505, 45 | 0.14953397556500755, 46 | - 0.21648093400458224, 47 | - 0.2936540407357981, 48 | 0.14722311196952223, 49 | 0.571801654887122, 50 | 0.5718268077650818, 51 | 0.31467894133619284, 52 | 0.10358846582214751, 53 | 0.01928853172409497, 54 | 0.0015763102184365595 55 | ] 56 | 57 | # high-pass 58 | decompositionHighFilter = [ 59 | -0.0015763102184365595, 60 | 0.01928853172409497, 61 | - 0.10358846582214751, 62 | 0.31467894133619284, 63 | - 0.5718268077650818, 64 | 0.571801654887122, 65 | - 0.14722311196952223, 66 | - 0.2936540407357981, 67 | 0.21648093400458224, 68 | 0.14953397556500755, 69 | - 0.16708131276294505, 70 | - 0.09233188415030412, 71 | 0.10675224665906288, 72 | 0.0648872162123582, 73 | - 0.05705124773905827, 74 | - 0.04452614190225633, 75 | 0.023733210395336858, 76 | 0.026670705926689853, 77 | - 0.006262167954438661, 78 | - 0.013051480946517112, 79 | - 0.00011863003387493042, 80 | 0.004943343605456594, 81 | 0.0011187326669886426, 82 | - 0.0013405962983313922, 83 | - 0.0006284656829644715, 84 | 0.0002135815619103188, 85 | 0.00019864855231101547, 86 | - 1.535917123021341e-07, 87 | - 3.741237880730847e-05, 88 | - 8.520602537423464e-06, 89 | 3.3326344788769603e-06, 90 | 1.768712983622886e-06, 91 | 7.691632689865049e-08, 92 | - 1.1760987670250871e-07, 93 | - 3.06883586303703e-08, 94 | - 2.507934454941929e-09 95 | ] 96 | 97 | # reconstruction filters 98 | # low pass 99 | reconstructionLowFilter = [ 100 | 0.0015763102184365595, 101 | 0.01928853172409497, 102 | 0.10358846582214751, 103 | 0.31467894133619284, 104 | 0.5718268077650818, 105 | 0.571801654887122, 106 | 0.14722311196952223, 107 | - 0.2936540407357981, 108 | - 0.21648093400458224, 109 | 0.14953397556500755, 110 | 0.16708131276294505, 111 | - 0.09233188415030412, 112 | - 0.10675224665906288, 113 | 0.0648872162123582, 114 | 0.05705124773905827, 115 | - 0.04452614190225633, 116 | - 0.023733210395336858, 117 | 0.026670705926689853, 118 | 0.006262167954438661, 119 | - 0.013051480946517112, 120 | 0.00011863003387493042, 121 | 0.004943343605456594, 122 | - 0.0011187326669886426, 123 | - 0.0013405962983313922, 124 | 0.0006284656829644715, 125 | 0.0002135815619103188, 126 | - 0.00019864855231101547, 127 | - 1.535917123021341e-07, 128 | 3.741237880730847e-05, 129 | - 8.520602537423464e-06, 130 | - 3.3326344788769603e-06, 131 | 1.768712983622886e-06, 132 | - 7.691632689865049e-08, 133 | - 1.1760987670250871e-07, 134 | 3.06883586303703e-08, 135 | - 2.507934454941929e-09 136 | ] 137 | 138 | # high-pass 139 | reconstructionHighFilter = [ 140 | -2.507934454941929e-09, 141 | - 3.06883586303703e-08, 142 | - 1.1760987670250871e-07, 143 | 7.691632689865049e-08, 144 | 1.768712983622886e-06, 145 | 3.3326344788769603e-06, 146 | - 8.520602537423464e-06, 147 | - 3.741237880730847e-05, 148 | - 1.535917123021341e-07, 149 | 0.00019864855231101547, 150 | 0.0002135815619103188, 151 | - 0.0006284656829644715, 152 | - 0.0013405962983313922, 153 | 0.0011187326669886426, 154 | 0.004943343605456594, 155 | - 0.00011863003387493042, 156 | - 0.013051480946517112, 157 | - 0.006262167954438661, 158 | 0.026670705926689853, 159 | 0.023733210395336858, 160 | - 0.04452614190225633, 161 | - 0.05705124773905827, 162 | 0.0648872162123582, 163 | 0.10675224665906288, 164 | - 0.09233188415030412, 165 | - 0.16708131276294505, 166 | 0.14953397556500755, 167 | 0.21648093400458224, 168 | - 0.2936540407357981, 169 | - 0.14722311196952223, 170 | 0.571801654887122, 171 | - 0.5718268077650818, 172 | 0.31467894133619284, 173 | - 0.10358846582214751, 174 | 0.01928853172409497, 175 | - 0.0015763102184365595 176 | ] 177 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db19.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 19 wavelet """ 2 | 3 | 4 | class Daubechies19: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db19/ 11 | """ 12 | __name__ = "Daubechies Wavelet 19" 13 | __motherWaveletLength__ = 38 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 8.666848839034483e-10, 20 | -1.1164020670405678e-08, 21 | 4.636937775802368e-08, 22 | 1.447088298804088e-08, 23 | -6.86275565779811e-07, 24 | 1.531931476697877e-06, 25 | 3.0109643163099385e-06, 26 | -1.664017629722462e-05, 27 | 5.105950487090694e-06, 28 | 8.711270467250443e-05, 29 | -0.00012460079173506306, 30 | -0.0002606761356811995, 31 | 0.0007358025205041731, 32 | 0.00034180865344939543, 33 | -0.002687551800734441, 34 | 0.0007689543592242488, 35 | 0.007040747367080495, 36 | -0.005866922281112195, 37 | -0.013988388678695632, 38 | 0.019375549889114482, 39 | 0.021623767409452484, 40 | -0.04567422627778492, 41 | -0.026501236250778635, 42 | 0.0869067555554507, 43 | 0.02758435062488713, 44 | -0.14278569504021468, 45 | -0.03351854190320226, 46 | 0.21234974330662043, 47 | 0.07465226970806647, 48 | -0.28583863175723145, 49 | -0.22809139421653665, 50 | 0.2608949526521201, 51 | 0.6017045491300916, 52 | 0.5244363774668862, 53 | 0.26438843174202237, 54 | 0.08127811326580564, 55 | 0.01428109845082521, 56 | 0.0011086697631864314 57 | ] 58 | 59 | # high-pass 60 | decompositionHighFilter = [ 61 | -0.0011086697631864314, 62 | 0.01428109845082521, 63 | -0.08127811326580564, 64 | 0.26438843174202237, 65 | -0.5244363774668862, 66 | 0.6017045491300916, 67 | -0.2608949526521201, 68 | -0.22809139421653665, 69 | 0.28583863175723145, 70 | 0.07465226970806647, 71 | -0.21234974330662043, 72 | -0.03351854190320226, 73 | 0.14278569504021468, 74 | 0.02758435062488713, 75 | -0.0869067555554507, 76 | -0.026501236250778635, 77 | 0.04567422627778492, 78 | 0.021623767409452484, 79 | -0.019375549889114482, 80 | -0.013988388678695632, 81 | 0.005866922281112195, 82 | 0.007040747367080495, 83 | -0.0007689543592242488, 84 | -0.002687551800734441, 85 | -0.00034180865344939543, 86 | 0.0007358025205041731, 87 | 0.0002606761356811995, 88 | -0.00012460079173506306, 89 | -8.711270467250443e-05, 90 | 5.105950487090694e-06, 91 | 1.664017629722462e-05, 92 | 3.0109643163099385e-06, 93 | -1.531931476697877e-06, 94 | -6.86275565779811e-07, 95 | -1.447088298804088e-08, 96 | 4.636937775802368e-08, 97 | 1.1164020670405678e-08, 98 | 8.666848839034483e-10 99 | ] 100 | 101 | # reconstruction filters 102 | # low pass 103 | reconstructionLowFilter = [ 104 | 0.0011086697631864314, 105 | 0.01428109845082521, 106 | 0.08127811326580564, 107 | 0.26438843174202237, 108 | 0.5244363774668862, 109 | 0.6017045491300916, 110 | 0.2608949526521201, 111 | -0.22809139421653665, 112 | -0.28583863175723145, 113 | 0.07465226970806647, 114 | 0.21234974330662043, 115 | -0.03351854190320226, 116 | -0.14278569504021468, 117 | 0.02758435062488713, 118 | 0.0869067555554507, 119 | -0.026501236250778635, 120 | -0.04567422627778492, 121 | 0.021623767409452484, 122 | 0.019375549889114482, 123 | -0.013988388678695632, 124 | -0.005866922281112195, 125 | 0.007040747367080495, 126 | 0.0007689543592242488, 127 | -0.002687551800734441, 128 | 0.00034180865344939543, 129 | 0.0007358025205041731, 130 | -0.0002606761356811995, 131 | -0.00012460079173506306, 132 | 8.711270467250443e-05, 133 | 5.105950487090694e-06, 134 | -1.664017629722462e-05, 135 | 3.0109643163099385e-06, 136 | 1.531931476697877e-06, 137 | -6.86275565779811e-07, 138 | 1.447088298804088e-08, 139 | 4.636937775802368e-08, 140 | -1.1164020670405678e-08, 141 | 8.666848839034483e-10 142 | ] 143 | 144 | # high-pass 145 | reconstructionHighFilter = [ 146 | 8.666848839034483e-10, 147 | 1.1164020670405678e-08, 148 | 4.636937775802368e-08, 149 | -1.447088298804088e-08, 150 | -6.86275565779811e-07, 151 | -1.531931476697877e-06, 152 | 3.0109643163099385e-06, 153 | 1.664017629722462e-05, 154 | 5.105950487090694e-06, 155 | -8.711270467250443e-05, 156 | -0.00012460079173506306, 157 | 0.0002606761356811995, 158 | 0.0007358025205041731, 159 | -0.00034180865344939543, 160 | -0.002687551800734441, 161 | -0.0007689543592242488, 162 | 0.007040747367080495, 163 | 0.005866922281112195, 164 | -0.013988388678695632, 165 | -0.019375549889114482, 166 | 0.021623767409452484, 167 | 0.04567422627778492, 168 | -0.026501236250778635, 169 | -0.0869067555554507, 170 | 0.02758435062488713, 171 | 0.14278569504021468, 172 | -0.03351854190320226, 173 | -0.21234974330662043, 174 | 0.07465226970806647, 175 | 0.28583863175723145, 176 | -0.22809139421653665, 177 | -0.2608949526521201, 178 | 0.6017045491300916, 179 | -0.5244363774668862, 180 | 0.26438843174202237, 181 | -0.08127811326580564, 182 | 0.01428109845082521, 183 | -0.0011086697631864314 184 | ] 185 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db2.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 2 wavelet """ 2 | 3 | 4 | class Daubechies2: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db2/ 11 | """ 12 | __name__ = "Daubechies Wavelet 2" 13 | __motherWaveletLength__ = 4 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -0.12940952255092145, 20 | 0.22414386804185735, 21 | 0.836516303737469, 22 | 0.48296291314469025 23 | ] 24 | 25 | # high-pass 26 | decompositionHighFilter = [ 27 | -0.48296291314469025, 28 | 0.836516303737469, 29 | - 0.22414386804185735, 30 | - 0.12940952255092145, 31 | ] 32 | 33 | # reconstruction filters 34 | # low pass 35 | reconstructionLowFilter = [ 36 | 0.48296291314469025, 37 | 0.836516303737469, 38 | 0.22414386804185735, 39 | - 0.12940952255092145 40 | ] 41 | 42 | # high-pass 43 | reconstructionHighFilter = [ 44 | -0.12940952255092145, 45 | - 0.22414386804185735, 46 | 0.836516303737469, 47 | - 0.48296291314469025 48 | ] 49 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db20.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 20 wavelet """ 2 | 3 | 4 | class Daubechies20: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db20/ 11 | """ 12 | __name__ = "Daubechies Wavelet 20" 13 | __motherWaveletLength__ = 40 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -2.998836489615753e-10, 20 | 4.05612705554717e-09, 21 | -1.814843248297622e-08, 22 | 2.0143220235374613e-10, 23 | 2.633924226266962e-07, 24 | -6.847079596993149e-07, 25 | -1.0119940100181473e-06, 26 | 7.241248287663791e-06, 27 | -4.376143862182197e-06, 28 | -3.710586183390615e-05, 29 | 6.774280828373048e-05, 30 | 0.00010153288973669777, 31 | -0.0003851047486990061, 32 | -5.349759844340453e-05, 33 | 0.0013925596193045254, 34 | -0.0008315621728772474, 35 | -0.003581494259744107, 36 | 0.00442054238676635, 37 | 0.0067216273018096935, 38 | -0.013810526137727442, 39 | -0.008789324924555765, 40 | 0.03229429953011916, 41 | 0.0058746818113949465, 42 | -0.061722899624668884, 43 | 0.005632246857685454, 44 | 0.10229171917513397, 45 | -0.024716827337521424, 46 | -0.1554587507060453, 47 | 0.039850246458519104, 48 | 0.22829105082013823, 49 | -0.016727088308801888, 50 | -0.3267868004335376, 51 | -0.13921208801128787, 52 | 0.36150229873889705, 53 | 0.6104932389378558, 54 | 0.4726961853103315, 55 | 0.21994211355113222, 56 | 0.06342378045900529, 57 | 0.010549394624937735, 58 | 0.0007799536136659112 59 | ] 60 | 61 | # high-pass 62 | decompositionHighFilter = [ 63 | -0.0007799536136659112, 64 | 0.010549394624937735, 65 | -0.06342378045900529, 66 | 0.21994211355113222, 67 | -0.4726961853103315, 68 | 0.6104932389378558, 69 | -0.36150229873889705, 70 | -0.13921208801128787, 71 | 0.3267868004335376, 72 | -0.016727088308801888, 73 | -0.22829105082013823, 74 | 0.039850246458519104, 75 | 0.1554587507060453, 76 | -0.024716827337521424, 77 | -0.10229171917513397, 78 | 0.005632246857685454, 79 | 0.061722899624668884, 80 | 0.0058746818113949465, 81 | -0.03229429953011916, 82 | -0.008789324924555765, 83 | 0.013810526137727442, 84 | 0.0067216273018096935, 85 | -0.00442054238676635, 86 | -0.003581494259744107, 87 | 0.0008315621728772474, 88 | 0.0013925596193045254, 89 | 5.349759844340453e-05, 90 | -0.0003851047486990061, 91 | -0.00010153288973669777, 92 | 6.774280828373048e-05, 93 | 3.710586183390615e-05, 94 | -4.376143862182197e-06, 95 | -7.241248287663791e-06, 96 | -1.0119940100181473e-06, 97 | 6.847079596993149e-07, 98 | 2.633924226266962e-07, 99 | -2.0143220235374613e-10, 100 | -1.814843248297622e-08, 101 | -4.05612705554717e-09, 102 | -2.998836489615753e-10 103 | ] 104 | 105 | # reconstruction filters 106 | # low pass 107 | reconstructionLowFilter = [ 108 | 0.0007799536136659112, 109 | 0.010549394624937735, 110 | 0.06342378045900529, 111 | 0.21994211355113222, 112 | 0.4726961853103315, 113 | 0.6104932389378558, 114 | 0.36150229873889705, 115 | -0.13921208801128787, 116 | -0.3267868004335376, 117 | -0.016727088308801888, 118 | 0.22829105082013823, 119 | 0.039850246458519104, 120 | -0.1554587507060453, 121 | -0.024716827337521424, 122 | 0.10229171917513397, 123 | 0.005632246857685454, 124 | -0.061722899624668884, 125 | 0.0058746818113949465, 126 | 0.03229429953011916, 127 | -0.008789324924555765, 128 | -0.013810526137727442, 129 | 0.0067216273018096935, 130 | 0.00442054238676635, 131 | -0.003581494259744107, 132 | -0.0008315621728772474, 133 | 0.0013925596193045254, 134 | -5.349759844340453e-05, 135 | -0.0003851047486990061, 136 | 0.00010153288973669777, 137 | 6.774280828373048e-05, 138 | -3.710586183390615e-05, 139 | -4.376143862182197e-06, 140 | 7.241248287663791e-06, 141 | -1.0119940100181473e-06, 142 | -6.847079596993149e-07, 143 | 2.633924226266962e-07, 144 | 2.0143220235374613e-10, 145 | -1.814843248297622e-08, 146 | 4.05612705554717e-09, 147 | -2.998836489615753e-10 148 | ] 149 | 150 | # high-pass 151 | reconstructionHighFilter = [ 152 | -2.998836489615753e-10, 153 | -4.05612705554717e-09, 154 | -1.814843248297622e-08, 155 | -2.0143220235374613e-10, 156 | 2.633924226266962e-07, 157 | 6.847079596993149e-07, 158 | -1.0119940100181473e-06, 159 | -7.241248287663791e-06, 160 | -4.376143862182197e-06, 161 | 3.710586183390615e-05, 162 | 6.774280828373048e-05, 163 | -0.00010153288973669777, 164 | -0.0003851047486990061, 165 | 5.349759844340453e-05, 166 | 0.0013925596193045254, 167 | 0.0008315621728772474, 168 | -0.003581494259744107, 169 | -0.00442054238676635, 170 | 0.0067216273018096935, 171 | 0.013810526137727442, 172 | -0.008789324924555765, 173 | -0.03229429953011916, 174 | 0.0058746818113949465, 175 | 0.061722899624668884, 176 | 0.005632246857685454, 177 | -0.10229171917513397, 178 | -0.024716827337521424, 179 | 0.1554587507060453, 180 | 0.039850246458519104, 181 | -0.22829105082013823, 182 | -0.016727088308801888, 183 | 0.3267868004335376, 184 | -0.13921208801128787, 185 | -0.36150229873889705, 186 | 0.6104932389378558, 187 | -0.4726961853103315, 188 | 0.21994211355113222, 189 | -0.06342378045900529, 190 | 0.010549394624937735, 191 | -0.0007799536136659112 192 | ] 193 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db3.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 3 wavelet """ 2 | 3 | 4 | class Daubechies3: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db3/ 11 | """ 12 | __name__ = "Daubechies Wavelet 3" 13 | __motherWaveletLength__ = 6 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.035226291882100656, 20 | - 0.08544127388224149, 21 | - 0.13501102001039084, 22 | 0.4598775021193313, 23 | 0.8068915093133388, 24 | 0.3326705529509569 25 | ] 26 | 27 | # high-pass 28 | decompositionHighFilter = [ 29 | -0.3326705529509569, 30 | 0.8068915093133388, 31 | - 0.4598775021193313, 32 | - 0.13501102001039084, 33 | 0.08544127388224149, 34 | 0.035226291882100656 35 | ] 36 | 37 | # reconstruction filters 38 | # low pass 39 | reconstructionLowFilter = [ 40 | 0.3326705529509569, 41 | 0.8068915093133388, 42 | 0.4598775021193313, 43 | - 0.13501102001039084, 44 | - 0.08544127388224149, 45 | 0.035226291882100656 46 | ] 47 | 48 | # high-pass 49 | reconstructionHighFilter = [ 50 | 0.035226291882100656, 51 | 0.08544127388224149, 52 | - 0.13501102001039084, 53 | - 0.4598775021193313, 54 | 0.8068915093133388, 55 | - 0.3326705529509569 56 | ] 57 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db4.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 4 wavelet """ 2 | 3 | 4 | class Daubechies4: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db4/ 11 | """ 12 | __name__ = "Daubechies Wavelet 4" 13 | __motherWaveletLength__ = 8 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -0.010597401784997278, 20 | 0.032883011666982945, 21 | 0.030841381835986965, 22 | - 0.18703481171888114, 23 | - 0.02798376941698385, 24 | 0.6308807679295904, 25 | 0.7148465705525415, 26 | 0.23037781330885523 27 | ] 28 | 29 | # high-pass 30 | decompositionHighFilter = [ 31 | -0.23037781330885523, 32 | 0.7148465705525415, 33 | - 0.6308807679295904, 34 | - 0.02798376941698385, 35 | 0.18703481171888114, 36 | 0.030841381835986965, 37 | - 0.032883011666982945, 38 | - 0.010597401784997278, 39 | ] 40 | 41 | # reconstruction filters 42 | # low pass 43 | reconstructionLowFilter = [ 44 | 0.23037781330885523, 45 | 0.7148465705525415, 46 | 0.6308807679295904, 47 | - 0.02798376941698385, 48 | - 0.18703481171888114, 49 | 0.030841381835986965, 50 | 0.032883011666982945, 51 | - 0.010597401784997278, 52 | ] 53 | 54 | # high-pass 55 | reconstructionHighFilter = [ 56 | -0.010597401784997278, 57 | - 0.032883011666982945, 58 | 0.030841381835986965, 59 | 0.18703481171888114, 60 | - 0.02798376941698385, 61 | - 0.6308807679295904, 62 | 0.7148465705525415, 63 | - 0.23037781330885523, 64 | ] 65 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db5.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 5 wavelet """ 2 | 3 | 4 | class Daubechies5: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db5/ 11 | """ 12 | __name__ = "Daubechies Wavelet 5" 13 | __motherWaveletLength__ = 10 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.003335725285001549, 20 | - 0.012580751999015526, 21 | - 0.006241490213011705, 22 | 0.07757149384006515, 23 | - 0.03224486958502952, 24 | - 0.24229488706619015, 25 | 0.13842814590110342, 26 | 0.7243085284385744, 27 | 0.6038292697974729, 28 | 0.160102397974125 29 | ] 30 | 31 | # high-pass 32 | decompositionHighFilter = [ 33 | -0.160102397974125, 34 | 0.6038292697974729, 35 | - 0.7243085284385744, 36 | 0.13842814590110342, 37 | 0.24229488706619015, 38 | - 0.03224486958502952, 39 | - 0.07757149384006515, 40 | - 0.006241490213011705, 41 | 0.012580751999015526, 42 | 0.003335725285001549 43 | ] 44 | 45 | # reconstruction filters 46 | # low pass 47 | reconstructionLowFilter = [ 48 | 0.160102397974125, 49 | 0.6038292697974729, 50 | 0.7243085284385744, 51 | 0.13842814590110342, 52 | - 0.24229488706619015, 53 | - 0.03224486958502952, 54 | 0.07757149384006515, 55 | - 0.006241490213011705, 56 | - 0.012580751999015526, 57 | 0.003335725285001549, 58 | ] 59 | 60 | # high-pass 61 | reconstructionHighFilter = [ 62 | 0.003335725285001549, 63 | 0.012580751999015526, 64 | - 0.006241490213011705, 65 | - 0.07757149384006515, 66 | - 0.03224486958502952, 67 | 0.24229488706619015, 68 | 0.13842814590110342, 69 | - 0.7243085284385744, 70 | 0.6038292697974729, 71 | - 0.160102397974125, 72 | ] 73 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db6.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 6 wavelet """ 2 | 3 | 4 | class Daubechies6: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db6/ 11 | """ 12 | __name__ = "Daubechies Wavelet 6" 13 | __motherWaveletLength__ = 12 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -0.00107730108499558, 20 | 0.004777257511010651, 21 | 0.0005538422009938016, 22 | - 0.031582039318031156, 23 | 0.02752286553001629, 24 | 0.09750160558707936, 25 | - 0.12976686756709563, 26 | - 0.22626469396516913, 27 | 0.3152503517092432, 28 | 0.7511339080215775, 29 | 0.4946238903983854, 30 | 0.11154074335008017 31 | ] 32 | 33 | # high-pass 34 | decompositionHighFilter = [ 35 | -0.1115407433500807, 36 | 0.4946238903983854, 37 | - 0.7511339080215775, 38 | 0.3152503517092432, 39 | 0.22626469396516913, 40 | - 0.12976686756709563, 41 | - 0.09750160558707936, 42 | 0.02752286553001629, 43 | 0.031582039318031156, 44 | 0.0005538422009938016, 45 | - 0.004777257511010651, 46 | - 0.00107730108499558 47 | ] 48 | 49 | # reconstruction filters 50 | # low pass 51 | reconstructionLowFilter = [ 52 | 0.11154074335008017, 53 | 0.4946238903983854, 54 | 0.7511339080215775, 55 | 0.3152503517092432, 56 | - 0.22626469396516913, 57 | - 0.12976686756709563, 58 | 0.09750160558707936, 59 | 0.02752286553001629, 60 | - 0.031582039318031156, 61 | 0.0005538422009938016, 62 | 0.004777257511010651, 63 | - 0.00107730108499558 64 | ] 65 | 66 | # high-pass 67 | reconstructionHighFilter = [ 68 | -0.00107730108499558, 69 | - 0.004777257511010651, 70 | 0.0005538422009938016, 71 | 0.031582039318031156, 72 | 0.02752286553001629, 73 | - 0.09750160558707936, 74 | - 0.12976686756709563, 75 | 0.22626469396516913, 76 | 0.3152503517092432, 77 | - 0.7511339080215775, 78 | 0.4946238903983854, 79 | - 0.11154074335008017 80 | ] 81 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db7.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 7 wavelet """ 2 | 3 | 4 | class Daubechies7: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db7/ 11 | """ 12 | __name__ = "Daubechies Wavelet 7" 13 | __motherWaveletLength__ = 14 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.0003537138000010399, 20 | - 0.0018016407039998328, 21 | 0.00042957797300470274, 22 | 0.012550998556013784, 23 | - 0.01657454163101562, 24 | - 0.03802993693503463, 25 | 0.0806126091510659, 26 | 0.07130921926705004, 27 | - 0.22403618499416572, 28 | - 0.14390600392910627, 29 | 0.4697822874053586, 30 | 0.7291320908465551, 31 | 0.39653931948230575, 32 | 0.07785205408506236, 33 | ] 34 | 35 | # high-pass 36 | decompositionHighFilter = [ 37 | -0.07785205408506236, 38 | 0.39653931948230575, 39 | - 0.7291320908465551, 40 | 0.4697822874053586, 41 | 0.14390600392910627, 42 | - 0.22403618499416572, 43 | - 0.07130921926705004, 44 | 0.0806126091510659, 45 | 0.03802993693503463, 46 | - 0.01657454163101562, 47 | - 0.012550998556013784, 48 | 0.00042957797300470274, 49 | 0.0018016407039998328, 50 | 0.0003537138000010399 51 | ] 52 | 53 | # reconstruction filters 54 | # low pass 55 | reconstructionLowFilter = [ 56 | 0.07785205408506236, 57 | 0.39653931948230575, 58 | 0.7291320908465551, 59 | 0.4697822874053586, 60 | - 0.14390600392910627, 61 | - 0.22403618499416572, 62 | 0.07130921926705004, 63 | 0.0806126091510659, 64 | - 0.03802993693503463, 65 | - 0.01657454163101562, 66 | 0.012550998556013784, 67 | 0.00042957797300470274, 68 | - 0.0018016407039998328, 69 | 0.0003537138000010399 70 | ] 71 | 72 | # high-pass 73 | reconstructionHighFilter = [ 74 | 0.0003537138000010399, 75 | 0.0018016407039998328, 76 | 0.00042957797300470274, 77 | - 0.012550998556013784, 78 | - 0.01657454163101562, 79 | 0.03802993693503463, 80 | 0.0806126091510659, 81 | - 0.07130921926705004, 82 | - 0.22403618499416572, 83 | 0.14390600392910627, 84 | 0.4697822874053586, 85 | - 0.7291320908465551, 86 | 0.39653931948230575, 87 | - 0.07785205408506236 88 | ] 89 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db8.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 8 wavelet """ 2 | 3 | 4 | class Daubechies8: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db8/ 11 | """ 12 | __name__ = "Daubechies Wavelet 8" 13 | __motherWaveletLength__ = 16 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -0.00011747678400228192, 20 | 0.0006754494059985568, 21 | - 0.0003917403729959771, 22 | - 0.00487035299301066, 23 | 0.008746094047015655, 24 | 0.013981027917015516, 25 | - 0.04408825393106472, 26 | - 0.01736930100202211, 27 | 0.128747426620186, 28 | 0.00047248457399797254, 29 | - 0.2840155429624281, 30 | - 0.015829105256023893, 31 | 0.5853546836548691, 32 | 0.6756307362980128, 33 | 0.3128715909144659, 34 | 0.05441584224308161 35 | ] 36 | 37 | # high-pass 38 | decompositionHighFilter = [ 39 | -0.05441584224308161, 40 | 0.3128715909144659, 41 | - 0.6756307362980128, 42 | 0.5853546836548691, 43 | 0.015829105256023893, 44 | - 0.2840155429624281, 45 | - 0.00047248457399797254, 46 | 0.128747426620186, 47 | 0.01736930100202211, 48 | - 0.04408825393106472, 49 | - 0.013981027917015516, 50 | 0.008746094047015655, 51 | 0.00487035299301066, 52 | - 0.0003917403729959771, 53 | - 0.0006754494059985568, 54 | - 0.00011747678400228192 55 | ] 56 | 57 | # reconstruction filters 58 | # low pass 59 | reconstructionLowFilter = [ 60 | 0.05441584224308161, 61 | 0.3128715909144659, 62 | 0.6756307362980128, 63 | 0.5853546836548691, 64 | - 0.015829105256023893, 65 | - 0.2840155429624281, 66 | 0.00047248457399797254, 67 | 0.128747426620186, 68 | - 0.01736930100202211, 69 | - 0.04408825393106472, 70 | 0.013981027917015516, 71 | 0.008746094047015655, 72 | - 0.00487035299301066, 73 | - 0.0003917403729959771, 74 | 0.0006754494059985568, 75 | - 0.00011747678400228192 76 | ] 77 | 78 | # high-pass 79 | reconstructionHighFilter = [ 80 | -0.00011747678400228192, 81 | - 0.0006754494059985568, 82 | - 0.0003917403729959771, 83 | 0.00487035299301066, 84 | 0.008746094047015655, 85 | - 0.013981027917015516, 86 | - 0.04408825393106472, 87 | 0.01736930100202211, 88 | 0.128747426620186, 89 | - 0.00047248457399797254, 90 | - 0.2840155429624281, 91 | 0.015829105256023893, 92 | 0.5853546836548691, 93 | - 0.6756307362980128, 94 | 0.3128715909144659, 95 | - 0.05441584224308161, 96 | ] 97 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/db9.py: -------------------------------------------------------------------------------- 1 | """ Daubechies 9 wavelet """ 2 | 3 | 4 | class Daubechies9: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/db9/ 11 | """ 12 | __name__ = "Daubechies Wavelet 9" 13 | __motherWaveletLength__ = 18 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 3.9347319995026124e-05, 20 | - 0.0002519631889981789, 21 | 0.00023038576399541288, 22 | 0.0018476468829611268, 23 | - 0.004281503681904723, 24 | - 0.004723204757894831, 25 | 0.022361662123515244, 26 | 0.00025094711499193845, 27 | - 0.06763282905952399, 28 | 0.030725681478322865, 29 | 0.14854074933476008, 30 | - 0.09684078322087904, 31 | - 0.29327378327258685, 32 | 0.13319738582208895, 33 | 0.6572880780366389, 34 | 0.6048231236767786, 35 | 0.24383467463766728, 36 | 0.03807794736316728 37 | ] 38 | 39 | # high-pass 40 | decompositionHighFilter = [ 41 | -0.03807794736316728, 42 | 0.24383467463766728, 43 | - 0.6048231236767786, 44 | 0.6572880780366389, 45 | - 0.13319738582208895, 46 | - 0.29327378327258685, 47 | 0.09684078322087904, 48 | 0.14854074933476008, 49 | - 0.030725681478322865, 50 | - 0.06763282905952399, 51 | - 0.00025094711499193845, 52 | 0.022361662123515244, 53 | 0.004723204757894831, 54 | - 0.004281503681904723, 55 | - 0.0018476468829611268, 56 | 0.00023038576399541288, 57 | 0.0002519631889981789, 58 | 3.9347319995026124e-05 59 | ] 60 | 61 | # reconstruction filters 62 | # low pass 63 | reconstructionLowFilter = [ 64 | 0.03807794736316728, 65 | 0.24383467463766728, 66 | 0.6048231236767786, 67 | 0.6572880780366389, 68 | 0.13319738582208895, 69 | - 0.29327378327258685, 70 | - 0.09684078322087904, 71 | 0.14854074933476008, 72 | 0.030725681478322865, 73 | - 0.06763282905952399, 74 | 0.00025094711499193845, 75 | 0.022361662123515244, 76 | - 0.004723204757894831, 77 | - 0.004281503681904723, 78 | 0.0018476468829611268, 79 | 0.00023038576399541288, 80 | - 0.0002519631889981789, 81 | 3.9347319995026124e-05 82 | ] 83 | 84 | # high-pass 85 | reconstructionHighFilter = [ 86 | 3.9347319995026124e-05, 87 | 0.0002519631889981789, 88 | 0.00023038576399541288, 89 | - 0.0018476468829611268, 90 | - 0.004281503681904723, 91 | 0.004723204757894831, 92 | 0.022361662123515244, 93 | - 0.00025094711499193845, 94 | - 0.06763282905952399, 95 | - 0.030725681478322865, 96 | 0.14854074933476008, 97 | 0.09684078322087904, 98 | - 0.29327378327258685, 99 | - 0.13319738582208895, 100 | 0.6572880780366389, 101 | - 0.6048231236767786, 102 | 0.24383467463766728, 103 | - 0.03807794736316728, 104 | ] 105 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/dmey.py: -------------------------------------------------------------------------------- 1 | """ Discrete Meyer (FIR Approximation) wavelet """ 2 | 3 | 4 | class Meyer: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/dmey/ 11 | """ 12 | __name__ = "Meyer Wavelet" 13 | __motherWaveletLength__ = 62 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.0, 20 | -1.009999956941423e-12, 21 | 8.519459636796214e-09, 22 | -1.111944952595278e-08, 23 | -1.0798819539621958e-08, 24 | 6.066975741351135e-08, 25 | -1.0866516536735883e-07, 26 | 8.200680650386481e-08, 27 | 1.1783004497663934e-07, 28 | -5.506340565252278e-07, 29 | 1.1307947017916706e-06, 30 | -1.489549216497156e-06, 31 | 7.367572885903746e-07, 32 | 3.20544191334478e-06, 33 | -1.6312699734552807e-05, 34 | 6.554305930575149e-05, 35 | -0.0006011502343516092, 36 | -0.002704672124643725, 37 | 0.002202534100911002, 38 | 0.006045814097323304, 39 | -0.006387718318497156, 40 | -0.011061496392513451, 41 | 0.015270015130934803, 42 | 0.017423434103729693, 43 | -0.03213079399021176, 44 | -0.024348745906078023, 45 | 0.0637390243228016, 46 | 0.030655091960824263, 47 | -0.13284520043622938, 48 | -0.035087555656258346, 49 | 0.44459300275757724, 50 | 0.7445855923188063, 51 | 0.44459300275757724, 52 | -0.035087555656258346, 53 | -0.13284520043622938, 54 | 0.030655091960824263, 55 | 0.0637390243228016, 56 | -0.024348745906078023, 57 | -0.03213079399021176, 58 | 0.017423434103729693, 59 | 0.015270015130934803, 60 | -0.011061496392513451, 61 | -0.006387718318497156, 62 | 0.006045814097323304, 63 | 0.002202534100911002, 64 | -0.002704672124643725, 65 | -0.0006011502343516092, 66 | 6.554305930575149e-05, 67 | -1.6312699734552807e-05, 68 | 3.20544191334478e-06, 69 | 7.367572885903746e-07, 70 | -1.489549216497156e-06, 71 | 1.1307947017916706e-06, 72 | -5.506340565252278e-07, 73 | 1.1783004497663934e-07, 74 | 8.200680650386481e-08, 75 | -1.0866516536735883e-07, 76 | 6.066975741351135e-08, 77 | -1.0798819539621958e-08, 78 | -1.111944952595278e-08, 79 | 8.519459636796214e-09, 80 | -1.009999956941423e-12, 81 | ] 82 | 83 | # high-pass 84 | decompositionHighFilter = [ 85 | 1.009999956941423e-12, 86 | 8.519459636796214e-09, 87 | 1.111944952595278e-08, 88 | -1.0798819539621958e-08, 89 | -6.066975741351135e-08, 90 | -1.0866516536735883e-07, 91 | -8.200680650386481e-08, 92 | 1.1783004497663934e-07, 93 | 5.506340565252278e-07, 94 | 1.1307947017916706e-06, 95 | 1.489549216497156e-06, 96 | 7.367572885903746e-07, 97 | -3.20544191334478e-06, 98 | -1.6312699734552807e-05, 99 | -6.554305930575149e-05, 100 | -0.0006011502343516092, 101 | 0.002704672124643725, 102 | 0.002202534100911002, 103 | -0.006045814097323304, 104 | -0.006387718318497156, 105 | 0.011061496392513451, 106 | 0.015270015130934803, 107 | -0.017423434103729693, 108 | -0.03213079399021176, 109 | 0.024348745906078023, 110 | 0.0637390243228016, 111 | -0.030655091960824263, 112 | -0.13284520043622938, 113 | 0.035087555656258346, 114 | 0.44459300275757724, 115 | -0.7445855923188063, 116 | 0.44459300275757724, 117 | 0.035087555656258346, 118 | -0.13284520043622938, 119 | -0.030655091960824263, 120 | 0.0637390243228016, 121 | 0.024348745906078023, 122 | -0.03213079399021176, 123 | -0.017423434103729693, 124 | 0.015270015130934803, 125 | 0.011061496392513451, 126 | -0.006387718318497156, 127 | -0.006045814097323304, 128 | 0.002202534100911002, 129 | 0.002704672124643725, 130 | -0.0006011502343516092, 131 | -6.554305930575149e-05, 132 | -1.6312699734552807e-05, 133 | -3.20544191334478e-06, 134 | 7.367572885903746e-07, 135 | 1.489549216497156e-06, 136 | 1.1307947017916706e-06, 137 | 5.506340565252278e-07, 138 | 1.1783004497663934e-07, 139 | -8.200680650386481e-08, 140 | -1.0866516536735883e-07, 141 | -6.066975741351135e-08, 142 | -1.0798819539621958e-08, 143 | 1.111944952595278e-08, 144 | 8.519459636796214e-09, 145 | 1.009999956941423e-12, 146 | 0.0, 147 | ] 148 | 149 | # reconstruction filters 150 | # low pass 151 | reconstructionLowFilter = [ 152 | -1.009999956941423e-12, 153 | 8.519459636796214e-09, 154 | -1.111944952595278e-08, 155 | -1.0798819539621958e-08, 156 | 6.066975741351135e-08, 157 | -1.0866516536735883e-07, 158 | 8.200680650386481e-08, 159 | 1.1783004497663934e-07, 160 | -5.506340565252278e-07, 161 | 1.1307947017916706e-06, 162 | -1.489549216497156e-06, 163 | 7.367572885903746e-07, 164 | 3.20544191334478e-06, 165 | -1.6312699734552807e-05, 166 | 6.554305930575149e-05, 167 | -0.0006011502343516092, 168 | -0.002704672124643725, 169 | 0.002202534100911002, 170 | 0.006045814097323304, 171 | -0.006387718318497156, 172 | -0.011061496392513451, 173 | 0.015270015130934803, 174 | 0.017423434103729693, 175 | -0.03213079399021176, 176 | -0.024348745906078023, 177 | 0.0637390243228016, 178 | 0.030655091960824263, 179 | -0.13284520043622938, 180 | -0.035087555656258346, 181 | 0.44459300275757724, 182 | 0.7445855923188063, 183 | 0.44459300275757724, 184 | -0.035087555656258346, 185 | -0.13284520043622938, 186 | 0.030655091960824263, 187 | 0.0637390243228016, 188 | -0.024348745906078023, 189 | -0.03213079399021176, 190 | 0.017423434103729693, 191 | 0.015270015130934803, 192 | -0.011061496392513451, 193 | -0.006387718318497156, 194 | 0.006045814097323304, 195 | 0.002202534100911002, 196 | -0.002704672124643725, 197 | -0.0006011502343516092, 198 | 6.554305930575149e-05, 199 | -1.6312699734552807e-05, 200 | 3.20544191334478e-06, 201 | 7.367572885903746e-07, 202 | -1.489549216497156e-06, 203 | 1.1307947017916706e-06, 204 | -5.506340565252278e-07, 205 | 1.1783004497663934e-07, 206 | 8.200680650386481e-08, 207 | -1.0866516536735883e-07, 208 | 6.066975741351135e-08, 209 | -1.0798819539621958e-08, 210 | -1.111944952595278e-08, 211 | 8.519459636796214e-09, 212 | -1.009999956941423e-12, 213 | 0.0, 214 | ] 215 | 216 | # high-pass 217 | reconstructionHighFilter = [ 218 | 0.0, 219 | 1.009999956941423e-12, 220 | 8.519459636796214e-09, 221 | 1.111944952595278e-08, 222 | -1.0798819539621958e-08, 223 | -6.066975741351135e-08, 224 | -1.0866516536735883e-07, 225 | -8.200680650386481e-08, 226 | 1.1783004497663934e-07, 227 | 5.506340565252278e-07, 228 | 1.1307947017916706e-06, 229 | 1.489549216497156e-06, 230 | 7.367572885903746e-07, 231 | -3.20544191334478e-06, 232 | -1.6312699734552807e-05, 233 | -6.554305930575149e-05, 234 | -0.0006011502343516092, 235 | 0.002704672124643725, 236 | 0.002202534100911002, 237 | -0.006045814097323304, 238 | -0.006387718318497156, 239 | 0.011061496392513451, 240 | 0.015270015130934803, 241 | -0.017423434103729693, 242 | -0.03213079399021176, 243 | 0.024348745906078023, 244 | 0.0637390243228016, 245 | -0.030655091960824263, 246 | -0.13284520043622938, 247 | 0.035087555656258346, 248 | 0.44459300275757724, 249 | -0.7445855923188063, 250 | 0.44459300275757724, 251 | 0.035087555656258346, 252 | -0.13284520043622938, 253 | -0.030655091960824263, 254 | 0.0637390243228016, 255 | 0.024348745906078023, 256 | -0.03213079399021176, 257 | -0.017423434103729693, 258 | 0.015270015130934803, 259 | 0.011061496392513451, 260 | -0.006387718318497156, 261 | -0.006045814097323304, 262 | 0.002202534100911002, 263 | 0.002704672124643725, 264 | -0.0006011502343516092, 265 | -6.554305930575149e-05, 266 | -1.6312699734552807e-05, 267 | -3.20544191334478e-06, 268 | 7.367572885903746e-07, 269 | 1.489549216497156e-06, 270 | 1.1307947017916706e-06, 271 | 5.506340565252278e-07, 272 | 1.1783004497663934e-07, 273 | -8.200680650386481e-08, 274 | -1.0866516536735883e-07, 275 | -6.066975741351135e-08, 276 | -1.0798819539621958e-08, 277 | 1.111944952595278e-08, 278 | 8.519459636796214e-09, 279 | 1.009999956941423e-12, 280 | ] 281 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/haar.py: -------------------------------------------------------------------------------- 1 | """ Haar Wavelet """ 2 | 3 | 4 | class Haar: 5 | """ 6 | Properties 7 | ---------- 8 | asymmetric, orthogonal, bi-orthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/haar/ 11 | """ 12 | __name__ = "Haar Wavelet" 13 | __motherWaveletLength__ = 2 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.7071067811865476, 20 | 0.7071067811865476 21 | ] 22 | 23 | # high-pass 24 | decompositionHighFilter = [ 25 | -0.7071067811865476, 26 | 0.7071067811865476 27 | ] 28 | 29 | # reconstruction filters 30 | # low pass 31 | reconstructionLowFilter = [ 32 | 0.7071067811865476, 33 | 0.7071067811865476 34 | ] 35 | 36 | # high-pass 37 | reconstructionHighFilter = [ 38 | 0.7071067811865476, 39 | - 0.7071067811865476 40 | ] 41 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym10.py: -------------------------------------------------------------------------------- 1 | """ Symlet 10 wavelet """ 2 | 3 | 4 | class Symlet10: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym2/ 11 | """ 12 | __name__ = "Symlet Wavelet 10" 13 | __motherWaveletLength__ = 20 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.0007701598091144901, 20 | 9.563267072289475e-05, 21 | -0.008641299277022422, 22 | -0.0014653825813050513, 23 | 0.0459272392310922, 24 | 0.011609893903711381, 25 | -0.15949427888491757, 26 | -0.07088053578324385, 27 | 0.47169066693843925, 28 | 0.7695100370211071, 29 | 0.38382676106708546, 30 | -0.03553674047381755, 31 | -0.0319900568824278, 32 | 0.04999497207737669, 33 | 0.005764912033581909, 34 | -0.02035493981231129, 35 | -0.0008043589320165449, 36 | 0.004593173585311828, 37 | 5.7036083618494284e-05, 38 | -0.0004593294210046588, 39 | ] 40 | 41 | # high-pass 42 | decompositionHighFilter = [ 43 | 0.0004593294210046588, 44 | 5.7036083618494284e-05, 45 | -0.004593173585311828, 46 | -0.0008043589320165449, 47 | 0.02035493981231129, 48 | 0.005764912033581909, 49 | -0.04999497207737669, 50 | -0.0319900568824278, 51 | 0.03553674047381755, 52 | 0.38382676106708546, 53 | -0.7695100370211071, 54 | 0.47169066693843925, 55 | 0.07088053578324385, 56 | -0.15949427888491757, 57 | -0.011609893903711381, 58 | 0.0459272392310922, 59 | 0.0014653825813050513, 60 | -0.008641299277022422, 61 | -9.563267072289475e-05, 62 | 0.0007701598091144901, 63 | ] 64 | 65 | # reconstruction filters 66 | # low pass 67 | reconstructionLowFilter = [ 68 | -0.0004593294210046588, 69 | 5.7036083618494284e-05, 70 | 0.004593173585311828, 71 | -0.0008043589320165449, 72 | -0.02035493981231129, 73 | 0.005764912033581909, 74 | 0.04999497207737669, 75 | -0.0319900568824278, 76 | -0.03553674047381755, 77 | 0.38382676106708546, 78 | 0.7695100370211071, 79 | 0.47169066693843925, 80 | -0.07088053578324385, 81 | -0.15949427888491757, 82 | 0.011609893903711381, 83 | 0.0459272392310922, 84 | -0.0014653825813050513, 85 | -0.008641299277022422, 86 | 9.563267072289475e-05, 87 | 0.0007701598091144901, 88 | ] 89 | 90 | # high-pass 91 | reconstructionHighFilter = [ 92 | 0.0007701598091144901, 93 | -9.563267072289475e-05, 94 | -0.008641299277022422, 95 | 0.0014653825813050513, 96 | 0.0459272392310922, 97 | -0.011609893903711381, 98 | -0.15949427888491757, 99 | 0.07088053578324385, 100 | 0.47169066693843925, 101 | -0.7695100370211071, 102 | 0.38382676106708546, 103 | 0.03553674047381755, 104 | -0.0319900568824278, 105 | -0.04999497207737669, 106 | 0.005764912033581909, 107 | 0.02035493981231129, 108 | -0.0008043589320165449, 109 | -0.004593173585311828, 110 | 5.7036083618494284e-05, 111 | 0.0004593294210046588, 112 | ] 113 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym11.py: -------------------------------------------------------------------------------- 1 | """ Symlet 11 wavelet """ 2 | 3 | 4 | class Symlet11: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym11/ 11 | """ 12 | __name__ = "Symlet Wavelet 11" 13 | __motherWaveletLength__ = 22 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.00017172195069934854, 20 | -3.8795655736158566e-05, 21 | -0.0017343662672978692, 22 | 0.0005883527353969915, 23 | 0.00651249567477145, 24 | -0.009857934828789794, 25 | -0.024080841595864003, 26 | 0.0370374159788594, 27 | 0.06997679961073414, 28 | -0.022832651022562687, 29 | 0.09719839445890947, 30 | 0.5720229780100871, 31 | 0.7303435490883957, 32 | 0.23768990904924897, 33 | -0.2046547944958006, 34 | -0.1446023437053156, 35 | 0.03526675956446655, 36 | 0.04300019068155228, 37 | -0.0020034719001093887, 38 | -0.006389603666454892, 39 | 0.00011053509764272153, 40 | 0.0004892636102619239, 41 | ] 42 | 43 | # high-pass 44 | decompositionHighFilter = [ 45 | -0.0004892636102619239, 46 | 0.00011053509764272153, 47 | 0.006389603666454892, 48 | -0.0020034719001093887, 49 | -0.04300019068155228, 50 | 0.03526675956446655, 51 | 0.1446023437053156, 52 | -0.2046547944958006, 53 | -0.23768990904924897, 54 | 0.7303435490883957, 55 | -0.5720229780100871, 56 | 0.09719839445890947, 57 | 0.022832651022562687, 58 | 0.06997679961073414, 59 | -0.0370374159788594, 60 | -0.024080841595864003, 61 | 0.009857934828789794, 62 | 0.00651249567477145, 63 | -0.0005883527353969915, 64 | -0.0017343662672978692, 65 | 3.8795655736158566e-05, 66 | 0.00017172195069934854, 67 | ] 68 | 69 | # reconstruction filters 70 | # low pass 71 | reconstructionLowFilter = [ 72 | 0.0004892636102619239, 73 | 0.00011053509764272153, 74 | -0.006389603666454892, 75 | -0.0020034719001093887, 76 | 0.04300019068155228, 77 | 0.03526675956446655, 78 | -0.1446023437053156, 79 | -0.2046547944958006, 80 | 0.23768990904924897, 81 | 0.7303435490883957, 82 | 0.5720229780100871, 83 | 0.09719839445890947, 84 | -0.022832651022562687, 85 | 0.06997679961073414, 86 | 0.0370374159788594, 87 | -0.024080841595864003, 88 | -0.009857934828789794, 89 | 0.00651249567477145, 90 | 0.0005883527353969915, 91 | -0.0017343662672978692, 92 | -3.8795655736158566e-05, 93 | 0.00017172195069934854, 94 | ] 95 | 96 | # high-pass 97 | reconstructionHighFilter = [ 98 | 0.00017172195069934854, 99 | 3.8795655736158566e-05, 100 | -0.0017343662672978692, 101 | -0.0005883527353969915, 102 | 0.00651249567477145, 103 | 0.009857934828789794, 104 | -0.024080841595864003, 105 | -0.0370374159788594, 106 | 0.06997679961073414, 107 | 0.022832651022562687, 108 | 0.09719839445890947, 109 | -0.5720229780100871, 110 | 0.7303435490883957, 111 | -0.23768990904924897, 112 | -0.2046547944958006, 113 | 0.1446023437053156, 114 | 0.03526675956446655, 115 | -0.04300019068155228, 116 | -0.0020034719001093887, 117 | 0.006389603666454892, 118 | 0.00011053509764272153, 119 | -0.0004892636102619239, 120 | ] 121 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym12.py: -------------------------------------------------------------------------------- 1 | """ Symlet 12 wavelet """ 2 | 3 | 4 | class Symlet12: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym12/ 11 | """ 12 | __name__ = "Symlet Wavelet 12" 13 | __motherWaveletLength__ = 24 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.00011196719424656033, 20 | -1.1353928041541452e-05, 21 | -0.0013497557555715387, 22 | 0.00018021409008538188, 23 | 0.007414965517654251, 24 | -0.0014089092443297553, 25 | -0.024220722675013445, 26 | 0.0075537806116804775, 27 | 0.04917931829966084, 28 | -0.03584883073695439, 29 | -0.022162306170337816, 30 | 0.39888597239022, 31 | 0.7634790977836572, 32 | 0.46274103121927235, 33 | -0.07833262231634322, 34 | -0.17037069723886492, 35 | 0.01530174062247884, 36 | 0.05780417944550566, 37 | -0.0026043910313322326, 38 | -0.014589836449234145, 39 | 0.00030764779631059454, 40 | 0.002350297614183465, 41 | -1.8158078862617515e-05, 42 | -0.0001790665869750869, 43 | ] 44 | 45 | # high-pass 46 | decompositionHighFilter = [ 47 | 0.0001790665869750869, 48 | -1.8158078862617515e-05, 49 | -0.002350297614183465, 50 | 0.00030764779631059454, 51 | 0.014589836449234145, 52 | -0.0026043910313322326, 53 | -0.05780417944550566, 54 | 0.01530174062247884, 55 | 0.17037069723886492, 56 | -0.07833262231634322, 57 | -0.46274103121927235, 58 | 0.7634790977836572, 59 | -0.39888597239022, 60 | -0.022162306170337816, 61 | 0.03584883073695439, 62 | 0.04917931829966084, 63 | -0.0075537806116804775, 64 | -0.024220722675013445, 65 | 0.0014089092443297553, 66 | 0.007414965517654251, 67 | -0.00018021409008538188, 68 | -0.0013497557555715387, 69 | 1.1353928041541452e-05, 70 | 0.00011196719424656033, 71 | ] 72 | 73 | # reconstruction filters 74 | # low pass 75 | reconstructionLowFilter = [ 76 | -0.0001790665869750869, 77 | -1.8158078862617515e-05, 78 | 0.002350297614183465, 79 | 0.00030764779631059454, 80 | -0.014589836449234145, 81 | -0.0026043910313322326, 82 | 0.05780417944550566, 83 | 0.01530174062247884, 84 | -0.17037069723886492, 85 | -0.07833262231634322, 86 | 0.46274103121927235, 87 | 0.7634790977836572, 88 | 0.39888597239022, 89 | -0.022162306170337816, 90 | -0.03584883073695439, 91 | 0.04917931829966084, 92 | 0.0075537806116804775, 93 | -0.024220722675013445, 94 | -0.0014089092443297553, 95 | 0.007414965517654251, 96 | 0.00018021409008538188, 97 | -0.0013497557555715387, 98 | -1.1353928041541452e-05, 99 | 0.00011196719424656033, 100 | ] 101 | 102 | # high-pass 103 | reconstructionHighFilter = [ 104 | 0.00011196719424656033, 105 | 1.1353928041541452e-05, 106 | -0.0013497557555715387, 107 | -0.00018021409008538188, 108 | 0.007414965517654251, 109 | 0.0014089092443297553, 110 | -0.024220722675013445, 111 | -0.0075537806116804775, 112 | 0.04917931829966084, 113 | 0.03584883073695439, 114 | -0.022162306170337816, 115 | -0.39888597239022, 116 | 0.7634790977836572, 117 | -0.46274103121927235, 118 | -0.07833262231634322, 119 | 0.17037069723886492, 120 | 0.01530174062247884, 121 | -0.05780417944550566, 122 | -0.0026043910313322326, 123 | 0.014589836449234145, 124 | 0.00030764779631059454, 125 | -0.002350297614183465, 126 | -1.8158078862617515e-05, 127 | 0.0001790665869750869, 128 | ] 129 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym13.py: -------------------------------------------------------------------------------- 1 | """ Symlet 13 wavelet """ 2 | 3 | 4 | class Symlet13: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym13/ 11 | """ 12 | __name__ = "Symlet Wavelet 13" 13 | __motherWaveletLength__ = 26 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 6.820325263075319e-05, 20 | -3.573862364868901e-05, 21 | -0.0011360634389281183, 22 | -0.0001709428585302221, 23 | 0.0075262253899681, 24 | 0.005296359738725025, 25 | -0.02021676813338983, 26 | -0.017211642726299048, 27 | 0.013862497435849205, 28 | -0.0597506277179437, 29 | -0.12436246075153011, 30 | 0.19770481877117801, 31 | 0.6957391505614964, 32 | 0.6445643839011856, 33 | 0.11023022302137217, 34 | -0.14049009311363403, 35 | 0.008819757670420546, 36 | 0.09292603089913712, 37 | 0.017618296880653084, 38 | -0.020749686325515677, 39 | -0.0014924472742598532, 40 | 0.0056748537601224395, 41 | 0.00041326119884196064, 42 | -0.0007213643851362283, 43 | 3.690537342319624e-05, 44 | 7.042986690694402e-05, 45 | ] 46 | 47 | # high-pass 48 | decompositionHighFilter = [ 49 | -7.042986690694402e-05, 50 | 3.690537342319624e-05, 51 | 0.0007213643851362283, 52 | 0.00041326119884196064, 53 | -0.0056748537601224395, 54 | -0.0014924472742598532, 55 | 0.020749686325515677, 56 | 0.017618296880653084, 57 | -0.09292603089913712, 58 | 0.008819757670420546, 59 | 0.14049009311363403, 60 | 0.11023022302137217, 61 | -0.6445643839011856, 62 | 0.6957391505614964, 63 | -0.19770481877117801, 64 | -0.12436246075153011, 65 | 0.0597506277179437, 66 | 0.013862497435849205, 67 | 0.017211642726299048, 68 | -0.02021676813338983, 69 | -0.005296359738725025, 70 | 0.0075262253899681, 71 | 0.0001709428585302221, 72 | -0.0011360634389281183, 73 | 3.573862364868901e-05, 74 | 6.820325263075319e-05, 75 | ] 76 | 77 | # reconstruction filters 78 | # low pass 79 | reconstructionLowFilter = [ 80 | 7.042986690694402e-05, 81 | 3.690537342319624e-05, 82 | -0.0007213643851362283, 83 | 0.00041326119884196064, 84 | 0.0056748537601224395, 85 | -0.0014924472742598532, 86 | -0.020749686325515677, 87 | 0.017618296880653084, 88 | 0.09292603089913712, 89 | 0.008819757670420546, 90 | -0.14049009311363403, 91 | 0.11023022302137217, 92 | 0.6445643839011856, 93 | 0.6957391505614964, 94 | 0.19770481877117801, 95 | -0.12436246075153011, 96 | -0.0597506277179437, 97 | 0.013862497435849205, 98 | -0.017211642726299048, 99 | -0.02021676813338983, 100 | 0.005296359738725025, 101 | 0.0075262253899681, 102 | -0.0001709428585302221, 103 | -0.0011360634389281183, 104 | -3.573862364868901e-05, 105 | 6.820325263075319e-05, 106 | ] 107 | 108 | # high-pass 109 | reconstructionHighFilter = [ 110 | 6.820325263075319e-05, 111 | 3.573862364868901e-05, 112 | -0.0011360634389281183, 113 | 0.0001709428585302221, 114 | 0.0075262253899681, 115 | -0.005296359738725025, 116 | -0.02021676813338983, 117 | 0.017211642726299048, 118 | 0.013862497435849205, 119 | 0.0597506277179437, 120 | -0.12436246075153011, 121 | -0.19770481877117801, 122 | 0.6957391505614964, 123 | -0.6445643839011856, 124 | 0.11023022302137217, 125 | 0.14049009311363403, 126 | 0.008819757670420546, 127 | -0.09292603089913712, 128 | 0.017618296880653084, 129 | 0.020749686325515677, 130 | -0.0014924472742598532, 131 | -0.0056748537601224395, 132 | 0.00041326119884196064, 133 | 0.0007213643851362283, 134 | 3.690537342319624e-05, 135 | -7.042986690694402e-05, 136 | ] 137 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym14.py: -------------------------------------------------------------------------------- 1 | """ Symlet 14 wavelet """ 2 | 3 | 4 | class Symlet14: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym14/ 11 | """ 12 | __name__ = "Symlet Wavelet 14" 13 | __motherWaveletLength__ = 28 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -2.5879090265397886e-05, 20 | 1.1210865808890361e-05, 21 | 0.00039843567297594335, 22 | -6.286542481477636e-05, 23 | -0.002579441725933078, 24 | 0.0003664765736601183, 25 | 0.01003769371767227, 26 | -0.002753774791224071, 27 | -0.029196217764038187, 28 | 0.004280520499019378, 29 | 0.03743308836285345, 30 | -0.057634498351326995, 31 | -0.03531811211497973, 32 | 0.39320152196208885, 33 | 0.7599762419610909, 34 | 0.4753357626342066, 35 | -0.05811182331771783, 36 | -0.15999741114652205, 37 | 0.02589858753104667, 38 | 0.06982761636180755, 39 | -0.002365048836740385, 40 | -0.019439314263626713, 41 | 0.0010131419871842082, 42 | 0.004532677471945648, 43 | -7.321421356702399e-05, 44 | -0.0006057601824664335, 45 | 1.9329016965523917e-05, 46 | 4.4618977991475265e-05, 47 | ] 48 | 49 | # high-pass 50 | decompositionHighFilter = [ 51 | -4.4618977991475265e-05, 52 | 1.9329016965523917e-05, 53 | 0.0006057601824664335, 54 | -7.321421356702399e-05, 55 | -0.004532677471945648, 56 | 0.0010131419871842082, 57 | 0.019439314263626713, 58 | -0.002365048836740385, 59 | -0.06982761636180755, 60 | 0.02589858753104667, 61 | 0.15999741114652205, 62 | -0.05811182331771783, 63 | -0.4753357626342066, 64 | 0.7599762419610909, 65 | -0.39320152196208885, 66 | -0.03531811211497973, 67 | 0.057634498351326995, 68 | 0.03743308836285345, 69 | -0.004280520499019378, 70 | -0.029196217764038187, 71 | 0.002753774791224071, 72 | 0.01003769371767227, 73 | -0.0003664765736601183, 74 | -0.002579441725933078, 75 | 6.286542481477636e-05, 76 | 0.00039843567297594335, 77 | -1.1210865808890361e-05, 78 | -2.5879090265397886e-05, 79 | ] 80 | 81 | # reconstruction filters 82 | # low pass 83 | reconstructionLowFilter = [ 84 | 4.4618977991475265e-05, 85 | 1.9329016965523917e-05, 86 | -0.0006057601824664335, 87 | -7.321421356702399e-05, 88 | 0.004532677471945648, 89 | 0.0010131419871842082, 90 | -0.019439314263626713, 91 | -0.002365048836740385, 92 | 0.06982761636180755, 93 | 0.02589858753104667, 94 | -0.15999741114652205, 95 | -0.05811182331771783, 96 | 0.4753357626342066, 97 | 0.7599762419610909, 98 | 0.39320152196208885, 99 | -0.03531811211497973, 100 | -0.057634498351326995, 101 | 0.03743308836285345, 102 | 0.004280520499019378, 103 | -0.029196217764038187, 104 | -0.002753774791224071, 105 | 0.01003769371767227, 106 | 0.0003664765736601183, 107 | -0.002579441725933078, 108 | -6.286542481477636e-05, 109 | 0.00039843567297594335, 110 | 1.1210865808890361e-05, 111 | -2.5879090265397886e-05, 112 | ] 113 | 114 | # high-pass 115 | reconstructionHighFilter = [ 116 | -2.5879090265397886e-05, 117 | -1.1210865808890361e-05, 118 | 0.00039843567297594335, 119 | 6.286542481477636e-05, 120 | -0.002579441725933078, 121 | -0.0003664765736601183, 122 | 0.01003769371767227, 123 | 0.002753774791224071, 124 | -0.029196217764038187, 125 | -0.004280520499019378, 126 | 0.03743308836285345, 127 | 0.057634498351326995, 128 | -0.03531811211497973, 129 | -0.39320152196208885, 130 | 0.7599762419610909, 131 | -0.4753357626342066, 132 | -0.05811182331771783, 133 | 0.15999741114652205, 134 | 0.02589858753104667, 135 | -0.06982761636180755, 136 | -0.002365048836740385, 137 | 0.019439314263626713, 138 | 0.0010131419871842082, 139 | -0.004532677471945648, 140 | -7.321421356702399e-05, 141 | 0.0006057601824664335, 142 | 1.9329016965523917e-05, 143 | -4.4618977991475265e-05, 144 | ] 145 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym15.py: -------------------------------------------------------------------------------- 1 | """ Symlet 15 wavelet """ 2 | 3 | 4 | class Symlet15: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym15/ 11 | """ 12 | __name__ = "Symlet Wavelet 15" 13 | __motherWaveletLength__ = 30 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 9.712419737963348e-06, 20 | -7.35966679891947e-06, 21 | -0.00016066186637495343, 22 | 5.512254785558665e-05, 23 | 0.0010705672194623959, 24 | -0.0002673164464718057, 25 | -0.0035901654473726417, 26 | 0.003423450736351241, 27 | 0.01007997708790567, 28 | -0.01940501143093447, 29 | -0.03887671687683349, 30 | 0.021937642719753955, 31 | 0.04073547969681068, 32 | -0.04108266663538248, 33 | 0.11153369514261872, 34 | 0.5786404152150345, 35 | 0.7218430296361812, 36 | 0.2439627054321663, 37 | -0.1966263587662373, 38 | -0.1340562984562539, 39 | 0.06839331006048024, 40 | 0.06796982904487918, 41 | -0.008744788886477952, 42 | -0.01717125278163873, 43 | 0.0015261382781819983, 44 | 0.003481028737064895, 45 | -0.00010815440168545525, 46 | -0.00040216853760293483, 47 | 2.171789015077892e-05, 48 | 2.866070852531808e-05, 49 | ] 50 | 51 | # high-pass 52 | decompositionHighFilter = [ 53 | -2.866070852531808e-05, 54 | 2.171789015077892e-05, 55 | 0.00040216853760293483, 56 | -0.00010815440168545525, 57 | -0.003481028737064895, 58 | 0.0015261382781819983, 59 | 0.01717125278163873, 60 | -0.008744788886477952, 61 | -0.06796982904487918, 62 | 0.06839331006048024, 63 | 0.1340562984562539, 64 | -0.1966263587662373, 65 | -0.2439627054321663, 66 | 0.7218430296361812, 67 | -0.5786404152150345, 68 | 0.11153369514261872, 69 | 0.04108266663538248, 70 | 0.04073547969681068, 71 | -0.021937642719753955, 72 | -0.03887671687683349, 73 | 0.01940501143093447, 74 | 0.01007997708790567, 75 | -0.003423450736351241, 76 | -0.0035901654473726417, 77 | 0.0002673164464718057, 78 | 0.0010705672194623959, 79 | -5.512254785558665e-05, 80 | -0.00016066186637495343, 81 | 7.35966679891947e-06, 82 | 9.712419737963348e-06, 83 | ] 84 | 85 | # reconstruction filters 86 | # low pass 87 | reconstructionLowFilter = [ 88 | 2.866070852531808e-05, 89 | 2.171789015077892e-05, 90 | -0.00040216853760293483, 91 | -0.00010815440168545525, 92 | 0.003481028737064895, 93 | 0.0015261382781819983, 94 | -0.01717125278163873, 95 | -0.008744788886477952, 96 | 0.06796982904487918, 97 | 0.06839331006048024, 98 | -0.1340562984562539, 99 | -0.1966263587662373, 100 | 0.2439627054321663, 101 | 0.7218430296361812, 102 | 0.5786404152150345, 103 | 0.11153369514261872, 104 | -0.04108266663538248, 105 | 0.04073547969681068, 106 | 0.021937642719753955, 107 | -0.03887671687683349, 108 | -0.01940501143093447, 109 | 0.01007997708790567, 110 | 0.003423450736351241, 111 | -0.0035901654473726417, 112 | -0.0002673164464718057, 113 | 0.0010705672194623959, 114 | 5.512254785558665e-05, 115 | -0.00016066186637495343, 116 | -7.35966679891947e-06, 117 | 9.712419737963348e-06, 118 | ] 119 | 120 | # high-pass 121 | reconstructionHighFilter = [ 122 | 9.712419737963348e-06, 123 | 7.35966679891947e-06, 124 | -0.00016066186637495343, 125 | -5.512254785558665e-05, 126 | 0.0010705672194623959, 127 | 0.0002673164464718057, 128 | -0.0035901654473726417, 129 | -0.003423450736351241, 130 | 0.01007997708790567, 131 | 0.01940501143093447, 132 | -0.03887671687683349, 133 | -0.021937642719753955, 134 | 0.04073547969681068, 135 | 0.04108266663538248, 136 | 0.11153369514261872, 137 | -0.5786404152150345, 138 | 0.7218430296361812, 139 | -0.2439627054321663, 140 | -0.1966263587662373, 141 | 0.1340562984562539, 142 | 0.06839331006048024, 143 | -0.06796982904487918, 144 | -0.008744788886477952, 145 | 0.01717125278163873, 146 | 0.0015261382781819983, 147 | -0.003481028737064895, 148 | -0.00010815440168545525, 149 | 0.00040216853760293483, 150 | 2.171789015077892e-05, 151 | -2.866070852531808e-05, 152 | ] 153 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym16.py: -------------------------------------------------------------------------------- 1 | """ Symlet 16 wavelet """ 2 | 3 | 4 | class Symlet16: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym16/ 11 | """ 12 | __name__ = "Symlet Wavelet 16" 13 | __motherWaveletLength__ = 32 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 6.230006701220761e-06, 20 | -3.113556407621969e-06, 21 | -0.00010943147929529757, 22 | 2.8078582128442894e-05, 23 | 0.0008523547108047095, 24 | -0.0001084456223089688, 25 | -0.0038809122526038786, 26 | 0.0007182119788317892, 27 | 0.012666731659857348, 28 | -0.0031265171722710075, 29 | -0.031051202843553064, 30 | 0.004869274404904607, 31 | 0.032333091610663785, 32 | -0.06698304907021778, 33 | -0.034574228416972504, 34 | 0.39712293362064416, 35 | 0.7565249878756971, 36 | 0.47534280601152273, 37 | -0.054040601387606135, 38 | -0.15959219218520598, 39 | 0.03072113906330156, 40 | 0.07803785290341991, 41 | -0.003510275068374009, 42 | -0.024952758046290123, 43 | 0.001359844742484172, 44 | 0.0069377611308027096, 45 | -0.00022211647621176323, 46 | -0.0013387206066921965, 47 | 3.656592483348223e-05, 48 | 0.00016545679579108483, 49 | -5.396483179315242e-06, 50 | -1.0797982104319795e-05, 51 | ] 52 | 53 | # high-pass 54 | decompositionHighFilter = [ 55 | 1.0797982104319795e-05, 56 | -5.396483179315242e-06, 57 | -0.00016545679579108483, 58 | 3.656592483348223e-05, 59 | 0.0013387206066921965, 60 | -0.00022211647621176323, 61 | -0.0069377611308027096, 62 | 0.001359844742484172, 63 | 0.024952758046290123, 64 | -0.003510275068374009, 65 | -0.07803785290341991, 66 | 0.03072113906330156, 67 | 0.15959219218520598, 68 | -0.054040601387606135, 69 | -0.47534280601152273, 70 | 0.7565249878756971, 71 | -0.39712293362064416, 72 | -0.034574228416972504, 73 | 0.06698304907021778, 74 | 0.032333091610663785, 75 | -0.004869274404904607, 76 | -0.031051202843553064, 77 | 0.0031265171722710075, 78 | 0.012666731659857348, 79 | -0.0007182119788317892, 80 | -0.0038809122526038786, 81 | 0.0001084456223089688, 82 | 0.0008523547108047095, 83 | -2.8078582128442894e-05, 84 | -0.00010943147929529757, 85 | 3.113556407621969e-06, 86 | 6.230006701220761e-06, 87 | ] 88 | 89 | # reconstruction filters 90 | # low pass 91 | reconstructionLowFilter = [ 92 | -1.0797982104319795e-05, 93 | -5.396483179315242e-06, 94 | 0.00016545679579108483, 95 | 3.656592483348223e-05, 96 | -0.0013387206066921965, 97 | -0.00022211647621176323, 98 | 0.0069377611308027096, 99 | 0.001359844742484172, 100 | -0.024952758046290123, 101 | -0.003510275068374009, 102 | 0.07803785290341991, 103 | 0.03072113906330156, 104 | -0.15959219218520598, 105 | -0.054040601387606135, 106 | 0.47534280601152273, 107 | 0.7565249878756971, 108 | 0.39712293362064416, 109 | -0.034574228416972504, 110 | -0.06698304907021778, 111 | 0.032333091610663785, 112 | 0.004869274404904607, 113 | -0.031051202843553064, 114 | -0.0031265171722710075, 115 | 0.012666731659857348, 116 | 0.0007182119788317892, 117 | -0.0038809122526038786, 118 | -0.0001084456223089688, 119 | 0.0008523547108047095, 120 | 2.8078582128442894e-05, 121 | -0.00010943147929529757, 122 | -3.113556407621969e-06, 123 | 6.230006701220761e-06, 124 | ] 125 | 126 | # high-pass 127 | reconstructionHighFilter = [ 128 | 6.230006701220761e-06, 129 | 3.113556407621969e-06, 130 | -0.00010943147929529757, 131 | -2.8078582128442894e-05, 132 | 0.0008523547108047095, 133 | 0.0001084456223089688, 134 | -0.0038809122526038786, 135 | -0.0007182119788317892, 136 | 0.012666731659857348, 137 | 0.0031265171722710075, 138 | -0.031051202843553064, 139 | -0.004869274404904607, 140 | 0.032333091610663785, 141 | 0.06698304907021778, 142 | -0.034574228416972504, 143 | -0.39712293362064416, 144 | 0.7565249878756971, 145 | -0.47534280601152273, 146 | -0.054040601387606135, 147 | 0.15959219218520598, 148 | 0.03072113906330156, 149 | -0.07803785290341991, 150 | -0.003510275068374009, 151 | 0.024952758046290123, 152 | 0.001359844742484172, 153 | -0.0069377611308027096, 154 | -0.00022211647621176323, 155 | 0.0013387206066921965, 156 | 3.656592483348223e-05, 157 | -0.00016545679579108483, 158 | -5.396483179315242e-06, 159 | 1.0797982104319795e-05, 160 | ] 161 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym17.py: -------------------------------------------------------------------------------- 1 | """ Symlet 17 wavelet """ 2 | 3 | 4 | class Symlet17: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym17/ 11 | """ 12 | __name__ = "Symlet Wavelet 17" 13 | __motherWaveletLength__ = 34 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 4.297343327345983e-06, 20 | 2.7801266938414138e-06, 21 | -6.293702597554192e-05, 22 | -1.3506383399901165e-05, 23 | 0.0004759963802638669, 24 | -0.000138642302680455, 25 | -0.0027416759756816018, 26 | 0.0008567700701915741, 27 | 0.010482366933031529, 28 | -0.004819212803176148, 29 | -0.03329138349235933, 30 | 0.01790395221434112, 31 | 0.10475461484223211, 32 | 0.0172711782105185, 33 | -0.11856693261143636, 34 | 0.1423983504146782, 35 | 0.6507166292045456, 36 | 0.681488995344925, 37 | 0.18053958458111286, 38 | -0.15507600534974825, 39 | -0.08607087472073338, 40 | 0.016158808725919346, 41 | -0.007261634750928767, 42 | -0.01803889724191924, 43 | 0.009952982523509598, 44 | 0.012396988366648726, 45 | -0.001905407689852666, 46 | -0.003932325279797902, 47 | 5.8400428694052584e-05, 48 | 0.0007198270642148971, 49 | 2.520793314082878e-05, 50 | -7.607124405605129e-05, 51 | -2.4527163425833e-06, 52 | 3.7912531943321266e-06, 53 | ] 54 | 55 | # high-pass 56 | decompositionHighFilter = [ 57 | -3.7912531943321266e-06, 58 | -2.4527163425833e-06, 59 | 7.607124405605129e-05, 60 | 2.520793314082878e-05, 61 | -0.0007198270642148971, 62 | 5.8400428694052584e-05, 63 | 0.003932325279797902, 64 | -0.001905407689852666, 65 | -0.012396988366648726, 66 | 0.009952982523509598, 67 | 0.01803889724191924, 68 | -0.007261634750928767, 69 | -0.016158808725919346, 70 | -0.08607087472073338, 71 | 0.15507600534974825, 72 | 0.18053958458111286, 73 | -0.681488995344925, 74 | 0.6507166292045456, 75 | -0.1423983504146782, 76 | -0.11856693261143636, 77 | -0.0172711782105185, 78 | 0.10475461484223211, 79 | -0.01790395221434112, 80 | -0.03329138349235933, 81 | 0.004819212803176148, 82 | 0.010482366933031529, 83 | -0.0008567700701915741, 84 | -0.0027416759756816018, 85 | 0.000138642302680455, 86 | 0.0004759963802638669, 87 | 1.3506383399901165e-05, 88 | -6.293702597554192e-05, 89 | -2.7801266938414138e-06, 90 | 4.297343327345983e-06, 91 | ] 92 | 93 | # reconstruction filters 94 | # low pass 95 | reconstructionLowFilter = [ 96 | 3.7912531943321266e-06, 97 | -2.4527163425833e-06, 98 | -7.607124405605129e-05, 99 | 2.520793314082878e-05, 100 | 0.0007198270642148971, 101 | 5.8400428694052584e-05, 102 | -0.003932325279797902, 103 | -0.001905407689852666, 104 | 0.012396988366648726, 105 | 0.009952982523509598, 106 | -0.01803889724191924, 107 | -0.007261634750928767, 108 | 0.016158808725919346, 109 | -0.08607087472073338, 110 | -0.15507600534974825, 111 | 0.18053958458111286, 112 | 0.681488995344925, 113 | 0.6507166292045456, 114 | 0.1423983504146782, 115 | -0.11856693261143636, 116 | 0.0172711782105185, 117 | 0.10475461484223211, 118 | 0.01790395221434112, 119 | -0.03329138349235933, 120 | -0.004819212803176148, 121 | 0.010482366933031529, 122 | 0.0008567700701915741, 123 | -0.0027416759756816018, 124 | -0.000138642302680455, 125 | 0.0004759963802638669, 126 | -1.3506383399901165e-05, 127 | -6.293702597554192e-05, 128 | 2.7801266938414138e-06, 129 | 4.297343327345983e-06, 130 | ] 131 | 132 | # high-pass 133 | reconstructionHighFilter = [ 134 | 4.297343327345983e-06, 135 | -2.7801266938414138e-06, 136 | -6.293702597554192e-05, 137 | 1.3506383399901165e-05, 138 | 0.0004759963802638669, 139 | 0.000138642302680455, 140 | -0.0027416759756816018, 141 | -0.0008567700701915741, 142 | 0.010482366933031529, 143 | 0.004819212803176148, 144 | -0.03329138349235933, 145 | -0.01790395221434112, 146 | 0.10475461484223211, 147 | -0.0172711782105185, 148 | -0.11856693261143636, 149 | -0.1423983504146782, 150 | 0.6507166292045456, 151 | -0.681488995344925, 152 | 0.18053958458111286, 153 | 0.15507600534974825, 154 | -0.08607087472073338, 155 | -0.016158808725919346, 156 | -0.007261634750928767, 157 | 0.01803889724191924, 158 | 0.009952982523509598, 159 | -0.012396988366648726, 160 | -0.001905407689852666, 161 | 0.003932325279797902, 162 | 5.8400428694052584e-05, 163 | -0.0007198270642148971, 164 | 2.520793314082878e-05, 165 | 7.607124405605129e-05, 166 | -2.4527163425833e-06, 167 | -3.7912531943321266e-06, 168 | ] 169 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym18.py: -------------------------------------------------------------------------------- 1 | """ Symlet 18 wavelet """ 2 | 3 | 4 | class Symlet18: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym18/ 11 | """ 12 | __name__ = "Symlet Wavelet 18" 13 | __motherWaveletLength__ = 36 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 2.6126125564836423e-06, 20 | 1.354915761832114e-06, 21 | -4.5246757874949856e-05, 22 | -1.4020992577726755e-05, 23 | 0.00039616840638254753, 24 | 7.021273459036268e-05, 25 | -0.002313871814506099, 26 | -0.00041152110923597756, 27 | 0.009502164390962365, 28 | 0.001642986397278216, 29 | -0.030325091089369604, 30 | -0.005077085160757053, 31 | 0.08421992997038655, 32 | 0.03399566710394736, 33 | -0.15993814866932407, 34 | -0.052029158983952786, 35 | 0.47396905989393956, 36 | 0.7536291401017928, 37 | 0.40148386057061813, 38 | -0.032480573290138676, 39 | -0.07379920729060717, 40 | 0.028529597039037808, 41 | 0.006277944554311694, 42 | -0.03171268473181454, 43 | -0.0032607442000749834, 44 | 0.015012356344250213, 45 | 0.001087784789595693, 46 | -0.005239789683026608, 47 | -0.00018877623940755607, 48 | 0.0014280863270832796, 49 | 4.741614518373667e-05, 50 | -0.0002658301102424104, 51 | -9.858816030140058e-06, 52 | 2.955743762093081e-05, 53 | 7.847298055831765e-07, 54 | -1.5131530692371587e-06, 55 | ] 56 | 57 | # high-pass 58 | decompositionHighFilter = [ 59 | 1.5131530692371587e-06, 60 | 7.847298055831765e-07, 61 | -2.955743762093081e-05, 62 | -9.858816030140058e-06, 63 | 0.0002658301102424104, 64 | 4.741614518373667e-05, 65 | -0.0014280863270832796, 66 | -0.00018877623940755607, 67 | 0.005239789683026608, 68 | 0.001087784789595693, 69 | -0.015012356344250213, 70 | -0.0032607442000749834, 71 | 0.03171268473181454, 72 | 0.006277944554311694, 73 | -0.028529597039037808, 74 | -0.07379920729060717, 75 | 0.032480573290138676, 76 | 0.40148386057061813, 77 | -0.7536291401017928, 78 | 0.47396905989393956, 79 | 0.052029158983952786, 80 | -0.15993814866932407, 81 | -0.03399566710394736, 82 | 0.08421992997038655, 83 | 0.005077085160757053, 84 | -0.030325091089369604, 85 | -0.001642986397278216, 86 | 0.009502164390962365, 87 | 0.00041152110923597756, 88 | -0.002313871814506099, 89 | -7.021273459036268e-05, 90 | 0.00039616840638254753, 91 | 1.4020992577726755e-05, 92 | -4.5246757874949856e-05, 93 | -1.354915761832114e-06, 94 | 2.6126125564836423e-06, 95 | ] 96 | 97 | # reconstruction filters 98 | # low pass 99 | reconstructionLowFilter = [ 100 | -1.5131530692371587e-06, 101 | 7.847298055831765e-07, 102 | 2.955743762093081e-05, 103 | -9.858816030140058e-06, 104 | -0.0002658301102424104, 105 | 4.741614518373667e-05, 106 | 0.0014280863270832796, 107 | -0.00018877623940755607, 108 | -0.005239789683026608, 109 | 0.001087784789595693, 110 | 0.015012356344250213, 111 | -0.0032607442000749834, 112 | -0.03171268473181454, 113 | 0.006277944554311694, 114 | 0.028529597039037808, 115 | -0.07379920729060717, 116 | -0.032480573290138676, 117 | 0.40148386057061813, 118 | 0.7536291401017928, 119 | 0.47396905989393956, 120 | -0.052029158983952786, 121 | -0.15993814866932407, 122 | 0.03399566710394736, 123 | 0.08421992997038655, 124 | -0.005077085160757053, 125 | -0.030325091089369604, 126 | 0.001642986397278216, 127 | 0.009502164390962365, 128 | -0.00041152110923597756, 129 | -0.002313871814506099, 130 | 7.021273459036268e-05, 131 | 0.00039616840638254753, 132 | -1.4020992577726755e-05, 133 | -4.5246757874949856e-05, 134 | 1.354915761832114e-06, 135 | 2.6126125564836423e-06, 136 | ] 137 | 138 | # high-pass 139 | reconstructionHighFilter = [ 140 | 2.6126125564836423e-06, 141 | -1.354915761832114e-06, 142 | -4.5246757874949856e-05, 143 | 1.4020992577726755e-05, 144 | 0.00039616840638254753, 145 | -7.021273459036268e-05, 146 | -0.002313871814506099, 147 | 0.00041152110923597756, 148 | 0.009502164390962365, 149 | -0.001642986397278216, 150 | -0.030325091089369604, 151 | 0.005077085160757053, 152 | 0.08421992997038655, 153 | -0.03399566710394736, 154 | -0.15993814866932407, 155 | 0.052029158983952786, 156 | 0.47396905989393956, 157 | -0.7536291401017928, 158 | 0.40148386057061813, 159 | 0.032480573290138676, 160 | -0.07379920729060717, 161 | -0.028529597039037808, 162 | 0.006277944554311694, 163 | 0.03171268473181454, 164 | -0.0032607442000749834, 165 | -0.015012356344250213, 166 | 0.001087784789595693, 167 | 0.005239789683026608, 168 | -0.00018877623940755607, 169 | -0.0014280863270832796, 170 | 4.741614518373667e-05, 171 | 0.0002658301102424104, 172 | -9.858816030140058e-06, 173 | -2.955743762093081e-05, 174 | 7.847298055831765e-07, 175 | 1.5131530692371587e-06, 176 | ] 177 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym19.py: -------------------------------------------------------------------------------- 1 | """ Symlet 19 wavelet """ 2 | 3 | 4 | class Symlet19: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym19/ 11 | """ 12 | __name__ = "Symlet Wavelet 19" 13 | __motherWaveletLength__ = 38 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 5.487732768215838e-07, 20 | -6.463651303345963e-07, 21 | -1.1880518269823984e-05, 22 | 8.873312173729286e-06, 23 | 0.0001155392333357879, 24 | -4.612039600210587e-05, 25 | -0.000635764515004334, 26 | 0.00015915804768084938, 27 | 0.0021214250281823303, 28 | -0.0011607032572062486, 29 | -0.005122205002583014, 30 | 0.007968438320613306, 31 | 0.01579743929567463, 32 | -0.02265199337824595, 33 | -0.046635983534938946, 34 | 0.0070155738571741596, 35 | 0.008954591173043624, 36 | -0.06752505804029409, 37 | 0.10902582508127781, 38 | 0.578144945338605, 39 | 0.7195555257163943, 40 | 0.2582661692372836, 41 | -0.17659686625203097, 42 | -0.11624173010739675, 43 | 0.09363084341589714, 44 | 0.08407267627924504, 45 | -0.016908234861345205, 46 | -0.02770989693131125, 47 | 0.004319351874894969, 48 | 0.008262236955528255, 49 | -0.0006179223277983108, 50 | -0.0017049602611649971, 51 | 0.00012930767650701415, 52 | 0.0002762187768573407, 53 | -1.6821387029373716e-05, 54 | -2.8151138661550245e-05, 55 | 2.0623170632395688e-06, 56 | 1.7509367995348687e-06, 57 | ] 58 | 59 | # high-pass 60 | decompositionHighFilter = [ 61 | -1.7509367995348687e-06, 62 | 2.0623170632395688e-06, 63 | 2.8151138661550245e-05, 64 | -1.6821387029373716e-05, 65 | -0.0002762187768573407, 66 | 0.00012930767650701415, 67 | 0.0017049602611649971, 68 | -0.0006179223277983108, 69 | -0.008262236955528255, 70 | 0.004319351874894969, 71 | 0.02770989693131125, 72 | -0.016908234861345205, 73 | -0.08407267627924504, 74 | 0.09363084341589714, 75 | 0.11624173010739675, 76 | -0.17659686625203097, 77 | -0.2582661692372836, 78 | 0.7195555257163943, 79 | -0.578144945338605, 80 | 0.10902582508127781, 81 | 0.06752505804029409, 82 | 0.008954591173043624, 83 | -0.0070155738571741596, 84 | -0.046635983534938946, 85 | 0.02265199337824595, 86 | 0.01579743929567463, 87 | -0.007968438320613306, 88 | -0.005122205002583014, 89 | 0.0011607032572062486, 90 | 0.0021214250281823303, 91 | -0.00015915804768084938, 92 | -0.000635764515004334, 93 | 4.612039600210587e-05, 94 | 0.0001155392333357879, 95 | -8.873312173729286e-06, 96 | -1.1880518269823984e-05, 97 | 6.463651303345963e-07, 98 | 5.487732768215838e-07, 99 | ] 100 | 101 | # reconstruction filters 102 | # low pass 103 | reconstructionLowFilter = [ 104 | 1.7509367995348687e-06, 105 | 2.0623170632395688e-06, 106 | -2.8151138661550245e-05, 107 | -1.6821387029373716e-05, 108 | 0.0002762187768573407, 109 | 0.00012930767650701415, 110 | -0.0017049602611649971, 111 | -0.0006179223277983108, 112 | 0.008262236955528255, 113 | 0.004319351874894969, 114 | -0.02770989693131125, 115 | -0.016908234861345205, 116 | 0.08407267627924504, 117 | 0.09363084341589714, 118 | -0.11624173010739675, 119 | -0.17659686625203097, 120 | 0.2582661692372836, 121 | 0.7195555257163943, 122 | 0.578144945338605, 123 | 0.10902582508127781, 124 | -0.06752505804029409, 125 | 0.008954591173043624, 126 | 0.0070155738571741596, 127 | -0.046635983534938946, 128 | -0.02265199337824595, 129 | 0.01579743929567463, 130 | 0.007968438320613306, 131 | -0.005122205002583014, 132 | -0.0011607032572062486, 133 | 0.0021214250281823303, 134 | 0.00015915804768084938, 135 | -0.000635764515004334, 136 | -4.612039600210587e-05, 137 | 0.0001155392333357879, 138 | 8.873312173729286e-06, 139 | -1.1880518269823984e-05, 140 | -6.463651303345963e-07, 141 | 5.487732768215838e-07, 142 | ] 143 | 144 | # high-pass 145 | reconstructionHighFilter = [ 146 | 5.487732768215838e-07, 147 | 6.463651303345963e-07, 148 | -1.1880518269823984e-05, 149 | -8.873312173729286e-06, 150 | 0.0001155392333357879, 151 | 4.612039600210587e-05, 152 | -0.000635764515004334, 153 | -0.00015915804768084938, 154 | 0.0021214250281823303, 155 | 0.0011607032572062486, 156 | -0.005122205002583014, 157 | -0.007968438320613306, 158 | 0.01579743929567463, 159 | 0.02265199337824595, 160 | -0.046635983534938946, 161 | -0.0070155738571741596, 162 | 0.008954591173043624, 163 | 0.06752505804029409, 164 | 0.10902582508127781, 165 | -0.578144945338605, 166 | 0.7195555257163943, 167 | -0.2582661692372836, 168 | -0.17659686625203097, 169 | 0.11624173010739675, 170 | 0.09363084341589714, 171 | -0.08407267627924504, 172 | -0.016908234861345205, 173 | 0.02770989693131125, 174 | 0.004319351874894969, 175 | -0.008262236955528255, 176 | -0.0006179223277983108, 177 | 0.0017049602611649971, 178 | 0.00012930767650701415, 179 | -0.0002762187768573407, 180 | -1.6821387029373716e-05, 181 | 2.8151138661550245e-05, 182 | 2.0623170632395688e-06, 183 | -1.7509367995348687e-06, 184 | ] 185 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym2.py: -------------------------------------------------------------------------------- 1 | """ Symlet 2 wavelet """ 2 | 3 | 4 | class Symlet2: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym2/ 11 | """ 12 | __name__ = "Symlet Wavelet 2" 13 | __motherWaveletLength__ = 4 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -0.12940952255092145, 20 | 0.22414386804185735, 21 | 0.836516303737469, 22 | 0.48296291314469025, 23 | ] 24 | 25 | # high-pass 26 | decompositionHighFilter = [ 27 | -0.48296291314469025, 28 | 0.836516303737469, 29 | -0.22414386804185735, 30 | -0.12940952255092145, 31 | ] 32 | 33 | # reconstruction filters 34 | # low pass 35 | reconstructionLowFilter = [ 36 | 0.48296291314469025, 37 | 0.836516303737469, 38 | 0.22414386804185735, 39 | -0.12940952255092145, 40 | ] 41 | 42 | # high-pass 43 | reconstructionHighFilter = [ 44 | -0.12940952255092145, 45 | -0.22414386804185735, 46 | 0.836516303737469, 47 | -0.48296291314469025, 48 | ] 49 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym20.py: -------------------------------------------------------------------------------- 1 | """ Symlet 20 wavelet """ 2 | 3 | 4 | class Symlet20: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym20/ 11 | """ 12 | __name__ = "Symlet Wavelet 20" 13 | __motherWaveletLength__ = 40 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 3.695537474835221e-07, 20 | -1.9015675890554106e-07, 21 | -7.919361411976999e-06, 22 | 3.025666062736966e-06, 23 | 7.992967835772481e-05, 24 | -1.928412300645204e-05, 25 | -0.0004947310915672655, 26 | 7.215991188074035e-05, 27 | 0.002088994708190198, 28 | -0.0003052628317957281, 29 | -0.006606585799088861, 30 | 0.0014230873594621453, 31 | 0.01700404902339034, 32 | -0.003313857383623359, 33 | -0.031629437144957966, 34 | 0.008123228356009682, 35 | 0.025579349509413946, 36 | -0.07899434492839816, 37 | -0.02981936888033373, 38 | 0.4058314443484506, 39 | 0.75116272842273, 40 | 0.47199147510148703, 41 | -0.0510883429210674, 42 | -0.16057829841525254, 43 | 0.03625095165393308, 44 | 0.08891966802819956, 45 | -0.0068437019650692274, 46 | -0.035373336756604236, 47 | 0.0019385970672402002, 48 | 0.012157040948785737, 49 | -0.0006111263857992088, 50 | -0.0034716478028440734, 51 | 0.0001254409172306726, 52 | 0.0007476108597820572, 53 | -2.6615550335516086e-05, 54 | -0.00011739133516291466, 55 | 4.525422209151636e-06, 56 | 1.22872527779612e-05, 57 | -3.2567026420174407e-07, 58 | -6.329129044776395e-07, 59 | ] 60 | 61 | # high-pass 62 | decompositionHighFilter = [ 63 | 6.329129044776395e-07, 64 | -3.2567026420174407e-07, 65 | -1.22872527779612e-05, 66 | 4.525422209151636e-06, 67 | 0.00011739133516291466, 68 | -2.6615550335516086e-05, 69 | -0.0007476108597820572, 70 | 0.0001254409172306726, 71 | 0.0034716478028440734, 72 | -0.0006111263857992088, 73 | -0.012157040948785737, 74 | 0.0019385970672402002, 75 | 0.035373336756604236, 76 | -0.0068437019650692274, 77 | -0.08891966802819956, 78 | 0.03625095165393308, 79 | 0.16057829841525254, 80 | -0.0510883429210674, 81 | -0.47199147510148703, 82 | 0.75116272842273, 83 | -0.4058314443484506, 84 | -0.02981936888033373, 85 | 0.07899434492839816, 86 | 0.025579349509413946, 87 | -0.008123228356009682, 88 | -0.031629437144957966, 89 | 0.003313857383623359, 90 | 0.01700404902339034, 91 | -0.0014230873594621453, 92 | -0.006606585799088861, 93 | 0.0003052628317957281, 94 | 0.002088994708190198, 95 | -7.215991188074035e-05, 96 | -0.0004947310915672655, 97 | 1.928412300645204e-05, 98 | 7.992967835772481e-05, 99 | -3.025666062736966e-06, 100 | -7.919361411976999e-06, 101 | 1.9015675890554106e-07, 102 | 3.695537474835221e-07, 103 | ] 104 | 105 | # reconstruction filters 106 | # low pass 107 | reconstructionLowFilter = [ 108 | -6.329129044776395e-07, 109 | -3.2567026420174407e-07, 110 | 1.22872527779612e-05, 111 | 4.525422209151636e-06, 112 | -0.00011739133516291466, 113 | -2.6615550335516086e-05, 114 | 0.0007476108597820572, 115 | 0.0001254409172306726, 116 | -0.0034716478028440734, 117 | -0.0006111263857992088, 118 | 0.012157040948785737, 119 | 0.0019385970672402002, 120 | -0.035373336756604236, 121 | -0.0068437019650692274, 122 | 0.08891966802819956, 123 | 0.03625095165393308, 124 | -0.16057829841525254, 125 | -0.0510883429210674, 126 | 0.47199147510148703, 127 | 0.75116272842273, 128 | 0.4058314443484506, 129 | -0.02981936888033373, 130 | -0.07899434492839816, 131 | 0.025579349509413946, 132 | 0.008123228356009682, 133 | -0.031629437144957966, 134 | -0.003313857383623359, 135 | 0.01700404902339034, 136 | 0.0014230873594621453, 137 | -0.006606585799088861, 138 | -0.0003052628317957281, 139 | 0.002088994708190198, 140 | 7.215991188074035e-05, 141 | -0.0004947310915672655, 142 | -1.928412300645204e-05, 143 | 7.992967835772481e-05, 144 | 3.025666062736966e-06, 145 | -7.919361411976999e-06, 146 | -1.9015675890554106e-07, 147 | 3.695537474835221e-07, 148 | ] 149 | 150 | # high-pass 151 | reconstructionHighFilter = [ 152 | 3.695537474835221e-07, 153 | 1.9015675890554106e-07, 154 | -7.919361411976999e-06, 155 | -3.025666062736966e-06, 156 | 7.992967835772481e-05, 157 | 1.928412300645204e-05, 158 | -0.0004947310915672655, 159 | -7.215991188074035e-05, 160 | 0.002088994708190198, 161 | 0.0003052628317957281, 162 | -0.006606585799088861, 163 | -0.0014230873594621453, 164 | 0.01700404902339034, 165 | 0.003313857383623359, 166 | -0.031629437144957966, 167 | -0.008123228356009682, 168 | 0.025579349509413946, 169 | 0.07899434492839816, 170 | -0.02981936888033373, 171 | -0.4058314443484506, 172 | 0.75116272842273, 173 | -0.47199147510148703, 174 | -0.0510883429210674, 175 | 0.16057829841525254, 176 | 0.03625095165393308, 177 | -0.08891966802819956, 178 | -0.0068437019650692274, 179 | 0.035373336756604236, 180 | 0.0019385970672402002, 181 | -0.012157040948785737, 182 | -0.0006111263857992088, 183 | 0.0034716478028440734, 184 | 0.0001254409172306726, 185 | -0.0007476108597820572, 186 | -2.6615550335516086e-05, 187 | 0.00011739133516291466, 188 | 4.525422209151636e-06, 189 | -1.22872527779612e-05, 190 | -3.2567026420174407e-07, 191 | 6.329129044776395e-07, 192 | ] 193 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym3.py: -------------------------------------------------------------------------------- 1 | """ Symlet 3 wavelet """ 2 | 3 | 4 | class Symlet3: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym3/ 11 | """ 12 | __name__ = "Symlet Wavelet 3" 13 | __motherWaveletLength__ = 6 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.035226291882100656, 20 | -0.08544127388224149, 21 | -0.13501102001039084, 22 | 0.4598775021193313, 23 | 0.8068915093133388, 24 | 0.3326705529509569, 25 | ] 26 | 27 | # high-pass 28 | decompositionHighFilter = [ 29 | -0.3326705529509569, 30 | 0.8068915093133388, 31 | -0.4598775021193313, 32 | -0.13501102001039084, 33 | 0.08544127388224149, 34 | 0.035226291882100656, 35 | ] 36 | 37 | # reconstruction filters 38 | # low pass 39 | reconstructionLowFilter = [ 40 | 0.3326705529509569, 41 | 0.8068915093133388, 42 | 0.4598775021193313, 43 | -0.13501102001039084, 44 | -0.08544127388224149, 45 | 0.035226291882100656, 46 | ] 47 | 48 | # high-pass 49 | reconstructionHighFilter = [ 50 | 0.035226291882100656, 51 | 0.08544127388224149, 52 | -0.13501102001039084, 53 | -0.4598775021193313, 54 | 0.8068915093133388, 55 | -0.3326705529509569, 56 | ] 57 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym4.py: -------------------------------------------------------------------------------- 1 | """ Symlet 4 wavelet """ 2 | 3 | 4 | class Symlet4: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym4/ 11 | """ 12 | __name__ = "Symlet Wavelet 4" 13 | __motherWaveletLength__ = 8 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -0.07576571478927333, 20 | -0.02963552764599851, 21 | 0.49761866763201545, 22 | 0.8037387518059161, 23 | 0.29785779560527736, 24 | -0.09921954357684722, 25 | -0.012603967262037833, 26 | 0.0322231006040427, 27 | ] 28 | 29 | # high-pass 30 | decompositionHighFilter = [ 31 | -0.0322231006040427, 32 | -0.012603967262037833, 33 | 0.09921954357684722, 34 | 0.29785779560527736, 35 | -0.8037387518059161, 36 | 0.49761866763201545, 37 | 0.02963552764599851, 38 | -0.07576571478927333, 39 | ] 40 | 41 | # reconstruction filters 42 | # low pass 43 | reconstructionLowFilter = [ 44 | 0.0322231006040427, 45 | -0.012603967262037833, 46 | -0.09921954357684722, 47 | 0.29785779560527736, 48 | 0.8037387518059161, 49 | 0.49761866763201545, 50 | -0.02963552764599851, 51 | -0.07576571478927333, 52 | ] 53 | 54 | # high-pass 55 | reconstructionHighFilter = [ 56 | -0.07576571478927333, 57 | 0.02963552764599851, 58 | 0.49761866763201545, 59 | -0.8037387518059161, 60 | 0.29785779560527736, 61 | 0.09921954357684722, 62 | -0.012603967262037833, 63 | -0.0322231006040427, 64 | ] 65 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym5.py: -------------------------------------------------------------------------------- 1 | """ Symlet 5 wavelet """ 2 | 3 | 4 | class Symlet5: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym5/ 11 | """ 12 | __name__ = "Symlet Wavelet 5" 13 | __motherWaveletLength__ = 10 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.027333068345077982, 20 | 0.029519490925774643, 21 | -0.039134249302383094, 22 | 0.1993975339773936, 23 | 0.7234076904024206, 24 | 0.6339789634582119, 25 | 0.01660210576452232, 26 | -0.17532808990845047, 27 | -0.021101834024758855, 28 | 0.019538882735286728, 29 | ] 30 | 31 | # high-pass 32 | decompositionHighFilter = [ 33 | -0.019538882735286728, 34 | -0.021101834024758855, 35 | 0.17532808990845047, 36 | 0.01660210576452232, 37 | -0.6339789634582119, 38 | 0.7234076904024206, 39 | -0.1993975339773936, 40 | -0.039134249302383094, 41 | -0.029519490925774643, 42 | 0.027333068345077982, 43 | ] 44 | 45 | # reconstruction filters 46 | # low pass 47 | reconstructionLowFilter = [ 48 | 0.019538882735286728, 49 | -0.021101834024758855, 50 | -0.17532808990845047, 51 | 0.01660210576452232, 52 | 0.6339789634582119, 53 | 0.7234076904024206, 54 | 0.1993975339773936, 55 | -0.039134249302383094, 56 | 0.029519490925774643, 57 | 0.027333068345077982, 58 | ] 59 | 60 | # high-pass 61 | reconstructionHighFilter = [ 62 | 0.027333068345077982, 63 | -0.029519490925774643, 64 | -0.039134249302383094, 65 | -0.1993975339773936, 66 | 0.7234076904024206, 67 | -0.6339789634582119, 68 | 0.01660210576452232, 69 | 0.17532808990845047, 70 | -0.021101834024758855, 71 | -0.019538882735286728, 72 | ] 73 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym6.py: -------------------------------------------------------------------------------- 1 | """ Symlet 6 wavelet """ 2 | 3 | 4 | class Symlet6: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym6/ 11 | """ 12 | __name__ = "Symlet Wavelet 6" 13 | __motherWaveletLength__ = 12 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.015404109327027373, 20 | 0.0034907120842174702, 21 | -0.11799011114819057, 22 | -0.048311742585633, 23 | 0.4910559419267466, 24 | 0.787641141030194, 25 | 0.3379294217276218, 26 | -0.07263752278646252, 27 | -0.021060292512300564, 28 | 0.04472490177066578, 29 | 0.0017677118642428036, 30 | -0.007800708325034148, 31 | ] 32 | 33 | # high-pass 34 | decompositionHighFilter = [ 35 | 0.007800708325034148, 36 | 0.0017677118642428036, 37 | -0.04472490177066578, 38 | -0.021060292512300564, 39 | 0.07263752278646252, 40 | 0.3379294217276218, 41 | -0.787641141030194, 42 | 0.4910559419267466, 43 | 0.048311742585633, 44 | -0.11799011114819057, 45 | -0.0034907120842174702, 46 | 0.015404109327027373, 47 | ] 48 | 49 | # reconstruction filters 50 | # low pass 51 | reconstructionLowFilter = [ 52 | -0.007800708325034148, 53 | 0.0017677118642428036, 54 | 0.04472490177066578, 55 | -0.021060292512300564, 56 | -0.07263752278646252, 57 | 0.3379294217276218, 58 | 0.787641141030194, 59 | 0.4910559419267466, 60 | -0.048311742585633, 61 | -0.11799011114819057, 62 | 0.0034907120842174702, 63 | 0.015404109327027373, 64 | ] 65 | 66 | # high-pass 67 | reconstructionHighFilter = [ 68 | 0.015404109327027373, 69 | -0.0034907120842174702, 70 | -0.11799011114819057, 71 | 0.048311742585633, 72 | 0.4910559419267466, 73 | -0.787641141030194, 74 | 0.3379294217276218, 75 | 0.07263752278646252, 76 | -0.021060292512300564, 77 | -0.04472490177066578, 78 | 0.0017677118642428036, 79 | 0.007800708325034148, 80 | ] 81 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym7.py: -------------------------------------------------------------------------------- 1 | """ Symlet 7 wavelet """ 2 | 3 | 4 | class Symlet7: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym7/ 11 | """ 12 | __name__ = "Symlet Wavelet 7" 13 | __motherWaveletLength__ = 14 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.002681814568257878, 20 | -0.0010473848886829163, 21 | -0.01263630340325193, 22 | 0.03051551316596357, 23 | 0.0678926935013727, 24 | -0.049552834937127255, 25 | 0.017441255086855827, 26 | 0.5361019170917628, 27 | 0.767764317003164, 28 | 0.2886296317515146, 29 | -0.14004724044296152, 30 | -0.10780823770381774, 31 | 0.004010244871533663, 32 | 0.010268176708511255, 33 | ] 34 | 35 | # high-pass 36 | decompositionHighFilter = [ 37 | -0.010268176708511255, 38 | 0.004010244871533663, 39 | 0.10780823770381774, 40 | -0.14004724044296152, 41 | -0.2886296317515146, 42 | 0.767764317003164, 43 | -0.5361019170917628, 44 | 0.017441255086855827, 45 | 0.049552834937127255, 46 | 0.0678926935013727, 47 | -0.03051551316596357, 48 | -0.01263630340325193, 49 | 0.0010473848886829163, 50 | 0.002681814568257878, 51 | ] 52 | 53 | # reconstruction filters 54 | # low pass 55 | reconstructionLowFilter = [ 56 | 0.010268176708511255, 57 | 0.004010244871533663, 58 | -0.10780823770381774, 59 | -0.14004724044296152, 60 | 0.2886296317515146, 61 | 0.767764317003164, 62 | 0.5361019170917628, 63 | 0.017441255086855827, 64 | -0.049552834937127255, 65 | 0.0678926935013727, 66 | 0.03051551316596357, 67 | -0.01263630340325193, 68 | -0.0010473848886829163, 69 | 0.002681814568257878, 70 | ] 71 | 72 | # high-pass 73 | reconstructionHighFilter = [ 74 | 0.002681814568257878, 75 | 0.0010473848886829163, 76 | -0.01263630340325193, 77 | -0.03051551316596357, 78 | 0.0678926935013727, 79 | 0.049552834937127255, 80 | 0.017441255086855827, 81 | -0.5361019170917628, 82 | 0.767764317003164, 83 | -0.2886296317515146, 84 | -0.14004724044296152, 85 | 0.10780823770381774, 86 | 0.004010244871533663, 87 | -0.010268176708511255, 88 | ] 89 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym8.py: -------------------------------------------------------------------------------- 1 | """ Symlet 8 wavelet """ 2 | 3 | 4 | class Symlet8: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym8/ 11 | """ 12 | __name__ = "Symlet Wavelet 8" 13 | __motherWaveletLength__ = 16 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | -0.0033824159510061256, 20 | -0.0005421323317911481, 21 | 0.03169508781149298, 22 | 0.007607487324917605, 23 | -0.1432942383508097, 24 | -0.061273359067658524, 25 | 0.4813596512583722, 26 | 0.7771857517005235, 27 | 0.3644418948353314, 28 | -0.05194583810770904, 29 | -0.027219029917056003, 30 | 0.049137179673607506, 31 | 0.003808752013890615, 32 | -0.01495225833704823, 33 | -0.0003029205147213668, 34 | 0.0018899503327594609, 35 | ] 36 | 37 | # high-pass 38 | decompositionHighFilter = [ 39 | -0.0018899503327594609, 40 | -0.0003029205147213668, 41 | 0.01495225833704823, 42 | 0.003808752013890615, 43 | -0.049137179673607506, 44 | -0.027219029917056003, 45 | 0.05194583810770904, 46 | 0.3644418948353314, 47 | -0.7771857517005235, 48 | 0.4813596512583722, 49 | 0.061273359067658524, 50 | -0.1432942383508097, 51 | -0.007607487324917605, 52 | 0.03169508781149298, 53 | 0.0005421323317911481, 54 | -0.0033824159510061256, 55 | ] 56 | 57 | # reconstruction filters 58 | # low pass 59 | reconstructionLowFilter = [ 60 | 0.0018899503327594609, 61 | -0.0003029205147213668, 62 | -0.01495225833704823, 63 | 0.003808752013890615, 64 | 0.049137179673607506, 65 | -0.027219029917056003, 66 | -0.05194583810770904, 67 | 0.3644418948353314, 68 | 0.7771857517005235, 69 | 0.4813596512583722, 70 | -0.061273359067658524, 71 | -0.1432942383508097, 72 | 0.007607487324917605, 73 | 0.03169508781149298, 74 | -0.0005421323317911481, 75 | -0.0033824159510061256, 76 | ] 77 | 78 | # high-pass 79 | reconstructionHighFilter = [ 80 | -0.0033824159510061256, 81 | 0.0005421323317911481, 82 | 0.03169508781149298, 83 | -0.007607487324917605, 84 | -0.1432942383508097, 85 | 0.061273359067658524, 86 | 0.4813596512583722, 87 | -0.7771857517005235, 88 | 0.3644418948353314, 89 | 0.05194583810770904, 90 | -0.027219029917056003, 91 | -0.049137179673607506, 92 | 0.003808752013890615, 93 | 0.01495225833704823, 94 | -0.0003029205147213668, 95 | -0.0018899503327594609, 96 | ] 97 | -------------------------------------------------------------------------------- /StreamingWavelet/wavelets_coeff/sym9.py: -------------------------------------------------------------------------------- 1 | """ Symlet 9 wavelet """ 2 | 3 | 4 | class Symlet9: 5 | """ 6 | Properties 7 | ---------- 8 | near symmetric, orthogonal, biorthogonal 9 | 10 | All values are from http://wavelets.pybytes.com/wavelet/sym9/ 11 | """ 12 | __name__ = "Symlet Wavelet 9" 13 | __motherWaveletLength__ = 18 # length of the mother wavelet 14 | __transformWaveletLength__ = 2 # minimum wavelength of input signal 15 | 16 | # decomposition filter 17 | # low-pass 18 | decompositionLowFilter = [ 19 | 0.0014009155259146807, 20 | 0.0006197808889855868, 21 | -0.013271967781817119, 22 | -0.01152821020767923, 23 | 0.03022487885827568, 24 | 0.0005834627461258068, 25 | -0.05456895843083407, 26 | 0.238760914607303, 27 | 0.717897082764412, 28 | 0.6173384491409358, 29 | 0.035272488035271894, 30 | -0.19155083129728512, 31 | -0.018233770779395985, 32 | 0.06207778930288603, 33 | 0.008859267493400484, 34 | -0.010264064027633142, 35 | -0.0004731544986800831, 36 | 0.0010694900329086053, 37 | ] 38 | 39 | # high-pass 40 | decompositionHighFilter = [ 41 | -0.0010694900329086053, 42 | -0.0004731544986800831, 43 | 0.010264064027633142, 44 | 0.008859267493400484, 45 | -0.06207778930288603, 46 | -0.018233770779395985, 47 | 0.19155083129728512, 48 | 0.035272488035271894, 49 | -0.6173384491409358, 50 | 0.717897082764412, 51 | -0.238760914607303, 52 | -0.05456895843083407, 53 | -0.0005834627461258068, 54 | 0.03022487885827568, 55 | 0.01152821020767923, 56 | -0.013271967781817119, 57 | -0.0006197808889855868, 58 | 0.0014009155259146807, 59 | ] 60 | 61 | # reconstruction filters 62 | # low pass 63 | reconstructionLowFilter = [ 64 | 0.0010694900329086053, 65 | -0.0004731544986800831, 66 | -0.010264064027633142, 67 | 0.008859267493400484, 68 | 0.06207778930288603, 69 | -0.018233770779395985, 70 | -0.19155083129728512, 71 | 0.035272488035271894, 72 | 0.6173384491409358, 73 | 0.717897082764412, 74 | 0.238760914607303, 75 | -0.05456895843083407, 76 | 0.0005834627461258068, 77 | 0.03022487885827568, 78 | -0.01152821020767923, 79 | -0.013271967781817119, 80 | 0.0006197808889855868, 81 | 0.0014009155259146807, 82 | ] 83 | 84 | # high-pass 85 | reconstructionHighFilter = [ 86 | 0.0014009155259146807, 87 | -0.0006197808889855868, 88 | -0.013271967781817119, 89 | 0.01152821020767923, 90 | 0.03022487885827568, 91 | -0.0005834627461258068, 92 | -0.05456895843083407, 93 | -0.238760914607303, 94 | 0.717897082764412, 95 | -0.6173384491409358, 96 | 0.035272488035271894, 97 | 0.19155083129728512, 98 | -0.018233770779395985, 99 | -0.06207778930288603, 100 | 0.008859267493400484, 101 | 0.010264064027633142, 102 | -0.0004731544986800831, 103 | -0.0010694900329086053, 104 | ] 105 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | import setuptools 2 | 3 | with open("README.md", "r", encoding="utf-8") as fh: 4 | long_description = fh.read() 5 | 6 | setuptools.setup( 7 | name="StreamingWavelet", 8 | version="1.0.6", 9 | author="Yu-Yang Qian", 10 | url='https://github.com/ZinYY/StreamingWavelet', 11 | author_email="qianyy@lamda.nju.edu.cn", 12 | install_requires=['numpy>=1.19.0'], 13 | license='MIT', 14 | description="This is an implementation for Streaming Wavelet Operator, " 15 | "which sequentially apply wavelet transform to a sequence efficiently.\n" 16 | "Reference: Qian et al., Efficient Non-stationary Online Learning by Wavelets\n" 17 | "with Applications to Online Distribution Shift Adaptation.\n" 18 | "In Proceedings of the 41st International Conference on Machine Learning (ICML 2024).", 19 | long_description=long_description, 20 | long_description_content_type="text/markdown", 21 | packages=setuptools.find_packages(), 22 | platforms='any', 23 | classifiers=[ 24 | "Programming Language :: Python :: 3", 25 | "License :: OSI Approved :: MIT License", 26 | "Operating System :: OS Independent", 27 | ] 28 | ) 29 | --------------------------------------------------------------------------------