├── License.md ├── README.md ├── dirspec.py ├── infospec.py ├── interpspec.py ├── plotspec.py ├── private ├── EMEP.py ├── IMLM.py ├── check_data.py ├── diwasp_csd.py ├── elev.py ├── hsig.py ├── pres.py ├── smoothspec.py ├── spectobasis.py ├── velx.py ├── vely.py └── wavenumber.py └── writespec.py /License.md: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # pyDiwasp 2 | conversion of diwasp package (DIWASP: DIrectional WAve SPectrum analysis Version 1.4) for python 3 | converted from https://github.com/metocean/diwasp 4 | 5 | I would LOVE help making this into better package of the original diwasp tool. Please check issues for needed functionality adds. 6 | 7 | ## Toolbox contents: 8 | ### Main functions: 9 | - dirspec.m Main function for directional wave analysis 10 | - readspec.m Reads in DIWASP format spectrum files 11 | - writespec.m Writes DIWASP format spectrum files 12 | - plotspec.m Plots DIWASP spectrums 13 | - testspec.m Testing function for the estimation methods 14 | - makespec.m Makes a fake spectrum and generates fake data for testing dirspec.m 15 | - infospec.m Returns information about a directional spectrum 16 | - data_structures.m is a help file describing the new Version 1.1 data structures 17 | 18 | ## Private functions (some can be used as stand alone functions): 19 | ### The transfer functions 20 | - /private/elev.m 21 | - /private/pres.m 22 | - /private/velx.m 23 | - /private/vely.m 24 | - /private/velz.m 25 | - /private/slpx.m 26 | - /private/slpy.m 27 | - /private/vels.m 28 | - /private/accs.m 29 | 30 | ### The estimation functions 31 | - /private/DFTM.m 32 | - /private/EMLM.m 33 | - /private/IMLM.m 34 | - /private/EMEP.m 35 | - /private/BDM.m 36 | 37 | ### Miscellaneous functions 38 | - /private/smoothspec.m 39 | - /private/wavenumber.m 40 | - /private/makerandomsea.m 41 | - /private/makewavedata.m 42 | - /private/Hsig.m 43 | - /private/gsamp.m 44 | - /private/check_data.m 45 | 46 | 47 | carying original license agreement and copyright 48 | 49 | ## License agreement 50 | DIWASP, is free software; you can redistribute it and/or modify it under the terms of the 51 | GNU General Public License as published by the Free Software Foundation. 52 | However, the DIWASP license includes the following addendum concerning its usage: 53 | This software and any derivatives of it shall only be used for educational purposes or 54 | scientific research without the intention of any financial gain. 55 | Use of this software or derivatives for any purpose that results in financial gain 56 | for a person or organization without written consent from the author is a breach of the license agreement. 57 | This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; 58 | without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 59 | In addition the author is not liable in any way for consequences arising from the application of 60 | software output for any design or decision-making process. 61 | The GNU General Public License forms the main part of the license agreement included in the package. 62 | 63 | Copyright (C) 2002 David Johnson Coastal Oceanography Group, CWR, UWA, Perth 64 | 65 | -------------------------------------------------------------------------------- /dirspec.py: -------------------------------------------------------------------------------- 1 | import warnings 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | from scipy.signal import detrend 5 | from interpspec import interpspec 6 | from infospec import infospec 7 | from writespec import writespec 8 | from plotspec import plotspec 9 | from private.velx import velx 10 | from private.vely import vely 11 | from private.pres import pres 12 | from private.elev import elev 13 | from private.wavenumber import wavenumber 14 | from private.IMLM import IMLM 15 | from private.EMEP import EMEP 16 | from private.smoothspec import smoothspec 17 | from private.diwasp_csd import diwasp_csd 18 | from private.check_data import check_data 19 | 20 | def dirspec(ID, SM, EP, Options_=None): 21 | """ 22 | DIWASP V1.4 function 23 | dirspec: main spectral estimation routine 24 | 25 | [SMout,EPout]=dirspec(ID,SM,EP,{options}) 26 | 27 | Outputs: 28 | SMout A spectral matrix structure containing the results 29 | Epout The estimation parameters structure with the values actually used for the computation including any default settings. 30 | 31 | Inputs: 32 | ID An instrument data structure containing the measured data 33 | SM A spectral matrix structure; data in field SM.S is ignored. 34 | EP The estimation parameters structure. For default values enter EP as [] 35 | [options] options entered as cell array with parameter/value pairs: e.g.{'MESSAGE',1,'PLOTTYPE',2}; 36 | Available options with default values: 37 | 'MESSAGE',1, Level of screen display: 0,1,2 (increasing output) 38 | 'PLOTTYPE',1, Plot type: 0 none, 1 3d surface, 2 polar type plot, 3 3d surface(compass angles), 4 polar plot(compass angles) 39 | 'FILEOUT','' Filename for output file: empty string means no file output 40 | 41 | Input structures ID and SM are required. Either [EP] or [options] can be included but must be in order if both are included. 42 | "help data_structures" for information on the DIWASP data structures 43 | 44 | All of the implemented calculation algorithms are as described by: 45 | Hashimoto,N. 1997 "Analysis of the directional wave spectrum from field data" 46 | In: Advances in Coastal Engineering Vol.3. Ed:Liu,P.L-F. Pub:World Scientific,Singapore 47 | 48 | 49 | Original copyright (C) 2002 Coastal Oceanography Group, CWR, UWA, Perth 50 | 51 | Translated by Chuan Li and Spicer Bak, 52 | Field Research Facility, US Army Corps of Engineers 53 | """ 54 | 55 | Options = {'MESSAGE':1, 'PLOTTYPE':1, 'FILEOUT':''} 56 | 57 | if Options_ is not None: 58 | nopts = len(Options_) 59 | else: 60 | nopts = 0 61 | 62 | ID = check_data(ID, 1) 63 | if len(ID) == 0: 64 | return [], [] 65 | SM = check_data(SM, 2) 66 | if len(SM) == 0: 67 | return [], [] 68 | EP = check_data(EP, 3) 69 | if len(EP) == 0: 70 | return [], [] 71 | 72 | if nopts != 0: 73 | if nopts % 2 != 0: 74 | warnings.warn('Options must be in Name/Value pairs - setting to ' 75 | 'defaults') 76 | else: 77 | for i in range(int(nopts / 2)): 78 | arg = Options_[2 * i + 1] 79 | field = Options_[2 * i] 80 | Options[field] = arg 81 | 82 | ptype = Options['PLOTTYPE'] 83 | displ = Options['MESSAGE'] 84 | 85 | 86 | print('\ncalculating.....\n\ncross power spectra') 87 | 88 | data = detrend(ID['data'], axis=0) 89 | ndat, szd = np.shape(ID['data']) 90 | 91 | #get resolution of FFT - if not specified, calculate a sensible value 92 | if 'nfft' not in EP: 93 | nfft = int(2 ** (8 + np.round(np.log2(ID['fs'])))) 94 | EP['nfft'] = nfft 95 | else: 96 | nfft = int(EP['nfft']) 97 | if nfft > ndat: 98 | raise Exception('Data length of {} too small'.format(ndat)) 99 | 100 | #calculate the cross-power spectra 101 | xps = np.empty((szd, szd, int(nfft / 2)), 'complex128') 102 | for m in range(szd): 103 | for n in range(szd): 104 | xpstmp, Ftmp = diwasp_csd(data[:, m], data[:, n], 105 | nfft, ID['fs'], flag=2) 106 | xps[m, n, :] = xpstmp[1:int(nfft / 2) + 1] 107 | F = Ftmp[1:int(nfft / 2) + 1] 108 | nf = int(nfft / 2) 109 | print('wavenumbers') 110 | wns = wavenumber(2 * np.pi * F, ID['depth'] * np.ones(np.shape(F))) 111 | pidirs = np.linspace(-np.pi, np.pi - 2 * np.pi / EP['dres'], 112 | num=EP['dres']) 113 | 114 | #calculate transfer parameters 115 | print('transfer parameters\n') 116 | trm = np.empty((szd, nf, len(pidirs))) 117 | kx = np.empty((szd, szd, nf, len(pidirs))) 118 | for m in range(szd): 119 | trm[m, :, :] = eval(ID['datatypes'][m])(2 * np.pi * F, pidirs, wns, 120 | ID['layout'][2, m], ID['depth']) 121 | for n in range(szd): 122 | kx[m, n, :, :] = wns[:, np.newaxis] * ((ID['layout'][0, n] - 123 | ID['layout'][0, m]) * np.cos(pidirs) + (ID['layout'][1, n] - 124 | ID['layout'][1, m]) * np.sin(pidirs)) 125 | 126 | Ss = np.empty((szd, nf), dtype='complex128') 127 | for m in range(szd): 128 | tfn = trm[m, :, :] 129 | Sxps = xps[m, m, :] 130 | Ss[m, :] = Sxps / (np.max(tfn, axis=1) * np.conj(np.max(tfn, axis=1))) 131 | 132 | ffs = np.logical_and(F >= np.min(SM['freqs']), F <= np.max(SM['freqs'])) 133 | SM1 = dict() 134 | SM1['freqs'] = F[ffs] 135 | SM1['funit'] = 'Hz' 136 | SM1['dirs'] = pidirs 137 | SM1['dunit'] = 'rad' 138 | 139 | # call appropriate estimation function 140 | print('directional spectra using {} method'.format(EP['method'])) 141 | SM1['S'] = eval(EP['method'])(xps[:, :, ffs], trm[:, ffs, :], 142 | kx[:, :, ffs, :], Ss[:, ffs], pidirs, EP['iter'], displ) 143 | SM1['S'][np.logical_or(np.isnan(SM1['S']), SM1['S'] < 0)] = 0 144 | 145 | #Interpolate onto user specified matrix 146 | print('\ninterpolating onto specified matrix...\n') 147 | SMout = interpspec(SM1, SM, method='linear') 148 | 149 | #smooth spectrum 150 | if EP['smooth'].upper() == 'ON': 151 | print('\nsmoothing spectrum...\n') 152 | SMout = smoothspec(SMout, [[1, 0.5, 0.25], [1, 0.5, 0.25]]) 153 | 154 | infospec(SMout) 155 | 156 | #write out spectrum matrix in DIWASP format 157 | filename = Options['FILEOUT'] 158 | if len(filename) > 0: 159 | print('writing out spectrum matrix to file') 160 | writespec(SMout,filename) 161 | 162 | #plot spectrum 163 | if ptype > 0: 164 | print('finished...plotting spectrum') 165 | plotspec(SMout, ptype) 166 | T = 'Directional spectrum estimate using {} method'.format(EP['method']) 167 | plt.title(T) 168 | plt.show() 169 | 170 | return SMout, EP 171 | -------------------------------------------------------------------------------- /infospec.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from private.hsig import hsig 3 | 4 | def infospec(SM): 5 | """ 6 | DIWASP V1.4 function 7 | infospec: calculates and displays information about a directional spectrum 8 | 9 | [Hsig,Tp,DTp,Dp]=infospec(SM) 10 | 11 | Outputs: 12 | Hsig Signficant wave height 13 | Tp Peak period 14 | DTp Direction of spectral peak 15 | Dp Dominant direction 16 | 17 | Inputs: 18 | SM A spectral matrix structure containing the file data 19 | 20 | Hsig is the significant wave height. Tp is the peak frequency, the highest point in the one dimensional spectrum. 21 | DTp is the main direction of the peak period (i.e the highest point in the two-dimensional directional spectrum). 22 | Dp is the dominant direction defined as the direction with the highest energy integrated over all frequencies. 23 | 24 | "help data_structures" for information on the DIWASP data structures 25 | 26 | Copyright (C) 2002 Coastal Oceanography Group, CWR, UWA, Perth 27 | """ 28 | 29 | H = hsig(SM) 30 | 31 | S = np.sum(np.real(SM['S']), 1) 32 | 33 | I = np.argmax(S) 34 | Tp = 1 / (SM['freqs'][I]) 35 | I = np.argmax(np.real(SM['S'][I, :])) 36 | DTp = SM['dirs'][I] 37 | I = np.argmax(np.real(np.sum(SM['S'], 0))) 38 | Dp = SM['dirs'][I] 39 | 40 | print('Infospec::') 41 | print('Significant wave height: {}'.format(H)) 42 | print('Peak period: {}'.format(Tp)) 43 | print('Direction of peak period: {} axis angle / {} ' 44 | 'compass bearing'.format(DTp, compangle(DTp, SM['xaxisdir']))) 45 | print('Dominant direction: {} axis angle / {} ' 46 | 'compass bearing'.format(Dp, compangle(Dp, SM['xaxisdir']))) 47 | 48 | return H, Tp, DTp, Dp 49 | 50 | def compangle(dirs, xaxisdir): 51 | return (180 + xaxisdir * np.ones(np.shape(dirs)) - dirs) % 360 52 | -------------------------------------------------------------------------------- /interpspec.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from warnings import warn 3 | from scipy.interpolate import griddata 4 | from private.hsig import hsig 5 | from private.spectobasis import spectobasis 6 | 7 | def interpspec(SMin, SMout, method='linear'): 8 | """ 9 | DIWASP V1.4 function 10 | interpspec: interpolates between spectral matrix bases 11 | 12 | SMout=interpspec(SMin,SMout) 13 | 14 | Outputs: 15 | SMout Output spectral matrix structure with interpolated power density 16 | 17 | Inputs: 18 | SMin A spectral matrix structure containing the original spectra 19 | SMout A spectral matrix defining the new spectral matrix 20 | 21 | SMout only needs to have the frequency and directional axes defined - 22 | spectral density ignored 23 | 24 | "help data_structures" for information on the DIWASP data structures 25 | """ 26 | Hs1 = hsig(SMin) 27 | 28 | SMin, facin = spectobasis(SMin) 29 | SMtmp, facout = spectobasis(SMout) 30 | 31 | s1 = SMin['freqs'][:, np.newaxis] * np.sin(SMin['dirs']) 32 | c1 = SMin['freqs'][:, np.newaxis] * np.cos(SMin['dirs']) 33 | s2 = SMtmp['freqs'][:, np.newaxis] * np.sin(SMtmp['dirs']) 34 | c2 = SMtmp['freqs'][:, np.newaxis] * np.cos(SMtmp['dirs']) 35 | 36 | if np.array_equal(s1, s2) and np.array_equal(c1, c2): 37 | warn('No interpolation required, skipping') 38 | Stmp = SMin['S'] 39 | else: 40 | Stmp = griddata((s1.flatten(), c1.flatten()), SMin['S'].flatten(), 41 | (s2.flatten(), c2.flatten()), method=method).reshape(s2.shape) 42 | 43 | Stmp[np.isnan(Stmp)] = 0 44 | SMout['S'] = Stmp / facout 45 | 46 | # check Hsig of mapped spectrum and check sufficiently close to original 47 | Hs2 = hsig(SMout) 48 | if (Hs2 - Hs1) / Hs1 > 0.02: 49 | warn('User defined grid may be too coarse; try increasing' + 50 | ' resolution of ''SM[\'freqs\']'' or ''SM[\'dirs\']''') 51 | 52 | return SMout -------------------------------------------------------------------------------- /plotspec.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import numpy as np 3 | from private.spectobasis import spectobasis 4 | 5 | def plotspec(SM, ptype): 6 | """ 7 | DIWASP V1.4 function 8 | plotspec: plots the spectral matrix in 3D or polar form 9 | 10 | plotspec(SM,ptype) 11 | 12 | Inputs: 13 | SM A spectral matrix structure 14 | ptype plot type: 15 | 1 3D surface plot 16 | 2 polar type plot 17 | 3 3D surface plot (compass bearing angles direction from) 18 | 4 polar type plot (compass bearing angles direction from) 19 | 20 | The 3D surface plot type is a MATLAB surface plot with SM.freqs on the x axis, SM.dirs on the y axis and the spectral density, SM.S as the z value. 21 | The polar type plot is a MATLAB polar plot with the direction showing values in SM.dirs, the radius showing values in SM.freqs 22 | and contours representing the spectral density, SM.S. An example of the polar type plot is shown on the front cover of the manual. 23 | For plot types 1 and 2, the direction is the direction of propagation relative to the Cartesian axis. 24 | For options 3 and 4 the direction is coming from as a true compass bearing (this has changed from previous versions). 25 | Directions are corrected internally from the SM.xaxisdir and SM.dunit 26 | fields that define the orientation of the axes and directional units in the spectral matrix. 27 | 28 | "help data_structures" for information on the DIWASP data structures 29 | 30 | Copyright (C) 2002 Coastal Oceanography Group, CWR, UWA, Perth 31 | """ 32 | 33 | fig = plt.figure(tight_layout=True) 34 | 35 | SM, sfac = spectobasis(SM) #Convert to basis matrix 36 | dirs = SM['dirs'] 37 | ffreqs = SM['freqs'] / (2 * np.pi) 38 | S = 2 * np.pi ** 2 * np.real(SM['S'])/ 180 39 | 40 | #Convert directions to nautical 41 | if ptype == 3 or ptype == 4: 42 | if 'xaxisdir' in SM.keys(): 43 | xaxisdir = SM['xaxisdir'] 44 | else: 45 | xaxisdir = 90 46 | dirs = dirs + np.pi + np.pi * (90 - xaxisdir) / 180 47 | 48 | #Surface plots 49 | if ptype == 1 or ptype == 3: 50 | if ptype == 3: 51 | dirs %= 2 * np.pi 52 | order = np.argsort(dirs) 53 | dirs = (180 * dirs / np.pi)[order] 54 | ddir, df = np.meshgrid(dirs, ffreqs) 55 | S = S[:, order] 56 | ax = fig.add_subplot(111, projection='3d') 57 | ax.set_xlabel('frequency [Hz]') 58 | if ptype == 1: 59 | ax.set_ylabel('direction [degrees]') 60 | ax.set_xlim(0, np.max(ffreqs)) 61 | ax.set_ylim(-180, 180) 62 | ax.set_zlim(0, np.max(S)) 63 | S[:, dirs > 180] = np.nan 64 | else: 65 | ax.set_ylabel('direction [bearings]') 66 | ax.set_xlim(0, np.max(ffreqs)) 67 | ax.set_ylim(0, 360) 68 | ax.set_zlim(0, np.max(S)) 69 | ax.plot_surface(df, ddir, np.real(S)) 70 | ax.set_zlabel('m^2s / deg') 71 | ax.view_init(30, -135) 72 | 73 | #Polar plots 74 | elif ptype == 2 or ptype == 4: 75 | ddir, df = np.meshgrid(dirs, ffreqs) 76 | ax = fig.add_subplot(111, projection='polar') 77 | ax.set_rlim(0, 0.8 * np.max(ffreqs)) 78 | c = ax.contour(ddir, df, np.real(S), 20) 79 | fig.colorbar(c) 80 | if ptype == 2: 81 | ax.set_ylabel('direction [degrees] / frequency [Hz]') 82 | else: 83 | ax.set_ylabel('direction [bearing] / frequency [Hz]') 84 | ax.set_xlabel('m^2 s / deg') 85 | -------------------------------------------------------------------------------- /private/EMEP.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import warnings 3 | 4 | 5 | def solve_with_nan_handling(C, Z): 6 | """ 7 | Solve the least-squares problem C * x = Z with MATLAB-like behavior for NaN values. 8 | If NaNs are present in C or Z, return a vector of zeros with appropriate dimensions. 9 | """ 10 | # Check for NaN in C or Z 11 | if np.isnan(C).any() or np.isnan(Z).any(): 12 | # MATLAB returns zeros in this scenario 13 | num_cols = C.shape[1] if C.ndim > 1 else 1 14 | return np.zeros((num_cols,)) 15 | 16 | # Perform least-squares solving 17 | try: 18 | solution, _, _, _ = np.linalg.lstsq(C, Z, rcond=None) 19 | return solution 20 | except np.linalg.LinAlgError: 21 | return np.zeros((C.shape[1],)) 22 | 23 | def EMEP(xps, trm, kx, Ss, pidirs, miter, displ): 24 | szd = xps.shape[0] 25 | freqs = xps.shape[2] 26 | ddirs = trm.shape[2] 27 | 28 | ddir = abs(pidirs[1] - pidirs[0]) 29 | pi = np.pi 30 | 31 | if displ < 2: 32 | warnings.simplefilter("ignore") 33 | 34 | Co = np.real(xps) 35 | Quad = -np.imag(xps) 36 | 37 | sigCo = np.zeros_like(Co) 38 | sigQuad = np.zeros_like(Quad) 39 | xpsx = np.zeros_like(Co) 40 | for ff in range(freqs): 41 | xpsx[:,:,ff] = np.outer(np.real(np.diag(xps[:,:,ff])), 42 | np.real(np.diag(xps[:,:,ff]).T)) 43 | sigCo[:,:,ff] = np.sqrt(0.5 * (xpsx[:,:,ff] + Co[:,:,ff]**2 - Quad[:,:,ff]**2)) 44 | sigQuad[:,:,ff] = np.sqrt(0.5 * (xpsx[:,:,ff] - Co[:,:,ff]**2 + Quad[:,:,ff]**2)) 45 | 46 | S = np.zeros((freqs, ddirs)) 47 | 48 | phi = np.zeros((szd+2, freqs)) 49 | H = np.zeros((ddirs, szd+2, freqs)) 50 | for ff in range(freqs): 51 | index = 0 52 | 53 | for m in range(szd): 54 | for n in range(m, szd): 55 | expx = np.exp(-1j * kx[m, n, ff, :ddirs]) 56 | Hh = trm[m, ff, :ddirs] 57 | Hhs = np.conj(trm[n, ff, :ddirs]) 58 | Htemp = Hh * Hhs * expx 59 | 60 | if Htemp[0] != Htemp[1]: 61 | phi[index,ff] = (np.real(xps[m, n, ff]) / (sigCo[m, n, ff] * Ss[0, ff])) 62 | H[0:ddirs,index,ff] = np.real(Htemp) / sigCo[m, n, ff] 63 | index += 1 64 | 65 | if kx[m, n, 0, 0] + kx[m, n, 0, 1] != 0: 66 | phi[index,ff] = (np.imag(xps[m, n, ff]) / (sigQuad[m, n, ff] * Ss[0, ff])) 67 | H[0:ddirs,index,ff] = np.imag(Htemp) / sigQuad[m, n, ff] 68 | index += 1 69 | 70 | M = index 71 | 72 | cosnt = np.zeros((ddirs, M, M // 2 + 1)) 73 | sinnt = np.zeros((ddirs, M, M // 2 + 1)) 74 | for eni in range(1, M // 2 + 2): 75 | cosnt[:, :, eni - 1] = np.cos(eni * pidirs)[:, None] 76 | sinnt[:, :, eni - 1] = np.sin(eni * pidirs)[:, None] 77 | cosn = np.cos(np.arange(1, M // 2 + 2)[:, None] * pidirs) 78 | sinn = np.sin(np.arange(1, M // 2 + 2)[:, None] * pidirs) 79 | 80 | for ff in range(freqs): 81 | if displ >= 1: 82 | print(f"Calculating for frequency {ff + 1} of {freqs}") 83 | 84 | Hi = H[0:ddirs,0:M,ff] 85 | Phione = np.outer(np.ones_like(pidirs), phi[:M,ff]) 86 | 87 | keepgoing = True 88 | n = 0 89 | AIC = [] 90 | 91 | a1held, b1held = [], [] 92 | while keepgoing: 93 | n += 1 94 | 95 | if n <= M // 2 + 1: 96 | if displ > 0: 97 | print(f"Model: {n}") 98 | 99 | a1, b1 = np.zeros(n), np.zeros(n) 100 | a2, b2 = np.ones(n) * 100, np.ones(n) * 100 101 | count = 0 102 | rlx = 1.0 103 | 104 | while np.max(np.abs(a2)) > 0.01 or np.max(np.abs(b2)) > 0.01: 105 | count += 1 106 | Fn = (a1 @ cosn[:n, :] + b1 @ sinn[:n, :]).T 107 | 108 | Fnexp = np.exp(Fn)[:, None] * np.ones((1, M)) 109 | PhiHF = (Phione - Hi) * Fnexp 110 | Z = np.sum(PhiHF, axis=0) / np.sum(Fnexp, axis=0) 111 | 112 | X = np.zeros((n, M)) 113 | Y = np.zeros((n, M)) 114 | for eni in range(n): 115 | X[eni,0:M] = Z * (( 116 | np.sum(Fnexp * cosnt[:,:,eni], axis=0) / \ 117 | np.sum(Fnexp, axis=0) 118 | ) - ( 119 | np.sum(PhiHF * cosnt[:,:,eni], axis=0) / \ 120 | np.sum(PhiHF, axis=0) 121 | )) 122 | Y[eni,0:M] = Z * (( 123 | np.sum(Fnexp * sinnt[:,:,eni], axis=0) / \ 124 | np.sum(Fnexp, axis=0) 125 | ) - ( 126 | np.sum(PhiHF * sinnt[:,:,eni], axis=0) / \ 127 | np.sum(PhiHF, axis=0) 128 | )) 129 | 130 | C = np.hstack((X.T, Y.T)) 131 | 132 | out = solve_with_nan_handling(C, Z) 133 | 134 | a2old, b2old = a2.copy(), b2.copy() 135 | a2, b2 = out[:n], out[n:2*n] 136 | 137 | if ( 138 | np.sum(np.abs(a2) - np.abs(a2old) > 100) > 0 139 | or np.sum(np.abs(b2) - np.abs(b2old) > 100) > 0 140 | or count > miter 141 | ): 142 | if rlx > 0.0625: 143 | rlx *= 0.5 144 | if displ == 2: 145 | print(f"Relaxing computation...factor: {rlx:.4f}") 146 | 147 | count = 0 148 | a1[:n], b1[:n] = 0.0, 0.0 149 | else: 150 | if displ == 2: 151 | print("Computation fully relaxed...bailing out") 152 | keepgoing = False 153 | break 154 | else: 155 | a1 = a1 + rlx*a2 156 | b1 = b1 + rlx*b2 157 | 158 | error = Z - a2 @ X - b2 @ Y 159 | AIC.append(M * (np.log(2 * pi * np.var(error)) + 1) + 4 * n + 2) 160 | 161 | if n > 1 and (AIC[-1] > AIC[-2] or np.isnan(AIC[-1])): 162 | keepgoing = False 163 | 164 | a1held.append(a1[:n]) 165 | b1held.append(b1[:n]) 166 | best = n 167 | 168 | if not keepgoing: 169 | if n > 1: 170 | max_len = max(len(arr) for arr in a1held) 171 | padded_a1held = [np.pad( 172 | arr, (0, max_len - len(arr))) for arr in a1held] 173 | padded_b1held = [np.pad( 174 | arr, (0, max_len - len(arr))) for arr in b1held] 175 | a1 = np.array(padded_a1held)[n - 2, :n - 1] 176 | b1 = np.array(padded_b1held)[n - 2, :n - 1] 177 | best = n - 1 178 | else: 179 | a1 = np.zeros(1) 180 | b1 = np.zeros(1) 181 | 182 | else: 183 | keepgoing = False 184 | 185 | if displ == 2: 186 | print(f"Best: {best}") 187 | 188 | G = np.exp(a1@cosn[:best, :] + \ 189 | b1@sinn[:best, :]).T 190 | SG = G / (np.sum(G) * ddir) 191 | S[ff, 0:ddirs] = Ss[0, ff] * SG.T 192 | 193 | return S 194 | -------------------------------------------------------------------------------- /private/IMLM.py: -------------------------------------------------------------------------------- 1 | import warnings 2 | import numpy as np 3 | from numpy.linalg import inv 4 | 5 | def IMLM(xps, trm, kx, Ss, pidirs, miter, displ): 6 | 7 | gamma = 0.1 8 | alpha = 0.1 9 | 10 | szd = np.shape(xps)[0] 11 | ffreqs = np.shape(xps)[2] 12 | ddirs = np.shape(trm)[2] 13 | 14 | ddir = 8 * np.arctan(1) / ddirs 15 | 16 | if displ < 2: 17 | warnings.simplefilter('ignore') 18 | 19 | Htemp = np.empty((ddirs, szd, szd), dtype='complex128') 20 | iHtemp = np.empty((ddirs, szd, szd), dtype='complex128') 21 | ixps = np.empty((szd, szd), dtype='complex128') 22 | S = np.empty((ffreqs, ddirs), dtype='complex128') 23 | 24 | for ff in range(ffreqs): 25 | if displ >= 1: 26 | print('calculating for frequency {} of {}'.format(ff + 1, ffreqs)) 27 | 28 | for m in range(szd): 29 | for n in range(szd): 30 | H = trm[n, ff, :] 31 | Hs = np.conj(trm[m, ff, :]) 32 | expx = np.exp(1j * kx[m, n, ff, :]) 33 | iexpx = np.exp(-1j * kx[m, n, ff, :]) 34 | Htemp[:, m, n] = H * Hs * expx 35 | iHtemp[:, m, n] = H * Hs * iexpx 36 | 37 | invcps = inv(xps[:, :, ff]) 38 | Sftmp = np.zeros(ddirs, dtype='complex128') 39 | for m in range(szd): 40 | for n in range(szd): 41 | xtemp = invcps[m, n] * Htemp[:, m, n] 42 | Sftmp += xtemp 43 | Eo = 1 / Sftmp 44 | kappa = 1 / (ddir * np.sum(Eo)) 45 | Eo *= kappa 46 | E = Eo 47 | T = Eo 48 | 49 | for it in range(miter): 50 | for m in range(szd): 51 | for n in range(szd): 52 | expG = iHtemp[:, m, n] * E 53 | ixps[m, n] = np.sum(expG) * ddir 54 | invcps = inv(ixps) 55 | Sftmp = np.zeros(ddirs, dtype='complex128') 56 | for m in range(szd): 57 | for n in range(szd): 58 | xtemp = invcps[m, n] * Htemp[:, m, n] 59 | Sftmp = Sftmp + xtemp 60 | Told = T 61 | T = 1 / Sftmp 62 | 63 | kappa = 1 / (ddir * np.sum(T)) 64 | T = T * kappa 65 | 66 | ei = gamma * ((Eo - T) + alpha * (T - Told)) 67 | E = E + ei 68 | kappa = 1 / (ddir * np.sum(E)) 69 | E = E * kappa 70 | 71 | 72 | 73 | S[ff, :] = Ss[0, ff] * E 74 | 75 | 76 | 77 | warnings.simplefilter('default') 78 | 79 | return S 80 | -------------------------------------------------------------------------------- /private/check_data.py: -------------------------------------------------------------------------------- 1 | import warnings 2 | import numpy as np 3 | 4 | def check_data(DDS, type_): 5 | """ 6 | internal DIWASP1.1 function 7 | checks data structures 8 | 9 | DDS=check_data(DDS,type) 10 | DDS: the data structure 11 | type: 1, Instrument data structure; 12 | 2, Spectral matrix structure; 13 | 3, Estimation parameters structure. 14 | 15 | Updated on 29/04/2013 by r.guedes to fix some errors that were not being 16 | detected (e.g., ID.datatype not being a cell array). 17 | """ 18 | 19 | #-------------------------------------------------------------------------- 20 | # Defaults 21 | #-------------------------------------------------------------------------- 22 | SM = dict() 23 | EP = dict() 24 | SM['xaxisdir'] = 90 25 | EP['dres'] = 180 26 | EP['nfft'] = [] 27 | EP['method'] = 'IMLM' 28 | EP['iter'] = 100 29 | error = '' 30 | 31 | #-------------------------------------------------------------------------- 32 | # Instrument data structure 33 | #-------------------------------------------------------------------------- 34 | if type_ == 1: 35 | if type(DDS) != dict: 36 | print('DIWASP data_check: Instrument data type is not a ' 37 | 'dictionary') 38 | nc = 1 39 | if 'layout' in DDS: 40 | nr, nc = np.shape(DDS['layout']) 41 | if nr < 3: 42 | if nr == 2: 43 | np.array(DDS['layout'])[2, :] = 0 44 | else: 45 | error = 'layout' 46 | 47 | if ('datatypes' not in DDS or type(DDS['datatypes']) not in 48 | (list, np.ndarray) or np.squeeze(DDS['datatypes']).ndim != 1 49 | or max(np.shape(DDS['datatypes'])) != nc): 50 | error = 'datatypes' 51 | else: 52 | DDS['datatypes'] = np.reshape(DDS['datatypes'], 53 | np.size(DDS['datatypes'])) 54 | 55 | if 'depth' not in DDS or type(DDS['depth']) not in (int, float): 56 | error = 'depth' 57 | 58 | if 'fs' not in DDS or type(DDS['fs']) not in (int, float): 59 | error = 'fs' 60 | 61 | if 'data' in DDS: 62 | if np.ndim(DDS['data']) < 2 or np.shape(DDS['data'])[1] != nc: 63 | error = 'data' 64 | else: 65 | DDS['data'] = np.zeros((1, nc)) 66 | 67 | if len(error) != 0: 68 | print('\nInstrument data structure error: field [{}] not specified ' 69 | 'correctly'.format(error)) 70 | DDS = [] 71 | return DDS 72 | 73 | # ------------------------------------------------------------------------- 74 | # Special matrix 75 | # ------------------------------------------------------------------------- 76 | if type_ == 2: 77 | if type(DDS) != dict: 78 | print('DIWASP data_check: Special matrix data type is not a ' 79 | 'structure') 80 | 81 | if 'freqs' in DDS and np.squeeze(DDS['freqs']).ndim == 1: 82 | nf = np.size(DDS['freqs']) 83 | else: 84 | error = 'freqs' 85 | 86 | if 'dirs' in DDS and np.squeeze(DDS['dirs']).ndim == 1: 87 | nd = np.size(DDS['dirs']) 88 | else: 89 | error = 'dirs' 90 | 91 | if 'S' in DDS: 92 | if (np.shape(DDS['S'])[0] != nf or np.ndim(DDS['S']) < 2 or 93 | np.shape(DDS['S'])[1] != nd): 94 | error = 'S' 95 | else: 96 | DDS['S'] = [] 97 | 98 | if 'xaxisdir' in DDS: 99 | if type(DDS['xaxisdir']) not in (int, float): 100 | error = 'xaxisdir' 101 | else: 102 | DDS['xaxisdir'] = SM['xaxisdir'] 103 | 104 | if 'dunit' not in DDS: 105 | DDS['dunit'] = 'cart' 106 | 107 | if 'funit' not in DDS: 108 | DDS['funit'] = 'hz' 109 | 110 | if len(error) != 0: 111 | print('\nSpectral matrix structure error: field [{}] not ' 112 | 'specified correctly'.format(error)) 113 | DDS = [] 114 | return DDS 115 | 116 | #-------------------------------------------------------------------------- 117 | # Estimation parameters 118 | #-------------------------------------------------------------------------- 119 | if type_ == 3: 120 | if type(DDS) != dict: 121 | print('DIWASP data_check: Estimation parameter data type is not a ' 122 | 'structure') 123 | 124 | if 'dres' in DDS: 125 | if type(DDS['dres']) not in (int, float): 126 | error = 'dres' 127 | elif DDS['dres'] < 10: 128 | DDS['dres'] = 10 129 | warnings.warn('dres is too small and has been set to 10') 130 | 131 | else: 132 | DDS['dres'] = EP['dres'] 133 | 134 | if 'nfft' in DDS: 135 | if type(DDS['nfft']) not in (int, float): 136 | error = 'nfft' 137 | elif DDS['nfft'] < 64: 138 | DDS['nfft'] = 64 139 | warnings.warn('nfft is too small and has been set to 64') 140 | else: 141 | DDS['nfft'] = EP['nfft'] 142 | 143 | if 'iter' in DDS: 144 | if type(DDS['iter']) not in (int, float): 145 | error = 'iter' 146 | else: 147 | DDS['iter'] = EP['iter'] 148 | 149 | if 'smooth' in DDS: 150 | if DDS['smooth'].upper() != 'OFF': 151 | DDS['smooth'] = 'ON' 152 | else: 153 | DDS['smooth'] = 'ON' 154 | 155 | if 'method' in DDS: 156 | if DDS['method'].upper() not in ('DFTM', 'EMLM', 'IMLM', 'EMEP', 157 | 'BDM'): 158 | error = 'method' 159 | else: 160 | DDS['method'] = EP['method'] 161 | 162 | if len(error) != 0: 163 | print('\nEstimation parameters structure error: field [{}] not ' 164 | 'specified correctly') 165 | DDS = [] 166 | return DDS 167 | 168 | if type_ not in (1, 2, 3): 169 | print() 170 | warnings.warn('DIWASP data_check: Data type unknown') 171 | DDS = [] 172 | 173 | return DDS 174 | -------------------------------------------------------------------------------- /private/diwasp_csd.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from scipy.signal import csd 3 | 4 | def diwasp_csd(x, y, nfft, fs, flag=1): 5 | """ 6 | Diwasp cross spectral density. 7 | If flag = 1, use scipy's cross spectral density function 8 | If flag = 2, use custom cross spectral density function 9 | 10 | [Pxy, f] = diwasp_csd(x,y,nfft,fs) 11 | """ 12 | 13 | if flag == 1: 14 | f, S = csd(y, x, fs=fs, window='hamming', nperseg=nfft, 15 | noverlap=0, nfft=nfft, detrend=False) 16 | elif flag == 2: # match mlab version if Signal Processing Toolbox not available 17 | hann = 0.5 * (1 - np.cos(2 * np.pi * np.arange(1, int(nfft / 2) + 1) / 18 | (nfft + 1))) 19 | win = np.hstack((hann, np.flipud(hann))) 20 | nw = np.size(win) 21 | nseg = int(np.size(x) / nw) 22 | S = np.zeros(nfft, dtype='complex128') 23 | for iseg in range(nseg): 24 | ind = nw * iseg + np.arange(nw) 25 | xw = win * x[ind] 26 | yw = win * y[ind] 27 | Px = np.fft.fft(xw, nfft) 28 | Py = np.fft.fft(yw, nfft) 29 | Pxy = Py * np.conj(Px) 30 | S += Pxy 31 | nfac = fs * nseg * np.linalg.norm(win) ** 2 32 | S = np.hstack((S[0], 2 * S[1:int(nfft / 2)], S[int(nfft / 2)])) / nfac 33 | f = (fs / nfft) * np.arange(int(nfft / 2) + 1).T 34 | 35 | return S, f 36 | -------------------------------------------------------------------------------- /private/elev.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | def elev(ffreqs, dirs, wns, z, depth): 4 | 5 | trm = np.ones((np.size(ffreqs), np.size(dirs))) 6 | 7 | return trm -------------------------------------------------------------------------------- /private/hsig.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | def hsig(argin): 4 | """ 5 | DIWASP function to calculate significant wave height 6 | 7 | Hs=Hsig(SM) 8 | 9 | Hs is significant wave height of spectral matrix SM 10 | 11 | "help data_structures" for information on the DIWASP data structures 12 | 13 | Copyright (C) 2002 Coastal Oceanography Group, CWR, UWA, Perth 14 | """ 15 | 16 | SM = argin 17 | df = SM['freqs'][1] - SM['freqs'][0] 18 | ddir = SM['dirs'][1] - SM['dirs'][0] 19 | S = SM['S'] 20 | 21 | Hs = 4 * np.sqrt(np.sum(np.sum(np.real(S))) * df * ddir) 22 | 23 | return Hs -------------------------------------------------------------------------------- /private/pres.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | def pres(ffreqs, dirs, wns, z, depth): 4 | Kz = np.cosh(z * wns) / np.cosh(depth * wns) 5 | #include a maximum cuttoff for the pressure response function 6 | Kz[Kz < 0.1] = 0.1 7 | trm = Kz[:, np.newaxis] * np.ones(np.shape(dirs)) 8 | 9 | return trm -------------------------------------------------------------------------------- /private/smoothspec.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | def smoothspec(S, kernel): 4 | """ 5 | smooths a directional spectrum using the 6 | first dimension is frequency 7 | kernel is 2*3 matrix with smoothing parameters 8 | """ 9 | 10 | f1 = kernel[0][2] 11 | f2 = kernel[0][1] 12 | f3 = kernel[0][0] 13 | d1 = kernel[1][2] 14 | d2 = kernel[1][1] 15 | d3 = kernel[1][0] 16 | tot = 2 * f1 + 2 * f2 + f3 + 2 * d1 + 2 * d2 + d3 17 | 18 | nf, nd = np.shape(S['S']) 19 | 20 | Sin = S['S'] 21 | Sin[np.isnan(Sin)] = 0 22 | 23 | S['S'][2:nf - 2, 2:nd - 2] = (f1 * Sin[:nf - 4, 2:nd - 2] + f2 * 24 | Sin[1:nf - 3, 2:nd - 2] + f3 * Sin[2:nf - 2, 2:nd - 2] + f2 * 25 | Sin[3:nf - 1, 2:nd - 2] + f1 * Sin[4:nf, 2:nd - 2] + d1 * 26 | Sin[2:nf - 2, :nd - 4] + d2 * Sin[2:nf - 2, 1:nd - 3] + d3 * 27 | Sin[2:nf - 2, 2:nd - 2] + d2 * Sin[2:nf - 2, 3:nd - 1] + d1 * 28 | Sin[2:nf - 2, 4:nd]) / tot 29 | 30 | return S 31 | -------------------------------------------------------------------------------- /private/spectobasis.py: -------------------------------------------------------------------------------- 1 | import copy 2 | import numpy as np 3 | 4 | def spectobasis(SM): 5 | """Converts any spectral matrix to rad/s and cartesian radians""" 6 | 7 | SM = copy.deepcopy(SM) 8 | 9 | Sfac = 1.0 10 | if 'funit' in SM.keys() and SM['funit'].lower() == 'hz': 11 | SM['freqs'] *= 2 * np.pi 12 | Sfac /= (2 * np.pi) 13 | 14 | r2d = np.pi / 180 15 | if 'dunit' in SM.keys(): 16 | if SM['dunit'][:3].lower() == 'car': 17 | SM['dirs'] = SM['dirs'] * r2d 18 | Sfac /= r2d 19 | elif SM['dunit'][:3].lower() == 'nau': 20 | if 'xaxisdir' in SM.keys(): 21 | SM['dirs'] += (90 - SM['xaxisdir']) 22 | SM['dirs'] = r2d * (270 - SM['dirs']) 23 | Sfac /= r2d 24 | 25 | if 'S' in SM.keys() and (isinstance(SM['S'], np.ndarray) and \ 26 | SM['S'].size > 0): 27 | SM['S'] *= Sfac 28 | 29 | return SM, Sfac -------------------------------------------------------------------------------- /private/velx.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | def velx(ffreqs, dirs, wns, z, depth): 4 | 5 | Kz = np.cosh(z * wns) / np.sinh(depth * wns) 6 | #include a maximum cuttoff for the velocity response function 7 | Kz[Kz < 0.1] = 0.1 8 | trm = (ffreqs * Kz)[:, np.newaxis] * np.cos(dirs) 9 | 10 | return trm -------------------------------------------------------------------------------- /private/vely.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | def vely(ffreqs, dirs, wns, z, depth): 4 | Kz = np.cosh(z * wns) / np.sinh(depth * wns) 5 | #include a maximum cuttoff for the velocity response function 6 | Kz[Kz < 0.1] = 0.1 7 | trm = np.transpose((ffreqs * Kz, )) * np.sin(dirs) 8 | 9 | return trm -------------------------------------------------------------------------------- /private/wavenumber.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | def wavenumber(sigma, h): 4 | """" 5 | k = wavenumber(sigma,h) 6 | 7 | k is the matrix of same size as sigma and h containing the calculated wave numbers 8 | 9 | sigma is the wave frequencies in rad/s 10 | h is the water depth 11 | 12 | sigma and h must be scalars,vectors or matricies of the same dimensions 13 | 14 | 15 | modified from R.Dalrymple's java code 16 | """ 17 | g = 9.81 18 | 19 | a0 = (sigma * sigma * h) / g 20 | b1 = 1 / np.tanh(a0 ** 0.75) 21 | a1 = a0 * (b1 ** 0.666) 22 | da1 = 1000 23 | 24 | d1 = np.ones(np.shape(h)) 25 | while np.max(d1) == 1: 26 | d1 = np.abs(da1 / a1) > 0.00000001 27 | th = np.tanh(a1) 28 | ch = np.cosh(a1) 29 | f1 = a0 - (a1 * th) 30 | f2 = -a1 * (1 / ch) ** 2 - th 31 | da1 = -f1 / f2 32 | a1 += da1 33 | 34 | k = a1 / h 35 | 36 | return k -------------------------------------------------------------------------------- /writespec.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | def writespec(SM, filename): 4 | """ 5 | DIWASP V1.4 function 6 | writespec: writes spectrum matrix to file using DIWASP format 7 | 8 | writespec(SM,filename) 9 | 10 | Inputs: 11 | SM A spectral matrix structure 12 | filename String containing the filename including file extension if required 13 | 14 | All inputs required 15 | 16 | "help data_structures" for information on the DIWASP data structures 17 | 18 | Copyright (C) 2002 Coastal Oceanography Group, CWR, UWA, Perth 19 | """ 20 | 21 | nf = np.max(SM['freqs'].shape) 22 | nd = np.max(SM['dirs'].shape) 23 | 24 | streamout = np.empty((nf + nd + 4 + nf * nd)) 25 | 26 | streamout[0] = SM['xaxisdir'] 27 | streamout[1] = nf 28 | streamout[2] = nd 29 | streamout[3:nf + 3] = SM['freqs'] 30 | streamout[nf + 3:nf + nd + 3] = SM['dirs'] 31 | streamout[nf + nd + 3] = 999 32 | streamout[nf + nd + 4:nf + nd + 4 + nf * nd] = np.reshape(np.real(SM['S']), 33 | (nf * nd)) 34 | 35 | streamout = streamout.T 36 | 37 | np.savetxt(filename, streamout) 38 | --------------------------------------------------------------------------------